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Learn the latest models, advancements, and trends from the top practitioners behind two of data science’s hottest topics
Comprising multiple tracks, this focus area is where leading experts in the rapidly expanding fields of Deep Learning and Machine Learning gather to discuss the latest advances, trends, and models in this exciting field.
Attend talks, tutorials, and workshops and hear from the creators and top practitioners as they teach the latest models and trends in Machine Learning and Deep Learning to solve problems in business and society. Some of the topics you’ll learn at this data science and machine learning conference include:
Machine Learning
Deep Learning
Deep Reinforcement Learning
Neural Networks
LSTM, CNNs, RNNs, & GANs,
Computer Vision
Pattern Recognition
Tensorflow
Scikit-learn
Keras
Caffe 2
PyTorch
Theano
Apache Spark & MlLib
and many more…
Federated Learning
Transfer Learning
Autonomous Machines
MLOps and Kubeflow
Recommendation Systems
Never Ending Learning for ML
Causal Inference
Some of Our Past ML & DL Speakers
Dr. Jon Krohn
Jon Krohn is Co-Founder and Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the data science industry’s most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at leading universities and conferences, as well as via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.
Deep Learning with PyTorch and TensorFlow(Training)
NLP with GPT-4 and other LLMs: From Training to Deployment with Hugging Face and PyTorch Lightning(Training)
Matt Harrison
Matt Harrison has been using Python since 2000. He runs MetaSnake, a Python and Data Science consultancy and corporate training shop. In the past, he has worked across the domains of search, build management and testing, business intelligence, and storage.
He has presented and taught tutorials at conferences such as Strata, SciPy, SCALE, PyCON, and OSCON as well as local user conferences.
Machine Learning with XGBoost(Workshop)
Idiomatic Pandas(Workshop)
Dr. Hongxia Yang, PhD
Dr. Hongxia Yang, PhD from Duke University, led the team to develop AI open sourced platforms and systems such as AliGraph, M6, Luoxi. Dr. Yang has published nearly 100 top conference and journal papers, and held more than 20 patents. She has been awarded the highest prize of the 2019 World Artificial Intelligence Conference, Super AI Leader (SAIL Award), the second prize of the 2020 National Science and Technology Progress Award (China’s Top tech award), the first prize of Science and Technology Progress of the Chinese Institute of Electronics in 2021, and the Forbes China Top 50 Women in Science and Technology in 2022. She used to work as the Senior Staff Data Scientist and Director in Alibaba Group, Principal Data Scientist at Yahoo! Inc and Research Staff Member at IBM T.J. Watson Research Center, joint adjunct professor at Zhejiang University Shanghai Advanced Research Institute respectively.
Towards the Next Generation of Artificial Intelligence with its Applications in Practice(Talk)
David P. Woodruff, PhD
David Woodruff is a professor at Carnegie Mellon University in the Computer Science Department. Before that he was a research scientist at the IBM Almaden Research Center, which he joined in 2007 after completing his Ph.D. at MIT in theoretical computer science. His research interests include data stream algorithms, distributed algorithms, machine learning, numerical linear algebra, optimization, sketching, and sparse recovery. He is the recipient of the 2020 Simons Investigator Award, the 2014 Presburger Award, and Best Paper Awards at STOC 2013, PODS 2010, and PODS, 2020. At IBM he was a member of the Academy of Technology and a Master Inventor.
Testing Positive Semidefiniteness and Eigenvalue Approximation(Talk)
Irina Rish, PhD
Irina Rish is an Associate Professor in the Computer Science and Operations Research Department at the Université de Montréal (UdeM) and a core faculty member of MILA – Quebec AI Institute. She holds Canada Excellence Research Chair (CERC) in Autonomous AI and a Canadian Institute for Advanced Research (CIFAR) Canada AI Chair. She received her MSc and PhD in AI from University of California, Irvine and MSc in Applied Mathematics from Moscow Gubkin Institute. Dr. Rish’s research focus is on machine learning, neural data analysis and neuroscience-inspired AI. Before joining UdeM and MILA in 2019, Irina was a research scientist at the IBM T.J. Watson Research Center, where she worked on various projects at the intersection of neuroscience and AI, and led the Neuro-AI challenge. She received multiple IBM awards, including IBM Eminence & Excellence Award and IBM Outstanding Innovation Award in 2018, IBM Outstanding Technical Achievement Award in 2017, and IBM Research Accomplishment Award in 2009. Dr. Rish holds 64 patents, has published over 80 research papers in peer-reviewed conferences and journals, several book chapters, three edited books, and a monograph on Sparse Modeling.
