All session videos and recordings are available on Ai+ two weeks from the conference end date.
ODSC East will be more inclusive than ever — from in-person sessions to digital experiences available to everyone, from anywhere. Get ready for the successful hybrid ODSC Conference. Combining small 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.
ODSC is the best community data science event on the planet. It is comprehensive and totally community-focused: it’s the conference to engage, to build, to develop, and to learn from the whole data science community.
Kirk Borne – Principal Scientist and Executive Advisor at Booz Allen Hamilton @ ODSC East 2019
What a spectacular first day attending ODSC East. This has been a wonderful day full of new knowledge, new connections, and the discovery of problems to solve and solutions alike.
Data Scientist, USA
It’s been such a wonderful week learning about all the incredible work that’s being done within the field of data science – with too many incredible sessions to list at the moment.
Machine Learning Engineer | Data Scientist, USA
It was a wonderful experience, and I literally am going back with an enhanced understanding of so many concepts, while learning about many new products and theories. Thanks, ODSC for this opportunity.
Product Specialist, India
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12 Focus Areas. 3 Days. One Machine Learning Conference
The largest applied machine learning conference is now 3 days, including an extra day for our mini-bootcamp. Choose from in-person or virtual that includes, 3 full training days, and 2 talks/workshops days. You get even more talks, training, and workshops spread over 8 focus areas. Accelerate your machine learning knowledge, training, and network all in one event.
Conference Focus Areas
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COVID Precautions: currently there are no mask or vaccination mandates in Boston, MA. That may change and we encourage the attendees to take these steps to the level that they are comfortable.
For more information please our What to Expect page here.
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The data science and the tech communities should strive to be more inclusive and offer opportunities for everyone to learn and grow their career in data science. To help achieve this goal ODSC offers diversity and inclusion scholarship passes to minorities, underrepresented groups, graduate students, and others who express interest in the content ODSC offers. Application is OPEN! You can apply here
You can apply for a student or academic discount of up to 15% off the current price here. You will receive a response from our team within 48 hours of submitting your application.
If you have a group of 3 to 13 or more, please email us at info@odsc.com to enquire about additional discounts. Please mention the size of your group and the types of passes required.
In exchange for a free pass, ODSC is seeking volunteers to help with our program and event planning. See more details HERE.
For this year’s event, ODSC will double donations and fundraising to Support of Ukraine. Please support Ukraine, its refugees and help those who stayed fighting for their country. All donations would be sent to 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.
As Chief Decision Scientist at Google Cloud, Cassie Kozyrkov advises leadership teams on decision process, AI strategy, and building data-driven organizations. She is the innovator behind bringing the practice of Decision Intelligence to Google, personally training over 15,000 Googlers. Prior to joining Google, Cassie worked as a data scientist and consultant. She holds degrees in mathematical statistics, economics, psychology, and neuroscience.
Making Data Useful(Track Keynote)
Michael W. Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He is also an Amazon Scholar as well as a faculty scientist at the Lawrence Berkeley National Laboratory. He works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning, including randomized matrix algorithms and randomized numerical linear algebra, geometric network analysis tools for structure extraction in large informatics graphs, scalable implicit regularization methods, computational methods for neural network analysis, physics informed machine learning, and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis. He received his PhD from Yale University with a dissertation in computational statistical mechanics, and he has worked and taught at Yale University in the mathematics department, at Yahoo Research, and at Stanford University in the mathematics department. Among other things, he is on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI), he was on the National Research Council’s Committee on the Analysis of Massive Data, he co-organized the Simons Institute’s fall 2013 and 2018 programs on the foundations of data science, he ran the Park City Mathematics Institute’s 2016 PCMI Summer Session on The Mathematics of Data, and he runs the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets. He is the Director of the NSF/TRIPODS-funded FODA (Foundations of Data Analysis) Institute at UC Berkeley. More information is available at https://www.stat.berkeley.edu/~mmahoney/.
WeightWatcher, an Open-Source Diagnostic Tool for Analyzing Deep Neural Nets(Talk)
Jeannette M. Wing is the Executive Vice President for Research at Columbia University and Professor of Computer Science. In her EVPR role, she has overall responsibility for the University’s research enterprise at all New York locations and internationally. The New York locations include the Morningside and Manhattanville campuses, Columbia University Irving Medical Center, Lamont-Doherty Earth Observatory, and Nevis Laboratories. She joined Columbia in 2017 as the inaugural Avanessians Director of the Data Science Institute.
