March 30th – April 1st, 2021

Machine Learning & Deep Learning Track

Learn the latest models, advancements, and trends from the top practitioners behind two of data science’s hottest topics
Register now

Brought by

    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 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 Current ML & DL Speakers

    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 Cold-Start Problem with Machine Learning Examples(Half-Day Training)

    Atypical Applications of Typical Machine Learning Algorithms(Half-Day Training)

    Machine Intelligence ScientistDraper

    Teal Guidici, PhD

    Teal Guidici is a Senior Machine Intelligence Scientist at Draper where she uses statistical techniques and machine learning algorithms to develop creative solutions for interesting data-driven problems in areas including biomedicine, finance, and remote sensing. Prior to Draper, she did graduate work creating new methods to analyze patterns of co-variation in complex datasets and applied these methods applied to high throughput metabolomics data. She has additional experience in survey design and data analysis in consumer marketing research. Dr. Guidici has a B.S. in Theoretical Mathematics from MIT, a M.S. in Bioinformatics and a Ph.D. in Statistics from the University of Michigan.

    Echo State Networks for Time-Series Data (Tutorial)

    Global Lead Data Science InstructorGeneral Assembly

    Matt Brems

    Matt currently leads instruction for GA’s Data Science Immersive in Washington, D.C. and most enjoys bridging the gap between theoretical statistics and real-world insights. Matt is a recovering politico, having worked as a data scientist for a political consulting firm through the 2016 election. Prior to his work in politics, he earned his Master’s degree in statistics from The Ohio State University. Matt is passionate about making data science more accessible and putting the revolutionary power of machine learning into the hands of as many people as possible. When he isn’t teaching, he’s thinking about how to be a better teacher, falling asleep to Netflix, and/or cuddling with his pug.

    Good, Fast, Cheap: How to do Data Science with Missing Data(Half-Day Training)

    Staff Associate - Machine LearningColumbia University in the City of New York

    Thomas Fan

    Thomas J. Fan is a Staff Associate at the Data Science Institute at Columbia University. He is one of the core developers of scikit-learn, an open source machine learning library written in Python. Thomas holds a Masters in Mathematics from NYU and Masters in Physics from Stony Brook University. He also maintains skorch, a scikit-learn compatible neural network library that wraps PyTorch. He believes that developing open source software is one of the best ways to maximize one’s impact.

    Introduction to Scikit-learn: Machine Learning in Python(Half-Day Training)

    Intermediate Machine Learning with Scikit-learn: Cross-validation, Parameter Tuning, Pandas Interoperability, and Missing Values(Half-Day Training)

    Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection(Half-Day Training)

    Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression(Half-Day Training)

    Professor of Machine Learning, AI, and Medicine, Director, Professor of Electrical and Computer EngineeringUniversity of Cambridge, UCLA

    Mihaela van der Schaar, PhD

    Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

    Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

    Mihaela’s work has also led to 35 USA patents (many widely cited and adopted in standards) and 45+ contributions to international standards for which she received 3 International ISO (International Organization for Standardization) Awards.

    In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise spans signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

    Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.

    In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).

    Why Medicine is Creating Exciting New Frontiers for Machine Learning(Keynote)

    Co-Founder, Principal Data Scientist & FacultyRoboflow.com, BetaVector, General Assembly

    Joseph Nelson

    Joseph Nelson is co-founder and CEO of Roboflow, a computer vision developer tool. Roboflow enables anyone to build better computer vision models quickly. Joseph previously co-founded Represently (acq. 2018). He has been named Distinguished Faculty at General Assembly and worked at companies big (Facebook) and small (failed startups). Joseph is a managing partner at BetaVector, a data science consultancy he co-founded. He’s easily reached on Twitter: @josephofiowa

    Applied Deep Learning: Building a Chess Object Detection Model with Computer Vision(Half-Day Training)

    Developer Advocate, TensorFlowGoogle

    Robert Crowe

    A recovering data scientist and TensorFlow addict, Robert has a passion for helping developers quickly learn what they need to be productive.

    From Experimentation to Products: The Production ML Journey(Talk)

    Vice President of Data AnalyticsOptum

    Margaret Good, PhD

    Margaret (Meg) Good, Ph.D., specializes in health economics, health policy, and survey research methods. She has been with Optum since 2005. In her current role as Vice President of Data Analytics, Dr. Good advises Optum businesses on how to use analytics to achieve strategic objectives for their products and services. She supports the advancement and use of artificial intelligence, machine learning, advanced analytics, and emerging technologies at Optum. Before joining the OEA team, Dr. Good served as the Vice President of Health Economics & Outcomes Research in Optum Life Sciences. This team conducts observational research studies using administrative claims data, patient and provider surveys, EHR/medical chart data, and other secondary data sources. Prior to joining Optum, she was a faculty member in the Department of Public Policy at the University of Maryland, Baltimore County where she taught courses in health policy and research methods. She also worked at the University of Minnesota where she worked in a research collaborative funded by the Robert Wood Johnson Foundation to help states expand access to health insurance and health coverage among disadvantaged populations. Dr. Good earned her PhD and MS in health services research and policy at the University of Minnesota and her undergraduate degree at Williams College. She has presented her research at national conferences and has authored or co-authored publications that include articles in the Journal of the American Medical Association, Inquiry, Medical Care Research & Review, and the Journal of Health Politics, Policy and Law.

