As Chief Decision Scientist at Google Cloud, Cassie Kozyrkov advises leadership teams on 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.
ODSC EAST 2023 will be announced soon, register your interest and get early access to exclusive discounts on all ticket options
Speakers
Hours of Content
Companies
Hybrid Attendees
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
Build AI Better
Over the course of 3 days, ODSC East will provide expert-led instruction in machine learning, deep learning, NLP, MLOps, and more through hands-on training sessions, immersive workshops, and talks. You’ll also have the chance to share insights and build new connections through a wide range of events from the ODSC Networking Reception to the Ai+ Career Expo.
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
ODSC is the best community data science event on the planet. It 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 Scientist and Executive Advisor at Booz Allen Hamilton @ ODSC East 2019
What a spectacular first day attending ODSC East. This has been a wonderful day full of new knowledge, new connections, and the discovery of problems to solve and solutions alike.
Data Scientist, USA
It’s been such a wonderful week learning about all the incredible work that’s being done within the field of data science – with too many incredible sessions to list at the moment.
Machine Learning Engineer | Data Scientist, USA
It was a wonderful experience, and I literally am going back with an enhanced understanding of so many concepts, while learning about many new products and theories. Thanks, ODSC for this opportunity.
Product Specialist, India
Why You Should Attend the Leading Data Science Conference
HANDS-ON TRAINING
Build job-ready skills and stay up-to-date with the latest advances in machine learning, NLP, data analytics, responsible AI, and more with ODSC East’s expert-led, immersive, training sessions.
With 300 hours of content, the conference features a wide range of sessions for data scientists at every level, from beginner to expert.

NETWORKING
Connect with and learn from thousands of your peers and data science experts during ODSC East’s numerous in-person and virtual events. Meet with our expert speakers to ask questions and continue the discussion during Meet the Speaker and Book Signing events. Or, set a goal to meet as many of your peers as possible at the ODSC Networking Reception.

AI EXPO AND DEMO HALL
Meet representatives from some of the leading AI startups and companies at the AI Expo and Demo Hall. Visit their booths, or see their products demoed live to learn about the latest advancements in AI in enterprise and discover how to build AI better in your organization.
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Access to 80+ ODSC Talks ( Wed-Thur)
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Access to 12 Virtual ODSC Training Sessions (Tue- Thur)
On-demand access to Conference Recordings*
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Virtual Accelerate AI Business Summit (Business Talks & Panels on Tue)
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Access to the vast resources of WorldData.AI
Access to 32 Virtual ODSC Workshops (Tue- Thur)
Access to 12 Virtual ODSC Training Sessions (Tue- Thur)
On-demand access to Conference Recordings*
1 year Premium Subscription on AI+
Access to Monday Bootcamp Fundamentals
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Virtual Accelerate AI Business Summit (Business Talks & Panels on Tue)
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Access to Virtual Events
Access to the vast resources of WorldData.AI
Access to 32 Virtual ODSC Workshops (Tue- Thur)
Access to 12 Virtual ODSC Training Sessions (Tue- Thur)
On-demand access to Conference Recordings*
Access to Monday Bootcamp Fundamentals
1 year Premium Subscription on AI+
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$796
Virtual Workshops & Talks (3 Days Tue-Thu)
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Virtual Accelerate AI Business Summit (Business Talks & Panels on Tue)
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Access to the vast resources of WorldData.AI
Access to 32 Virtual ODSC Workshops (Tue- Thur)
Access to 12 Virtual ODSC Training Sessions (Tue- Thur)
On-demand access to Conference Recordings*
Access to Monday Bootcamp Fundamentals
1 year Premium Subscription on AI+
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$1,196
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Virtual Accelerate AI Business Summit (Business Talks & Panels on Tue)
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Access to Virtual Events
Access to the vast resources of WorldData.AI
Access to 32 Virtual ODSC Workshops (Tue- Thur)
Access to 12 Virtual ODSC Training Sessions (Tue- Thur)
On-demand access to Conference Recordings*
Access to Monday Bootcamp Fundamentals
1 year Premium Subscription on AI+
Virtual Mini-Bootcamp
$1,996
All Virtual Access and Pre-conference training & Access to AI+ (On-Demand or Live training)
Access to ODSC Keynotes & Talks
Virtual Accelerate AI Business Summit (Business Talks & Panels on Tue)
Access to Virtual AI Expo & Demo Talks (Wed - Thur)
Access to Virtual Events
Access to the vast resources of WorldData.AI
Access to 32 Virtual ODSC Workshops (Tue- Thur)
Access to 12 Virtual ODSC Training Sessions (Tue- Thur)
On-demand access to Conference Recordings*
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1 year Premium Subscription on AI+
Save 75% on Full Price
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Hotel DEAL
Sheraton Boston Hotel
39 Dalton St, Boston, MA 02199
For a limited time, hotel rooms start from just $240 + taxes and fees per night.
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WHAT TO EXPECT
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If you have a group of 3 to 13 or more, please email us at info@odsc.com to enquire about additional discounts. Please mention the size of your group and the types of passes required.
Donate to our Fundraise
For this year’s event, ODSC East is donating to Girls Who Code and Black Girls Who Code. We invite you to join us and donate to our fundraiser. Both these organisations are dedicated to increasing the number of women in engineering and helping them become innovators in the STEM fields. Please donate what you can via our registration. No purchase is necessary to donate and 100% of funds raised are donated.
ODSC East 2022
Previous Speakers & Instructors