Prior to Columbia, Dr. Wing was Corporate Vice President of Microsoft Research, served on the faculty and as department head in computer science at Carnegie Mellon University, and served as Assistant Director for Computer and Information Science and Engineering at the National Science Foundation.
Dr. Wing’s research contributions have been in the areas of trustworthy AI, security and privacy, specification and verification, concurrent and distributed systems, programming languages, and software engineering. Her 2006 seminal essay, titled “Computational Thinking,’’ is credited with helping to establish the centrality of computer science to problem-solving in fields where previously it had not been embraced, and thereby influencing K-12 and university curricula worldwide.
She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers. She received distinguished service awards from the ACM and the Computing Research Association and an honorary doctorate degree from Linköping University, Sweden. She earned her bachelor’s, master’s, and doctoral degrees in computer science, all from the Massachusetts Institute of Technology.
Trustworthy AI(Talk)
Joel Z. Leibo is a research scientist at DeepMind. He obtained his PhD in 2013 from MIT where he worked on the computational neuroscience of face recognition. Nowadays, Joel’s research is aimed at the following questions: How can we get deep reinforcement learning agents to perform complex cognitive behaviors like cooperating with one another in groups? How should we evaluate the performance of deep reinforcement learning agents? How can we model processes like cumulative culture that gave rise to unique aspects of human intelligence?
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot(Talk)
Gaël Varoquaux is a research director working on data science and health at Inria (French Computer Science National research). His research focuses on using data and machine learning for scientific inference, with applications to health and social science, as well as developing tools that make it easier for non-specialists to use machine learning. He has long applied it to brain-imaging data to understand cognition. Years before the NSA, he was hoping to make bleeding-edge data processing available across new fields, and he has been working on a mastermind plan building easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, Mayavi and nilearn, a nominated member of the PSF, and often teaches scientific computing with Python using the scipy lecture notes.
Prediction with Missing Values(Tutorial)
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)
Since joining Syracuse University in 2014, Jeff has focused on how to effectively manage and coordinate data science projects within and across teams. Via his research and consulting, Jeff has become an expert in this field, having worked across a range of organizations and published 30+ peer-reviewed academic papers that (1) explore the challenges in executing data science projects, and (2) evaluate potential frameworks via experiments and real-world case studies.
Prior to joining Syracuse University and consulting on data science efforts, Jeff worked at JPMorgan Chase, where he reported to the firm’s Global Chief Information Officer and drove technology innovation across the organization, while also having other leadership roles within the bank, such as leading the IT effort for credit risk analytics within the consumer bank.
Managing Data Science Projects via an Agile Framework(Business Talk)
Julie Josse is a senior researcher in statistics and machine learning applied to health at Inria, a French research institute in digital sciences, and Professor at Ecole Polytechnique (Paris). She is an expert in the treatment of missing values (inference, multiple imputation, matrix completion, MNAR, supervised learning with missing values) and has created a website on the topic (https://rmisstastic.netlify.app/) for users. Her research also focuses on causal inference techniques (causal inference with missing values, combining RCT and observational data) for personalized medicine. Julie Josse is dedicated to reproducible research with R statistical software: she has developed packages including FactoMineR and missMDA to transfer her work.
Patrick von Platen is a research engineer at Hugging Face and one of the core maintainers of the popular Transformers library. He specializes in speech recognition, encoder-decoder models and long-range sequence modeling. Before joining Hugging Face, Patrick conducted research in speech recognition at Uber AI, Cambridge University, and RWTH Aachen University.
Transformers & Datasets for Research and Production(Training)
Violeta has been interested in understanding the causes of social inequalities and to what extent bad experiences early in life propagate to negative outcomes later. When she realized ML can result in widening already existing social gaps, she became an advocate for the responsible development and deployment of ML. Violeta currently works as a data scientist at ABN Amro. Before that, she worked in consultancy and obtained her PhD in applied econometrics. Violeta likes sharing her knowledge with others by the form of workshops on data science and online courses. Violeta proposes that developers of ML solutions alone cannot ensure their safety but, rather, that the additional efforts of multidisciplinary experts as well as proper regulation is also needed.