    Responsible Use of AI in Health Care(Talk)

    Professor, Lab Director, Sr. AI ScientistCornell Tech, Rochester Institute of Technology, Paige

    Christopher Kanan, PhD

    Christopher Kanan is an Assistant Professor at the Rochester Institute of Technology (RIT), a Visiting Assistant Professor at Cornell Tech, and a Senior AI Scientist at Paige. At RIT, his lab works on lifelong machine learning and language driven computer vision, which has been supported by awards from NSF, AFOSR, ONR, DARPA, Adobe Research, and other industrial partners. He is also Associate Director of RIT’s Center for Human-aware AI and he is a member of RIT’s McNair Scholars advisory board. At Paige, a startup that has raised $95M to improve the diagnosis of cancer, he led the AI R&D team during its first 1.5 years and continues to advise its AI teams. He received a PhD in computer science from the University of California at San Diego, where he worked on brain-inspired algorithms for object recognition, neural networks, active vision, and cognitive modeling. He received an MS in computer science from the University of Southern California. Before joining RIT, he was a postdoctoral scholar at the California Institute of Technology, and later worked as a Research Technologist at NASA’s Jet Propulsion Laboratory, where he used deep learning to develop vision systems for autonomous ships. He is the recipient of the 2016 Rising Star Award and the 2019 Distinguished Scholarship Award in RIT’s College of Science. He is an IEEE Senior Member and has published over 50 refereed papers, many of which are in top venues across AI such as CVPR, ICCV, NeurIPS, AAAI, ICLR, ACL, etc.

    Continual Learning in Deep Neural Networks: Methods and Applications(Tutorial)

    Principal AI ScientistZS Associates

    Srinivas Chilukuri

    Srinivas leads ZS AI Research Lab with a focus on frontier innovation and development of cutting edge algorithms. Srinivas’s core expertise areas include automated machine learning, natural language processing, and marketing AI across industries. He has authored several thought leadership articles and presented at conferences. Prior to joining ZS, Srinivas spent time as a solution architect building expert systems to automate product design and manufacturing across multiple industries viz., automobile, power systems, medical devices and retail

    Improving Structured Data Ml Processes with Generative Adversarial Networks(Business Talk)

    deep learning with TensorFlow
    Chief Data ScientistUntapt

    Dr. Jon Krohn

    Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, which was released by Addison-Wesley in 2019 and became an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy, as well as online via O’Reilly, YouTube, and his A4N podcast on A.I. news. Jon holds a doctorate in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010.

    Linear Algebra, Calculus, and Probability: The Math ML Experts Master(Tutorial)

    Senior Data ScientistGreenhouse Software

    Mona Khalil

    Mona is a Senior Data Scientist 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.

    Evaluation of Statistical Models based on Stakeholder Needs(Tutorial)

    Assistant Professor of Machine LearningEindhoven University of Technology

    Joaquin Vanschoren, PhD

    Joaquin Vanschoren is Assistant Professor in Machine Learning at the Eindhoven University of Technology. His research focuses on machine learning, meta-learning, and understanding and automating learning. He founded and leads OpenML.org, an open science platform for machine learning. He received several demo and open data awards, has been tutorial speaker at NeurIPS and ECMLPKDD, and invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NIPS, and many other occasions. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and meta-learning workshop series at NIPS and ICML. He is also co-editor of the book ’Automatic Machine Learning: Methods, Systems, Challenges’.

    Tutorial on Automated Machine Learning(Workshop)

    Assistant ProfessorUniversity of Wisconsin-Madison

    Sharon Yixuan Li, PhD

    Sharon Yixuan Li is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin Madison. Previously she spent a wonderful year as a postdoc researcher in the Computer Science department at Stanford University, working with Chris Ré. Sharon Yixuan Li completed her PhD from Cornell University in 2017, where Sharon was fortunate to be advised by John E. Hopcroft. Her thesis committee members are Kilian Q. Weinberger and Thorsten Joachims. Sharon has spent time at Google AI twice as an intern, and Facebook AI as a Research Scientist. Sharon was named Forbes 30 Under 30 in Science in 2020.

    Sharon Yixuan Li broad research interests are in deep learning and machine learning. Her time in both academia and industry has shaped her view and approach in research. The goal of the research is to enable transformative algorithms and practices towards reliable open-world learning, which can function safely and adaptively in the presence of evolving and unpredictable data stream. Our works explore, understand, and mitigate the many challenges where failure modes can naturally occur in deploying machine learning models in the open world.

