Dr. Oren Etzioni has served as the Chief Executive Officer of the Allen Institute for AI (AI2) since its inception in 2014. He has been a Professor at the University of Washington’s Computer Science department since 1991, and a Venture Partner at the Madrona Venture Group since 2000. He has garnered several awards including Seattle’s Geek of the Year (2013), the Robert Engelmore Memorial Award (2007), the IJCAI Distinguished Paper Award (2005), AAAI Fellow (2003), and a National Young Investigator Award (1993). He has been the founder or co-founder of several companies, including Farecast (sold to Microsoft in 2008) and Decide (sold to eBay in 2013). He has written commentary on AI for The New York Times, Nature, Wired, and the MIT Technology Review. He helped to pioneer meta-search (1994), online comparison shopping (1996), machine reading (2006), and Open Information Extraction (2007). He has authored over 100 technical papers that have garnered over 2,000 highly influential citations on Semantic Scholar. He received his Ph.D. from Carnegie Mellon in 1991 and his B.A. from Harvard in 1986.
Semantic Scholar, NLP, and the Fight Against COVID-19(Track Keynote)
Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, which was released by Addison-Wesley in 2019 and became an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy, as well as online via O’Reilly, YouTube, and his A4N podcast on A.I. news. Jon holds a doctorate in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010.
Jeannette M. Wing is Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University. From 2013 to 2017, she was a Corporate Vice President of Microsoft Research. She is an Adjunct Professor of Computer Science at Carnegie Mellon where she twice served as the Head of the Computer Science Department and had been on the faculty since 1985. From 2007-2010 she was the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation. She received her S.B., S.M., and Ph.D. degrees in Computer Science, all from the Massachusetts Institute of Technology.
Professor Wing’s general research interests are in the areas of trustworthy computing, specification and verification, concurrent and distributed systems, programming languages, and software engineering. Her current interests are in the foundations of security and privacy, with a new focus on trustworthy AI. She was or is on the editorial board of twelve journals, including the Journal of the ACM and Communications of the ACM. Professor Wing is known for her work on linearizability, behavioral subtyping, attack graphs, and privacy-compliance checkers. Her 2006 seminal essay, titled вЂњComputational Thinking,вЂќ is credited with helping to establish the centrality of computer science to problem-solving in fields where previously it had not been embraced. She is currently a member of: the National Library of Medicine Blue Ribbon Panel; the Science, Engineering, and Technology Advisory Committee for the American Academy for Arts and Sciences; the Board of Trustees for the Institute of Pure and Applied Mathematics; the Advisory Board for the Association for Women in Mathematics; and the Alibaba DAMO Technical Advisory Board.
She has been chair and/or a member of many other academic, government, and industry advisory boards. She received the CRA Distinguished Service Award in 2011 and the ACM Distinguished Service Award in 2014. She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE).
Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), and A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina). Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.
Ion Stoica is a Professor in the EECS Department at University of California at Berkeley. HHe does research on cloud computing and networked computer systems. Past work includes Apache Spark, Apache Mesos, Tachyon, Chord DHT, and Dynamic Packet State (DPS). He is an ACM Fellow and has received numerous awards, including the SIGOPS Hall of Fame Award (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001). In 2013, he co-founded Databricks a startup to commercialize technologies for Big Data processing, and in 2006 he co-founded Conviva, a startup to commercialize technologies for large scale video distribution.
Oriol Vinyals is a Principal Scientist at Google DeepMind, and a team lead of the Deep Learning group. His work focuses on Deep Learning and Artificial Intelligence. Prior to joining DeepMind, Oriol was part of the Google Brain team. He holds a Ph.D. in EECS from the University of California, Berkeley and is a recipient of the 2016 MIT TR35 innovator award. His research has been featured multiple times at the New York Times, Financial Times, WIRED, BBC, etc., and his articles have been cited over 85000 times. Some of his contributions such as seq2seq, knowledge distillation, or TensorFlow are used in Google Translate, Text-To-Speech, and Speech recognition, serving billions of queries every day, and he was the lead researcher of the AlphaStar project, creating an agent that defeated a top professional at the game of StarCraft, achieving Grandmaster level, also featured as the cover of Nature. At DeepMind he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, deep learning and reinforcement learning.
Gaël Varoquaux is an Inria faculty researcher working on data science and brain imaging. He has a joint position at Inria (French Computer Science National research) and in the Neurospin brain research institute. His research focuses on using data and machine learning for scientific inference, applying it to brain-imaging data to understand cognition, as well as developing tools that make it easier for non-specialists to use machine learning. Years before the NSA, he was hoping to make bleeding-edge data processing available across new fields, and he has been working on a mastermind plan building easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, Mayavi and nilearn, a nominated member of the PSF, and often teaches scientific computing with Python using the scipy lecture notes.
The Life of Scikit-learn: from Tech to People(Track Keynote)
As a Marketing Manager for data science and open-source, Marinela uses her cross-domain expertise in statistics, business and marketing, to position SAS as a leader in the Data Science and Machine Learning Platform market. She focuses on helping customers apply advanced analytics, machine learning, natural language processing and forecasting to solve their most complex problems. Over the past 5 years, Profi honed her skills mining data, developing models and technical/business solutions, including deploying AI at scale. Her experience spans banking, manufacturing, retails and energy. She is a keynote speaker and presenter at different global conferences, where she shares trend and priorities of the data science industry. She is a published author, contributor to several eBooks, and blog writer on major industry and data science blogs. She has a bachelor’s in economics, an MBA and a master’s in statistics. She is passionate about getting more younger passionate to code and pursue careers in STEM.
John leads Program Management for Microsoft Azure AI and is responsible for designing products and services that data scientists and ML experts around the world love and use. He leads a team of program managers, researchers, and designers responsible for products and services including Azure Machine Learning, Azure Cognitive Services, ML.NET, and ONNX Runtime. Prior to this role, John has led the Program Management team for Microsoft’s Developer Division, including Visual Studio, Visual Studio Code, and Azure Notebooks. He has also held positions as director of marketing for Visual Studio, as well as a program manager for Microsoft’s participation in several standards organizations, including ISO, IETF, and ECMA.
Prior to joining Microsoft in 1998, John worked as a writer and editor for several computer and technology publications, including BYTE Magazine, PC/Computing, and Corporate Computing, as well as being the Chief Information Officer for Imagine Publishing.
Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.
Machine Learning in R Part I(Full-Day Training)
Machine Learning in R Part II(Full-Day Training)
Vida Williams is a revered leader in the tech space. She currently serves as the Head of Data at SingleStone, and was recently named the company’s Chief Diversity Officer as an intentional approach towards mitigating bias in the Data and Technology industries. Vida is also a professor and Innovator in Resident at Virginia Commonwealth University, a gubernatorial appointee and co-chair of the Virginia Biotechnology Research Partnership Authority, and an impassioned activist for data privacy. She has contributed to TedX, TomTom Festival, The Frontier Partners, and countless client presentations. Vida also volunteers her data expertise to non-profits, offering free workshops and consulting. She leads organizations through exercises that help them to identify areas of improvement, new and better ways of showcasing their impact on the community, and how to use their data to foster growth.
Joseph M. Hellerstein is the Jim Gray Professor of Computer Science at the University of California, Berkeley, whose work focuses on data-centric systems and the way they drive computing. He is an ACM Fellow, an Alfred P. Sloan Research Fellow and the recipient of three ACM-SIGMOD “Test of Time” awards for his research. Fortune Magazine has included him in their list of 50 smartest people in technology , and MIT’s Technology Review magazine included his work on their TR10 list of the 10 technologies “most likely to change our world”. Hellerstein is the co-founder and Chief Strategy Officer of Trifacta, a software vendor providing intelligent interactive solutions to the messy problem of wrangling data. He has served on the technical advisory boards of a number of computing and Internet companies including Dell EMC, SurveyMonkey, Captricity, and Datometry, and previously served as the Director of Intel Research, Berkeley.
Gill Bejerano is an Associate Professor of Developmental Biology and Computer Science at Stanford University, and a member of the Stanford Artificial Intelligence (AI) Laboratory and Bio-X program. Mr. Bejerano is a pioneer of Human Genome research. He is the discoverer of “Ultraconserved Elements”, human genomic regions that defy understanding of molecular evolution. He has also done influential work in applying Markovian models to biosequence analysis, and in showing that co-option of junk DNA into functional roles is an under-appreciated force shaping the evolution of the Human Genome. He serves Member of the Technical Advisory Board of Numenta, Inc. He holds a triple BSc in Mathematics, Physics and Computer Science (summa cum laude) and a PhD in Computer Science (Machine Learning applications in Bioinformatics) from the Hebrew University of Jerusalem.
Anca Dragan is an Assistant Professor in EECS at UC Berkeley, where she runs the InterACT lab. Her goal is to enable robots to work with, around, and in support of people. She works on algorithms that enable robots to a) coordinate with people in shared spaces, and b) learn what people want them to do. Anca did her PhD in the Robotics Institute at Carnegie Mellon University on legible motion planning. At Berkeley, she helped found the Berkeley AI Research Lab, is a co-PI for the Center for Human-Compatible AI, and has been honored by the Presidential Early Career Award for Scientists and Engineers (PECASE), the Sloan fellowship, the NSF CAREER award, the Okawa award, MIT’s TR35, and an IJCAI Early Career Spotlight.
David Duvenaud is an assistant professor in computer science and statistics at the University of Toronto, where he holds a Canada Research Chair in generative models. His postdoctoral research was done at Harvard University, where he worked on hyperparameter optimization, variational inference, and automatic chemical design. He did his Ph.D. at the University of Cambridge, studying Bayesian nonparametrics with Zoubin Ghahramani and Carl Rasmussen. David spent two summers in the machine vision team at Google Research, and also co-founded Invenia, an energy forecasting and trading company. David is a founding member of the Vector Institute for Artificial Intelligence.
Kurt received his Ph.D. degree in Computer Science from Indiana University in 1984 and then joined the research division of AT&T Bell Laboratories. In 1991 he joined Synopsys, Inc. where he ultimately became Chief Technical Officer and Senior Vice-President of Research. In 1998 Kurt became Professor of Electrical Engineering and Computer Science at the University of California at Berkeley. Kurt’s research now focuses on systems issues associated with the application of Deep Learning to computer vision, speech recognition, natural language processing, and finance.
Kurt has published six books, over 250 refereed articles, and is among the most highly cited authors in Hardware and Design Automation. Kurt was elected a Fellow of the IEEE in 1996. At the 50th Design Automation Conference Kurt received a number of awards reflecting achievements over the 50 year history of the conference. These included “Top Ten Cited Author” and “Top Ten Cited Paper.” He was also recognized as among one of only three people to have received four Best Paper Awards in the history of the conference. Kurt’s research on Deep Learning has also received Best Paper Awards at the Embedded Vision Workshop and at the International Conference on Parallel Processing.
As an entrepreneur Kurt has served as an angel investor and advisor to over twenty-five start-up companies including C-Cube Microsystems, Coverity, Simplex, and Tensilica. Kurt co-founded DeepScale with his PhD student Forrest Iandola. DeepScale was acquired by Tesla in 2019.
