Nov 1st-3rd, 2022
IN-PERSON & VIRTUAL:
Hyatt Regency San Francisco Airport

Conference & Expo
November 1st – 3rd, 2022
IN-PERSON & VIRTUAL
Hyatt Regency San Francisco Airport
Early Bird Offer Ends Friday
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Hours of Content
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ODSC West 2021
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RECONNECT @ ODSC
You’re In Good Company
ODSC West will be as inclusive as we have demonstrated before — from in-person sessions to digital experiences available to everyone, from anywhere. Get ready for the hybrid ODSC West Conference! Combining immersive in-person sessions and hands-on training with innovative and insightful virtual ones — it’s going to be one fantastic event to reconnect, all done with your safety in mind.
The Conference was amazing! Thank you to the staff and volunteers of the Open Data Science Conference for putting an amazing conference together! I can’t wait to attend next year!
Data Analyst, USA
I had the amazing opportunity to attend #ODSCWest this past week in San Francisco with my team. I was able to learn more about cutting-edge AI and ML techniques and ways that we can utilize these at our company!
Data Scientist, USA
#ODSCWest Awesome insightful talks and workshops! Buzzwords: MLOps, FeatureStore, ML MetadataStore, Automated retraining, and many more…
Data Science Engineer, USA
Amazing to see so many professionals sharing their knowledge. Exciting concepts which will gain further momentrum no matter which industry you are working in. Check it out!
Process & Quality Manager, Canada
The Leading Conference for
MACHINE LEARNING
DATA SCIENCE
DEEP LEARNING
DATA ANALYTICS
DATA ENGINEERING
NLP & NLU
RESPONSIBLE AI
CYBERSECURITY
MLOPS
COMPUTER VISION
SOME OF OUR CONFIRMED SPEAKERS

Pieter Abbeel, PhD
Professor Pieter Abbeel is Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab. Abbeel’s research strives to build ever more intelligent systems, which has his lab push the frontiers of deep reinforcement learning, deep unsupervised learning, especially as it pertains to robotics. Abbeel’s Intro to AI class has been taken by over 100K students through edX, and his Deep Unsupervised Learning materials are standard references for AI researchers. Abbeel has founded several companies, including Gradescope (AI to help instructors with grading homework, projects and exams) and Covariant (AI for robotic automation of warehouses and factories). He advises many AI and robotics start-ups, and is a frequently sought after speaker worldwide for C-suite sessions on AI future and strategy. Abbeel has received many awards and honors, including ACM Prize, IEEE Fellow, PECASE, NSF-CAREER, ONR-YIP, AFOSR-YIP, Darpa-YFA, TR35, and 10+ best paper awards/finalists. His work is frequently featured in the press, including the New York Times, Wall Street Journal, BBC, Rolling Stone, Wired, and Tech Review.

Andreas Mueller, PhD
Andreas Mueller is a Principal Research SDE at Microsoft (previously Columbia, NYU, Amazon), and author of the O’Reilly book “Introduction to machine learning with Python”, describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and has been co-maintaining it for several years. Andreas is also a Software Carpentry instructor.

Raluca Ada Popa, PhD
Raluca Ada Popa is an assistant professor of computer science at UC Berkeley. She is interested in security, systems, and applied cryptography. Raluca developed practical systems that protect data confidentiality by computing over encrypted data, as well as designed new encryption schemes that underlie these systems. Some of her systems have been adopted into or inspired systems such as SEEED of SAP AG, Microsoft SQL Server’s Always Encrypted Service, and others. Raluca received her PhD in computer science as well as her two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of an Intel Early Career Faculty Honor award, George M. Sprowls Award for best MIT CS doctoral thesis, a Google PhD Fellowship, a Johnson award for best CS Masters of Engineering thesis from MIT, and a CRA Outstanding undergraduate award from the ACM.

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.

Sadie St Lawrence
Sadie St Lawrence is the Founder and CEO of Women in Data, a community of 30,000+ data leaders, practitioners, and citizens whose mission is to increase diversity in data careers. Women in Data has been named a Top 50 Leading Company of The Year, and has been rated as the #1 community for Women in AI and Tech. Sadie has trained over 400,000 people in data science and has developed multiple programs in machine learning and career development. Sadie has been awarded, Top 30 Most Inspiring Women in AI, Top 10 Most Admired Businesswomen to Watch in 2021, Top 21 Influencer in Data, and is the recipient of the Outstanding Service Award from UC Davis. In addition, she serves on boards, and is the host of the Data Bytes podcast.

