Boston | April 13th – April 17th, 2020

Machine Learning & Deep Learning Track

Learn the latest models, advancements and trends from the top practitioners behind two of data science’s hottest topics
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Comprising of 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 demonstrate and 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 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 Current ML & DL Speakers

Professor, Researcher, Author of 'Python Machine Learning'University of Wisconsin-Madison

Sebastian Raschka, PhD

Sebastian Raschka is a machine learning researcher developing new deep learning architectures to solve problems in the field of biometrics with a focus on face recognition and privacy protection. Among others, his research activities include applications of machine learning to solve problems in (computational) biology. After receiving his doctorate from Michigan State University, Sebastian recently joined the University of Wisconsin-Madison as Assistant Professor of Statistics. Sebastian Raschka is also the author of the bestselling book “Python Machine Learning”, which received the ACM Best of Computing award in 2016 and was translated into many different languages, including German, Korean, Chinese, Japanese, Russian, Polish, and Italian. In his free time, Sebastian loves to contribute to open source projects, and methods that he implemented are now successfully used in machine learning competitions such as Kaggle.

Chief Data Scientist, Author of Deep Learning IllstratedUntapt

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)

Sihem Romdhani
Software EngineerVeeva Systems

Sihem Romdhani

Sihem Romdhani received her MASc degree in Machine Learning from the Department of Electrical and Computer Engineering at the University of Waterloo-Canada, where her research was focused on Deep Learning for Speech Recognition. She is currently working with Veeva Systems as a Data Scientist, where she is building ML models for Natural Language Processing. She has led multiple projects on text parsing, sequence tagging, and information extraction from unstructured text data. She has also worked on recommendation systems using different ML algorithms including Reinforcement Learning. Sihem is very interested in AI and how to solve new and challenging problems. Throughout her education, academic research, and work in the industry, she gathered experiences and knowledge that she enjoys sharing by actively doing public presentations.

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)

Head of Data Science and AnalyticsBadoo (now MagicLab, which owns several apps)

Leonardo De Marchi

Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks and Manchester United, and with large social networks, like Justgiving.

He now works as Head of Data Scientist and Analytics in Badoo, the largest dating site with over 420 million users. He is also the lead instructor at ideai.io, a company specialized in Reinforcement Learning, Deep Learning, and Machine Learning training.

Modern and Old Reinforcement Learning(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.

Co-creator of the Julia language, Co-founder & CTOJulia Computing, Inc.

Jeff Bezanson, PhD

Jeff is one of the creators of Julia, co-founding the project at MIT in 2009 and eventually receiving a Ph.D. related to the language in 2015. He continues to work on the compiler and system internals, while also working to expand Julia’s commercial reach as a co-founder of Julia Computing, Inc.

Author & Maintainer of PyTorch

Adam Paszke

Adam is an author and maintainer of PyTorch. He has worked with large organizations like Facebook AI Research, NVIDIA and Google, despite the fact that he has graduated from the Master’s program in Computer Science at the University of Warsaw only last year. Currently, he is also finishing his second major in Mathematics. His general interests include graph theory, programming languages, numerical computing and machine learning.

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)

ODSC Speaker - Victor Lo
Head of Data Science & Artificial IntelligenceFidelity Investments

Victor Lo, PhD

Victor S.Y. Lo is a seasoned Big Data, Marketing, Risk, and Finance leader with over 25 years of extensive consulting and corporate experience employing data-driven solutions in a wide variety of business areas, including Customer Relationship Management, Market Research, Advertising Strategy, Risk Management, Financial Econometrics, Insurance Analytics, Product Development, Healthcare Analytics, Operations Management, Transportation, and Human Resources. He is actively engaged with causal inference and is a pioneer of Uplift/True-lift modeling, a key subfield of data science.

Victor has managed teams of quantitative analysts in multiple organizations. He currently leads the AI and Data Science Center of Excellence, Workplace Investing at Fidelity Investments. Previously he managed advanced analytics/data science teams in Personal Investing, Corporate Treasury, Managerial Finance, and Healthcare and Total Well-being at Fidelity Investments. Prior to Fidelity, he was VP and Manager of Modeling and Analysis at FleetBoston Financial (now Bank of America), and Senior Associate at Mercer Management Consulting (now Oliver Wyman).