Making Deep Learning Efficient(Track Keynote)
Zoubin Ghahramani is Chief Scientist of Uber and a world leader in the field of machine learning, significantly advancing the state-of-the-art in algorithms that can learn from data. He is known in particular for fundamental contributions to probabilistic modeling and Bayesian approaches to machine learning systems and AI. Zoubin also maintains his roles as Professor of Information Engineering at the University of Cambridge and Deputy Director of the Leverhulme Centre for the Future of Intelligence. He was one of the founding directors of the Alan Turing Institute (the UK’s national institute for Data Science and AI), and is a Fellow of St John’s College Cambridge and of the Royal Society.
Aleksandra Korolova is a WiSE Gabilan Assistant Professor of Computer Science at the University of Southern California (USC), where she researches algorithms and technologies that enable data-driven innovations while preserving privacy and fairness. Prior to joining USC, Aleksandra was a research scientist at Google and a computer science Ph.D. student at Stanford. Aleksandra is a recipient of the 2020 NSF CAREER award, a co-winner of the 2011 PET Award for outstanding research in privacy enhancing technologies for exposing privacy violations of microtargeted advertising and a runner-up for the 2015 PET Award for RAPPOR, the first commercial deployment of differential privacy. Aleksandra’s most recent work, on discrimination in ad delivery, has received CSCW Honorable Mention Award and Recognition of Contribution to Diversity and Inclusion, was cited in Facebook’s Civil Rights Audit Report, and invited for a briefing for Members of the House Financial Services Committee.
A Teaching Associate Professor in the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern work force to wisely communicate and handle a data-driven future at the nation’s first Master of Science in analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management. Previously, he was Director and Senior Scientist at Elder Research, where he mentored and led a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government. Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.
Eric P. Xing is a Professor of Computer Science at Carnegie Mellon University, and the Founder, CEO, and Chief Scientist of Petuum Inc., a 2018 World Economic Forum Technology Pioneer company that builds standardized artificial intelligence development platform and operating system for broad and general industrial AI applications. He completed his undergraduate study at Tsinghua University, and holds a PhD in Molecular Biology and Biochemistry from the State University of New Jersey, and a PhD in Computer Science from the University of California, Berkeley. His main research interests are the development of machine learning and statistical methodology, and large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems. Prof. Xing currently serves or has served the following roles: associate editor of the Journal of the American Statistical Association (JASA), Annals of Applied Statistics (AOAS), IEEE Journal of Pattern Analysis and Machine Intelligence (PAMI) and the PLoS Journal of Computational Biology; action editor of the Machine Learning Journal (MLJ) and Journal of Machine Learning Research (JMLR); member of the United States Department of Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) advisory group. He is a recipient of the Carnegie Science Award, National Science Foundation (NSF) Career Award, the Alfred P. Sloan Research Fellowship in Computer Science, the United States Air Force Office of Scientific Research Young Investigator Award, the IBM Open Collaborative Research Faculty Award, as well as several best paper awards. Prof Xing is a board member of the International Machine Learning Society; he has served as the Program Chair (2014) and General Chair (2019) of the International Conference of Machine Learning (ICML); he is also the Associate Department Head of the Machine Learning Department, founding director of the Center for Machine Learning and Health at Carnegie Mellon University; and he is a Fellow of the Association of Advancement of Artificial Intelligence (AAAI), and an IEEE Fellow.
Yashesh Shroff is a Lead Strategy Planner at Intel where he focuses on enabling the AI ecosystem on heterogeneous compute. Recently, as a product manager, he was responsible for the AI and media/game graphics software ecosystem showcasing Intel’s latest-gen graphics architecture (10nm). He has over 15 years of technical and enabling experience, spanning optical modeling, statistical analysis, and capital equipment supply chain at Intel. He has over 20 published papers and 4 patents. He has a Ph.D. in EECS from UC Berkeley and a joint MBA from UC Berkeley Haas & Columbia Graduate School of Business.
I’m the Chief Data Scientist at Bill.com and have many years of experience as a scientist and researcher. My recent focus is in machine learning, deep learning, applied statistics and engineering. Before, I was a Postdoctoral Scholar at Lawrence Berkeley National Lab, received my PhD in Physics from Boston University and my B.S. in Astrophysics from University of California Santa Cruz. I have 2 patents and 11 publications to date and have spoken about data at various conferences around the world.
Anne Lifton has ten years of experience in data science and 3 years in data science management. She has worked across a range of industries from medical devices to retail to engineering and specializes in reducing the cycle time to delivery of models.
Gerald Quon is an Assistant Professor in the Department of Molecular and Cellular Biology at the University of California at Davis. He obtained his Ph.D. in Computer Science from the University of Toronto, M.Sc. in Biochemistry from the University of Toronto, and B. Math in Computer Science from the University of Waterloo. He also completed postdoctoral research training at MIT. His lab focuses on applications of machine learning to human genetics, genomics and health, and is funded by the National Science Foundation, National Institutes of Health, the Chan Zuckerberg Initiative, and the American Cancer Society.
Raluca Ada Popa is an assistant professor of computer science at UC Berkeley. She is interested in security, systems, and applied cryptography. Raluca developed practical systems that protect data confidentiality by computing over encrypted data, as well as designed new encryption schemes that underlie these systems. Some of her systems have been adopted into or inspired systems such as SEEED of SAP AG, Microsoft SQL Server’s Always Encrypted Service, and others. Raluca received her PhD in computer science as well as her two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of an Intel Early Career Faculty Honor award, George M. Sprowls Award for best MIT CS doctoral thesis, a Google PhD Fellowship, a Johnson award for best CS Masters of Engineering thesis from MIT, and a CRA Outstanding undergraduate award from the ACM.
Josh is the Founder of Stealth Startup and his research focuses on applying deep reinforcement learning, generative models, and synthetic data to problems in robotic perception and control. Additionally, he co-organizes a machine learning training program for engineers to learn about production-ready deep learning called Full Stack Deep Learning. Previously, Josh was a Research Scientist at OpenAI working at the intersection of machine learning and robotics. Josh did his PhD in Computer Science at UC Berkeley advised by Pieter Abbeel. He have also been a management consultant at McKinsey and an Investment Partner at Dorm Room Fund.
Alejandro is a Ph.D. Candidate at Stanford University and Professor Ron Howard’s last doctoral student. His research focus is on approximation of multi-attribute utility functions with the use of thresholds and the implications that this approach has on the creation of better decision support automation. He is passionate about operationalizing decision making within organizations in a way that creates an “alive process”. Before coming to Stanford, he was an Operations consultant and an Operations Manager for several manufacturing businesses in his native Venezuela. He has led large teams in delivering results in operations rich fields such as food and heavy metal manufacturing.
Alex Ratner has Ph.D. in computer science at Stanford, advised by Chris Re, where his research focuses on weak supervision: the idea of using higher-level, noisier input from domain experts to train complex state-of-the-art models where limited or no hand-labeled training data is available. He leads the development of the Snorkel framework (snorkel.stanford.edu) for weakly supervised ML, which has been applied to machine learning problems in domains like genomics, radiology, and political science. He is supported by a Stanford Bio-X SIGF fellowship.
An AI expert and health AI pioneer, Suchi Saria’s research has led to myriad new inventions to improve patient care. Her work first demonstrated the use of machine learning to make early detection possible in sepsis, a life-threatening condition (Science Trans. Med. 2015). In Parkinson’s, her work showed a first demonstration of using readily-available sensors to easily track and measure symptom severity at home, to optimize treatment management (JAMA Neurology 2018). On the technical front, her work at the intersection of machine learning and causal inference has led to new ideas for building and evaluating reliable ML (ACM FAT 2019). Suchi currently holds a John C. Malone endowed chair at Johns Hopkins University, with appointments across engineering, public health, and medicine. She is also the Founder of Bayesian Health, aiming to revolutionize the delivery of healthcare by empowering providers and health systems with real-time access to essential clinical inferences. She is the recipient of numerous prizes and honors, including being named a Sloan Research Fellow, a National Academy of Medicine Emerging Leader in Health and Medicine, MIT Technology Review’s 35 Innovators Under 35, and a World Economic Forum Young Global Leader.
John Zedlewski is the director of GPU-accelerated machine learning on the NVIDIA Rapids team. Previously, he worked on deep learning for self-driving cars at NVIDIA, deep learning for radiology at Enlitic, and machine learning for structured healthcare data at Castlight. He has an MA/ABD in economics from Harvard with a focus in computational econometrics and an AB in computer science from Princeton.
GPU-accelerated Data Science with RAPIDS (Workshop)
Sriram Sankararaman is an assistant professor in the Departments of Computer Science, Human Genetics, and Computational Medicine at UCLA where he leads the machine learning and genomic lab. His research interests lie at the interface of computer science, statistics and biology and is interested in developing statistical machine learning algorithms to make sense of large-scale biomedical data and in using these tools to understand the interplay between evolution, our genomes and traits. He received a B.Tech. in Computer Science from the Indian Institute of Technology, Madras, a Ph.D. in Computer Science from UC Berkeley and was a post-doctoral fellow in Harvard Medical School before joining UCLA. He is a recipient of the Alfred P. Sloan Foundation fellowship (2017), Okawa Foundation grant (2017), the UCLA Hellman fellowship (2017), the NIH Pathway to Independence Award (2014), a Simons Research fellowship (2014), and a Harvard Science of the Human Past fellowship (2012) as well as the Northrop-Grumman Excellence in Teaching Award at UCLA (2019).
Sijun He is a machine learning engineer at Twitter Cortex, where he works on content understanding with deep learning and NLP. Previously, he was a data scientist at Autodesk. Sijun holds an MS in statistics from Stanford University.
As the Executive Director of the Human Rights Data Analysis Group, Megan Price designs strategies and methods for statistical analysis of human rights data for projects in a variety of locations including Guatemala, Colombia, and Syria. Her work in Guatemala includes serving as the lead statistician on a project in which she analyzed documents from the National Police Archive; she has also contributed analyses submitted as evidence in two court cases in Guatemala. Her work in Syria includes serving as the lead statistician and author on three reports, commissioned by the Office of the United Nations High Commissioner of Human Rights (OHCHR), on documented deaths in that country. Megan is a member of the Technical Advisory Board for the Office of the Prosecutor at the International Criminal Court and a Research Fellow at the Carnegie Mellon University Center for Human Rights Science. She is the Human Rights Editor for the Statistical Journal of the International Association for Official Statistics (IAOS) and on the editorial board of Significance Magazine. She earned her doctorate in biostatistics and a Certificate in Human Rights from the Rollins School of Public Health at Emory University. She also holds a master of science degree and bachelor of science degree in Statistics from Case Western Reserve University.
Vinod Bakthavachalam is a Data Scientist working with the Content Strategy and Enterprise teams, focusing on using Coursera’s data to understand what are the most valuable skills across roles, industries, and geographies. Prior to Coursera, he worked in quantitative finance and studied Economics, Statistics, and Molecular & Cellular Biology at UC Berkeley.
Causal Inference in Data Science(Workshop)
Piero Molino is a Senior Research Scientist at Uber AI with focus on machine learning for language and dialogue. Piero completed a PhD on Question Answering at the University of Bari, Italy. Founded QuestionCube, a startup that built a framework for semantic search and QA. Worked for Yahoo Labs in Barcelona on learning to rank, IBM Watson in New York on natural language processing with deep learning and then joined Geometric Intelligence, where he worked on grounded language understanding. After Uber acquired Geometric Intelligence, he became one of the founding members of Uber AI Labs. At Uber he works on research topics including Dialogue Systems, Language Generation, Graph Representation Learning, Computer Vision, Reinforcement Learning and Meta Learning. He also worked on several deployed applications like COTA, an ML and NLP model for Customer Support, Dialogue Systems for driver hands free dispatch, pickup and communications, and on the Uber Eats Reommender System. He is the author of Ludwig, a code-free deep learnin toolbox.
