Experience ODSC LIVE
The latest tools, breakthrough models & frameworks streamed live to your laptop.
April 16th to April 17th, 2020
ODSC Livestream & Recordings
Hear from the best and brightest in data science and AI. Topics include:
Livestream Sessions
Recordings
Hours of Content
ODSC is the best community data science event on the planet. There are other events that cover special topics, or industries, etc., but ODSC is comprehensive and totally community-focused: it's the conference to engage, to build, to develop, and to learn from the whole data science community.
Kirk Borne - Principal Data Scientist and Executive Advisor at Booz Allen Hamilton @ ODSC East 2019
East 2020 Speakers & Instructors
Announcing our first 50 speakers and instructors. 230+ more coming soon!
Tom M. Mitchell, PhD
Tom is a researcher in Machine Learning, Artificial Intelligence, and Cognitive Neuroscience. He founded the Machine Learning Department at Carnegie Mellon University and led it as Department Head for its first 10 years. Currently, he serves as the Interim Dean of the School of Computer Science at Carnegie Mellon.
Lester Mackey, PhD
Lester Mackey is a machine learning researcher at Microsoft Research, where he develops new tools, models, and theory for large-scale learning tasks driven by applications from healthcare, climate, recommender systems, and the social good. Lester moved to Microsoft from Stanford University, where he was an assistant professor of Statistics and (by courtesy) of Computer Science. He earned his PhD in Computer Science and MA in Statistics from UC Berkeley and his BSE in Computer Science from Princeton University. He co-organized the second-place team in the $1M Netflix Prize competition for collaborative filtering, won the $50K Prize4Life ALS disease progression prediction challenge, won prizes for temperature and precipitation forecasting in the yearlong real-time $800K Subseasonal Climate Forecast Rodeo, and received a best student paper award at the International Conference on Machine Learning.
Dr. Anju Kambadur
Dr. Prabhanjan (Anju) Kambadur heads the AI Engineering group at Bloomberg. Anju leads a group of 100+ researchers and engineers who build solutions for Bloomberg clients in the areas of machine learning, natural language processing (NLP) and natural language understanding, information extraction, knowledge graphs, question answering, and table understanding. Previously, Anju was a research staff member in the Business Analytics and Mathematical Sciences Department at IBM Research’s Thomas J. Watson Research Center, where he worked on problems in machine learning, such as matrix sketching, genome-wide association studies, temporal causal modeling, and high-performance computing. He received his PhD from Indiana University. Anju has published peer-reviewed articles in the fields of high-performance computing, machine learning, and natural language processing.
Cassie Kozyrkov, PhD
As Chief Decision Scientist at Google Cloud, Cassie Kozyrkov advises leadership teams on the decision process, AI strategy, and building data-driven organizations. She is the innovator behind bringing the practice of Decision Intelligence to Google, personally training over 15,000 Googlers. Prior to joining Google, Cassie worked as a data scientist and consultant. She holds degrees in mathematical statistics, economics, psychology, and neuroscience.
Mark Weber
Mark Weber is an applied researcher and operations lead at the MIT-IBM Watson AI Lab, a community of over 200 scientists from MIT and IBM Research focused on fundamental advancements in AI. As the token M.B.A. in the group, Mark leads internal strategy and operations as well as the business program for member companies co-investing in the lab, taking on the creative challenge of bridging fundamental science to real-world impact. Mark also leads the lab’s strategic engagements with IDEO and the International Monetary Fund. In his research, Mark is focused on developing and utilizing Graph Convolutional Networks (GCNs) for anti-money laundering.
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. At MIT, Mark led engagements with Media Lab member companies and governments, including the development of b_verify, an open-source blockchain-based protocol for verifiable records in agricultural finance and supply chains.
Classically trained in Notre Dame’s intensive “great books” program, 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. Mark’s recreational joys include board games, non-fiction reading, and ultramarathon trail running. Learn more at www.markrweber.com.
Hanspeter Pfister, PhD
Hanspeter Pfister is An Wang Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. Before joining Harvard he worked for over a decade at Mitsubishi Electric Research Laboratories where he was Associate Director and Senior Research Scientist. He was the chief architect of VolumePro, Mitsubishi Electric’s award-winning real-time volume rendering hardware for PCs. Dr. Pfister has a Ph.D. in Computer Science from the State University of New York at Stony Brook and an M.S. in Electrical Engineering from ETH Zurich, Switzerland. He is the recipient of the 2010 IEEE Visualization Technical Achievement Award and the Petra T. Shattuck Excellence in Teaching Award in 2009. He is co-editor of the first textbook on Point-Based Computer Graphics, published by Elsevier in 2007, and was Technical Papers Chair for SIGGRAPH 2012.