Statistics Professor | Faculty ScientistUC Berkeley | Lawrence Berkeley National Laboratory
Michael Mahoney, PhD
Michael W. Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He is also an Amazon Scholar as well as a faculty scientist at the Lawrence Berkeley National Laboratory. He works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning, including randomized matrix algorithms and randomized numerical linear algebra, geometric network analysis tools for structure extraction in large informatics graphs, scalable implicit regularization methods, computational methods for neural network analysis, physics informed machine learning, and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis. He received his PhD from Yale University with a dissertation in computational statistical mechanics, and he has worked and taught at Yale University in the mathematics department, at Yahoo Research, and at Stanford University in the mathematics department. Among other things, he is on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI), he was on the National Research Council’s Committee on the Analysis of Massive Data, he co-organized the Simons Institute’s fall 2013 and 2018 programs on the foundations of data science, he ran the Park City Mathematics Institute’s 2016 PCMI Summer Session on The Mathematics of Data, and he runs the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets. He is the Director of the NSF/TRIPODS-funded FODA (Foundations of Data Analysis) Institute at UC Berkeley. More information is available at https://www.stat.berkeley.edu/~mmahoney/.
WeightWatcher, an Open-Source Diagnostic Tool for Analyzing Deep Neural Nets(Talk)

Avanessians Director, Data Science Institute | Professor of Computer ScienceColumbia University
Jeannette M. Wing, PhD
Jeannette M. Wing is the Executive Vice President for Research at Columbia University and Professor of Computer Science. In her EVPR role, she has overall responsibility for the University’s research enterprise at all New York locations and internationally. The New York locations include the Morningside and Manhattanville campuses, Columbia University Irving Medical Center, Lamont-Doherty Earth Observatory, and Nevis Laboratories. She joined Columbia in 2017 as the inaugural Avanessians Director of the Data Science Institute.
Prior to Columbia, Dr. Wing was Corporate Vice President of Microsoft Research, served on the faculty and as department head in computer science at Carnegie Mellon University, and served as Assistant Director for Computer and Information Science and Engineering at the National Science Foundation.
Dr. Wing’s research contributions have been in the areas of trustworthy AI, security and privacy, specification and verification, concurrent and distributed systems, programming languages, and software engineering. Her 2006 seminal essay, titled “Computational Thinking,’’ is credited with helping to establish the centrality of computer science to problem-solving in fields where previously it had not been embraced, and thereby influencing K-12 and university curricula worldwide.
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. She received distinguished service awards from the ACM and the Computing Research Association and an honorary doctorate degree from Linköping University, Sweden. She earned her bachelor’s, master’s, and doctoral degrees in computer science, all from the Massachusetts Institute of Technology.

Research Scientist DeepMind
Joel Z. Leibo, PhD
Joel Z. Leibo is a research scientist at DeepMind. He obtained his PhD in 2013 from MIT where he worked on the computational neuroscience of face recognition. Nowadays, Joel’s research is aimed at the following questions: How can we get deep reinforcement learning agents to perform complex cognitive behaviors like cooperating with one another in groups? How should we evaluate the performance of deep reinforcement learning agents? How can we model processes like cumulative culture that gave rise to unique aspects of human intelligence?
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot(Talk)

Research Director | Director, Scikit-learnINRIA
Gaël Varoquaux, PhD
Gaël Varoquaux is a research director working on data science and health at Inria (French Computer Science National research). His research focuses on using data and machine learning for scientific inference, with applications to health and social science, as well as developing tools that make it easier for non-specialists to use machine learning. He has long applied it to brain-imaging data to understand cognition. 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.

Associate ProfessorSyracuse University
Jeffrey Saltz, PhD
Since joining Syracuse University in 2014, Jeff has focused on how to effectively manage and coordinate data science projects within and across teams. Via his research and consulting, Jeff has become an expert in this field, having worked across a range of organizations and published 30+ peer-reviewed academic papers that (1) explore the challenges in executing data science projects, and (2) evaluate potential frameworks via experiments and real-world case studies.
Prior to joining Syracuse University and consulting on data science efforts, Jeff worked at JPMorgan Chase, where he reported to the firm’s Global Chief Information Officer and drove technology innovation across the organization, while also having other leadership roles within the bank, such as leading the IT effort for credit risk analytics within the consumer bank.
Managing Data Science Projects via an Agile Framework(Business Talk)