Daniel Whitenack (aka Data Dan) is a PhD trained data scientist who has been developing artificial intelligence applications in the real world for over 10 years. He knows how to see beyond the hype of AI and machine learning to build systems that create business value, and he has taught these skills to 1000’s of developers, data scientists, and engineers all around the world. Now with the AI Classroom event, Data Dan is bringing this knowledge to an live, online learning environment so that you can level up your career from anywhere!
Hanie Sedghi is a research scientist at Google Brain. She works on large-scale machine learning, especially latent variable probabilistic models. Her approach is to bond theory and practice in machine learning by designing algorithms with theoretical guarantees that also work efficiently in practice and lead the state of the art. Prior to joining Brain, she was a research scientist at Allen Institute for AI. Hanie received her PhD in Electrical Engineering from University of Southern California with a minor in Mathematics in 2015. She was also closely collaborating with Professor Anima Anandkumar at UC Irvine during her PhD studies. She received her M.Sc. and B.Sc. degrees from Sharif University of Technology, Tehran, Iran.
Brian Lucena is a Principal at Lucena Consulting and a consulting Data Scientist at Agentero. An applied mathematician in every sense, he is passionate about applying modern machine learning techniques to understand the world and act upon it. In previous roles, he has served as SVP of Analytics at PCCI, Principal Data Scientist at Clover Health, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.
Biplav Srivastava is a Professor of Computer Science at the AI Institute at the University of South Carolina. Previously, he was at IBM for nearly two decades in the roles of a Research Scientist, Distinguished Data Scientist and Master Inventor. Biplav is an ACM Distinguished Scientist, AAAI Senior Member, IEEE Senior Member and AAAS Leshner Fellow for Public Engagement on AI (2020-2021). His focus is on promoting goal-oriented, ethical, human-machine collaboration via natural interfaces using domain and user models, learning and planning. He applies these techniques in areas of social as well as commercial relevance with particular attention to issues of developing countries (e.g., transportation, water, health and governance). Biplav’s work has lead to many science firsts and high-impact commercial innovations ($B+), 150+ papers and 50+ US patents issued, and awards for papers, demos and hacks. He has interacted with commercial customers, universities and governments, been on multilateral bodies, and assisted business leaders on technical issues.
Stanislav Kirillov is the leading developer in the group of ML-platforms in Yandex where he develops machine learning tools, supporting and developing infrastructure for them.
Mikhail Burtsev is a head of Neural Networks and Deep Learning Laboratory at Moscow Institute of Physics and Technology. He is also founder and leader of open-source conversational AI framework DeepPavlov. Mikhail had proposed and co-organize a series of academic Conversational AI Challenges (including NIPS 2017, NeurIPS 2018, EMNLP 2020). His research interests are in fields of Natural Language Processing , Machine Learning, Artificial Intelligence and Complex Systems. Mikhail Burtsev has published more than 20 technical papers including – Nature, Artificial Life, Lecture Notes in Computer Science series and other peer reviewed venues.
Conversational AI with DeepPavlov(Workshop)
Jennifer Redmon joined Cisco in 2009 and serves as its, and industry’s 1st, Chief Data & Analytics Evangelist as well as sits on Forbes’ Technology Council. Jennifer is a leader in developing data-driven cultures and organizational analytical maturity through qualitative and quantitative methods. Her approach to fostering a data-driven workforce is taught at multiple higher ed. institutions around the world. Jennifer is passionate about giving back and consequently, founded Cisco’s Data Science and AI for Good initiative. She leads the company’s award-winning Data Science and AI for Suicide Prevention Team, which, inspired by and in collaboration with WHO researchers, focus on de-stigmatizing mental illness through cultural change. Her authorship focuses on data- and analytics-driven cultures, mental health in the workplace, suicide prevention, as well as innovative applications of data science and AI including social good. Jennifer holds an international MBA from Duke University with a concentration in Strategy, Bachelor’s Degrees in Economics and Art History from UC Davis, is in Georgia Tech’s Master of Science in Analytics Spring 2021 class, and is a certified Suicide First Responder.
Pablo Samuel Castro was born and raised in Quito, Ecuador, and moved to Montreal after high school to study at McGill. He stayed in Montreal for the next 10 years, finished his bachelors, worked at a flight simulator company, and then eventually obtained his masters and PhD at McGill, focusing on Reinforcement Learning. After his PhD Pablo did a 10-month postdoc in Paris before moving to Pittsburgh to join Google. He has worked at Google for over 8 years, and is currently a Staff Research Software Developer in Google Brain in Montreal, focusing on fundamental Reinforcement Learning research, as well as Machine Learning and Creativity. Aside from his interest in coding/AI/math, Pablo is actively trying to increase the presence of the latin american community in the AI research ecosystem. On the side, he’s an active musician (https://www.psctrio.com/), loves running (5 marathons so far, including Boston!), and enjoys discussing politics and activism.
Tina Eliassi-Rad is a Professor of Computer Science at Northeastern University. She is also a core faculty member at Northeastern University’s Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences at the University of Wisconsin-Madison. Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of big data from networked representations of physical and social phenomena. She has over 100 peer-reviewed publications (including a few best paper and best paper runner-up awardees). Tina’s work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, and ethics in machine learning. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2017, Tina served as the program co-chair for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining and as the program co-chair for the International Conference on Network Science . In 2020, she is serving as the program co-chair for the International Conference on Computational Social Science. Tina received an Outstanding Mentor Award from the Office of Science at the US Department of Energy in 2010; and became a Fellow of the ISI Foundation in Turin Italy in 2019.
Just Machine Learning(Talk)
Allen Downey is a Professor of Computer Science at Olin College of Engineering in Needham, MA. He is the author of several books related to computer science and data science, including Think Python, Think Stats, Think Bayes, and Think Complexity. Prof Downey has taught at Colby College and Wellesley College, and in 2009 he was a Visiting Scientist at Google. He received his Ph.D. in Computer Science from U.C. Berkeley, and M.S. and B.S. degrees from MIT.
Bayesian Statistics Made Simple(Workshop)
Bolei Zhou is an Assistant Professor with the Information Engineering Department at the Chinese University of Hong Kong. He received his PhD in computer science at the Massachusetts Institute of Technology. His research is on machine perception and decision, with a focus on visual scene understanding and interpretable AI systems. He received the MIT Tech Review’s Innovators under 35 in Asia-Pacific award, Facebook Fellowship, Microsoft Research Asia Fellowship, MIT Greater China Fellowship, and his research was featured in media outlets such as TechCrunch, Quartz, and MIT News. More about his research is at http://bzhou.ie.cuhk.edu.hk/.
Peter Bailis is the founder and CEO of Sisu Data, a data analytics platform that helps users understand the key drivers behind critical business metrics in real time. Peter is also an assistant professor of Computer Science at Stanford University, where he co-leads Stanford DAWN, a research project focused on making it dramatically easier to build machine learning enabled applications. He received his Ph.D. from UC Berkeley in 2015, for which he was awarded the ACM SIGMOD Jim Gray Doctoral Dissertation Award, and holds an A.B. from Harvard College in 2011, both in Computer Science.
Jenn Wortman Vaughan is a Senior Principal Researcher at Microsoft Research, New York City. Her research background is in machine learning and algorithmic economics. She is especially interested in the interaction between people and AI, and has often studied this interaction in the context of prediction markets and other crowdsourcing systems. In recent years, she has turned her attention to fair and interpretable machine learning as part of MSR’s FATE group. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009 and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn’s 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a handful of best paper awards. In her “spare” time, Jenn is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which has been held each year since 2006.
Rich Dutton is the Head of Machine Learning for Corporate Engineering at Google, where he leads a team of 15 engineers and data scientists across NYC and Austin. Prior to this role, Rich was a tech lead in Bigtable at Google following a 15 year career working in data and analytics across both tech and finance in the US (New York and Seattle), Europe and Asia.
How Google Uses AI and Machine Learning in the Enterprise(Business Talk)
Javed is an economist and data scientist with experience in banking, finance, forecasting, risk management, consulting, policy, and behavioral economics. He has led development of analytic applications for large organizations including Amazon and the Federal Reserve Board of Governors, and served as a researcher with the Office of Financial Research (U.S. Treasury). He holds a PhD in financial economics and MA in statistics from U.C. Berkeley, as well as undergraduate degrees in operations management and systems engineering from the University of Pennsylvania. Currently, Javed is a Senior Data Scientist on the Corporate Training team at Metis, where he works with companies to upskill their staff in data science and analytics.
Dave Stuart is a senior technical executive within the US Department of Defense, where he’s leading a large-scale effort to transform the workflows of thousands of enterprise business analysts through Jupyter and Python adoption, making tradecraft more efficient, sharable, and repeatable. Previously, Dave led multiple grassroots technology adoption efforts, developing innovative training methods that tangibly increased the technical proficiency of a large noncoding enterprise workforce.
Tian Zheng is Professor and Department Chair of Statistics at Columbia University. She develops novel methods for exploring and understanding patterns in complex data from different application domains. Her current projects are in the fields of statistical machine learning, spatiotemporal modeling and social network analysis. Professor Zheng’s research has been recognized by the 2008 Outstanding Statistical Application Award from the American Statistical Association (ASA), the Mitchell Prize from ISBA and a Google research award. She became a Fellow of American Statistical Association in 2014. Professor Zheng is the recipient of the 2017 Columbia’s Presidential Award for Outstanding Teaching. From 2018-2020, she has been the chair-elect, chair and past-chair for ASA’s section on Statistical Learning and Data Science.
Topic-Adjusted Visibility Metric for Scientific Articles(Track Keynote)
Marcin von Grotthuss is a Senior Computational Scientists at the Broad Institute of MIT and Harvard. He earned the Ph.D. in bioinformatics at Radboud University Nijmegen, Netherlands and gained his professional experiences at Sanford-Burnham Institute (La Jolla), University of Washington (Seattle), Harvard University, University of Cambridge, University of California Irvine, and the Brigham and Women’s Hospital / Harvard Medical School. In his work, initially, he applied machine-learning technics to estimate the biomedical properties of small molecules. Next, he worked on distant sequence-structure-function relationships in proteins. After that, he was focused on the genomics of model organisms and human genetics (1000 Genomes Project). Currently, he develops knowledge portals for complex traits like type 2 diabetes, stroke, and cardiovascular diseases. And he is one of the instrumental scientists in the consortium that builds a Biomedical Data Translator.
Dr. Marie desJardins is the Dean of the College of Organizational, Computational, and Information Sciences. In this role she will lead collaboration among faculty and other stakeholders in pursuing the growth and development of College programs and initiatives, align an interdisciplinary collaboration that increases pathways from undergraduate to graduate study in growing fields, and promote a culture of research and scholarly productivity through innovative teaching and engaged learning.
