Tom M. Mitchell is the Founders University Professor at Carnegie Mellon University, where he founded the world’s first Machine Learning Department, and authored the widely used textbook “Machine Learning.” Mitchell’s research over the years has focused on machine learning, artificial intelligence, cognitive neuroscience, and the impact of AI on society.
Mitchell currently co-directs the CMU–Squirrel AI Lab on Personalized Education at Scale, which is pursuing the use of artificial intelligence to transform personalized education over the coming years.
A former president of the Association for the Advancement of Artificial Intelligence (AAAI), Mitchell is a fellow of both the AAAI and the American Association for the Advancement of Science, and winner of the 2007 AAAI Distinguished Service Award. He was elected in 2010 to the U.S. National Academy of Engineering, and in 2016 to the American Academy of Arts and Sciences. His AI research has been featured in media ranging from the New York Times, to CBS’s 60 Minutes, to PBS’s “NOVA Science NOW,” to Chinese national television.
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.
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.
Deep Learning (with TensorFlow 2)(Full-Day Training)
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).
Dr. Lisa Amini is the Director of IBM Research Cambridge, which is also home to the MIT-IBM Watson AI Lab, and of IBM’s AI Horizons Network. Lisa was previously Director of Knowledge & Reasoning Research in the Cognitive Computing group at IBM’s TJ Watson Research Center in New York, and she is also an IBM Distinguished Engineer. Lisa was the founding Director of IBM Research Ireland, and the first woman Lab Director for an IBM Research Global (i.e., non-US) Lab (2010-2013). In this role she developed the strategy and led researchers in advancing science and technology for intelligent urban and environmental systems (Smarter Cities), with a focus on creating analytics, optimizations, and systems for sustainable energy, constrained resources (e.g., urban water management), transportation, and the linked open data systems that assimilate and share data and models for these domains. She earned her PhD degree in Computer Science from Columbia University.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Natural Language Processing with PyTorch(Half-Day Training)
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.
Ravi Ilango is a Principal Data Scientist at StatesTitle. He is passionate in developing deployable deep learning solutions. Previously he was at Foghorn Systems as a Sr. Data Scientist and has over 10 years of experience at Apple as a data Scientist & at Applied Materials in Supply Chain Program Management. Ravi has a Graduate Certificate in Data Mining & Machine Learning from Stanford and completed a Masters Program in Aeronautics and Production Engineering from IIT Madras. He has a BS in Mechanical Engineering, Madras University.
Natural Language Processing with PyTorch(Half-Day Training)
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.
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.
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).
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.
Jennifer Redmon joined Cisco in 2009 and serves as its Chief Data Evangelist. She and her team support Cisco’s journey to up-level the company’s analytical acumen through offers such as education. In 2019, she joined The Erika Legacy Foundation pro bono as its Director of Data Science and Artificial Intelligence to honor the foundation’s namesake, Erika, with whom Jennifer had been close friends prior to Erika’s death. Jennifer is passionate about ethical applications of data science, ML/AIs power to create a more compassionate world, DIY home projects, Peloton, and travel. Jennifer holds an international MBA from Duke University with a concentration in Strategy and Bachelor’s Degrees in Economics and Art History from UC Davis.
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.
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.
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.
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.
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.
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.
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.
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)
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)
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.
Stanislaw 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.
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.
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.
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.
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.
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)
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!
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.
Billy Okal is an engineer with expertise in robotics and machine learning. He is a technical lead in the autonomy team at Voyage Auto, developing core intelligence for self-driving cars for urban transportation. He was previously a senior engineer with Apple Special Projects Group (SPG). He received bachelors in electrical engineering and computer science and masters in computer science from Jacobs University in 2011 and 2013 respectively. During his PhD studies at the University of Freiburg, he developed algorithms for imparting high-level behavioral objectives on a robot’s decision-making systems as part of EU FP7’s SPENCER project that developed a passenger guidance robot for airports. His current research interests include interactive and sequential learning, long-term autonomy, and applications in integrated systems. He is actively involved in community programs including co-organizing and teaching at the Data science Africa (DSA) summer schools, black-in-ai, MISE foundation program, helping run the African robotics network (AFRON), and organizing inaugural sessions on robotics and AI at IEEE AFRICON 2011, 2013 conferences among others.
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.
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)
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.
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/.