Dr Felicity Bell, PhD, is a Research Fellow for the Future of Law and Innovation in the Profession (FLIP) Research Stream, funded by the Law Society of New South Wales and based at the University of New South Wales, Sydney, Australia. FLIP provides research and thought leadership on the core concerns of the legal profession into the future. Felicity’s research looks at law and technology, and how lawyers’ regulation, professionalism and ethics interact with innovation in law. She has published widely and recently co-authored the book Artificial Intelligence and the Legal Profession (Hart, 2020).
What do Lawyers Need (and want) from Legaltech?(Business Talk)
Andrew Giessel is the Director of Data Science and AI at Moderna, where he leads a team of data scientists applying machine learning to solve a diverse set of science, business, and engineering problems. While at Moderna, he and his team have helped the company grow from a 300 employee start-up to a commercial organization producing hundreds of millions of vaccine doses all over the world. Prior to Moderna, Andrew earned a Ph.D. and did postdoc in the Neurobiology department at Harvard Medical School. Andrew lives in the Boston area with his family.
Luke Metz is a research scientist at Google Brain working on meta-learning and learned optimizers. He’s interested in building general purpose, learned learning algorithms that not only perform well, but generalizes to new types of never before seen problems.
Learned Optimizers: Learning to Learn Optimization Algorithms(Talks)
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.
Mona is a Data Science Manager 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.
Leveling Up Your Organization’s Capacity for Data-informed Decisions(Talk)
SQL for Data Science(Training)
Ankur Taly is a Staff Research Scientist at Google, where he carries out research in Machine Learning and Explainable AI. Previously, he served as the Head of Data Science at Fiddler labs, where he was responsible for developing, productionizing, and evangelizing core explainable AI technology. Ankur is most well-known for his contribution to developing and applying Integrated Gradients— a new interpretability algorithm for deep networks. His research in this area has resulted in publications at top-tier machine learning conferences and prestigious journals like the American Academy of Ophthalmology (AAO) and Proceedings of the National Academy of Sciences (PNAS). Besides explainable AI, Ankur has a broad research background and has published 30+ papers in areas including computer security, programming languages, formal verification, and machine learning. He has served on several academic conference program committees, and instructed short courses at summer schools and conferences. Ankur earned his PhD in computer science from Stanford University in 2012 and a BTech in Computer Science from IIT Bombay in 2007.
Evaluating, Interpreting and Monitoring Machine Learning Models(Talk)
Jirika is working in Machine learning and Data science for several years. He has done Ph.D. in Medical Imaging. In parallel, he gains practical experience while he has been working for a few IT companies as a consultant or data scientist. Actually,he is focusing on exploring interesting world problems and solving them with state-of-the-art techniques. He has developed several open-source python packages, He is the core contributor of `PyTorch-Lightning` and `TorchMetrics` and actively participating in other well-known projects.
Thomas J. Fan is a Senior Software Engineer at Quansight Labs, working to sustain and evolve the PyData open-source ecosystem. He is a maintainer for scikit-learn, an open-source machine learning library written for Python. Previously, he worked at Columbia University, improving the interoperability between scikit-learn and AutoML systems. Thomas holds a Masters in Physics from Stony Brook University and a Masters in Mathematics from New York University.
Introduction to Scikit-learn: Machine Learning in Python(Training)
Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection(Training)
Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression(Training)
Razvan Amironesei is a Visiting Researcher in the Ethical AI team. While at Google’s Center for Responsible AI, his research and publications focus on developing a pluralistic data ethics framework by using responsible interpretive methods to analyze the construction of benchmark datasets. He is also researching the relationship between computer science pedagogy and humanistic social science, specific issues related to data annotation, the constitution of offensiveness in ML datasets, and the topic of algorithmic conservation. Previously, Razvan has done research and published on sociotechnical impacts of benchmark datasets at the Center for Applied Data Ethics at the University of San Francisco, and on the political and ethical formation of algorithms at the Institute for Practical Ethics at UC San Diego. Razvan has taught classes in English and French in Applied Ethics for Engineers, Bioethics, Political Theory, and on Religion and Politics in the US. His educational background is international and situated at the intersection of social sciences and the humanities. He completed postdoctoral studies at the Center on Global Justice at UC San Diego, a PhD in philosophy at Laval University in Canada, an MA in the history of science and technology in France and a Bachelor’s degree in the history of philosophy in Romania.