James Demmel, PhD
James Demmel is the Dr. Richard Carl Dehmel Distinguished Professor of Computer Science and Mathematics at the University of California at Berkeley, and former Chair of the EECS Dept. He also serves as Chief Strategy Officer for the start-up HPC-AI Tech, whose goal is to make large-scale machine learning much more efficient, with little programming effort required by users. Demmel’s research is in high performance computing, numerical linear algebra, and communication avoiding algorithms. He is known for his work on the widely used LAPACK and ScaLAPACK linear algebra libraries. He is a member of the National Academy of Sciences, National Academy of Engineering, and American Academy of Arts and Sciences; a Fellow of the AAAS, ACM, AMS, IEEE and SIAM; and winner of the IPDPS Charles Babbage Award, IEEE Computer Society Sidney Fernbach Award, the ACM Paris Kanellakis Award, the J. H. Wilkinson Prize in Numerical Analysis and Scientific Computing, and numerous best paper prizes.
Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training(Tutorial)

Scott Zoldi, PhD
Scott Zoldi is chief analytics officer at FICO responsible for advancing the company's leadership in artificial intelligence (AI) and analytics in its product and technology solutions. At FICO Scott has authored more than 120 analytic patents, with 71 granted and 49 pending. Scott is actively involved in the development of analytics applications, Responsible AI technologies and AI governance frameworks, the latter including FICO's blockchain-based [SZ1] model development governance methodology. Scott is a member of the Board of Advisors of FinRegLab, a Cybersecurity Advisory Board Member of the California Technology Council, and a Board Member of Tech San Diego and the San Diego Cyber Center of Excellence. He is also a member of the CNBC Technology Executive Council. Scott received his Ph.D. in theoretical and computational physics from Duke University.

Dr. Jennifer Prendki
Dr. Jennifer Prendki is the founder and CEO of Alectio, the first startup focused on DataPrepOps, a portmanteau term that she coined to refer to the nascent field focused on automating the optimization of a training dataset. She and her team are on a fundamental mission to help ML teams build models with less data (leading to both the reduction of ML operations costs and CO2 emissions) and have developed technology that dynamically selects and tunes a dataset that facilitates the training process of a specific ML model. Prior to Alectio, Jennifer was the VP of Machine Learning at Figure Eight; she also built an entire ML function from scratch at Atlassian, and led multiple Data Science projects on the Search team at Walmart Labs. She is recognized as one of the top industry experts on Data Preparation, Active Learning and ML lifecycle management, and is an accomplished speaker who enjoys addressing both technical and non-technical audiences.

Steve Tingiris
Steve is the author of the book Exploring GPT-3 from Packt Publishing and the managing director of Dabble Lab, a technology education and services company that helps businesses accelerate learning and adoption of artificial intelligence, blockchain, and other emerging technologies. Steve has been designing and building automation solutions for over 20 years and has consulted on AI and automation projects for companies including Amazon, Google, and Twilio. He also publishes technical tutorials on Dabble Lab’s YouTube channel— one of the most popular educational resources for conversational AI developers—and manages several open-source projects, including the Autopilot CLI, Twilio’s recommended tool for building Autopilot bots.

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.

Oliver Zeigermann
Oliver is a software developer from Hamburg Germany and has been a practitioner for more than 3 decades. He specializes in frontend development and machine learning. He is the author of many video courses and textbooks.
Image Recognition with OpenCV and TensorFlow(Training)

Joe Hellerstein, PhD
Joseph M. Hellerstein is the Jim Gray Professor of Computer Science at the University of California, Berkeley, whose work focuses on data-centric systems and the way they drive computing. He is an ACM Fellow, an Alfred P. Sloan Research Fellow and the recipient of three ACM-SIGMOD “Test of Time” awards for his research. Fortune Magazine has included him in their list of 50 smartest people in technology , and MIT’s Technology Review magazine included his work on their TR10 list of the 10 technologies “most likely to change our world”.
Hellerstein is a co-founder of Aqueduct, which is bringing new open source technology for Prediction Infrastructure to market. Previously he co-founded Trifacta, the pioneering company in Data Preparation, where he served as founding CEO and Chief Strategy Officer. Hellerstein has served on the technical advisory boards of a number of computing and Internet companies including Dell EMC, SurveyMonkey, Datometry and Acryl Data.