Prior to joining Simmons, Dr. desJardins served as Professor and Associate Dean for Academic Affairs at the University of Maryland, Baltimore County’s College of Engineering and Information Technology, where she oversaw multiple assessment activities, including leading a successful re-accreditation of four undergraduate engineering and computing undergraduate curriculum. A career educator, Dr. desJardins has published over 100 scientific papers in journals, conferences, and workshops. Her research is in artificial intelligence, focusing on the areas of machine learning, multi-agent systems, planning, interactive AI techniques, information management, reasoning with uncertainty, and decision theory. Dr. desJardins graduated magna cum laude from Harvard University with a Bachelor of Arts in engineering and computer science, and earned her PhD in computer science from the University of California, Berkeley.
Mark Weber is an applied researcher and Strategy & Operations Lead at the MIT-IBM Watson AI Lab, a $250 million partnership funding over 200 scientists making fundamental breakthroughs in AI. Through the lab’s corporate membership program, which he runs, Mark works closely with global leaders across multiple sectors on the creative challenge of bridging fundamental science to real-world impact. Mark’s current applied research includes neuro-symbolic generative modeling for construction monitoring, graph deep learning for anti-money laundering, and supply chain demand forecasting. Mark also oversees strategic engagements with IDEO, the International Monetary Fund, and the Internal Revenue Service. Prior to IBM Research, Mark was a graduate researcher at the MIT Media Lab and a fellow at the MIT Legatum Center for Development & Entrepreneurship while he earned his M.B.A in finance from MIT Sloan. There he led the development of an open-source protocol called b_verify for verifiable records in supply chain finance. Before his foray into technology, Mark spent the first chapters of his career focused on political economy and development. He produced three documentary films on these subjects, most notably the critically acclaimed film Poverty, Inc
Luis Serrano is a Quantum AI Research Scientist at Zapata Computing. He is the author of the book Grokking Machine Learning and maintains a popular YouTube channel where he explains machine learning in pedestrian terms.
Luis has previously worked in machine learning at Apple and Google, and at Udacity as the head of content for AI and data science. He has a PhD in mathematics from the University of Michigan, a master’s and bachelors from the University of Waterloo, and worked as a postdoctoral researcher in mathematics at the University of Quebec at Montreal.
Introduction to Generative Modeling Using Quantum Machine Learning(Half-Day Training)
Quant/researcher with extensive experience in building cutting-edge statistical and predictive models and advanced machine learning algorithms to solve practical problems in finance and consumer analytics, especially within credit, portfolio risk modeling, capital forecast models, and optimal control models.
Inverse Reinforcement Learning for Financial Applications(Business Talk)
Dr. Shanghang Zhang is a postdoctoral research fellow in the Berkeley AI Research (BAIR) Lab, the Department of Electrical Engineering and Computer Sciences, UC Berkeley, USA. She received her Ph.D. from Carnegie Mellon University in 2018. Her research interests cover deep learning, computer vision, and reinforcement learning, as reflected in her numerous publications in top-tier journals and conference proceedings, including NeurIPS, CVPR, ICCV, and AAAI. Her research mainly focuses on machine learning with limited training data, including low-shot learning, domain adaptation, and meta-learning, which enables the learning system to automatically adapt to real-world variations and new environments. She was one of the “2018 Rising Stars in EECS” (a highly selective program launched at MIT in 2012, which has since been hosted at UC Berkeley, Carnegie Mellon, and Stanford annually). She has also been selected for the Qualcomm Innovation Fellowship (QInF) Finalist Award and Chiang Chen Overseas Graduate Fellowship.
Charles is a PhD candidate at McGill University and Mila – the Québec AI Institute. He is interested in the design and development of improved diagnostic and decision-making tools in healthcare. He leads the Ubenwa Health, a collaboration between researchers in Canada and Nigeria, developing low-cost, AI-powered mobile app for the diagnosis of perinatal asphyxia from the infant cry. At the Montreal Children’s Hospital, Charles is involved in the APEX project, where he has been developing machine learning algorithms for analyzing cardiorespiratory behaviour of preterm newborns in order to determine their readiness for extubation. He has worked with Health Experiences Research Canada contributing to the design and leading the development of the HERS mobile app – a personalized recommendation tool for breast cancer patients. Charles is a Jeanne Sauvé Fellow and a Vanier Doctoral Scholar.
Robert Munro is an expert in combining Human and Machine Intelligence, working with Machine Learning approaches to Text, Speech, Image and Video Processing. Robert has founded several AI companies, building some of the top teams in Artificial Intelligence. He has worked in many diverse environments, from Sierra Leone, Haiti and the Amazon, to London, Sydney and Silicon Valley, in organizations ranging from startups to the United Nations. He has shipped Machine Learning Products at startups and at/with Amazon, Google, IBM & Microsoft.
Robert has published more than 50 papers on Artificial Intelligence and is a regular speaker about technology in an increasingly connected world. He has a PhD from Stanford University. Robert is the author of Human-in-the-Loop Machine Learning (Manning Publications, 2020)
Annie is a leader in data science with over a decade of industry experience spanning corporate research and startups. Currently, she is the head of the Cisco Data Science Lab in Vancouver. Coming from a research background with a PhD in Computer Science from McGill, Annie is a former Research Scientist at IBM T. J. Watson Research Center in New York and a two-time winner of an ACM Distinguished Paper Award (both in applying data science to software engineering) with five patents (granted and applied). Annie is active in the data science community as a Meetup organizer (Data Science for Social Good), speaker, and mentor.
Thomas J. Fan is a Senior Software Engineer at Quansight Labs, working to sustain and evolve the PyData open-source ecosystem. He is a maintainer for scikit-learn, an open-source machine learning library written for Python. Previously, he worked at Columbia University, improving the interoperability between scikit-learn and AutoML systems. Thomas holds a Masters in Physics from Stony Brook University and a Masters in Mathematics from New York University.
Introduction to Scikit-learn: Machine Learning in Python(Half-Day Training)
Seth Weidman is a data scientist at SentiLink, an Andreesen Horowitz-backed startup based in San Francisco; he works on SentiLink’s core models that prevent a various forms of fraud – especially synthetic identity fraud – and other malicious behavior for banks and lenders. Immediately before SentiLink, Seth did machine learning engineering at Facebook for the data centers team; he also wrote an introductory book on deep learning called Deep Learning from Scratch that was published by O’Reilly in 2019. Seth has degrees in mathematics and economics from the University of Chicago.
Joseph Nelson is co-founder and CEO of Roboflow, a computer vision developer tool. Roboflow enables anyone to build better computer vision models quickly. Joseph previously co-founded Represently (acq. 2018). He has been named Distinguished Faculty at General Assembly and worked at companies big (Facebook) and small (failed startups). Joseph is a managing partner at BetaVector, a data science consultancy he co-founded. He’s easily reached on Twitter: @josephofiowa
Julie Shah is an Associate Professor in the Department of Aeronautics and Astronautics at MIT and leads the Interactive Robotics Group of the Computer Science and Artificial Intelligence Laboratory. Shah received her SB (2004) and SM (2006) from the Department of Aeronautics and Astronautics at MIT, and her PhD (2010) in Autonomous Systems from MIT. Before joining the faculty, she worked at Boeing Research and Technology on robotics applications for aerospace manufacturing. She has developed innovative methods for enabling fluid human-robot teamwork in time-critical, safety-critical domains, ranging from manufacturing to surgery to space exploration. Her group draws on expertise in artificial intelligence, human factors, and systems engineering to develop interactive robots that emulate the qualities of effective human team members to improve the efficiency of human-robot teamwork. In 2014, Shah was recognized with an NSF CAREER award for her work on “Human-aware Autonomy for Team-oriented Environments,” and by the MIT Technology Review TR35 list as one of the world’s top innovators under the age of 35. Her work on industrial human-robot collaboration was also recognized by the Technology Review as one of the 10 Breakthrough Technologies of 2013, and she has received international recognition in the form of best paper awards and nominations from the International Conference on Automated Planning and Scheduling, the American Institute of Aeronautics and Astronautics, the IEEE/ACM International Conference on Human-Robot Interaction, the International Symposium on Robotics, and the Human Factors and Ergonomics Society.
Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks and Manchester United, and with large social networks, like Justgiving. His previous experience includes Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through an acquisition and an IPO.
He is also the lead instructor at ideai.io, a company specialized in Reinforcement Learning, Deep Learning and Machine Learning training. He is also a contractor for several companies and for the European Commission, as an expert in AI and Machine Learning. As an author he wrote “Hands On Deep Learning” and he authored an online training course for O’Reilly, Introduction to Reinforcement Learning. In the academic world, he also helped set up the PhD center on Interactive Artificial Intelligence and will take part in the Inner Assessment Board to assign funding to Irish research in AI.
NLP Fundamentals(Full-Day Training)
Jordan Bakerman holds a Ph.D. in statistics from North Carolina State University. His dissertation centered on using social media to forecast real world events, such as civil unrest and influenza rates. As an intern at SAS, Jordan wrote the SAS Programming for R Users course for students to efficiently transition from the R to SAS using a cookbook style approach. As an employee, Jordan has developed courses demonstrating how to integrate open source software within SAS products. He is passionate about statistics, programming, and helping others become better statisticians.
End to End Modeling and Machine Learning (Workshop)
Anthony is the lead engineer on the Training Data Product and Founder of Diffgram. Prior he was a Data Engineer at Skidmore, Owings & Merrill and Co-Founded DriveCarma.ca – an Automotive Data Platform.
Anna is CTO of OpenCV.AI – a for-profit arm of OpenCV.org, the most popular Computer Vision library in the world. Anna is an expert in Deep Learning for Computer Vision with 10-year experience in the industry. Previously Anna created open-source optimized Machine Learning libraries, and worked on state-of-the-art Deep Learning algorithms for autonomous driving, retail, medicine and AR, most of them specifically optimized for fast inference on small edge devices.
Ari Zitin holds bachelor’s degrees in both physics and mathematics from UNC-Chapel Hill. His research focused on collecting and analyzing low energy physics data to better understand the neutrino. Ari taught introductory and advanced physics and scientific programming courses at UC-Berkeley while working on a master’s in physics with a focus on nonlinear dynamics. While at SAS, Ari has worked to develop courses that teach how to use Python code to control SAS analytical procedures, including machine learning and optimization.
End to End Modeling and Machine Learning (Workshop)
Boris Paskhaver is a full-stack web developer based in New York City with experience building apps in React / Redux and Ruby on Rails. His favorite part of programming is the never-ending sense that there’s always something new to master — a secret language feature, a popular design pattern, an emerging library or — most importantly — a different way of looking at a problem.
Getting Started with Pandas for Data Analysis(Half-Day Training)
Bethany Poulin is a data scientist and educator with expertise in statistical analysis, data visualization, and complex algorithmic problem-solving. She has worked as a professional data scientist and educator for the last 4 years and loves sharing what she has learned with her students. Her unusual background in Fine Arts and experience teaching high school give her a unique perspective on both problem-solving and the learning-teaching process. Prior to teaching this part-time course, she was a lead instructor in our Data Science Immersive program on the Boston campus. She teaches simply because she loves students and enjoys being a part of their success. She holds a BFA in Professional Photography from Rochester Institute of Technology, did post bachelor’s studies and the University of Montana in Environmental Biology, where she was recognized nationally as a Morris K Udall Scholar, is one semester away from an MS in Data Science from the City University of New York. She is has presented at PyOhio 2018 and 2019 and gave a presentation at ODSC East in 2019. In her spare time, Bethany is an avid fly fisherman, potter, and maker.