Professor, Computer ScienceNortheastern University
Tina Eliassi-Rad, PhD
Tina Eliassi-Rad is a Professor of Computer Science at Northeastern University. She is also a core faculty member at Northeastern University’s Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences at the University of Wisconsin-Madison. Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of big data from networked representations of physical and social phenomena. She has over 100 peer-reviewed publications (including a few best paper and best paper runner-up awardees). Tina’s work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, and ethics in machine learning. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2017, Tina served as the program co-chair for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining and as the program co-chair for the International Conference on Network Science . In 2020, she is serving as the program co-chair for the International Conference on Computational Social Science. Tina received an Outstanding Mentor Award from the Office of Science at the US Department of Energy in 2010; and became a Fellow of the ISI Foundation in Turin Italy in 2019.
Just Machine Learning(Talk)

Research EngineerHugging Face
Patrick von Platen
Patrick von Platen is a research engineer at Hugging Face and one of the core maintainers of the popular Transformers library. He specializes in speech recognition, encoder-decoder models and long-range sequence modeling. Before joining Hugging Face, Patrick conducted research in speech recognition at Uber AI, Cambridge University, and RWTH Aachen University.
Transformers &
Datasets for Research and Production(Training)

Senior Data Scientist | Vice-chairABN Amro Bank | The Foundation for Best Practices in ML
Violeta Misheva, PhD
Violeta has been interested in understanding the causes of social inequalities and to what extent bad experiences early in life propagate to negative outcomes later. When she realized ML can result in widening already existing social gaps, she became an advocate for the responsible development and deployment of ML. Violeta currently works as a data scientist at ABN Amro. Before that, she worked in consultancy and obtained her PhD in applied econometrics. Violeta likes sharing her knowledge with others by the form of workshops on data science and online courses. Violeta proposes that developers of ML solutions alone cannot ensure their safety but, rather, that the additional efforts of multidisciplinary experts as well as proper regulation is also needed.

Research FellowUniversity of New South Wales
Dr Felicity Bell
Dr Felicity Bell, PhD, is a Research Fellow for the Future of Law and Innovation in the Profession (FLIP) Research Stream, funded by the Law Society of New South Wales and based at the University of New South Wales, Sydney, Australia. FLIP provides research and thought leadership on the core concerns of the legal profession into the future. Felicity’s research looks at law and technology, and how lawyers’ regulation, professionalism and ethics interact with innovation in law. She has published widely and recently co-authored the book Artificial Intelligence and the Legal Profession (Hart, 2020).
What do Lawyers Need (and want) from Legaltech?(Business Talk)

Sr Research EngineerGrid AI | PyTorch Lightning
Jirka Borovec, PhD
Jirika is working in Machine learning and Data science for several years. He has done Ph.D. in Medical Imaging. In parallel, he gains practical experience while he has been working for a few IT companies as a consultant or data scientist. Actually,he is focusing on exploring interesting world problems and solving them with state-of-the-art techniques. He has developed several open-source python packages, He is the core contributor of `PyTorch-Lightning` and `TorchMetrics` and actively participating in other well-known projects.

Data Science ManagerGreenhouse Software
Mona Khalil
Mona is a Data Science Manager at Greenhouse Software in New York City, where they contribute to data-informed decision making across the company and machine learning solutions to improve the hiring process for Greenhouse customers. They’ve previously worked in government, creating analytics and machine learning solutions to improve the lives of New Yorkers, and continue to be involved in civic projects through a number of volunteer and non-profit organizations. They’ve also been a statistics and data science educator with DataCamp, Emeritus, and in university settings. They hold a graduate degree in Developmental Psychology, and are passionate about contributing to the ethical use of data science methodology in the public and private sector.
Leveling Up Your Organization’s Capacity for Data-informed Decisions(Talk)
SQL for Data Science(Training)

Product Manager, MLOps and TF OSSGoogle
Robert Crowe
A data scientist and ML enthusiast, Robert has a passion for helping developers quickly learn what they need to be productive. Robert is currently the Senior Product Manager for TensorFlow Open-Source and MLOps at Google and helps ML teams meet the challenges of creating products and services with ML. Previously Robert led software engineering teams for both large and small companies, always focusing on moving fast to implement clean, elegant solutions to well-defined needs. You can find him on LinkedIn at robert-crowe.
MLOps v LMOps – What’s Different?(Talk)

Principal ML Solutions ArchitectDatabricks
Sean Owen
Sean is a principal solutions architect focusing on machine learning and data science at Databricks. He is an Apache Spark committer and PMC member, and co-author Advanced Analytics with Spark. Previously, he was director of Data Science at Cloudera and an engineer at Google.
MLOps: Relieving Technical Debt in ML with MLflow, Delta and Databricks(Talk)

Director of Data Science and Artificial IntelligenceModerna
Andrew Giessel, PhD
Andrew Giessel is the Director of Data Science and AI at Moderna, where he leads a team of data scientists applying machine learning to solve a diverse set of science, business, and engineering problems. While at Moderna, he and his team have helped the company grow from a 300 employee start-up to a commercial organization producing hundreds of millions of vaccine doses all over the world. Prior to Moderna, Andrew earned a Ph.D. and did postdoc in the Neurobiology department at Harvard Medical School. Andrew lives in the Boston area with his family.
Event Venue
Boston Hynes Convention Center
900 Boylston St.
Boston, MA 02115
Participate at ODSC East 2023
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