ImageNet and its Discontents. The Case for Responsible Interpretation in ML (Talk)
A data scientist and TensorFlow addict, Robert has a passion for helping developers quickly learn what they need to be productive. He’s used TensorFlow since the very early days and is excited about how it’s evolving quickly to become even better than it already is. Before moving to data science Robert led software engineering teams for both large and small companies, always focusing on clean, elegant solutions to well-defined needs.
From Experimentation to Products: The Production ML Journey(Talk)
Ashwin Machanavajjhala is an Assistant Professor in the Department of Computer Science, Duke University and an Associate Director at the Information Initiative@Duke (iiD). Previously, he was a Senior Research Scientist in the Knowledge Management group at Yahoo! Research. His primary research interests lie in algorithms for ensuring privacy in statistical databases and augmented reality applications. He is a recipient of the National Science Foundation Faculty Early CAREER award in 2013, and the 2008 ACM SIGMOD Jim Gray Dissertation Award Honorable Mention. Ashwin graduated with a Ph.D. from the Department of Computer Science, Cornell University and a B.Tech in Computer Science and Engineering from the Indian Institute of Technology, Madras.
Analyzing Sensitive Data Using Differential Privacy(Tutorial)
Sharmistha Chatterjee is a Data Science Evangelist with 16+ years of professional experience in the field of Machine Learning (AI research and productionizing scalable solutions) and Cloud applications. She has worked in both Fortune 500 companies, as well as in very early-stage startups. She is currently working as a Senior Manager of Data Sciences at Publicis Sapient where she leads the digital transformation of clients across industry verticals. She is an active blogger, an international speaker at various tech conferences, and a 2X Google Developer Expert in Machine Learning and Google Cloud. She is also the Hackernoon Tech award winner for 2020, been listed as 40 under 40 Data Scientist by AIM and & 21 tech trailblazers 2021 by Google. She is involved in mentoring startups in Google Startup for Accelerators Program. She has also completed Business in AI from the London School of Business recently.
Need of Adaptive Ethical ML Models in Post Pandemic Era(Talk)
Sean is a principal solutions architect focusing on machine learning and data science at Databricks. He is an Apache Spark committer and PMC member, and co-author Advanced Analytics with Spark. Previously, he was director of Data Science at Cloudera and an engineer at Google.
MLOps: Relieving Technical Debt in ML with MLflow, Delta and Databricks(Talk)
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.
Karin is currently the leading developer community programming in the Developer Relations team at StarTree. Karin initially began her career in entertainment marketing working with the likes of names like Eminem and Live Nation. She also launched a successful professional women’s network in two major cities in the U.S., organized events for her local Data Science meetup, and helped lead a on-going hackathon to put machine learning in the hands of cancer biologists. Her journey working in data eventually let her to a position as Program Manager for Community Development for the leading graph database in the world, Neo4j. Most recently, she was brought on to StarTree to improve the adoption and success of the overall developer community.
Real-Time Analytics: Going Beyond Stream Processing with Apache Pinot(Workshop)
Keegan is VP of Machine Learning at ArthurAI and is also an Adjunct Assistant Professor at Georgetown University. Previously, he was the Director of Machine Learning Research at Capital One and has also held roles at cyberdefense firms. He is a Co-Founder of the Conference on Applied Learning for Information Security (CAMLIS) and holds a PhD in Neuroscience from the University of Texas.
Veysel is a well known thought leader in healthcare NLP and works as a Lead Data Scientist and ML Engineer at John Snow Labs, improving the Spark NLP for the Healthcare library and delivering hands-on projects in Healthcare and Life Science. He is a seasoned data scientist with a strong background in every aspect of data science including NLP, machine learning, deep learning, and big data with over ten years of experience. He’s also pursuing his Ph.D. in ML at Leiden University, Netherlands, and delivers graduate-level lectures in Auto ML and Distributed Data Processing. Veysel has broad consulting experience in Statistics, Data Science, Software Architecture, MLOps, Machine Learning, and AI to several start-ups, boot camps, and companies around the globe. He also speaks at Data Science & AI events, conferences and workshops, and has delivered more than a hundred talks at international as well as national conferences and meetups.