Introduction to Shiny Application Development(Half-Day Training)
Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he’s a Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a search engine startup, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events efficiently. Serg is passionate about providing the often-missing link between data and decision-making. His book titled “Interpretable Machine Learning with Python” is scheduled to be released in early 2021 by UK-based publisher Packt.
David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a Ph.D. in computer science and master’s degrees in both computer science and business administration.
At the age of 8, David began learning the BASIC programming language while living in the outskirts of Alaska. He studied music performance but found the beginning of his career building a small software and consulting company in the late 90’s. David’s career spans almost 20 years including many startups as a lead engineer, building scalable data services from prototype to production. During his time at Sony/Gracenote, leading the development of a handful of prototypes were featured multiple years in the Consumer Electronics Show, spanning problems with recommender systems, content classification, and profiling type problems. More recently, David was a data scientist at a YC backed dating app company researching and building scalable recommendation pipelines.
Currently, David works at General Assembly as a Global Data Science Instructor, where he helped architect the first version of the data science immersive curriculum and pilot many new programs developed internally. Regularly delivering lectures in a hybrid format classroom on topics ranging from engineering, statistics, and ML.
Data Visualization: From Jupyter to Dashboards(Half-Day Training)
Bill Keogh is an insurance, analytics and technology Advisor, and the former Chairman and Chief Executive Officer of Advisen. He has spent more than 20 years in the insurance analytics and technology world, having developed a passion for insurance analytics in the early 1990’s. He collaborates with insurance, analytics and technology leaders to develop and implement successful business strategies. Bill is a founding board member of the International Society of Catastrophe Managers (ISCM) and currently a board member of the St. Joseph’s Maguire School of Risk Management. He is also an adviser at Innovation Underwriters, The Insurance Collaborative – a non-profit fostering innovation in Risk Management and Insurance.
Change-Makers Who are Transforming Insurance with AI (Business Talk)
Azalia Mirhoseini is a Senior Research Scientist at Google Brain. She is the co-founder/tech-lead of the Machine Learning for Systems Team in Google Brain where they focus on deep reinforcement learning based approaches to solve problems in computer systems and metalearning. She has a Ph.D. in Electrical and Computer Engineering from Rice University. She has received a number of awards, including the MIT Technology Review 35 under 35 award, the Best Ph.D. Thesis Award at Rice and a Gold Medal in the National Math Olympiad in Iran. Her work has been covered in various media outlets including MIT Technology Review, IEEE Spectrum, and Wired.
Stan Smith, Founder and CEO – Stan founded Gradient AI, first as a unique practice within Milliman to focus on the risk management and insurance industry’s most challenging business problems. He subsequently acquired the business from Milliman in 2018, founding Gradient as a rapid growth independent SaaS organization focused on the risk management and insurance industry’s most challenging business problems. With nearly 30 years of experience growing AI and technology organizations, Stan’s leadership ensures that Gradient is applying the latest Artificial Intelligence and Machine Learning technologies to the insurance industry, resulting in proven financial performance for its stellar list of customers and better treatment and outcomes for individuals.
Change-Makers Who are Transforming Insurance with AI(Business Talk)
Sears is a senior leader with expertise in the areas of data science and analytics as well as enterprise and internet technology. Over the past 15 years, Sears has spent time leading and innovating in numerous industries, including healthcare, telecommunications, and financial services. Sears currently leads MassMutual’s technology strategy, enterprise architecture, and data science & advanced analytics functions. The organization is focused on bringing automation, data science, analytics, machine learning and artificial intelligence to bear throughout the firm and driving change in the industry. Sears holds a Ph.D. in Computer Science, M.S. in Telecommunications, and B.S. in Electrical Engineering from the University of Colorado at Boulder and an M.B.A. from the Sloan School at Massachusetts Institute of Technology. Sears is a frequent speaker at industry conferences and has numerous patents and publications in the areas of machine learning, technology and financial services. Sears has been recognized as one of the life insurance industry’s top 25 innovators under 40 by LIMRA and a top 100 innovator by Corinium Intelligence. He is an advisor to Antara Health and a member of the analytics advisory board for Corinium Intelligence and the Innovation Committee for the American Council of Life Insurers.
Change-Makers Who are Transforming Insurance with AI (Business Talk)
Susan K. Gregurick, Ph.D., was appointed Associate Director for Data Science and Director of the Office of Data Science Strategy (ODSS) at the National Institutes of Health (NIH) on September 16, 2019. Under Dr. Gregurick’s leadership, the ODSS leads the implementation of the NIH Strategic Plan for Data Science through scientific, technical, and operational collaboration with the institutes, centers, and offices that comprise Dr. Gregurick was previously the Division Director for Biophysics, Biomedical Technology, and Computational Biosciences at the National Institute of General Medical Sciences (NIGMS). In this role Dr. Gregurick led the institutes effort to reimagine the NIGMS technology programs including early stage, concept development, focused technology programs, development and dissemination centers, through National and Regional Resources to support state-of-the-art facilities, equipment, technologies, research tools, software, and service. Prior to joining the NIH, Susan was a program manager for the Department of Energy where she oversaw the development and implementation of the DOE Systems Biology Knowledgebase, which is a framework to integrate data, models, and simulations together for a better understanding of energy and environmental processes. During Susan’s academic career she was a Professor of Computational Biology at the University of Maryland, Baltimore County and her research interests include dynamics of large biological macromolecules. Susan holds a Ph.D. in Computational Chemistry and her areas of expertise are computational biology, high-performance computing, neutron scattering and bioinformatics.
Going Beyond FAIR to Create a Connected Data Ecosystem(Business Talk)
Charlie has spent 25+ years in a variety of roles with P&C commercial insurance providers, has run multiple technology startups, and now finds himself running the insurance-focused innovation hub at Cambridge Innovation Center. He is often focused on how innovation can be leveraged to build systems that prevent problems occurring instead of just fixing the consequences of know problems over and over. He’s always looking for ways to go upstream to find ways that mitigate risk in a dramatic fashion in a way that eliminates the need for insurance. Companies that align with that philosophy will earn the customer’s attention even if they fall short.
Change-Makers Who are Transforming Insurance with AI (Business Talk)
Ian Johnson is a User Experience Engineer at Google. He also organizes of Bay Area d3, starts with SVG and then dives deep into d3 including DOM manipulation, categorical and quantitative scales, axis, brushes, color schemes, events and histograms. Ian likes to make sense of data by exploring it visually with D3.js!
Painting with Data: Introduction to d3.js(Half-Day Training)
Kenny is interested in bringing human capabilities – particularly language, vision, and the acquisition of everyday knowledge – to modern technology.
Ariel Rokem is a Research Assistant Professor at the University of Washington Department of Psychology. He received a PhD in neuroscience from UC Berkeley (2010) and additional postdoctoral training in computational neuroimaging at Stanford (2011 – 2015). He was also previously a Senior Data Scientist at the University of Washington eScience Institute (2015-2020)
He leads a research program in neuroinformatics, the development of data science tools, techniques and methods and their application to the analysis of neural data. One thrust of this research focuses specifically on the application of methods from statistical learning to analysis of diffusion MRI data acquired in human brain. This type of data sheds light on the role that human brain connections play in cognitive abilities, in diverse behaviors, and in neurological and psychiatric disorders.
Another thrust of the research focuses specifically on the development of systems for analysis (e.g., Mehta et al. 2017, Richford and Rokem, 2018) and sharing (e.g. Yeatman et al. 2018) of large-scale open datasets, to enable research with datasets that are increasingly becoming available through data-sharing initiatives, and to facilitate its reproducibility.
He is a member of the Software and Data Carpentry communities, where he has been an instructor since 2013 and an instructor trainer 2015. He also directs the annual Summer Institute for Neuroimaging and Data Science (NeuroHackademy). A contributor to multiple open-source software projects in the scientific Python ecosystem, he is a member of the editorial board of the Journal for Open Source Software (JOSS).
Jekaterina Novikova is a Director of Machine Learning at Winterlight Labs, Toronto/Canada, where she leads a team of research scientists and engineers. She focuses on strategic leadership in machine learning, helping teams to incorporate state-of-the-art research into business priorities, prioritize opportunities, and develop roadmaps. Jekaterina holds a PhD from the University of Bath/UK and did her PostDoc at the Heriot-Watt University in Edinburgh/UK.
Development and evaluation of natural language interfaces is a key area of Jekaterina’s research, with applications ranging from human-robot spoken dialogue systems to machine learning-based diagnostic platforms that detect cognitive and mental diseases from human speech. Jekaterina has authored over 40 peer-reviewed papers in this area, that were published and presented at top-tier conferences, such as EMNLP and ACL.
In recent years, Jekaterina was invited to be the keynote speaker at various conferences and workshops across the globe, such as CogX 2017 in London/UK, Re-Work AI Summit 2018 in Toronto/Canada, MLconf 2019 in San Francisco/US, ODSC East 2020 Virtual. Jekaterina’s work and outreach activities have been recognized with a nomination of “30 Influential Women Advancing AI in Canada”, as well as best research paper nominations at the conferences HAI and SigDIAL.
Kumaran Ponnambalam is an AI and Big Data leader with 15+ years of experience. He is currently the Director of AI for Webex Contact Center at Cisco. He focuses on creating robust, scalable AI platforms and models to drive effective customer engagements. In his current and previous roles, he has built data pipelines, ML models, analytics, and integrations around customer engagement. He has also authored several courses on the LinkedIn Learning Platform in Machine Learning and Big Data areas. He holds an MS in Information Technology and advanced certificates in Deep Learning and Data Science.
Experienced product manager and engineering leader with a focus on data science and data products, currently working on the Apache Arrow project with the Ursa Labs team. Previously, he led product development at Crunch.io, where he grew a geographically distributed, cross-functional team from 5 to 25, shaped the product vision and roadmap, and developed processes to deliver a high-quality product tailored to the needs of survey researchers.
He is also an open-source software developer, author of several packages for the R language, and contributor to many others in R and Python. Many of his projects sit at the intersection of data science and web services. In his projects and public talks, he is a strong advocate for intuitive user experience/API design and for comprehensive test coverage.
Prior to working in software development, he received a Ph.D. in Political Science from UC Berkeley and worked in data science at YouGov, where analyzed survey data and developed tools to streamline data pipelines and workflows. Among the statistical tools and methods he has used professionally and in peer-reviewed publications are experimental and quasi-experimental methods, text classification and sentiment analysis, survey weighting, and web scraping.
Liz Sander is a Data Science Lead at Civis Analytics. They lead a cross-functional team of data scientists and engineers building products to automate survey orchestration and derive insights from survey data. Liz has a PhD in Ecology and Evolution from the University of Chicago, where they studied the structure and stability of ecological networks.
Business Skills for Data Scientists(Business Talk)
Adewale (Wale) Akinfaderin is a Data Scientist at Amazon Web Services. His expertise is in machine learning, deep learning, statistical experimentation and general information theory. He has broad experience implementing and extending ML techniques to solve practical and business problems. In his spare time, he conducts research on Machine Learning for the Developing World.
Mathematics for Data Science and Machine Learning(Half-Day Training)
Scott is a Software Developer in SAS R&D where he integrates open source models and workflows into SAS technology. He’s passionate about making it easy for data scientists using Python to govern, deploy, monitor, and manage their complex models without needing to recode. Scott is a results-oriented research professional, with lots of experience solving complex data and analytical challenges and developing results-oriented and replicable solutions to improve customer outcomes. He is a passionate leader centered on mentoring and coaching teams and providing in-depth insight on challenging problems impacting performance. Scott graduated with a BS in Physics from Clemson University and a master’s and PhD in Physics from North Carolina State University.