Victor S.Y. Lo is a seasoned Big Data, Marketing, Risk, and Finance leader with over 25 years of extensive consulting and corporate experience employing data-driven solutions in a wide variety of business areas, including Customer Relationship Management, Market Research, Advertising Strategy, Risk Management, Financial Econometrics, Insurance Analytics, Product Development, Healthcare Analytics, Operations Management, Transportation, and Human Resources. He is actively engaged with causal inference and is a pioneer of Uplift/True-lift modeling, a key subfield of data science.
Victor has managed teams of quantitative analysts in multiple organizations. He currently leads the AI and Data Science Center of Excellence, Workplace Investing at Fidelity Investments. Previously he managed advanced analytics/data science teams in Personal Investing, Corporate Treasury, Managerial Finance, and Healthcare and Total Well-being at Fidelity Investments. Prior to Fidelity, he was VP and Manager of Modeling and Analysis at FleetBoston Financial (now Bank of America), and Senior Associate at Mercer Management Consulting (now Oliver Wyman).
For academic services, Victor has been a visiting research fellow and corporate executive-in-residence at Bentley University. He has also been serving on the steering committee of the Boston Chapter of the Institute for Operations Research and the Management Sciences (INFORMS) and on the editorial board for two academic journals. He is also an elected board member of the National Institute of Statistical Sciences (NISS). Victor earned a master’s degree in Operational Research and a PhD in Statistics and was a Postdoctoral Fellow in Management Science. He has co-authored a graduate-level econometrics book and published numerous articles in Data Mining, Marketing, Statistics, and Management Science literature, and is completing a graduate-level book on causal inference in business.
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.
Dean Wampler is an expert in data engineering for scalable streaming data systems and applications of machine learning and artificial intelligence (ML/AI). He is a Principal Software Engineer at Domino Data Lab. Previously he worked at Anyscale and Lightbend, where he worked on scalable ML with Ray and distributed streaming data systems with Apache Spark, Apache Kafka, Kubernetes, and other tools. Dean is the author of “Programming Scala”, “What Is Ray?”, “Fast Data Architectures for Streaming Applications”, “Functional Programming for Java Developers”, and the coauthor of “Programming Hive”, all from O’Reilly. He is a contributor to several open source projects, a frequent conference speaker. He also co-organizes several conferences around the world and several user groups in Chicago. Dean has a Ph.D. in Physics from the University of Washington. Find Dean on Twitter: @deanwampler.
Hands-on Reinforcement Learning with Ray RLlib(Half-Day Training)
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.
Ye Zhao is a professor in the Department of Computer Science at Kent State University, Ohio, USA. He has been working on computer graphics and visualization for more than 20 years. His current research interests include visual analytics of urban transportation data, multidimensional, text, and animated data visualization. He has published numerous refereed technical papers and served in many program committees of data visualization conferences. His work has been actively supported by NSF, including his recent work which develops several open source software for urban data processing, management, and visualization. Ye Zhao received his PhD degree in computer science from Stony Brook University and B.S./M.S. degrees from Tsinghua University.
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.
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.
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.
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.
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 masters and bachelors from the University of Waterloo, and worked as a postdoctoral researcher in mathematics at the University of Quebec at Montreal.
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)
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)
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.
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)
Charles Givre recently joined JP Morgan Chase works as a data scientist and technical product manager in the cybersecurity and technology controls group. Prior to joining JP Morgan, Mr. Givre worked as a lead data scientist for Deutsche Bank. Mr. Givre worked as a Senior Lead Data Scientist for Booz Allen Hamilton for seven years where he worked in the intersection of cybersecurity and data science. At Booz Allen, Mr. Givre worked on one of Booz Allen’s largest analytic programs where he led data science efforts and worked to expand the role of data science in the program. Mr. Givre is passionate about teaching others data science and analytic skills and has taught data science classes all over the world at conferences, universities and for clients. Mr. Givre taught data science classes at BlackHat, the O’Reilly Security Conference, the Center for Research in Applied Cryptography and Cyber Security at Bar Ilan University. He is a sought-after speaker and has delivered presentations at major industry conferences such as Strata-Hadoop World, Open Data Science Conference and others. One of Mr. Givre’s research interests is increasing the productivity of data science and analytic teams, and towards that end, he has been working extensively to promote the use of Apache Drill in security applications and is a committer and PMC Member for the Drill project. Mr. Givre teaches online classes for O’Reilly about Drill and Security Data Science and is a coauthor for the O’Reilly book Learning Apache Drill. Prior to joining Booz Allen, Mr. Givre, worked as a counterterrorism analyst at the Central Intelligence Agency for five years. Mr. Givre holds a Masters Degree in Middle Eastern Studies from Brandeis University, as well as a Bachelors of Science in Computer Science and a Bachelor’s of Music both from the University of Arizona. Mr. Givre blogs at thedataist.com and tweets @cgivre.
