ODSC West 2022 SPEAKERS
ODSC West will host more than 280 speakers and instructors. Speaker profiles are added weekly. Check back for updates. You’re welcome to check out some speaker blogs here.
ODSC West will host more than 280 speakers and instructors. Speaker profiles are added weekly. Check back for updates. You’re welcome to check out some speaker blogs here.
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 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.
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.
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.
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.
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 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.
Ville has been developing infrastructure for machine learning for over two decades. He has worked as an ML researcher in academia and as a leader at a number of companies, including Netflix where he led the ML infrastructure team that created Metaflow, a popular open-source framework for data science infrastructure. He is a co-founder and CEO of Outerbounds, a company developing modern human-centric ML. He is also the author of an upcoming book, Effective Data Science Infrastructure, published by Manning.
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.
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.
Hugo Bowne-Anderson is a data scientist, writer, educator & podcaster. His interests include promoting data & AI literacy/fluency, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. He does many of these at DataCamp, a data science training company educating over 3 million learners worldwide through interactive courses on the use of Python, R, SQL, Git, Bash and Spreadsheets in a data science context. He has spearheaded the development of over 25 courses in DataCamp’s Python curriculum, impacting over 170,000 learners worldwide through my own courses. He hosts and produce the data science podcast DataFramed, in which he uses long-format interviews with working data scientists to delve into what actually happens in the space and what impact it can and does have. He earned PhD in Mathematics from the University of New South Wales, Australia and has conducted biomedical research at the Max Planck Institute in Germany and Yale University, New Haven.
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.
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 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).
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.
Balaji is currently a Staff Research Scientist at Google Brain working on Machine Learning and its applications. Previously, he was a research scientist at DeepMind for 4.5+ years. Before that, he received a PhD in machine learning from Gatsby Unit, UCL supervised by Yee Whye Teh. His research interests are in scalable, probabilistic machine learning. More recently, he has focused on: – Uncertainty and out-of-distribution robustness in deep learning – Deep generative models including generative adversarial networks (GANs), normalizing flows and variational auto-encoders (VAEs) – Applying probabilistic deep learning ideas to solve challenging real-world problems.
Guy Van den Broeck is an Associate Professor and Samueli Fellow at UCLA, in the Computer Science Department, where he directs the Statistical and Relational Artificial Intelligence (StarAI) lab. His research interests are in Machine Learning, Knowledge Representation and Reasoning, and Artificial Intelligence in general. His work has been recognized with best paper awards from key artificial intelligence venues such as UAI, ILP, KR, and AAAI (honorable mention). He also serves as Associate Editor for the Journal of Artificial Intelligence Research (JAIR). Guy is the recipient of an NSF CAREER award, a Sloan Fellowship, and the IJCAI-19 Computers and Thought Award.
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).
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.
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. 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.
Cal Al-Dhubaib is a data scientist, entrepreneur, and professional speaker on Artificial Intelligence. He founded Pandata to help organizations plan, design, and scale human-centered AI solutions. Pandata has overseen 80+ transformative projects with leading global brands including Parker Hannifin, the Cleveland Museum of Art, FirstEnergy, and Penn State University.
Cal is especially passionate about orchestrating inclusive teams that are empowered to build Trusted AI solutions. He has been recognized as a Notable Immigrant Entrepreneur, Crain’s Cleveland 20 in their 20s, and two-time Cleveland Smart 50 recipient. In addition to becoming the first data science graduate from Case Western Reserve University, Cal is also known for his role in advocating for careers and educational pathways in Data Science through workforce development initiatives.
Utkarsh Contractor is the Director of AI at Aisera, where he leads the data science team working on machine learning and artificial intelligence applications in the fields of Natural Language Processing and Vision. He is also pursuing his graduate degree at Stanford University, focussing his research and experiments on computer vision, using CNNs to analyze surveillance scene imagery and footages. Utkarsh has a decade of industry experience in Information Retrieval and Machine Learning working at companies such as LinkedIn and AT&T Labs.