Stefanie Molin
Stefanie Molin is a data scientist and software engineer at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science. She is currently pursuing a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

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.
Running Any ML Code in Any ML Framework(Workshop)

Neil Sahota
Neil Sahota is an IBM Master Inventor, United Nations (UN) AI Advisor, author of the book Own the A.I. Revolution., and Chief Innovation Officer at UC Irvine. He is a business solution advisor to several large companies and sought-after keynote speaker. Over his 20+ year career, Neil has worked with enterprises on the business strategy to create next generation products/solutions powered by emerging technology as well as helping organizations create the culture, community, and ecosystem needed to achieve success such as the U.N.’s AI for Good initiative. Neil also actively pursues social good and volunteers with nonprofits. He is currently helping the Zero Abuse Project prevent child sexual abuse as well as Planet Home to engage youth culture in sustainability initiatives.

Meg Kurdziolek, PhD
Meg is currently a UX Researcher for Google Cloud AI and Industry Solutions, where she focuses her research on Explainable AI and Model Understanding. She has had a varied career working for start-ups and large corporations alike across fields such as EdTech, weather forecasting, and commercial robotics. She has published articles on topics such as information visualization, educational-technology design, human-robot interaction (HRI), and voice user interface (VUI) design. Meg is also a proud alumnus of Virginia Tech, where she received her Ph.D. in Human-Computer Interaction (HCI).

Josh Tobin, PhD
Josh Tobin is the founder and CEO of Gantry. Previously, Josh worked as a deep learning & robotics researcher at OpenAI and as a management consultant at McKinsey. He is also the creator of Full Stack Deep Learning (fullstackdeeplearning.com), the first course focused on the emerging engineering discipline of production machine learning. Josh did his PhD in Computer Science at UC Berkeley advised by Pieter Abbeel.

Carl Gold, PhD
Carl Gold is currently the Data Science Director at OfferFit.ai, an AI-as-a-Service reinforcement learning engine that maximizes customer upsell and retention. Before coming to OfferFit, Carl was Chief Data Scientist of Zuora, the Subscription Economy leading billing platform. Based on his experiences fighting churn for SaaS companies during his time at Zuora, Carl wrote the first book dedicated to customer churn analytics and data science: “Fighting Churn With Data”. Carl has a PhD from the California Institute of Technology and first author publications in leading Machine Learning and Neuroscience journals.
Fighting Churn With Data(Workshop)

Serg Masis
Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he’s a Climate and Agronomic Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a search engine startup, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events efficiently. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making. He wrote the bestselling book “Interpretable Machine Learning with Python” and is currently working on a new book titled “DIY AI” for Addison-Wesley for a broader audience of curious developers, makers, and hackers.
Enhance Trust with Machine Learning Model Error Analysis(Workshop)

Jennifer Dawn Davis, PhD
Jennifer Davis, Ph.D. is a Staff Field Data Scientist at Domino Data Labs, where she empowers clients on complex data science projects. She has completed two postdocs in computational and systems biology, trained at a supercomputing center at the University of Texas, Austin, and worked on hundreds of consulting projects with companies ranging from start-ups to the Fortune 100. Jennifer has previously presented topics at conferences for Association for Computing Machinery on LSTMs and Natural Language Generation and at conferences across the US and in Italy. Jennifer was part of a panel discussion for an IEEE conference on artificial intelligence in biology and medicine. She has practical experience teaching both corporate classes and at the college level. Jennifer enjoys working with clients and helping them achieve their goals.
Large Scale Deep Learning using the High-Performance Computing Library OpenMPI and DeepSpeed(Workshop)

Julien Simon
Julien is currently Chief Evangelist at Hugging Face. He’s recently spent 6 years at Amazon Web Services where he was the Global Technical Evangelist for AI & Machine Learning. Prior to joining AWS, Julien served for 10 years as CTO/VP Engineering in large-scale startups.