Recent Advances in Foundation Models: Scaling Laws, Emergent Behaviors, and AI Democratization(Talk)
Julia Lintern
Julia Lintern currently works as a Director of Data Science at Gartner. Previously, she worked as a Data Scientist for the New York Times. Julia began her career as a structures engineer designing repairs for damaged aircraft. Julia holds an MA in applied math from Hunter College, where she focused on visualizations of various numerical methods and discovered a deep appreciation for the combination of mathematics and visualizations. During certain seasons of her career, she has also worked on creative side projects such as Lia Lintern, her own fashion label.
Introduction to Machine Learning(Bootcamp)
Pradeep Ravikumar, PhD
Pradeep Ravikumar is a Professor in the Machine Learning Department, School of Computer Science at Carnegie Mellon University. He was previously an Associate Director at the Center for Big Data Analytics, at the University of Texas at Austin. His thesis has received honorable mentions in the ACM SIGKDD Dissertation award and the CMU School of Computer Science Distinguished Dissertation award. He is a Sloan Fellow, a Siebel Scholar, a recipient of the NSF CAREER Award, and was Program Chair for the International Conference on Artificial Intelligence and Statistics (AISTATS) in 2013. He is Associate Editor-in-Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and action editor for the Machine Learning journal, and the Journal of Machine Learning Research.
Dr. Ravikumar’s research group at CMU works on the foundations of statistical machine learning, with recent focus on “next generation” machine learning systems, that are explainable, robust to train and test time corruptions, and resilient to distribution shifts, and are learnt under resource constraints by leveraging or discovering various notions of “structure” and domain knowledge.
Robustness to Adversarial Inputs and Tail Risk via Boosting(Talk)
Daniel Gerlanc
Daniel Gerlanc has worked as a data scientist for more than decade and been writing software for nearly 20 years. He frequently teaches live trainings on oreilly.com and is the author of the video course Programming with Data: Python and Pandas. He has coauthored several open source R packages, published in peer-reviewed journals, and is a graduate of Williams College.
Programming with Data: Python and Pandas(Bootcamp)
Thomas J. Fan
Thomas J. Fan is a Staff Software Engineer at Quansight Labs and is a maintainer for scikit-learn, an open-source machine learning library for Python. Previously, Thomas worked at Columbia University to improve interoperability between scikit-learn and AutoML systems. He is a maintainer for skorch, a neural network library that wraps PyTorch. Thomas has a Masters in Mathematics from NYU and a Masters in Physics from Stony Brook University.
Introduction to scikit-learn: Machine Learning in Python (Training)
Dan Shiebler
As the Head of Machine Learning at Abnormal Security, Dan builds cybercrime detection algorithms to keep people and businesses safe. Before joining Abnormal Dan worked at Twitter: first as an ML researcher working on recommendation systems, and then as the head of web ads machine learning. Before Twitter Dan built smartphone sensor algorithms at TrueMotion and Computer Vision systems at the Serre Lab.
Daniel Lenton, PhD
Daniel Lenton is the creator of Ivy, which is an open-source framework with an ambitious mission to unify all other ML frameworks. Prior to starting Ivy, Daniel was a PhD student at Imperial College London, where he published research in the areas of machine learning, robotics and computer vision.
Unifying ML With One Line of Code(Tutorial)
Tejaswini Pedapati
Tejaswini Pedapati works at IBM Research. Her research is focused on interpretability and automating deep learning. To that end, she was involved in developing tools and algorithms to provide these capabilities for IBM products. She has a masters’ degree from Columbia University.
Introduction to AutoML: Hyperparameter Optimization and Neural Architecture Search(Tutorial)
Jeffrey Yau, PhD
Jeffrey Yau is currently Chief Data & A.I. Officer at Fanatics Collectibles. Most recently, he served as Global Head of Data Science, Analytics & Engineering at Amazon Music where he oversaw multiple teams who developed both insights-packed analytics and end-to-end statistical and machine learning systems. Prior to Amazon, Jeffrey worked at WalmartLabs as the VP of Data Science & Engineering where he led the team responsible for powering Walmart store mobile apps and the entire store finance system. Further, his team created end-to-end machine learning systems for key business initiatives and had a multi-billion dollar impact annually on Walmart U.S.