Aaron Richter is a software developer turned data engineer and data scientist. He has pioneered the development and implementation of large-scale data science infrastructure in both business and research environments. Inevitably, he spent a lot of time finding efficient ways to clean data, run pipelines, and tune models. Aaron is currently a Senior Data Scientist at Saturn Cloud, where he works to make data scientists faster and happier. He holds a PhD in machine learning from Florida Atlantic University.
Nina Hristozova is a Data Scientist at Thomson Reuters (TR) Labs. She has a BSc in Computer Science from the University of Glasgow, Scotland. As part of her role at TR she has worked on a wide range of projects applying ML and DL to a variety of NLP problems. Her current focus is on applied summarization of legal text. She is actively engaged with local technology Meetups to spread the love and knowledge for NLP through tech talks and workshops. In her free time she plays volleyball for the local team in Zug, enjoys going to the mountains and SUP in the lakes.
Diego Oppenheimer is the co-founder and CEO of Algorithmia. Previously, he designed, managed, and shipped some of Microsoft’s most used data analysis products including Excel, Power Pivot, SQL Server, and Power BI. He holds a Bachelor’s degree in Information Systems and a Master’s degree in Business Intelligence and Data Analytics from Carnegie Mellon University.
Ernie is SVP of Products at MANTA, focusing on solutions for lineage and metadata integration. He has over thirty years of experience in the data integration space, including twenty-plus years at IBM, working in a variety of roles with responsibilities in product management and technical sales support. For most of the past decade Ernie has been providing guidance in information governance and helping architect custom lineage solutions. Earlier in his career, Ernie was building decision support systems with fourth generation languages and data access middleware. Ernie maintains a blog on open metadata, data lineage and overall metadata management and governance.
He is a graduate of Boston College.
While not pursuing MANTA initiatives, working on his blog, or spending time with family, Ernie enjoys woodturning and motorcycles.
Bob Foreman has worked with LexisNexis and the open source big data HPCC Systems technology platform and the ECL programming language for more than 9 years, and has been a technical trainer for over 25 years. He is the developer and designer of the HPCC Systems Online Training Courses, and is the Senior Instructor for all classroom and remote training. This includes: Introduction to ECL (Part 1 and 2) – Concepts and Queries, the Extract, Transform and Load (ETL) Process, Advanced ECL (Part 1 and 2) – Working with Relational Data, Super files, Working with XML, and Free-form Text Parsing, ROXIE ECL – Indexes and Queries, Complex Query Development, and Applied ECL – ECL Code Generation Tools. He has over 30 years of industry experience in training, consulting, and technical writing with RDBMS platforms and most recently Big Data. Bob earned his A.S. in Electronic Engineering and was a Cryptologic Technician in US Navy for 5 years.
Nadja Herger is a Data Scientist at Thomson Reuters Labs, based in Switzerland. She is primarily focusing on Deep Learning PoCs within the Labs, where she is working on applied NLP projects in the legal and news domains, applying her skills to text classification, metadata extraction, and summarization tasks. Before joining Thomson Reuters, she obtained her Ph.D. in Climate Science from the University of New South Wales, Australia. She has successfully made the transition from working with Spatio-temporal data to working with text-based data on the job. Nadja is passionate about education, which is reflected in her ongoing mentorship of students within Thomson Reuters, as well as South African students from previously disadvantaged groups who are aspiring to get into Data Science.
Peter Houlihan specializes in planning and leading expeditions into understudied and threatened rainforests all over the world for conservation. Regularly operating in more than 20 countries across Africa, the Americas, and Asia, Peter is passionate about working with local scientists and communities and inspiring others to learn about our natural world. He has lived and worked extensively throughout the tropics, where he has led nearly 50 large scale expeditions and managed long term conservation programs, particularly in Borneo, Madagascar, the Amazon, Central America, and the Congo Basin. A tropical ecologist and conservation scientist by training, Peter is a Senior Research Fellow with UCLA’s Center for Tropical Research, a frequent visiting scientist at the Smithsonian Tropical Research Institute in Panama, International Advisor for the Borneo Nature Foundation, and an Adjunct Professor for Johns Hopkins University instructing international graduate-level field courses in tropical ecology and conservation.
Globally, Peter’s research, conservation work, and science communication have helped lead to the establishment of new protected areas and the documentation of species new to science. As a multilingual photographer, videographer, and producer, Peter fuses conservation science with high-impact media to inform broad international audiences about our planet. On camera, Peter has appeared alongside Sir David Attenborough in a BAFTA-nominated series for the BBC, across National Geographic platforms, and in an Emmy-nominated PBS documentary. A recent film about his scientific research in the Florida Everglades has received many accolades as an Official Selection of numerous film festivals. Peter is also a National Geographic Explorer, Photographer, and Expeditions Expert, a Fellow of the Explorers Club, and a gear tester for Patagonia.
Hugo supports the development and delivery of training programs for the HPCC Systems platform in the Brazil region since 2019. Hugo has worked for over 15 years on various technical roles in the IT industry with a focus on High Performance Computing. He is also a part time researcher on Information Systems and a member of the UK Academy for Information Systems.
Gopi brings 12-plus years of consulting and executive experience to his role as associate principal. The projects he leads are focused on big data, advanced data science, customer analytics, pricing, and sales and marketing effectiveness. Gopi is a seasoned decision science and analytics executive with extensive expertise in retail, gaming and hospitality, pharma, and P&C industries. He has led several large data and analytics transformation engagements for multiple Fortune 500 firms and helped them scale their data science capabilities to drive top-line and bottom-line growth. Gopi holds a master’s of technology in advanced communication systems and a bachelor’s of technology in electrical engineering from IIT Bombay.
Noemi Derzsy is a Senior Inventive Scientist at AT&T Chief Data Office within the Data Science and AI Research organization. Her research is centered on understanding and modeling customer behavior and experience through large-scale consumer and network data, using machine learning, network analysis/modeling, Spatio-temporal mining, text mining, and natural language processing techniques.
Prior to joining AT&T, Noemi was a Data Science Fellow at Insight Data Science NYC and a postdoctoral research associate at Social Cognitive Networks Academic Research Center at Rensselaer Polytechnic Institute. She holds a Ph.D. in Physics, MS in Computational Physics, and has a research background in Network Science and Computer Science.
Noemi is also involved in volunteering in the data science community. She is a NASA Datanaut and former organizer of the Data Umbrella meetup group and NYC Women in Machine Learning and Data Science meetup group.
Frank is a Senior Director and a key member of S&P Global Market Intelligence’s Quantamental Research group. His primary focus is to conduct systematic alpha research on global equities with publications on natural language processing, newly discovered stock selection anomalies, event-driven strategies, and industry-specific signals. Frank has master’s degrees in Financial Engineering from UCLA Anderson and in Finance from Boston College Carroll and has undergraduate degrees in Computer Science and Economics from the University of California, Davis.
Srinivas leads ZS AI Research Lab with a focus on frontier innovation and development of cutting edge algorithms. Srinivas’s core expertise areas include automated machine learning, natural language processing, and marketing AI across industries. He has authored several thought leadership articles and presented at conferences. Prior to joining ZS, Srinivas spent time as a solution architect building expert systems to automate product design and manufacturing across multiple industries viz., automobile, power systems, medical devices and retail
Russell Martin is a data scientist in residence at the Data Incubator, where he instructs fellows, teaches online courses, and leads training courses with corporate partners. Russ lived and worked in the UK for 17 years, including at Warwick University and the University of Liverpool, where he taught in the Department of Computer Science. He holds a PhD in applied mathematics from the Georgia Institute of Technology.
Teal Guidici is a Senior Machine Intelligence Scientist at Draper where she uses statistical techniques and machine learning algorithms to develop creative solutions for interesting data-driven problems in areas including biomedicine, finance, and remote sensing. Prior to Draper, she did graduate work creating new methods to analyze patterns of co-variation in complex datasets and applied these methods applied to high throughput metabolomics data. She has additional experience in survey design and data analysis in consumer marketing research. Dr. Guidici has a B.S. in Theoretical Mathematics from MIT, a M.S. in Bioinformatics and a Ph.D. in Statistics from the University of Michigan.
Echo State Networks for Time-Series Data (Tutorial)
Dr. Prakash (PKS Prakash) has over 12 years of data science experience with a focus on healthcare, manufacturing, pharmaceutical, and e-commerce domain. Prakash leads the advanced data science practice in patient analytics and customer-centric marketing at ZS. He has a doctorate from the University of Wisconsin-Madison, U.S., and also pursued his second doctorate in engineering from the University of Warwick, U.K. He holds a master’s degree from the University of Wisconsin-Madison, U.S., and a bachelor’s degree from the National Institute of Foundry and Forge Technology (NIFFT), India. He has co-founded Warwick Analytics, U.K. (based on his doctoral thesis). He has published widely in research areas of operational research & management, soft computing tools, and advance algorithms in leading journals such as IEEE-Trans, EJOR, and IJPR among others. He co-authored “Algorithms and Data Structures using R” and “R Deep Learning Cookbook” published by PACKT.
Dr. Ahmet Karagozoglu is the C.V. Starr Distinguished Professor in Finance & Investment Banking at Hofstra University and a visiting scholar at the Volatility and Risk Institute at New York University. He is also the founding academic director of the Martin B. Greenberg Trading Room at Hofstra’s Zarb School of Business since 2005. He was a visiting research professor at NYU’s Stern School of Business in 2019.
Dr. Karagozoglu’s primary research interests are in the areas of financial derivatives, risk management and market microstructure. His recently published articles focus on idiosyncratic volatility and news; short-sale constraints and information asymmetry; volatility and social media sentiment; credit risk and CDS markets; stress testing and model validation. Currently, he is investigating, with Dr. Nazli S. Alan and Dr. Robert F. Engle, the language complexity of earnings calls transcripts and volatility, using NLP, and the impact of COVID-19 pandemic on volatility in global equity markets.
Danny has an academic background in computational statistics. He believes that good data science requires good data engineering in order to create clean, accurate, and accessible data for data scientists. In the past, he’s given presentations on distributed deep learning, productionizing machine-learning models, and the importance of big data for machine learning in the modern world.
Yochay is an experienced tech leader and has been named in the 2020 Forbes 30 under 30 list for his achievements in AI advancement and for building cnvrg.io. Since the age of 7 Yochay has been writing code. He served in the Israeli Defence Force Intelligence unit for 4 years, and studied Computer Science at the Hebrew University of Jerusalem (HUJI) where he founded the HUJI Innovation Lab. Yochay has been consulting companies in AI and machine learning. After 3 years of consulting, Yochay, along with Co-founder Leah Kolben decided to create a tool to help data scientists and companies scale their AI and Machine Learning with cnvrg.io. The company continues to help data science teams from Fortune 500 companies manage, build and automate machine learning from research to production.
Joan Xiao is a Principal Data Scientist at Linc Global, a commerce-specialized customer care automation company. In her role, she applies novel natural language processing and machine learning techniques to improve customer experience. Previously she led machine learning and data science teams at various companies ranging from startup to Fortune 100. Joan received her Ph.D in Mathematics and MS in Computer Science from University of Pennsylvania.
