November 15th – 18th, 2021

Machine Learning & Deep Learning Track

Learn the latest models, advancements, and trends from the creators and top practitioners behind two of data science’s hottest topics
Register now | 75% ends soon

Machine Learning & Deep Learning

The Machine Learning & Deep Learning Track is where 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 Previous Speakers

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.

Deep Learning (with TensorFlow 2)(Full-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)

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.

Head of Data Science and AnalyticsBadoo (now MagicLab)

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 Part 1(Training)

Modern and Old Reinforcement Learning Part 2(Training)

Chief Data Evangelist, Director of Data ScienceCisco Systems Inc.

Jennifer Redmon

Jennifer Redmon joined Cisco in 2009 and serves as its, and industry’s 1st, Chief Data & Analytics Evangelist as well as sits on Forbes’ Technology Council. Jennifer is a leader in developing data-driven cultures and organizational analytical maturity through qualitative and quantitative methods. Her approach to fostering a data-driven workforce is taught at multiple higher ed. institutions around the world. Jennifer is passionate about giving back and consequently, founded Cisco’s Data Science and AI for Good initiative. She leads the company’s award-winning Data Science and AI for Suicide Prevention Team, which, inspired by and in collaboration with WHO researchers, focus on de-stigmatizing mental illness through cultural change. Her authorship focuses on data- and analytics-driven cultures, mental health in the workplace, suicide prevention, as well as innovative applications of data science and AI including social good. Jennifer holds an international MBA from Duke University with a concentration in Strategy, Bachelor’s Degrees in Economics and Art History from UC Davis, is in Georgia Tech’s Master of Science in Analytics Spring 2021 class, and is a certified Suicide First Responder.

Data Science for Suicide Prevention(Talk)

Data Science ManagerCisco Data Science Lab

Dr. Annie Ying

Annie is a leader in data science with over a decade of industry experience spanning corporate research and startups. Currently, she is the head of the Cisco Data Science Lab in Vancouver. Coming from a research background with a PhD in Computer Science from McGill, Annie is a former Research Scientist at IBM T. J. Watson Research Center in New York and a two-time winner of an ACM Distinguished Paper Award (both in applying data science to software engineering) with five patents (granted and applied). Annie is active in the data science community as a Meetup organizer (Data Science for Social Good), speaker, and mentor.

Data Science for Suicide Prevention(Talk)

 

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)

Risk Management SpecialistFederal Reserve Bank of Chicago

Lara Kattan

Lara is a risk specialist with the Federal Reserve Bank of Chicago and occasional adjunct at the University of Chicago’s Booth School of Business, teaching Python and R. Previously she’s taught a data science bootcamp and built risk models for large financial institutions at McKinsey & Co. She enjoys coffee, yoga and bikes.

Probabilistic Programming and Bayesian Inference with Python(Half-Day Training)

Chief Data Scientist, Author of "R for Everyone"Lander Analytics

Jared Lander

Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.

Machine Learning in R Part I(Training)

Machine Learning in R Part II(Training)

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)

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)

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)

CEOAlgorithmia

Diego Oppenheimer

Diego Oppenheimer is the co-founder and CEO of Algorithmia. Previously, he designed, managed, and shipped some of Microsoft’s most used data analysis products including Excel, Power Pivot, SQL Server, and Power BI. He holds a Bachelor’s degree in Information Systems and a Master’s degree in Business Intelligence and Data Analytics from Carnegie Mellon University.

Prioritize ML Operations at Any Maturity Level(Talk)

DirectorIBM Research

Lisa Amini, PhD

Dr. Lisa Amini is the Director of IBM Research Cambridge, which is also home to the MIT-IBM Watson AI Lab, and of IBM’s AI Horizons Network. Lisa was previously Director of Knowledge & Reasoning Research in the Cognitive Computing group at IBM’s TJ Watson Research Center in New York, and she is also an IBM Distinguished Engineer. Lisa was the founding Director of IBM Research Ireland, and the first woman Lab Director for an IBM Research Global (i.e., non-US) Lab (2010-2013). In this role she developed the strategy and led researchers in advancing science and technology for intelligent urban and environmental systems (Smarter Cities), with a focus on creating analytics, optimizations, and systems for sustainable energy, constrained resources (e.g., urban water management), transportation, and the linked open data systems that assimilate and share data and models for these domains. She earned her PhD degree in Computer Science from Columbia University.

Advances and Frontiers in Auto AI & Machine Learning(Track Keynote)

Principal Research Scientist Google DeepMind

Oriol Vinyals, PhD

Oriol Vinyals is a Principal Scientist at Google DeepMind, and a team lead of the Deep Learning group. His work focuses on Deep Learning and Artificial Intelligence. Prior to joining DeepMind, Oriol was part of the Google Brain team. He holds a Ph.D. in EECS from the University of California, Berkeley and is a recipient of the 2016 MIT TR35 innovator award. His research has been featured multiple times at the New York Times, Financial Times, WIRED, BBC, etc., and his articles have been cited over 85000 times. Some of his contributions such as seq2seq, knowledge distillation, or TensorFlow are used in Google Translate, Text-To-Speech, and Speech recognition, serving billions of queries every day, and he was the lead researcher of the AlphaStar project, creating an agent that defeated a top professional at the game of StarCraft, achieving Grandmaster level, also featured as the cover of Nature. At DeepMind he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, deep learning and reinforcement learning.

AlphaStar: Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning(Track Keynote)


Sessions on Machine Learning & Deep Learning Track

  • Training: Modern and Old Reinforcement Learning

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

  • Training: Deep Learning (with TensorFlow 2)

  • Training: Probabilistic Programming and Bayesian Inference with Python

  • Tutorial: Causal Inference in Data Science

  • Workshop: Decision Making for Urban Autonomous Vehicles

  • Workshop: Uplift Modeling Tutorial: From Predictive to Prescriptive Analytics

  • Tutorial: Echo State Networks for Time-Series Data

  • Talk: Building Content Embedding with Self Supervised Learning

  • Talk: Advances and Frontiers in Auto AI & Machine Learning

  • Talk: Intelligibility Throughout the Machine Learning Life Cycle

  • Talk: Data Science for Suicide Prevention

What You'll Learn

Talks + Workshops + Special Events on these topics:

Topics

  • Machine Learning

  • Deep Learning

  • Artificial Intelligence

  • Neural Networks

  • Natural Language Processing

  • Computer Vision

  • Pattern Recognition

Tools & Languages

  • R

  • Python SciPy, Pandas, etc

  • Scikit-learn

  • Tensorflow

  • Spark

  • MLlib

  • H20

Tools & Languages

  • WEKA

  • Pylearn2

  • Theano

  • Caffe

  • Torch

  • Azure Machine Learning API

  • and many more..

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 impacting the domains of business, society, healthcare, finance, manufacturing, and more

Sign Up for ODSC WEST | November 15th - 18th, 2021

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