Spark NLP for Healthcare: Modular Approach to Solve Problems at Scale in Healthcare NLP(Half-Day Training)
Accelerate your data science knowledge with the latest topics, tools, and trends presented by the best practitioners in the field
Looking for a conference that charges high ticket prices coupled mostly with vendor pitches that have zero relevance to you or your business? That’s not us
We have a relentless focus on the best, most relevant content for our audience. Sure we have partners, but we limit their number and make sure they’re relevant
Our focus is on the data science community and our contribution to that community’s growth and development, and on helping you, our attendee, gain relevant skills
As an applied data conference, we are committed to bringing you the most relevant topics and research that can have a direct impact on your work
We have fantastic networking activities at our events, so if you like your data science with a side of fun, we got you covered
Gain in-demand skills to enhance your data science career or simply become a better data scientist
Many topics to choose from, including 80+ workshops training sessions all under one roof
Learn from the best and brightest in AI and data science to accelerate your learning and insights
Learn from the core contributors behind hugely popular tools such as scikit-learn and Caffe
Connect with 6,500 fellow data scientists, presenters, innovators, practitioners, and influencers
Get hired by innovative companies ranging from Fortune 500s to startups at our Data Science Career Fair
Meet executives from multiple industries using AI and data science to transform their industries
Attend multiple networking events including breaks, lunches, and evening receptions
Stuart Russell is a Professor of Computer Science at the University of California at Berkeley, holder of the Smith-Zadeh Chair in Engineering, and Director of the Center for Human-Compatible AI. He is a recipient of the IJCAI Computers and Thought Award and held the Chaire Blaise Pascal in Paris. In 2021 he received the OBE from Her Majesty Queen Elizabeth and gave the Reith Lectures. He is an Honorary Fellow of Wadham College, Oxford, an Andrew Carnegie Fellow, and a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science. His book “Artificial Intelligence: A Modern Approach” (with Peter Norvig) is the standard text in AI, used in 1500 universities in 135 countries. His research covers a wide range of topics in artificial intelligence, with a current emphasis on the long-term future of artificial intelligence and its relation to humanity. He has developed a new global seismic monitoring system for the nuclear-test-ban treaty and is currently working to ban lethal autonomous weapons.
As Chief Decision Scientist at Google Cloud, Cassie Kozyrkov advises leadership teams on decision process, AI strategy, and building data-driven organizations. She is the innovator behind bringing the practice of Decision Intelligence to Google, personally training over 15,000 Googlers. Prior to joining Google, Cassie worked as a data scientist and consultant. She holds degrees in mathematical statistics, economics, psychology, and neuroscience.
Making Data Useful(Track Keynote)
Carlos Guestrin is a Professor in the Computer Science Department at Stanford University. His previous positions include the Amazon Professor of Machine Learning at the Computer Science & Engineering Department of the University of Washington, the Finmeccanica Associate Professor at Carnegie Mellon University, and the Senior Director of Machine Learning and AI at Apple, after the acquisition of Turi, Inc. (formerly GraphLab and Dato) — Carlos co-founded Turi, which developed a platform for developers and data scientist to build and deploy intelligent applications. He is a technical advisor for OctoML.ai. His team also released a number of popular open-source projects, including XGBoost, LIME, Apache TVM, MXNet, Turi Create, GraphLab/PowerGraph, SFrame, and GraphChi. Carlos received the IJCAI Computers and Thought Award and the Presidential Early Career Award for Scientists and Engineers (PECASE). He is also a recipient of the ONR Young Investigator Award, NSF Career Award, Alfred P. Sloan Fellowship, and IBM Faculty Fellowship, and was named one of the 2008 ‘Brilliant 10’ by Popular Science Magazine. Carlos’ work received awards at a number of conferences and journals, including ACL, AISTATS, ICML, IPSN, JAIR, JWRPM, KDD, NeurIPS, UAI, and VLDB. He is a former member of the Information Sciences and Technology (ISAT) advisory group for DARPA.
DJ Patil is perhaps the most influential data scientist in the world. Having been appointed by President Obama as the very first U.S. Chief Data Scientist, he was tasked with making the largest organization in history—the U.S. Federal Government—a data driven enterprise.
Working directly with the highest ranking officials in government, DJ’s efforts led to the establishment of nearly 40 Chief Data Officer roles across a vast array of departments and programs. Patil’s experience in national security initiatives is extensive, and for his efforts was awarded by Secretary Carter the Department of Defense Medal for Distinguished Public Service which the highest honor the department bestows on a civilian.