Michael Steliaros, PhD
Michael Steliaros is the global head of Quantitative Execution Services at Goldman Sachs. He is responsible for the research, development, and implementation of quantitative processes for portfolio and electronic trading as well as managing the bank’s relations with the quantitative client base. Previously, Michael held a variety of senior roles at BofAML in London and New York, most recently running the global agency portfolio trading and quantitative equity businesses. Earlier in his career, he spent a decade on the buy-side (most notably BGI and Winton) building quant stock-selection models and managing global market-neutral equity portfolios. Michael received a bachelor’s degree in Economics & Econometrics from the University of Nottingham, and an MSc and PhD in Finance from City University (CASS) Business School in London.
Mike Stonebraker, PhD
Dr. Stonebraker has been a pioneer of database research and technology for more than forty years. He was the main architect of the INGRES relational DBMS, and the object-relational DBMS, POSTGRES. These prototypes were developed at the University of California at Berkeley where Stonebraker was a Professor of Computer Science for twenty five years. More recently at M.I.T., he was a co-architect of the Aurora/Borealis stream processing engine, the C-Store column-oriented DBMS, the H-Store transaction processing engine, the SciDB array DBMS, and the Data Tamer data curation system.
Presently he serves as Chief Technology Officer of Paradigm4 and Tamr, Inc.
Zona Kostic, PhD
Zona Kostic is a research, teaching, and innovation fellow at Harvard University. Her professional interests are at the intersection of data visualization, machine learning, and digital realities, with current research activities being specifically focused on semantic visual similarity and smart web-based interfaces. Previously, Zona was part of the Harvard Visual Computing Group developing immersive visual analytics systems that support the overlay of digital information over physical objects. She published six books, and a number of research works at scientific journals and conferences. Zona is also a co-founder of ArchSpike, a small venture that integrates data science and visualization with market modeling, allowing users to design buildings that better respond to future market demands.
Matei Zaharia, PhD
Matei Zaharia is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks. He started the Apache Spark project during his PhD at UC Berkeley in 2009 and has worked broadly in datacenter systems, co-starting the Apache Mesos project and contributing as a committer on Apache Hadoop. Today, Matei tech-leads the MLflow development effort at Databricks. Matei’s research work was recognized through the 2014 ACM Doctoral Dissertation Award for the best PhD dissertation in computer science, an NSF CAREER Award and several best paper awards.
Jacob Andreas, PhD
Jacob Andreas is an assistant professor at MIT and a senior researcher at Microsoft Semantic Machines. His research focuses on language learning as a window into reasoning, planning and perception, and on more general machine learning problems involving compositionality and modularity. Jacob earned his Ph.D. from UC Berkeley, his M.Phil. from Cambridge (where he studied as a Churchill scholar) and his B.S. from Columbia. He has been the recipient of an NSF graduate fellowship, a Facebook fellowship, and paper awards at NAACL and ICML.
Byron Galbraith, PhD
Byron Galbraith is the Chief Data Scientist and co-founder of Talla, where he works to translate the latest advancements in machine learning and natural language processing to build AI-powered conversational agents. Byron has a PhD in Cognitive and Neural Systems from Boston University and an MS in Bioinformatics from Marquette University. His research expertise includes brain-computer interfaces, neuromorphic robotics, spiking neural networks, high-performance computing, and natural language processing. Byron has also held several software engineering roles including back-end system engineer, full-stack web developer, office automation consultant, and game engine developer at companies ranging in size from a two-person startup to a multi-national enterprise.
Diedre Downing
Diedre is a Lead Data Storytelling Trainer at StoryIQ where she helps organizations improve their communication with and about data. An accidental math teacher, Diedre learned the power of demystifying numbers in New York City classrooms and the power of influencing decision-makers with data during her time running WeTeachNYC.org for the NYC Department of Education. Diedre is an Adjunct Lecturer at Hunter College in New York and has spoken at NCTM, iNACOL, and Learning Forward about adult learning methodology and best practices in professional learning.
Dr. Jon Krohn
Jon Krohn is Chief Data Scientist at the machine learning company untapt. He presents an acclaimed series of tutorials published by Addison-Wesley, including Deep Learning with TensorFlow and Deep Learning for Natural Language Processing. Jon teaches his deep learning curriculum in-classroom at the New York City Data Science Academy and guest lectures at Columbia University. He holds a doctorate in neuroscience from the University of Oxford and, since 2010, has been publishing on machine learning in leading peer-reviewed journals. His book, Deep Learning Illustrated, is being published by Pearson in 2019.
