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.
Dr. Biplav Srivastava is a Distinguished Data Scientist and Master Inventor at IBM’s Chief Analytics Office. With over two decades of research experience in Artificial Intelligence, Services Computing and Sustainability, most of which was at IBM Research, Biplav is also an ACM Distinguished Scientist, AAAI Senior Member and IEEE Senior Member. 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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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 heterogenous compute. Recently, as 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.
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.
Reza Shiftehfar currently leads Uber’s Hadoop Platform teams. His teams help build and grow Uber’s reliable and scalable Big Data platform that serves petabytes of data utilizing technologies such as Apache Hadoop, Apache Hive, Apache Kafka, Apache Spark, and Presto. Reza is one of the founding engineers of Uber’s Data team and helped scale Uber’s data platform from a few terabytes to over 100 petabytes while reducing big data latency from 24+ hours to minutes. Reza holds a Ph.D. in Computer Science from the University of Illinois, Urbana-Champaign.
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.
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.
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.
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 is Senior Principal Researcher at Microsoft Research. Her research background is in machine learning and algorithmic economics. Within Microsoft, Jennifer has been leading efforts around transparency, intelligibility, and explanation under the umbrella of Aether, Microsoft’s company-wide initiative focused on responsible AI. Jennifer is very active in the research community and recently served as the (Program and General) Co-Chair of HCOMP 2019, the Workshops Chair of NeurIPS 2019, the Tutorial Co-Chair of both NIPS 2017 and NeurIPS 2018, the Workshops Co-Chair of both EC 2017 and EC 2018, the Press Co-Chair of ICML 2019, and the Secretary-Treasurer of SigEcom from 2015-2019. She is currently a Steering Committee Member of ACM FAccT and a Senior Advisor to WiML (which she co-founded back in 2006).
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 a Research Scientist at OpenAI working at the intersection of machine learning and robotics. His research focuses on applying deep reinforcement learning, generative models, and synthetic data to problems in robotic perception and control. Additionally, he co-organize a machine learning training program for engineers to learn about production-ready deep learning called Full Stack Deep Learning. 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.
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.
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 researched 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.
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.
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.
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!