Over the years, he has held various senior level positions in quantitative finance at global investment management firm AllianceBernstein, consulting firm Data Science at Silicon Valley Data Science, multinational financial services company Charles Schwab Corporation, and the world’s leading professional services firm KPMG. He began his career as a tenure-track Assistant Professor of Economics at Virginia Tech, and he was an adjunct professor at UC Berkeley, Cornell, and NYU, teaching machine learning and advanced statistical modeling for finance and business.
Noah Giansiracusa, PhD
Noah Giansiracusa (PhD in math from Brown University) is a tenured associate professor of mathematics and data science at Bentley University, a business school near Boston. His research interests range from algebraic geometry to machine learning to empirical legal studies. After publishing the book How Algorithms Create and Prevent Fake News in July 2021, Noah has gotten more involved in public writing and policy discussions concerning data-driven algorithms and their role in society. He’s written op-eds for Barron’s, Boston Globe, Wired, Slate, and Fast Company and is currently working on a second book, Robin Hood Math: How to Fight Back When the World Treats You Like a Number, with a Foreword by Nobel Prize-winning economist Paul Romer.
Deepfakes: How’re They Made, Detected, and How They Impact Society(Tutorial)
Akash Tandon
Akash Tandon is co-founder and CTO of Looppanel where he builds software to help product teams record, store and analyze user research data. He is a co-author of Advanced Analytics with PySpark, published by O’Reilly. Previously, Akash worked as a senior data engineer at Atlan, SocialCops and RedCarpet where he built data infrastructure for enterprise, government and finance use-cases. He has also been a participant and mentor in the Google Summer of Code program with the R Project for Statistical Computing.
From Big Data to NLP insights: Getting started with PySpark and Spark NLP(Workshop)
Freddy Boulton
Freddy Boulton started his career as a data scientist for Nielsen where he built predictive models of television viewing behavior to make television ratings more accurate. This gave him a first hand-view of one of the biggest challenges faced by industry data scientists – being able to easily communicate and share machine learning models with stakeholders. He is currently solving that problem by working on Gradio, an open-source python library that lets data scientists create fully interactive demos of machine learning models with just a few lines of code.
A Practical Tutorial on Building Machine Learning Demos with Gradio(Workshop)
Ali Rossi
Ali Rossi is a Data Science Tech Lead at Foursquare, working closely with their first-party foot traffic panel to deliver insights against a broad range of client business questions. She is passionate about consumer behavioral data, with experience building consumer panels, researching normalization methodologies, and developing methods to derive actionable insights. Previously, she worked in product management at Foursquare, Amazon and Nielsen, mainly focused on building analytics products using consumer-sourced data. She studied chemistry and mathematics at the University of Connecticut and is currently pursuing a Master of Science in computer science at the Georgia Institute of Technology.
Uncovering Behavioral Segments by Applying Unsupervised Learning to Location Data(Talk)
Tom Shafer, PhD
Tom Shafer works as a Lead Data Scientist at Elder Research, a recognized leader in data science, machine learning, and artificial intelligence consulting since its founding in 1995. As a lead scientist, Tom contributes technically to a wide variety of projects across the company, mentors data scientists, and helps to direct the company’s technical vision. His current interests focus on Bayesian modeling, interpretable ML, and data science workflow. Before joining Elder Research, Tom completed a PhD in Physics at the University of North Carolina, modeling nuclear radioactive decays using high-performance computing.
Beyond Credit Scoring: Interpretable Models for Responsible Machine Learning(Talk)
Brian Lucena, PhD
Brian Lucena is Principal at Numeristical, where he advises companies of all sizes on how to apply modern machine learning techniques to solve real-world problems with data. He is the creator of three Python packages: StructureBoost, ML-Insights, and SplineCalib. In previous roles he has served as Principal Data Scientist at Clover Health, Senior VP of Analytics at PCCI, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.
Uncertainty Quantification: Approaches and Methods(Training)
Madhav Thaker
Madhav is a Senior Data Scientist at Shopify where he focuses on building/evaluating recommendation systems. His role includes prototyping potential solutions and scaling them for production. Prior to Shopify, Madhav was a data science consultant where he focused on NLP projects for pharmaceutical companies. He then transitioned to Disney to develop personalized movie recommendations which sparked his passion for recommendation systems. In his free time, Madhav hosts free Q&A sessions for aspiring data scientists who are looking to get into this space.