Deep Learning (with TensorFlow 2)(Training)
Dr. Kirk Borne
Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.
Solving the Data Scientist’s Dilemma: the Cold-start Problem with 10+ Machine Learning Examples(Training)
Ali Vanderveld, PhD
Ali Vanderveld is the Director of Data Science at ShopRunner, where her team leverages data from a network of over 100 retailers to build products for their 6 million members. Prior to ShopRunner, she was a staff data scientist at Civis Analytics, a consulting and software startup that helps companies, nonprofits, and political organizations better utilize their data. She has also worked at Groupon and as a technical mentor for the Data Science for Social Good Fellowship. Ali has a PhD in theoretical astrophysics from Cornell University and got her to start working as an academic researcher at Caltech, the NASA Jet Propulsion Laboratory, and the University of Chicago, working on the development teams for several space telescope missions, including ESA’s Euclid.
Igor Halperin, PhD
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.
Gary Kazantsev, PhD
Gary is the Head of Quant Technology Strategy in the Office of the CTO at Bloomberg. Prior to taking on this role, he created and headed the company’s Machine Learning Engineering group, leading projects at the intersection of computational linguistics, machine learning and finance, such as sentiment analysis of financial news, market impact indicators, statistical text classification, social media analytics, question answering, and predictive modeling of financial markets.
Prior to joining Bloomberg in 2007, Gary had earned degrees in physics, mathematics, and computer science from Boston University.
He is engaged in advisory roles with FinTech and Machine Learning startups and has worked at a variety of technology and academic organizations over the last 20 years. In addition to speaking regularly at industry and academic events around the globe, he is a member of the KDD Data Science + Journalism workshop program committee and the advisory board for the AI & Data Science in Trading conference series. He is also a co-organizer of the annual Machine Learning in Finance conference at Columbia University.
Nir Shavit, PhD
Nir Shavit is the CEO of Neural Magic, a machine learning startup that is currently in stealth mode and is a professor in the Department of Electrical Engineering and Computer Science at MIT. Shavit is a co-author of the book The Art of Multiprocessor Programming, is a recipient of the 2004 Gödel Prize in theoretical computer science and of the 2012 Dijkstra Prize in Distributed Computing, and is an ACM fellow. His recent interests include systems issues in machine learning and techniques for understanding how neural tissue computes by extracting connectivity maps of neural tissue, a field called connectomics.
The Software GPU: Making Inference Scale in the Real World(Talk)
See all our First 50 speakers Here .More coming soon
Livestream & Recording Registration
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Streaming Access to ODSC Keynotes (Thu&Fri)
Immediate Access to ODSC East 2019 Recordings
ODSC Livestream Talks (Thu&Fri)
Remote Livestream Speaker Track Access
Access to ODSC 100+ East 2020 Recordings
Quick Pass Guide
Free Keynote Livestream Pass | Access one track on April 16th and 17th to 6 keynotes.
Livestream 2-Day Pass | Access three tracks on April 16ht & 17th that includes 6 keynotes and 50 breakout sessions, and an exclusive remote speaker track available only on livestream
Why Wait? You also get access to over 70+ recorded session on Machine Learning, Deep Learning, and more from ODSC East 2019 available here.
Livestream & East 2020 Video Access Pass |Â Get access to everything included in the Livestream 2-Day pass including East 2019 session recordings.
However, you also get access to over 150+ session recordings from East 2020 from across our 17 tracks and focus areas.Â
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Livestream FAQ
How will I access the Livestream?
You will receive instructions for accessing the Livestream 24-48 hours prior to the start of the event.
Is my registration/ticket transferable?
Yes, up until 7 days prior to the event. Email info@odsc.com with your full name, email address and the full name, email address, company name, and title of the person to whom you are transferring. Please be advised that transfers are not available within 7 days of the start of the event.
Where can I contact the organizer with any questions
Email info@odsc.com
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What is the Refund Policy
Cancellations within 30 days of the event are non-refundable. Please email info@odsc.com, if you would like to transfer your ticket. Ticket transfers may be requested up to 7 days before the conference.
Please note:
- Ticket prices and availability are subject to increase or decrease, at the discretion of ODSC, before and/or after you have made your purchase, and do not entitle the purchaser to a refund or credit (partial or full).
- Given the nature of live events, changes to specific sessions and/or presenters may be unavoidable and do not entitle the purchaser to a refund or credit (partial or full). Unavailability of or inability to gain access to preferred sessions does not entitle the purchaser to a refund or credit (partial or full).