Transfer Learning in NLP(Talk)
Changa has 20 years of Consulting experience implementing data and analytics solutions for cross sector clients with focus on reducing costs, improving operational efficiencies and adoption across different business functions. He has seen the transition from database solutions, packaged analytics to self-service advanced analytics over the last 2 decades.
Kristopher Overholt is a Sales and Solution Engineer at Algorithmia who works with machine learning operations, enterprise architecture, and data science workflows. He studied civil engineering at The University of Texas at Austin, where he completed his PhD in 2013. He has been working with enterprise customers for the last 6 years to help them move their data science and machine learning code into production.
Pavan heads the Tredence Engineering Org and his team specializes in building analytical products for Enterprise customers.
Automated Model Management with ML Works(Demo Talk)
Stephen Wong is the John S. Dunn Sr. Presidential Distinguished Chair and Chief Research Information Officer at Houston Methodist; Associate Director of Bioinformatics and Biostatistics Cores and Chair of Systems Medicine and Bioengineering Department at Houston Methodist Cancer Center; Director of T.T. and W.F. Chao Center for BRAIN and Director of Advanced Cellular and Tissue Microscopy Core at Houston Methodist Research Institute; and a Professor of Radiology, Neurosciences, Pathology and Laboratory Medicine of Cornell University. His research covers drug discovery, systems biology, biomedical imaging, and digital health for combating cancer and neurologic disorders. Previously, he was a professor at UCSF and Harvard University, handling major medical information and imaging system design and implementation at UCSF, Harvard Medical School, and the Brigham and Women’s Hospital. Stephen has served in technical and executive roles in other major technology-driven companies including HP, AT&T Bell Labs, Philips Healthcare, and Charles Schwab, where his group developed one of the first and largest web trading platforms. He received his Ph.D. and M.Sc. in Computer Science (AI) from Lehigh University, B.Eng. in Electrical Engineering (hons) from the University of Western Australia, and senior executive education from Stanford Graduate School of Business, MIT Sloan School of Management, and Columbia Business School. He reviews research grants regularly for NIH, DOD, research foundations and federal agencies of other countries and serves on advisory boards on several non-profit foundations and organizations. His research has been consistently funded by NIH for 25 years. He is a fellow of IEEE and a registered professional engineer.
Multimedia Artificial Intelligence in Precision Medicine(Business Talk)
Phoebe Liu is a senior data scientist with expertise in robotics and conversational AI. Previously, she was a robotics researcher in Japan, working in Hiroshi Ishiguro Laboratory at Advanced Telecommunications Research Institute International (ATR), where she also earned her PhD at Osaka University at the same time. She has 6+ years of research experience and 3 years of industry experience. Her research focus includes embodied agent, dialogue system, multimodal data annotation and behavior generation, topic modeling, learning from demonstration, style transfer, explainable AI, proactiveness, personalization, and artificial curiosity and has published many first-author papers in robotics conferences and journals.
Training Conversational Agents on Noisy Data(Demo Talk)
Yonatan Geifman is the CEO and a Co-Founder at Deci AI. He granted B.Sc. in mathematics and Computer Science from Ben-Gurion University, and M.Sc. and Ph.D in Computer Science from the Technion – Israel Institute of Technology. During his Ph.D. studies, Yonatan worked as a Research Intern in Google AI, Mountain View.
His research areas include deep learning, uncertainty estimation, active learning and neural architecture search (NAS), and his papers have been published in premier venues such as NeurIPS, ICML, and ICLR.
Dr. Geifman founded Deci with a mission to supercharge AI models for top performance, so they are ready for production at scale.
Viktoriia Samatova is a Head of Applied Innovation team of Data Scientists within Reuters Technology division focused on discovering and applying new technologies to enhance Reuters products and improving efficiency of news content production and discoverability. Prior to joining Reuters, Viktoriia spent over 5 years at State Street Bank’s Global Exchange division where she was managing product development of academic and financial industry content ingestion platform, which was the first company-wide product application utilizing emerging technologies of AI and machine learning.
Aravind heads the Data Science Org at Tredence, and his team works on the R&D & algorithm development for new Data Science solutions.
Predicting Model Failures in Production(Track Keynote)
Josh Poduska is the Chief Data Scientist at Domino Data Lab. He has 18 years of experience in the analytics space, particularly with designing and implementing data science solutions in the manufacturing and public sector domains. He has also led data science teams and strategy for several analytical software companies. Josh has a Masters in Applied Statistics from Cornell University.
Rachel House is a Senior Data Scientist on S&P Global’s Artificial Intelligence Engineering team. Prior to her tenure at S&P, Rachel served as a software developer in the ad tech industry and as a proposal writer in defense contracting. She has leveraged her dual background in technology and communication to build a portfolio of experience in the design and development of robust, elegant systems as well as the ability to pitch those creations to varied audiences.
Kaitlin Gili is a Quantum Applications Intern at Zapata Computing. She has previously worked at Los Alamos National Laboratory and the IBMQ hub within Keio University as a quantum algorithm intern, and at the University of Oxford as a visiting quantum hardware research student. Kaitlin is passionate about quantum computing outreach for young scientists and has previously delivered quantum computing workshops to Girls Who Code middle/high school programs. She received her Bachelors’s in Physics from Stevens Institute of Technology and will be starting her PhD in Physics at the University of Oxford in January 2021.
Introduction to Generative Modeling Using Quantum Machine Learning(Half-Day Training)
Prasanth Pulavarthi is Principal Program Manager for the AI Frameworks team at Microsoft. His team works on making ML practitioners and engineers more efficient through optimized libraries, tools, and communities. ONNX Runtime (https://onnxruntime.ai) is an open-source engine from his team that integrates with TensorFlow, PyTorch, and other frameworks to accelerate inferencing and training on a variety of cloud and edge hardware.
Prasanth is also the Co-Founder of ONNX (https://onnx.ai), the open standard for machine learning interoperability. ONNX is now a graduate projected in Linux Foundation Artificial Intelligence. He serves on the ONNX Steering Committee and is actively involved in the ONNX community.
Dr. James Pearce is a data science leader with over two decades of experience. He has worked with large Australian banks, insurance companies, credit bureaux, and data consultancies to help businesses and analysts get value out of their data using analytical techniques ranging from statistical analysis through to machine learning and AI. In addition, James is passionate about teaching machine learning and making sense of data. In his other roles, he has developed several market-leading data products, as well as machine learning systems that are used operationally to cut costs and increase revenue. In his spare time, James enjoys writing about analytics and data; he also writes fiction.
Hannes Hapke works in machine learning at Digits. Prior, he was a senior machine learning scientist for Concur Labs at SAP Concurfor Concur Labs at SAP Concur, where he explored innovative ways to use machine learning to improve the experience of a business traveler. Hannes has also solved machine learning and ML infrastructure problems in various industries including healthcare, retail, recruiting, and renewable energies. He was recognized as a Google Developer Expert for ML and has co-authored two machine learning publications: “Building Machine Learning Pipeline” by O’Reilly Media and “NLP in Action” by Manning Publications.
Mehrnoosh Sameki is a senior technical program manager at Microsoft, responsible for leading the product efforts on machine learning interpretability and fairness within the Azure Machine Learning platform. She earned her PhD degree in computer science at Boston University, where she currently serves as an adjunct assistant professor and lecturer, offering courses in responsible AI. Previously, she was a data scientist in the retail space, incorporating data science and machine learning to enhance customers’ personalized shopping experiences.
Paolo Tamagnini is a data science evangelist at KNIME and based in Berlin. After graduating with a master’s degree in data science at Sapienza University of Rome, Paolo gathered research experience at New York University in machine learning interpretability and visual analytics tools. Since working at KNIME, Paolo has presented different workshops in the USA and Europe and developed a number of reusable guided analytics applications for automated machine learning and human-in-the-loop analytics.
Jorge Torres is the Co-founder & CEO of MindsDB. He is also a visiting scholar at UC Berkeley researching machine learning automation and explainability. Prior to founding MindsDB, he worked for a number of data-intensive start-ups, most recently working with Aneesh Chopra (the first CTO in the US government) building data systems that analyze billions of patients records and lead to the highest savings for millions of patients. He started his work on scaling solutions using machine learning in early 2008 while working as the first full-time engineer at Couchsurfing where he helped grow the company from a few thousand users to a few million. Jorge had degrees in electrical engineering & computer science, including a master’s degree in computer systems with a focus on applied Machine Learning) from the Australian National University.
Fanny Perraudeau is a Senior Manager in Data Science & Software at Pendulum where she leads research into the application of state-of-the-art biostatistical, bioinformatic and computational methodology to better understand and leverage the human microbiome to deliver health innovations. She designs and implements novel clinical programs and develops novel algorithms and bioinformatics pipelines to associate clinical, genomic, mobile and consumer data streams with metagenomics datasets. She has a master in Engineering from Ecole Polytechnique, France and a PhD in Biostatistics from University of California, Berkeley, USA with a Designated Emphasis in Computational and Genomic Biology.
Introduction on Genomics Using R(Workshop)
Audrey Reznik has been in the IT industry (private and public sectors) for 27 years in multiple verticals. In the last 4 years, she worked as a Data Scientist at ExxonMobil where she created a Data Science Enablement team to help data scientists easily deploy ML models in a Hybrid Cloud environment. Audrey was instrumental in educating scientists about what the OpenShift platform was and how to use OpenShift containers (images) to organize, run, and visualize data analysis results. Audrey now works as a Data Scientist with the Red Hat Data Science Team where she is focused on next-generation applications. She is passionate about Data Science and in particular the current opportunities with ML and Federated Data.
Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine (UCI). He is working primarily on robustness and interpretability of machine learning algorithms, along with models that reason with text and structure for natural language processing. Sameer was a postdoctoral researcher at the University of Washington (w/ Carlos Guestrin and late Ben Taskar) and received his PhD from the University of Massachusetts, Amherst (w/ Andrew McCallum), during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs. He was selected as a DARPA Riser, and has been awarded the grand prize in the Yelp dataset challenge, the Yahoo! Key Scientific Challenges (story), UCI Mid-Career Excellence in research award, and recently received the Hellman Fellowship in 2020. His group has received funding from Amazon, Allen Institute for AI, NSF, DARPA, Adobe Research, Base 11, and FICO. Sameer has published extensively at machine learning and natural language processing conferences and workshops, including paper awards at KDD 2016, ACL 2018, EMNLP 2019, AKBC 2020, and ACL 2020.
Alejandro Perdomo-Ortiz is a Lead Quantum Application Scientist at Zapata Computing. He did his graduate studies, M.A and Ph.D. in Chemical Physics, at Harvard University. For over 12 years, he has worked to enhance the performance of quantum computing algorithms with physics-based approaches while maintaining a practical, application-relevant perspective. Before joining Zapata Computing, Alejandro spent over 5 years at NASA’s Quantum Artificial Intelligence Laboratory (NASA QuAIL), where he was the quantum machine learning technical lead. Between NASA and joining Zapata, he co-founded a consulting company called Qubitera LLC, which was acquired by Rigetti.
Introduction to Generative Modeling Using Quantum Machine Learning(Half-Day Training)
Diana Shaw is an engineer turned data scientist turned software developer with over 20 years making data-driven decisions. Her passion is helping customers apply advanced analytics, machine learning, natural language processing to solve complex problems. As a software developer and manager, she is focused on developing embedded automation and intelligence to make end-to-end analytics life-cycle orchestration easier and faster. Diana regularly leads discussions with executives, business analysts and data scientists around the globe, consulting on analytics strategies and broader technology objectives. She has a Bachelor’s in metallurgical engineering, an MBA, and a Master’s in analytics.