Manuela Veloso is Head of J.P. Morgan Chase AI Research and Herbert A. Simon University Professor Emerita at Carnegie Mellon University, where she was previously Faculty in the Computer Science Department and Head of the Machine Learning Department. She is past president of the Association for the Advancement of Artificial Intelligence (AAAI), and the co-founder and a Past President of the RoboCup Federation. In her career she has received numerous awards and honors, including: National Science Foundation CAREER Award, Allen Newell Medal for Excellence in Research, Radcliffe Fellow at the Radcliffe Institute for Advanced Study (Harvard University), Einstein Chair Professor of the Chinese Academy of Sciences, and the ACM/SIGART Autonomous Agents Research Award for “contributions to the field of artificial intelligence, in particular in planning, learning, multi-agent systems, and robotics.” Veloso is a Fellow of AAAI, AAAS, ACM, and IEEE. She was elected in 2022 to the National Academy of Engineering.
Neil Lawrence is the inaugural DeepMind Professor of Machine Learning. He has been working on machine learning models for over 20 years. He recently returned to academia after three years as Director of Machine Learning at Amazon. His main interest is the interaction of machine learning with the physical world. This interest was triggered by deploying machine learning in the African context, where ‘end-to-end’ solutions are normally required. This has inspired new research directions at the interface of machine learning and systems research, this work is funded by a Senior AI Fellowship from the Alan Turing Institute. Neil is also visiting Professor at the University of Sheffield and the co-host of Talking Machines.
Hilary Mason is the co-founder and CEO of Hidden Door. Prior to Hidden Door she was General Manager of the Machine Learning business unit at Cloudera (NYSE: CLDR). She previously founded Fast Forward Labs, an applied machine learning research and consulting startup which Cloudera acquired in 2017. Additionally, she was Data Scientist in Residence at Accel Partners, co-founded HackNY, and was Chief Scientist at bitly. Hilary has received numerous awards, is a regular keynote speaker, and has advised startups, corporations, and governments.
Making Story Computable: The Future of Co-creative Entertainment(Keynote)
Dr. Stonebraker has been a pioneer of database research and technology for more than forty years. He was the main architect of the INGRES relational DBMS, and the object-relational DBMS, POSTGRES. These prototypes were developed at the University of California at Berkeley where Stonebraker was a Professor of Computer Science for twenty five years. More recently at M.I.T., he was a co-architect of the Aurora/Borealis stream processing engine, the C-Store column-oriented DBMS, the H-Store transaction processing engine, the SciDB array DBMS, and the Data Tamer data curation system.
Presently he serves as Chief Technology Officer of Paradigm4 and Tamr, Inc.
Data Mastering at Scale(Track Keynote)
Professor Schmidhuber earned his Ph.D. in Computer Science from the Technical University of Munich (TUM). He is a Co-Founder and the Chief Scientist of the company NNAISENSE and was most recently Scientific Director at the Swiss AI Lab, IDSIA, and Professor of AI at the University of Lugano. He is also the recipient of numerous awards, author of over 350 peer-reviewed papers, a frequent keynote speaker and an adviser to various governments on AI strategies.
His lab’s deep learning neural networks have revolutionized machine learning and AI. By the mid-2010s, they were implemented on over 3 billion devices and used billions of times per day by customers of the world’s most valuable public companies’ products, e.g., for greatly improved speech recognition on all Android phones, greatly improved machine translation through Google Translate and Facebook (over 4 billion translations per day), Apple’s Siri and Quicktype on all iPhones, the answers of Amazon’s Alexa, and numerous other applications. In 2011, his team was the first to win official computer vision contests through deep neural nets with superhuman performance. In 2012, they had the first deep neural network to win a medical imaging contest (on cancer detection), attracting enormous interest from the industry. His research group also established the fields of artificial curiosity through generative adversarial neural networks, linear transformers and networks that learn to program other networks (since 1991), mathematically rigorous universal AI and recursive self-improvement in meta-learning machines that learn to learn (since 1987).
Margaret is a Senior Research Scientist in Google’s Research & Machine Intelligence group, working on artificial intelligence.