Rapid Data Exploration and Analysis with Apache Drill(Half-Day Training)
Thomas J. Fan is a Staff Associate at the Data Science Institute at Columbia University. He is one of the core developers of scikit-learn, an open-source machine learning library is written in Python. Thomas holds a Masters in Mathematics from NYU and Masters in Physics from Stony Brook University. He also maintains skorch, a scikit-learn compatible neural network library that wraps PyTorch. He believes that developing open-source software is one of the best ways to maximize one’s impact.
Introduction to Scikit-learn: Machine Learning in Python(Half-Day Training)
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.
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)
Seth Weidman is currently a data scientist at Sentilink, focused primarily on building machine learning models to stop synthetic fraud, and also on growing the company and improving data science practice.
In the seven years between college and beginning at Sentilink, he did a variety of jobs in the data science and analytics fields, with a heavy emphasis on teaching and writing. He worked as everything from a technical analyst role in a management consulting firm to a machine learning engineer at Facebook, helping to found the data science team at Trunk Club along the way. On the teaching and writing side, Seth taught at Metis (still the only accredited data science bootcamp) for 18 months, spoke at many community-oriented conferences (including 4 ODSCs), briefly worked as a developer advocate on the PyTorch team, and even wrote an introductory book on deep learning with O’Reilly.
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
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)
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.
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)
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.
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.
Rumi Chunara is an Assistant Professor at NYU, jointly appointed at the Tandon School of Engineering (in Computer Science) and the School of Global Public Health (in Biostatistics/Epidemiology). Her PhD is from the Harvard-MIT Division of Health Sciences and Technology. Her research group focuses on developing computational and statistical approaches for acquiring, integrating and using data to improve population-level public health. Considering health from a comprehensive, multi-level perspective means that she develops methods to work with data including social media, mobile phone, satellite imagery and other digital data sources as well as electronic health record, telemedicine and other clinical data. She is an MIT Technology Review’s 35 Innovators Under 35, NSF Career and Max Planck Sabbatical award winner and her work has been funded by diverse sources including the Gates Foundation, National Science Foundation, National Institutes of Health, Facebook, and the International Growth Centre.
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)
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.
Sujit Pal is an applied data scientist at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His areas of interests include Semantic Search, Natural Language Processing, Machine Learning and Deep Learning. At Elsevier, he has worked on several machine learning initiatives involving large image and text corpora, and other initiatives around recommendation systems and knowledge graph development. He has co-authored Deep Learning with Keras (https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-keras) and Deep Learning with Tensorflow 2.x and Keras (https://www.packtpub.com/data/deep-learning-with-tensorflow-2-0-and-keras-second-edition), and writes about technology on his blog Salmon Run (https://sujitpal.blogspot.com/).
Keras from Soup to Nuts – An Example Driven Tutorial(Half-Day Training)
Kumaran Ponnambalam is a seasoned veteran in everything data, with a reputation for delivering high-performance database and SaaS applications and currently specializing in leading Big Data Science and Engineering efforts.
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)
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)
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. She is an organizer of Data Umbrella meetup group and NYC Women in Machine Learning and Data Science meetup group, and she is a NASA Datanaut.
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 PhD in Physics, MS in Computational Physics, and has a research background in Network Science and Computer Science.
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)
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)
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.
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)
Kenny is interested in bringing human capabilities – particularly language, vision, and the acquisition of everyday knowledge – to modern technology.
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)
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).
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.
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.
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)
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)
Lara is a risk specialist with the Federal Reserve Bank of Chicago and occasional adjunct at the University of Chicago’s Booth School of Business, teaching Python and R. Previously she’s taught a data science bootcamp and built risk models for large financial institutions at McKinsey & Co. She enjoys coffee, yoga and bikes.
Probabilistic Programming and Bayesian Inference with Python(Half-Day Training)
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)