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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!

Head of AI EngineeringBloomberg

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.

AI Research at Bloomberg(Talk)

Applied Research ScientistMIT-IBM Watson AI Lab

Mark Weber

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

Introducing Graph Convolutional Networks for Finance(Talk)

Principal Data ScientistiRobot

Brandon Rohrer, PhD

I love solving puzzles and building things. Practicing data science gives me the opportunity to do both in equal measure. Like most data scientists, I came to the field indirectly. I started by studying robotics and human rehabilitation at MIT (MS ’99, PhD ’02), moved on to machine vision and machine learning at Sandia National Laboratories, then to predictive modeling of agriculture DuPont Pioneer, and cloud data science at Microsoft. Previously at Facebook I worked to get internet and electrical power to those in the world who didn’t have it, using deep learning and satellite imagery. In my spare time I like to rock climb, write robot learning algorithms, and go on walks with my wife and our dog, Reign of Terror.

Introduction to Deep Learning & Neural Networks I: Concepts(Training)

Introduction to Deep Learning & Neural Networks II: Practice(Training)

Professor of Computer Science, School of Engineering and Applied SciencesHarvard University

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.

Towards Visually Interactive Neural Probabilistic Models (Talk)

A.M. Turing Award Laureate, Professor, Co-founderMIT CSAIL, Tamr

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.

Research FellowHarvard University

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.

Smart Technologies in Enhancing Browsing Experiences(Talk)

Assistant ProfessorMIT CSAIL

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.

Opening the Pod Bay Doors: Building Intelligent Agents That Can Interpret, Generate and Learn from Natural Language(Tutorial)

learn data storytelling
Lead Data Storytelling TrainerStoryIQ

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.

The Art (and Importance) of Data Storytelling(Talk)

deep learning with TensorFlow
Chief Data Scientist, Author of Deep Learning IllustratedUntapt

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)

Kirk borne
Principal Data ScientistBooz Allen Hamilton

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)

Adapting Machine Learning Algorithms to Novel Use Cases(Training)

Director of Data ScienceShopRunner

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.

Using Computer Vision and NLP Together for Fashion Classification(Talk)

Research Professor of Financial Machine Learning | AI Asset ManagementNYU | Fidelity Investments

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.

Reinforcement learning and inverse reinforcement learning in finance(Talk)

Head of Quant Technology StrategyBloomberg

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.

Advances in Machine Learning: Finance Perspective(Talk)

Deep Learning and Software GPU expert Nir Shavit
CEO, ProfessorNeural Magic, MIT

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)

Software Engineer, Author & Creator of CaretRStudio

Max Kuhn, PhD

Max was a nonclinical statistician for 12 years in the pharmaceutical industry and for 6 years in the medical diagnostic industry. His degrees are in Biostatistics (Ph.D.) and Mathematics (B.S.). He has released several R packages for predictive modeling and machine learning, including caret, C50, and Cubist. He is the author of the Springer book Applied Predictive Modeling (with Kjell Johnson), which won the American Statistical Association’s Ziegel Award for best book in 2014.

See all our First 50 speakers Here .More coming soon

ODSC Live

Experience ODSC live. See why ODSC has a reputation for hosting one of the leading conferences in data science. Get access to content that can provide key insights and quickly boost your knowledge of AI

Best of the Best

Our Livestream brings you of the best of the best. Based on pre-registration popularity we bring you both the top speakers and the most highly anticipated sessions to ensure you get the best of ODSC East 2019

The Rest

We record most of our talks and workshop sessions.  Available after the conference, you will get access to an unparalleled selection of the latest breakthroughs, frameworks, models, and topics in data science

Livestream & Recording Registration

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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 |  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. 

 

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

What is the Privacy Policy?
In summary, registrant contact information is NOT shared with third parties without your consent. Registrant information is primarily used to verify registration and notify you of similar events held by ODSC in the future. We may share your contact information with sponsors, but only with your consent upon registering.

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).
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