Eitan Anzenberg, PhD
Eitan is the Chief Data Scientist at Bill.com and has many years of experience as a researcher. His recent focus is on machine learning, deep learning, applied statistics and software engineering. Before, he was a Postdoctoral Scholar at Lawrence Berkeley National Lab, received his PhD in Physics from Boston University and B.S. in Astrophysics from University of California Santa Cruz. Eitan holds 4 patents and 11 publications to date and has spoken about data at various conferences around the world.

Veena Mendiratta, PhD
Bio Coming Soon!
Using Change Detection Algorithms for Detecting Anomalous Behavior in Large Systems(Talk)

Dr. Anju Kambadur
Dr. Prabhanjan (Anju) Kambadur heads the AI Engineering group at Bloomberg. Anju leads a group of 100+ researchers and engineers who build solutions for Bloomberg clients in the areas of machine learning, natural language processing (NLP) and natural language understanding, information extraction, knowledge graphs, question answering, and table understanding. Previously, Anju was a research staff member in the Business Analytics and Mathematical Sciences Department at IBM Research’s Thomas J. Watson Research Center, where he worked on problems in machine learning, such as matrix sketching, genome-wide association studies, temporal causal modeling, and high-performance computing. He received his PhD from Indiana University. Anju has published peer-reviewed articles in the fields of high-performance computing, machine learning, and natural language processing.

Malte Pietsch
Malte Pietsch is CTO & Co-Founder at deepset. His current focus is on building deepset Cloud – a SaaS platform for developers to build, deploy and operate modern NLP pipelines. He holds a M.Sc. with honors from TU Munich and conducted research at Carnegie Mellon University. Before founding deepset he worked as a data scientist for multiple startups. He is an active open-source contributor and author of the NLP framework Haystack.
Building Modern Search Pipelines with Haystack, Large Language Models and Hybrid Retrieval(Talk)
THE LEADING DATA SCIENCE CONFERENCE WHY ATTEND
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 West’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 West’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.
West 2022 Registration
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* LIMITED TIME OFFER includes access to one live training on AI+ for FREE: August 24th, 2022 PyTorch 101 Building a Model Step-by-step with Daniel Voigt Godoy


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Hotel DEAL
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1333 Old Bayshore Hwy, Burlingame, CA 94010
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For this year’s event, ODSC will double donations and fundraising to Support of Ukraine. Please support Ukraine, and its refugees and help those who stayed fighting for their country. All donations would be sent to the Come Back Alive Foundation.
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8 Focus Areas. 3 Days. One Data Science Conference
The largest applied data science conference is now 3 days, including kickstart bootcamp days, 3 full training days, and 2 talks/workshops days. You get even more talks, trainings, and workshops spread over 8 focus areas. Accelerate your data science knowledge, training, and network all in one event. Start learning now with our pre-bootcamp live and on-demand training and hackathon.
EVENT SUMMARY
Pre-conference Training
– A real immersive learning experience –
Start learning now with live and on-demand pre-bootcamp sessions.
3 Days of Data Science Training
– Taught by World-Class Data Scientists –
Learn the latest data science concepts, tools, and techniques from the best.
2 Days of Keynotes, Talks, and Workshops
– Presented by the best in the field of data science –
Where Business Meets AI
Ai X Business Summit
Co-located at ODSC West, Ai X brings together the leading practitioners, innovation experts, and business professionals that drive artificial intelligence across a range of industries

Innovation
Discover how techniques from Machine Learning, Deep Learning, and Predictive Analytics are driving AI Innovation
Expertise
Gain AI expertise and learn from leading experts how to work with the frameworks and tools employed in AI
Management
Manage and deploy AI in the real world. Hear from AI management and heads of data science teams on a range of topics
Networking
Network with the AI experts, innovators, business professionals, and data scientists building the future of AI
Event Venue
Hyatt Regency,
South San Francisco
11333 Old Bayshore Hwy, Burlingame, CA 94010
Participate at ODSC West 2022
As part of the global data science community we value inclusivity, diversity, and fairness in the pursuit of knowledge and learning. We seek to deliver a conference agenda, speaker program, and attendee participation that moves the global data science community forward with these shared goals. Learn more on our code of conduct, speaker submissions, or speaker committee pages.
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Previous West Speakers
Check Out Some of The Top ODSC West 2021 Sessions in our interactive Guides
