
Conference & Expo
Nov 16th – 18th, 2021
IN-PERSON & VIRTUAL
at Hyatt Regency San Francisco Airport

Conference & Expo
November 1st – 3rd, 2022
IN-PERSON & VIRTUAL
Hyatt Regency San Francisco Airport
Speakers
Hours of Content
Companies
Hybrid Attendees
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ODSC West 2021
Thank you to all our speakers, attendees, and partners
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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
ODSC WEST 2022 TRACKS
West 2022 Registration

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Registration & Discount Resources
Hotel DEAL
Hyatt Regency Airport, South San Francisco
1333 Old Bayshore Hwy, Burlingame, CA 94010
For a limited time, hotel rooms start from just $185 + taxes and fees per night.
Book NOW here.
WHAT TO EXPECT
Please visit our What to Expect page here.
Pay by invoice/purchase order
You are able to buy your ticket via Invoice/Purchase Order (PO).
Please submit your request to receive a Purchase Order HERE.
Discounts
We will offer academic, government, non-profit, and start-up discounts.
To find out more please check later here.
How to Convince your manager to attend ODSC West 2022?
Let us help you convince your manager that your attendance will benefit your organisation.
Please check HERE for more information.
Volunteer
In exchange for a free pass, ODSC is seeking volunteers to help with our program and event planning. The application is OPEN already. Check more details later HERE.
DONATE TO OUR FUNDRAISE
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.
Please donate what you can via our registration. No purchase is necessary to donate and 100% of funds raised are donated.
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.
Confirmed ODSC Speakers

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.

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.

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.

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 and architect from Hamburg, Germany. He has been developing software with different approaches and programming languages for more than 3 decades. Lately, he has been focusing on Machine Learning and its interactions with humans.

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.

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.

Jess Garcia
Jess Garcia is the Founder of the global Cybersecurity/DFIR firm One eSecurity and a Senior Instructor with the SANS Institute.
During his 25 years in the field, Jess has led a myriad of complex multinational investigations for Fortune 500 companies and global organizations. As a SANS Instructor, Jess stands as one of the most prolific and veteran ones, having taught 10+ different highly technical Cybersecurity/DFIR courses in hundreds of conferences world-wide over the last 19 years.
Jess is also an active Cybersecurity/DFIR Researcher. With the mission of bringing Data Science/AI to the DFIR field, Jess launched in 2020 the DS4N6 initiative (www.ds4n6.io), under which he is leading the development of multiple open source tools, standards and analysis platforms for DS/AI+DFIR interoperability.

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.

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.

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.

Clinton Brownley, PhD
Clinton Brownley, Ph.D., is a data scientist at Meta (formerly Facebook), where he’s responsible for a variety of analytics projects designed to empower employees to do their best work. Prior to this role, he was a data scientist at WhatsApp, working to improve messaging and VoIP calling performance and reliability. Before WhatsApp, he worked on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions at Facebook.
As an avid student and teacher of modern data analysis and visualization techniques, Clinton teaches a graduate course in interactive data visualization for UC Berkeley’s MIDS program, taught a short-term graduate course in regression analysis and machine learning workshop for NYU’s A3SR program, leads an annual machine learning in Python workshop, and is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis”.
Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.

Rachel Kellam
Rachel is a Product Manager in Appen’s Autonomous Vehicles working group. In that role, she is working to provide high quality data on all levels of autonomy for motor vehicle clients. Prior to joining Appen, Rachel worked on data science tools to enable model interpretability, fairness testing and automated machine learning. Other passions of hers include using AI and technology to act as a catalyst towards solving humanitarian-centered problems for non-profits around the world.

Aric LaBarr, PhD
A Teaching Associate Professor in the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern work force to wisely communicate and handle a data-driven future at the nation’s first Master of Science in analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management. Previously, he was Director and Senior Scientist at Elder Research, where he mentored and led a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government. Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.

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.

Serg Masis
Serg Masís is a Data Scientist in agriculture with a lengthy background in entrepreneurship and web/app development, and the author of the bestselling book “Interpretable Machine Learning with Python”. Passionate about machine learning interpretability, responsible AI, behavioral economics, and causal inference.

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)

Chandra Khatri
Chandra Khatri is the Chief Scientist and Head of AI at Got It AI, wherein, his team is transforming AI space by leveraging state-of-the-art technologies to deliver the world’s first fully autonomous Conversational AI system. Under his leadership, Got It AI is democratizing Conversational AI and related ecosystems through automation. Prior to Got-It, Chandra was leading various AI applied and research groups at Uber, Amazon Alexa and eBay.
At Uber, he was leading Conversational AI, Multi-modal AI, and Recommendation Systems. At Amazon he was the founding member of the Alexa Prize Competition and Alexa AI, wherein he was leading the R&D and got the opportunity to significantly advance the field of Conversational AI, particularly Open-domain Dialog Systems, which is considered as the holy-grail of Conversational AI and is one of the open-ended problems in AI. And at eBay he was driving NLP, Deep Learning, and Recommendation Systems related applied research projects.
He graduated from Georgia Tech with a specialization in Deep Learning in 2015 and holds an undergraduate degree from BITS Pilani, India. His current areas of research include Artificial and General Intelligence, Democratization of AI, Reinforcement Learning, Language and Multi-modal Understanding, and Introducing Common Sense within Artificial Agents.
Self-Supervised and Unsupervised Learning for Conversational AI and NLP(Workshop)

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.

Craig Knoblock, PhD
Craig Knoblock is the Keston Executive Director of the Information Sciences Institute and a Research Professor of both Computer Science and Spatial Sciences at the University of Southern California. He received his Ph.D. from Carnegie Mellon University in computer science. His research focuses on techniques for describing, acquiring, and exploiting the semantics of data. He has worked extensively on source modeling, schema and ontology alignment, entity and record linkage, data cleaning and normalization, extracting data from the web, and combining all of these techniques to build knowledge graphs. Dr. Knoblock is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the Association of Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE).
More Speakers Coming Soon!
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 –
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.
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
Partnering With ODSC
ODSC Newsletter
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Previous West Speakers
Check Out Some of The Top ODSC West 2021 Sessions in our interactive Guides