For academic services, Victor has been a visiting research fellow and corporate executive-in-residence at Bentley University. He has also been serving on the steering committee of the Boston Chapter of the Institute for Operations Research and the Management Sciences (INFORMS) and on the editorial board for two academic journals. He is also an elected board member of the National Institute of Statistical Sciences (NISS). Victor earned a master’s degree in Operational Research and a PhD in Statistics and was a Postdoctoral Fellow in Management Science. He has co-authored a graduate-level econometrics book and published numerous articles in Data Mining, Marketing, Statistics, and Management Science literature, and is completing a graduate-level book on causal inference in business.

Uplift Modeling Tutorial: Predictive and Prescriptive Analytics(Tutorial)

Head of Data SciencePodium Education

Robert Alvarez, PhD

Robert loves to break deep technical concepts down to be as simple as possible, but no simpler.

Robert has data science experience in companies both large and small. He is currently Head of Data Science for Podium Education, where he builds models to improve student outcomes and an Adjunct Professor at Santa Clara University’s Leavey School of Business. Prior to Podium Education, he was a Senior Data Scientist at Metis teaching Data Science and Machine Learning. At Intel, he tackled problems in data center optimization using cluster analysis, enriched market sizing models by implementing sentiment analysis from social media feeds, and improved data-driven decision making in one of the top 5 global supply chains. At Tamr, he built models to unify large amounts of messy data across multiple silos for some of the largest corporations in the world. He earned a PhD in Applied Mathematics from Arizona State University where his research spanned image reconstruction, dynamical systems, mathematical epidemiology, and oncology.

Understanding the PyTorch Framework with Applications to Deep Learning(Training)

Joy Payton
Supervisor, Data EducationChildren's Hospital of Philadelphia

Joy Payton

Joy Payton is a cloud engineer, data scientist, and adjunct professor who specializes in helping biomedical professionals conduct reproducible computational research. In addition to moving medicine forward through principles of open science and reproducibility, Joy also enjoys teaching citizen scientists how to use public data repositories to understand their own communities better and advocate for change from a data-centric perspective. Her various roles allow Joy to lead efforts to teach people how to write their first line of code and help anyone who’s interested climb the data science learning curve. Currently employed by the Children’s Hospital of Philadelphia and Yeshiva University, Joy is always open to hearing about open-source, data-centric volunteer opportunities for herself and her students.

Data Science and Machine Learning in the Cloud for Cloud Novices(Training)


Click Here For Full Lineup
2020 Speakers

Focus Area: Machine Learning & Deep Learning      Session: Training 

Deep Learning (with TensorFlow 2)

Dr. Jon Krohn, Chief Data Scientist, Author of Deep Learning Illustrated, Untapt 

Focus Area: Deep Learning      Session: Talk 

The Software GPU: Making Inference Scale in the Real World

Nir Shavit, PhD, CEO, Professor, Neural Magic, MIT

Focus Area: Machine Learning & Deep Learning      Session: Training 

Modern and Old Reinforcement Learning

Leonardo De Marchi, Head of Data Science and Analytics, Badoo (now MagicLab, which owns several apps)

Focus Area: Machine Learning, Research Frontiers      Session: Tutorial 

Uplift Modeling Tutorial: Predictive and Prescriptive Analytics

Victor Lo, PhD, Head of Data Science & Artificial Intelligence, Fidelity Investments

Focus Area: Machine Learning, MLOps & Data Engineering      Session: Training

Data Science and Machine Learning in the Cloud for Cloud Novices

Joy Payton, Supervisor, Data Education, Children’s Hospital of Philadelphia

Focus Area: Machine Learning      Session: Talk 

Title Coming Soon!

Sebastian Raschka, PhD, Professor, Researcher, Author of ‘Python Machine Learning’, University of Wisconsin-Madison

You 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 field 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 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 field 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

Sessions 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

  • See the whole schedule!

Sign Up for ODSC EAST 2020 | April 13th – April 17th

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