Generating Content-based Recommendations for Millions of Merchants and Products(Talk)
Arvind Neelakantan, PhD
Arvind Neelakantan is a Research Lead and Manager at OpenAI working on deep learning research for real-world applications. He got his PhD from UMass Amherst where he was also a Google PhD Fellow. His work has received best paper awards at NeurIPS and at Automated Knowledge Base Construction workshop.
Text and Code Embeddings(Talk)
Nikolay Manchev, PhD
Nikolay is an experienced Data Science professional who currently leads the EMEA Data Science team at Domino Data Lab. He holds an MSc in Software Technologies, an MSc in Data Science, and is currently undertaking postgraduate research at King’s College London. His area of expertise is Statistics, Mathematics, and Data Science in general, and his research interests are in Neural Networks with emphasis on biological plausibility. He writes articles and blogs regularly and speaks at various European conferences (ODSC, Big Data Spain, Strata, Big Data London etc.) to build awareness about data science and artificial intelligence. He is also the organizer of the London Data Science and Machine Learning meetup and recipient of several technical mastery awards like the Oracle ACE Award and the IBM Outstanding Technical Achievement Award.
Avi Pfeffer, PhD
Dr. Avi Pfeffer is Chief Scientist at Charles River Analytics. Dr. Pfeffer is a leading researcher on a variety of computational intelligence techniques including probabilistic reasoning, machine learning, and computational game theory. Dr. Pfeffer has developed numerous innovative probabilistic representation and reasoning frameworks, such as probabilistic programming, which enables the development of probabilistic models using the full power of programming languages, and statistical relational learning, which provides the ability to combine probabilistic and relational reasoning. He is the lead developer of Charles River Analytics’ Figaro™ probabilistic programming language. As an Associate Professor at Harvard, he developed IBAL, the first general-purpose probabilistic programming language. While at Harvard, he also produced systems for representing, reasoning about, and learning the beliefs, preferences, and decision making strategies of people in strategic situations. Prior to joining Harvard, he invented object-oriented Bayesian networks and probabilistic relational models, which form the foundation of the field of statistical relational learning. Dr. Pfeffer serves as Action Editor of the Journal of Machine Learning Research and served as Associate Editor of Artificial Intelligence Journal and as Program Chair of the Conference on Uncertainty in Artificial Intelligence. He has published many journal and conference articles and is the author of a text on probabilistic programming. Dr. Pfeffer received his Ph.D. in computer science from Stanford University and his B.A. in computer science from the University of California, Berkeley.
See all our talks and hands-on workshop and training sessions
See all sessionsYou Will Meet
Top speakers and practitioners in Machine Learning and Deep Learning
Data Scientists and Data Analysts
Decision makers
Software Developers focused on Machine Learning and Deep Learning
Data Science Innovators
CEOs, CTOs, CIOs
Industry leaders
Core contributors in the fields of Machine Learning and Deep Learning
Data Science Enthusiasts
Why Attend?
Immerse yourself in talks, tutorials, and workshops on Machine Learning and Deep Learning tools, topics, models and advanced trends
Expand your network at this data science and machine learning conference and connect with like-minded attendees to discover how Machine Learning and Deep Learning knowledge can transform not only your data models but also your business and career
Meet and connect with the core contributors and top practitioners in the expanding and exciting fields of Machine Learning and Deep Learning
Learn how the rapid rise of intelligent machines is revolutionizing how we make sense of data in the real world and its coming impact on the domains of business, society, healthcare, finance, manufacturing, and more
ODSC EAST 2024 - April 23-25th
Register your interestSessions on Machine Learning & Deep Learning Track
Workshop: Deciphering the Black Box: Latest Tools and Techniques for Interpretability
Talk: Adversarial Attacks on Deep Neural Networks
Training: Integrating Pandas with Scikit-Learn, an Exciting New Workflow
Workshop: Machine Learning for Digital Identity
Talk: Adding Context and Cognition to Modern NLP Techniques
Training: Good, Fast, Cheap: How to do Data Science with Missing Data
Workshop: Open Data Hub workshop on OpenShift
Talk: Practical AI solutions within healthcare and biotechnology
Training: Apache Spark for Fast Data Science (and Fast Python Integration!) at Scale
Workshop: Reproducible Data Science Using Orbyter
Talk: Combining millions of products into one marketplace using computer vision and natural language processing
ODSC Newsletter
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