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.
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.
Ali Vanderveld is a Senior Staff Data Scientist at Wayfair, where she serves as a technical leader for machine learning, currently leading the development of novel search and recommendation technologies. Prior to Wayfair, she led a team focused on language AI at Amazon Web Services and was the Director of Data Science at ShopRunner. She has also worked at Civis Analytics, 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 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.
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.
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.
Martin is a Senior Clinical Programmer at BioMarin, where he builds dashboards and tools for making data-informed decisions. Previously, Martin built statistical tools and dashboards for the Diabetes Technology Society, a contributing author for Data Journalism in R on the Northeastern University School of Journalism blog/website, and other volunteer and non-profit organizations. He’s a data journalism instructor for California State University, Chico. Martin holds a graduate degree in Clinical Research and is passionate about data literacy and open source technologies.
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
He has presented and taught tutorials at conferences such as Strata, SciPy, SCALE, PyCON, and OSCON as well as local user conferences.
Azin is currently an applied research scientist on Georgian’s R&D team where she works with Georgian’s portfolio companies to help adopt applied research techniques to overcome business challenges. Azin holds a Master of Science in Computer Science from University of Toronto and a Bachelor of Computer Science from University of Tehran. Prior to joining Georgian, Azin was a research assistant at the University of Toronto and University Health Network (UHN) where she was working on the intersection of Machine Learning, Transfer Learning, and Computer Vision.
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.
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.
Brian Lucena is Principal at Numeristical, where he advises companies of all sizes on how to apply modern machine learning techniques to solve real-world problems with data. He is the creator of three Python packages: StructureBoost, ML-Insights, and SplineCalib. In previous roles he has served as Principal Data Scientist at Clover Health, Senior VP of Analytics at PCCI, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.
Dr. Sagar Samtani is an Assistant Professor and Grant Thornton Scholar in the Department of Operations and Decision Technologies at Indiana University. Dr. Samtani graduated with his Ph.D. from the AI Lab from University of Arizona. Dr. Samtani’s research interests are in AI for Cybersecurity, developing deep learning approaches for cyber threat intelligence, vulnerability assessment, open-source software, AI risk management, and Dark Web analytics. He has received funding from NSF’s SaTC, CICI, and SFS programs and has published over 40 peer-reviewed articles in leading information systems, machine learning, and cybersecurity venues. He is deeply involved with industry, serving on the Board of Directors for the DEFCON AI Village and Executive Advisory Council for the CompTIA ISAO.
Jayeeta is a Senior Data Scientist with 6+ years of industry experience. She received her MS in Quantitative Methods and Modeling from NY, and a BS in Economics and Statistics. Currently, Jayeeta works at Fitch Ratings, a global leader in financial information services. Jayeeta is an avid NLP researcher and gets to explore a lot of state-of-the-art models to build cool products and firmly believes that data, of all forms, is the best storyteller. She also led multiple NLP workshops in association with Women Who Code, GitNation among others. Jayeeta has also been invited to speak at International Conference on Machine Learning (ICML 2020), ODSC East, MLConf EU, WomenTech Global Conference, and Data Summit Connect. Jayeeta is passionate about promoting initiatives to inspire more women to take up STEM. Jayeeta lives in New York, she loves to cook, and spends her summers hiking and traveling with her husband. Connect here – https://linktr.ee/JayeetaP
Alex Ratner is the co-founder and CEO at Snorkel AI, and an Assistant Professor of Computer Science at the University of Washington. Prior to Snorkel AI and UW, he completed his Ph.D. in CS advised by Christopher Ré at Stanford, where he started and led the Snorkel open source project, and where his research focused on applying data management and statistical learning techniques to emerging machine learning workflows such as creating and managing training data and applying this to real-world problems in medicine, knowledge base construction, and more. Previously, he earned his A.B. in Physics from Harvard University.