Her research generally involves vision-language and grounded language generation, focusing on how to evolve artificial intelligence towards positive goals. This includes research on helping computers to communicate based on what they can process, as well as projects to create assistive and clinical technology from the state of the art in AI.
Her work combines computer vision, natural language processing, social media, many statistical methods, and insights from cognitive science.
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive, and biological sciences; in recent years, he has focused on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines, and applications to problems in distributed computing systems, natural language processing, signal processing, and statistical genetics. Previously, he was a professor at MIT. Michael is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences and a fellow of the American Association for the Advancement of Science, the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA, and SIAM. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. Michael holds a master’s degree in mathematics from Arizona State University and a PhD in cognitive science from the University of California, San Diego.
An AI expert and health AI pioneer, Suchi Saria’s research has led to myriad new inventions to improve patient care. Her work first demonstrated the use of machine learning to make early detection possible in sepsis, a life-threatening condition (Science Trans. Med. 2015). In Parkinson’s, her work showed a first demonstration of using readily-available sensors to easily track and measure symptom severity at home, to optimize treatment management (JAMA Neurology 2018). On the technical front, her work at the intersection of machine learning and causal inference has led to new ideas for building and evaluating reliable ML (ACM FAT 2019). Suchi currently holds a John C. Malone endowed chair at Johns Hopkins University, with appointments across engineering, public health, and medicine. She is also the Founder of Bayesian Health, aiming to revolutionize the delivery of healthcare by empowering providers and health systems with real-time access to essential clinical inferences. She is the recipient of numerous prizes and honors, including being named a Sloan Research Fellow, a National Academy of Medicine Emerging Leader in Health and Medicine, MIT Technology Review’s 35 Innovators Under 35, and a World Economic Forum Young Global Leader.
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)
Zoubin Ghahramani is Chief Scientist of Uber and a world leader in the field of machine learning, significantly advancing the state-of-the-art in algorithms that can learn from data. He is known in particular for fundamental contributions to probabilistic modeling and Bayesian approaches to machine learning systems and AI. Zoubin also maintains his roles as Professor of Information Engineering at the University of Cambridge and Deputy Director of the Leverhulme Centre for the Future of Intelligence. He was one of the founding directors of the Alan Turing Institute (the UK’s national institute for Data Science and AI), and is a Fellow of St John’s College Cambridge and of the Royal Society.
Gaël Varoquaux is a research director working on data science and health at Inria (French Computer Science National research). His research focuses on using data and machine learning for scientific inference, with applications to health and social science, as well as developing tools that make it easier for non-specialists to use machine learning. He has long applied it to brain-imaging data to understand cognition. Years before the NSA, he was hoping to make bleeding-edge data processing available across new fields, and he has been working on a mastermind plan building easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, Mayavi and nilearn, a nominated member of the PSF, and often teaches scientific computing with Python using the scipy lecture notes.
Prediction with Missing Values(Tutorial)
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.
Kurt received his Ph.D. degree in Computer Science from Indiana University in 1984 and then joined the research division of AT&T Bell Laboratories. In 1991 he joined Synopsys, Inc. where he ultimately became Chief Technical Officer and Senior Vice-President of Research. In 1998 Kurt became Professor of Electrical Engineering and Computer Science at the University of California at Berkeley. Kurt’s research now focuses on systems issues associated with the application of Deep Learning to computer vision, speech recognition, natural language processing, and finance.
Kurt has published six books, over 250 refereed articles, and is among the most highly cited authors in Hardware and Design Automation. Kurt was elected a Fellow of the IEEE in 1996. At the 50th Design Automation Conference Kurt received a number of awards reflecting achievements over the 50 year history of the conference. These included “Top Ten Cited Author” and “Top Ten Cited Paper.” He was also recognized as among one of only three people to have received four Best Paper Awards in the history of the conference. Kurt’s research on Deep Learning has also received Best Paper Awards at the Embedded Vision Workshop and at the International Conference on Parallel Processing.
As an entrepreneur Kurt has served as an angel investor and advisor to over twenty-five start-up companies including C-Cube Microsystems, Coverity, Simplex, and Tensilica. Kurt co-founded DeepScale with his PhD student Forrest Iandola. DeepScale was acquired by Tesla in 2019.