Cody Rich is a Solutions Engineer at Zaloni, specializing in enterprise software and solutions sales within data and analytics. You may recognize him from previous leadership roles at MetiStream and QGenda.
Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. Before his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. He also was a senior executive at a global fintech company with operations in 15 markets.
Earlier, he advised Central Banks in emerging markets, worked for the World Bank, raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He is the author for ‘Machine Learning for Algorithmic Trading’ and has been teaching data science at Datacamp and General Assembly.
How to Build and Test a Trading Strategy Using ML(Half-Day Training)
Tina is Founder and CEO of Collider Health, an ecosystem builder that works with organisations in both private and public sectors to transform health with sustainable impact at scale. Tina specialises in connecting government, business and academia to align thinking and take action. She works with the National AHSN AI Network and NHSX AI Lab to support an AI and data-driven tech ecosystem for the NHS and UK Research & Innovation on the Healthy Ageing Industrial Strategy Challenge Fund. Tina is also co-founder and CEO of Longevity International, which provides the Secretariat for the All Party Parliamentary Group for Longevity that launched The Health of the Nation Strategy supported by Rt Hon Matt Hancock MP, Secretary of State for Health and Care, in February 2020 to deliver the government manifesto for 5 extra years of healthy life expectancy to all British citizens by 2035 while mimimising health inequalities. Tina is now setting up the Business for Health: Coalition for a Healthier Nation, (a key recommendation in the Strategy) that sets out how business should invest long-term in sustainable preventative health in line with ESG mandates like for climate change: to enhance health and economic resilience and develop socially-driven products and services to keep people healthy and well. Tina’s book, ‘Live Longer with AI: How artificial intelligence is helping us extend our healthspans and live better too’ was published in October 2020 and is now available on Amazon here (and free for the NHS workforce via Health Education England)
Ross is a solutions engineer at Domino Data Lab who works with customers to resolve challenges across their data science life cycle and realize the benefits of utilizing an open data science platform. Prior to Domino, Ross was analytics and quantitative specialist at FactSet, supporting investment management firms in their use of FactSet’s suite of quantitative tools. Ross earned a bachelor’s degree in physics and a master’s degree in nuclear fusion at the University of York, UK.
Corey Weisinger is a Data Scientist with KNIME in Austin Texas. He studied Mathematics at Michigan State University focusing on Actuarial Techniques and Functional Analysis. Before coming to work for KNIME he worked as an Analytics Consultant for the Auto Industry in Detroit Michigan. He currently focuses on Signal Processing and Numeric Prediction techniques and is the Author of the Alteryx to KNIME guidebook.
Ivana Williams is a Staff Research Scientist at the Chan Zuckerberg Initiative. She is passionate about delivering state of the art machine learning and data science solutions in support of accelerating scientific discovery, unlocking insights from scientific publications, and delivering personalized content. Her recent research focuses on novel approaches to data representation and automated knowledge base construction.
Hannah Arnson serves as Director of Data Science with Pandata – a Cleveland-based AI consulting firm. There, she leverages her 10+ years of experience to lead AI solution design and development, with a focus on ethical and approachable AI. Hannah began her career as a neuroscientist, receiving a Ph.D. in neuroscience from Washington University in St. Louis, then continuing on to do postdoctoral research. During this time, she developed statistical and mathematical models to better understand topics ranging from the sense of smell to navigation in pigeons. As a data scientist, Hannah’s passions lie in finding patterns within complex datasets and educating to make these technical concepts accessible to all.
With nearly a decade of experience fixing your dirty data, Susan Walsh is The Classification Guru. She brings clarity and accuracy to data and procurement; helps teams work more effectively and efficiently; and cuts through the jargon to address the issues of dirty data and its consequences in an entertaining and engaging way. Susan is a specialist in data classification, supplier normalisation, taxonomy customisation, and data cleansing and can help your business find cost savings through spend and time management – supporting better, more informed business decisions. Susan has developed a methodology to accurately and efficiently classify, cleanse and check data for errors which will help prevent costly mistakes and could save days, if not weeks of laborious cleansing and classifying. Susan is passionate about helping you find the value in cleaning your ‘dirty data’ and raises awareness of the consequences of ignoring issues through her blogs, vlogs, webinars and speaking engagements.
Nishitha Kambhaladinne is a data science analyst in Janssen, a pharmaceutical company of Johnson & Johnson. She is part of the Commercial Data Sciences and Data Management Team. Nishitha joined Janssen through the Technology Leadership Development Program where she completed her rotations in data science. In her role, she works on a diversified portfolio of projects including real-world data analytics, machine learning to drive patient adherence, and NLP pipelines. Nishitha holds a bachelor’s degree in Economics and Information Technology from Rutgers University. Prior to joining Janssen, she founded a B2C startup for small to medium scale businesses, which was sold in 2017.
Jenna Eun is a principal data scientist in Janssen, a pharmaceutical company of Johnson & Johnson. She is part of the Commercial Data Sciences, Data Engineering & Data Enablement Leadership Team. In her role, she leads a portfolio of business-critical projects, including novel applications of machine learning to drive improved patient adherence, retention, and clinical outcome across key products and regions. Jenna holds a bachelor’s degree and a Ph.D. in Biochemistry from the University of Wisconsin-Madison where she conducted research in biophysics, chemical biology, and biomedical engineering. Prior to joining Janssen, she was selected as Helen Hay Whitney Howard Hughes Medical Institute postdoctoral fellow at Harvard University.
Marta Lopata is the Chief Growth Officer at Thinknum Alternative Data and Kgbase – no-code knowledge graph tool. Marta also serves as co-Founder and Publisher of Thinknum’s media outlet which attracted millions of readers with its alternative-data focused market insights about leading public and private companies. She is frequently quoted in the Business Insider, Institutional Investor, TechCrunch, and other business publications; and is often a featured panelist at major market-related conferences. Marta speaks 4 languages (Chinese, French, Polish, and English) and before immigrating to the US, she led startups in digital fabrication, automation, and sustainable materials in China and France.
Farah Shamout is an Assistant Professor Emerging Scholar in Computer Engineering at NYU Abu Dhabi. Her research expertise is in machine learning for healthcare, data analytics for large-scale multi-modal data, and model interpretability. Her projects focus on real-world clinical problems to inform decision-making, including diagnosis and prognosis using electronic health records and medical imaging. Previously, Farah completed her doctoral studies in Engineering Science at the University of Oxford as a Rhodes Scholar.
Soo has been working with Computer Vision, Machine Learning Engineers, and Research Scientists, across industries to create training datasets for the last 4+ years. As a Solutions Architect at iMerit, she helps our clients by connecting the dots between the technical details of tooling, designing annotation workflows, and integrating a remote data labeling team for the execution. Previously, Soo served as the Data Operations Manager at a geospatial analytics startup where she built and scaled a Data Operations team from the ground up, leading a team 10 analysts.
Sveta Kostinsky is a Director of Sales Engineering. She joined Samasource with deep expertise in scaling businesses, with a focus on partnerships, strategy, and technology sales. Previously at Cyngn, she was responsible for expanding company growth into the autonomous vehicles space. Sveta has a passion for technology and innovation and works closely with Samasource’s clients to design great solutions for them.
Marcelo Benedetti is a Senior Account Executive. Based in the SF Bay Area, he has nearly 3 years of experience at Samasource. He works primarily with clients in the e-commerce, retail, and biotech verticals. Marcelo supports Samasource’s partners by helping them deliver on their training data initiatives, getting their models to production more efficiently.
Yongin is a PhD candidate at UC Davis advised by Gerald Quon. His research focuses on computational biology involving applications of machine learning to answer questions in field of biology. More specifically, his current research focuses on the interpretation of deep neural network architectures trained on genomics data to understand the underlying gene to gene relationships.
Daniel Gray brings rich experience in technical solutions engineering as well as software engineering to his work with global enterprise organizations. Prior to joining AtScale to lead the Solutions Engineering team, Daniel spent many years in the analytics space including Hewlett-Packard’s Advanced Technology Center, Vertica, and Domino Data Lab. When he’s not in the office or onsite with customers, you’ll find Daniel running, climbing, hiking, and biking – basically anything outdoors.
Bill Wright is the head of AI/ML business development and technical strategy for global verticals at Red Hat. He is also a member of the Red Hat AI ILT, and participates in the ETSI ENI working group, to establish standards for the use of AI/ML in mobile network operations. He was the former Director of Product Management at Affirmed Networks, where he led their efforts in virtualization, management tools (ASAP) and security planning. Prior to this, he worked at VMware with Sanjay Aiyagari, where they conducted pioneering research with technology partners in the use of virtualization for network functions. They eventually disrupted the networking industry by delivering the first production NFV deployment at a multinational mobile operator, effectively kickstarting a new multi-billion dollar market. He lives in San Francisco with his wife Dahlia, son Nico, and an oversized French Bulldog named Sophie.
Martin Isaksson is the CEO and Co-Founder of PerceptiLabs, a startup aimed at accelerating machine learning by streamlining the workflow and advancing explainability of the models. To do this, PerceptiLabs created a visual modeling tool which gives full transparency into the process of machine learning development, combined with support functions for debugging and increased interpretability into the models.
Guillaume Moutier is a Sr. Principal Data Engineering Architect at Red Hat, focusing his work on data services, AI/ML workloads and data science platforms. Former CTO of Laval University in Canada, he is constantly looking for and promoting new and innovative solutions, but always with a focus on usability and business alignment brought by 20 years of IT management experience.
Robert Lundberg is the CTO and Co-Founder of PerceptiLabs, a startup aimed at accelerating machine learning by streamlining the workflow and advancing explainability of the models. To do this, PerceptiLabs created a visual modeling tool which gives full transparency into the process of machine learning development, combined with support functions for debugging and increased interpretability into the models.
Corey Nolet is a Data Scientist & Senior Engineer on the RAPIDS cuML team at NVIDIA, where he focuses on building and scaling machine learning algorithms to support extreme data loads at light-speed. Prior to working at NVIDIA, Corey spent over a decade building massive-scale analytics & data science platforms for HPC environments for the defense industry. Corey currently holds Bs. & Ms. degrees in Computer Science and is pursuing his Ph.D. in the same discipline, focused on scaling machine learning algorithms in distributed architectures. Corey has a passion for using data to make better sense of the world.
GPU-accelerated Data Science with RAPIDS(Workshop)
Jeffrey is a VP of Data Science, Data Engineering, and Platform Engineering at the Store Associate Technology of Walmart Global Technology. His prior roles include the Chief Data Scientist at AllianceBernstein, a global asset management firm that managed nearly $700 billion, Vice President and Head of Data Science at Silicon Valley Data Science, and senior leadership position at Charles Schwab Corporation and KPMG. He has also taught econometrics, statistics, and machine learning at UC Berkeley, Cornell, NYU, University of Pennsylvania, and Virginia Tech. Jeffrey is active in the data science community and often speaks at data science conferences and local events. He has many years of experience in applying a wide range of econometric and machine learning techniques to create analytic solutions for financial institutions, businesses, and policy institutions. Jeffrey holds a Ph.D. and an M.A. in Economics from the University of Pennsylvania and a B.S. in Mathematics and Economics from UCLA.