    Reliable Open-World Learning Against Out-of-distribution Data(Talk)

    Research Scientist and ManagerFacebook

    Yuandong Tian, PhD

    Yuandong Tian is a Research Scientist and Manager in Facebook AI Research, working on deep reinforcement learning in games and theoretical analysis of deep models. He led OpenGo, a super-human Go bot from Facebook AI. Prior to that, he was a Software Engineer/Researcher in Google Self-driving Car team during 2013-2014. He received Ph.D in Robotics Institute, Carnegie Mellon University on 2013, Bachelor and Master degree of Computer Science in Shanghai Jiao Tong University. He is the recipient of 2013 ICCV Marr Prize Honorable Mentions.

    Learning to Optimize High-Dimensional Optimization Problems(Talk)


    Some Previous ML & DL Speakers

    Instructor, Data ScientistData Dan

    Daniel Whitenack, PhD

    Daniel Whitenack (aka Data Dan) is a PhD trained data scientist who has been developing artificial intelligence applications in the real world for over 10 years. He knows how to see beyond the hype of AI and machine learning to build systems that create business value, and he has taught these skills to 1000’s of developers, data scientists, and engineers all around the world. Now with the AI Classroom event, Data Dan is bringing this knowledge to an live, online learning environment so that you can level up your career from anywhere!

    Advanced NLP with TensorFlow and PyTorch: LSTMs, Self-attention and Transformers(Full-Day Training)

    State of the art AI Methods with TensorFlow: Transfer Learning, RL and GANs(Training)

    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)

    Founder & Chief Scientist, ProfessorPetuum, Carnegie Mellon University

    Eric Xing, PhD

    Eric P. Xing is a Professor of Computer Science at Carnegie Mellon University, and the Founder, CEO, and Chief Scientist of Petuum Inc., a 2018 World Economic Forum Technology Pioneer company that builds standardized artificial intelligence development platform and operating system for broad and general industrial AI applications. He completed his undergraduate study at Tsinghua University, and holds a PhD in Molecular Biology and Biochemistry from the State University of New Jersey, and a PhD in Computer Science from the University of California, Berkeley. His main research interests are the development of machine learning and statistical methodology, and large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems. Prof. Xing currently serves or has served the following roles: associate editor of the Journal of the American Statistical Association (JASA), Annals of Applied Statistics (AOAS), IEEE Journal of Pattern Analysis and Machine Intelligence (PAMI) and the PLoS Journal of Computational Biology; action editor of the Machine Learning Journal (MLJ) and Journal of Machine Learning Research (JMLR); member of the United States Department of Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) advisory group. He is a recipient of the Carnegie Science Award, National Science Foundation (NSF) Career Award, the Alfred P. Sloan Research Fellowship in Computer Science, the United States Air Force Office of Scientific Research Young Investigator Award, the IBM Open Collaborative Research Faculty Award, as well as several best paper awards. Prof Xing is a board member of the International Machine Learning Society; he has served as the Program Chair (2014) and General Chair (2019) of the International Conference of Machine Learning (ICML); he is also the Associate Department Head of the Machine Learning Department, founding director of the Center for Machine Learning and Health at Carnegie Mellon University; and he is a Fellow of the Association of Advancement of Artificial Intelligence (AAAI), and an IEEE Fellow.

    Principal Data ScientistLinc Global

    Joan Xiao, PhD

    Joan Xiao is a Principal Data Scientist at Linc Global, a commerce-specialized customer care automation company. In her role, she applies novel natural language processing and machine learning techniques to improve customer experience. Previously she led machine learning and data science teams at various companies ranging from startup to Fortune 100. Joan received her Ph.D in Mathematics and MS in Computer Science from University of Pennsylvania.

    Transfer Learning in NLP(Talk)

    Machine Learning EngineerTwitter Cortex

    Sijun He

    Sijun He is a machine learning engineer at Twitter Cortex, where he works on content understanding with deep learning and NLP. Previously, he was a data scientist at Autodesk. Sijun holds an MS in statistics from Stanford University.

    Building Content Embedding with Self Supervised Learning(Talk)


    See all our talks and hands-on workshop and training sessions
    See all sessions

    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 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 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

    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 schedule!

    ODSC EAST 2021 | March 30th – April 1st

    Register Now
    Open Data Science

    Open Data Science
    Innovation Center
    101 Main St
    Cambridge, MA 02142
    info@odsc.com

    Menu
    Conferences
    Extras
    Copyright ODSC 2021. All Rights Reserved
    Privacy Settings
    We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
    Youtube
    Consent to display content from Youtube
    Vimeo
    Consent to display content from Vimeo
    Google Maps
    Consent to display content from Google
    Save