Making Deep Learning Efficient(Track Keynote)
Jeannette M. Wing is the Executive Vice President for Research at Columbia University and Professor of Computer Science. In her EVPR role, she has overall responsibility for the University’s research enterprise at all New York locations and internationally. The New York locations include the Morningside and Manhattanville campuses, Columbia University Irving Medical Center, Lamont-Doherty Earth Observatory, and Nevis Laboratories. She joined Columbia in 2017 as the inaugural Avanessians Director of the Data Science Institute.
Prior to Columbia, Dr. Wing was Corporate Vice President of Microsoft Research, served on the faculty and as department head in computer science at Carnegie Mellon University, and served as Assistant Director for Computer and Information Science and Engineering at the National Science Foundation.
Dr. Wing’s research contributions have been in the areas of trustworthy AI, security and privacy, specification and verification, concurrent and distributed systems, programming languages, and software engineering. Her 2006 seminal essay, titled “Computational Thinking,’’ is credited with helping to establish the centrality of computer science to problem-solving in fields where previously it had not been embraced, and thereby influencing K-12 and university curricula worldwide.
She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers. She received distinguished service awards from the ACM and the Computing Research Association and an honorary doctorate degree from Linköping University, Sweden. She earned her bachelor’s, master’s, and doctoral degrees in computer science, all from the Massachusetts Institute of Technology.
Trustworthy AI(Talk)
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)
Sarah Aerni is a Senior Manager of Data Science at Salesforce Einstein, where she leads teams building AI-powered applications across the Salesforce platform. Prior to Salesforce she led the healthcare & life science and Federal teams at Pivotal. Sarah obtained her PhD from Stanford University in Biomedical Informatics, performing research at the interface of biomedicine and machine learning. She also co-founded a company offering expert services in informatics to both academia and industry.
Supporting Your Machine Learning Teams: Testing, Modularity and Monitoring(Talk)
Dr. Oren Etzioni has served as the Chief Executive Officer of the Allen Institute for AI (AI2) since its inception in 2014. He has been a Professor at the University of Washington’s Computer Science department since 1991, and a Venture Partner at the Madrona Venture Group since 2000. He has garnered several awards including Seattle’s Geek of the Year (2013), the Robert Engelmore Memorial Award (2007), the IJCAI Distinguished Paper Award (2005), AAAI Fellow (2003), and a National Young Investigator Award (1993). He has been the founder or co-founder of several companies, including Farecast (sold to Microsoft in 2008) and Decide (sold to eBay in 2013). He has written commentary on AI for The New York Times, Nature, Wired, and the MIT Technology Review. He helped to pioneer meta-search (1994), online comparison shopping (1996), machine reading (2006), and Open Information Extraction (2007). He has authored over 100 technical papers that have garnered over 2,000 highly influential citations on Semantic Scholar. He received his Ph.D. from Carnegie Mellon in 1991 and his B.A. from Harvard in 1986.
Semantic Scholar, NLP, and the Fight Against COVID-19(Track Keynote)
Michael Kearns is a professor in the Computer and Information Science department at the University of Pennsylvania, where he holds the National Center Chair and has joint appointments in the Wharton School.He is founder of Penn’s Networked and Social Systems Engineering (NETS) program, and director of Penn’s Warren Center for Network and Data Sciences. Michael is also the co-author of the book The Ethical Algorithm that talks about the science of designing algorithms that embed social values like privacy and fairness. His research interests include topics in machine learning, algorithmic game theory, social networks, and computational finance. He has worked and consulted extensively in the technology and finance industries. He is a fellow of the American Academy of Arts and Sciences, the Association for Computing Machinery, and the Association for the Advancement of Artificial Intelligence.
Michael has worked extensively in quantitative and algorithmic trading on Wall Street (including at Lehman Brothers, Bank of America, and SAC Capital; see further details below). He often serve as an advisor to technology companies and venture capital firms. He is also involved in the seed-stage fund Founder Collective and occasionally invest in early-stage technology startups. Michael is also a member of the Scientific Advisory Board of the Alan Turing Institute, and of the Market Surveillance Advisory Group of FINRA.
Rachel Thomas is director of the USF Center for Applied Data Ethics and co-founder of fast.ai, which has been featured in The Economist, MIT Tech Review, and Forbes. She was selected by Forbes as one of 20 Incredible Women in AI, earned her math PhD at Duke, and was an early engineer at Uber. Rachel is a popular writer and keynote speaker.
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