November 1st - 3rd, 2022

Research Frontiers

The Most Advanced Active Research, Summarized
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Research Frontiers Track

Rapid Pace of Advancement

Data Science is a broad field and advancing at a tremendous pace. Every few months new research, models, and advances are announced.  For data science practitioners, it’s essential to keep abreast of the latest advances. However, given the demands on our time this can be a daunting task.

The Most Advanced Research, Summarized

The Research Frontiers track is the first of its kind. You don’t have to parse the contents of countless papers or attend academic conferences; instead, we bring the most relevant information to you.  World-class academics, researchers, and professionals summarize the latest research across focus areas, and detail what’s important.  

Featured Past Speakers

Adjunct Lecturer | FounderStanford University | Startup on real-time ML

Chip Huyen

Chip Huyen is an engineer and founder working to develop tools that leverage real-time machine learning. Through her work with Snorkel AI, NVIDIA, and Netflix, she has helped some of the world’s largest organizations deploy machine learning systems. She teaches Machine Learning Systems Design at Stanford. She’s also published four bestselling Vietnamese books.

Unifying Development and Production Environments for Machine Learning Projects(Workshop)

Director | Assistant ProfessorUCLA NLP | UCLA CS

Kai-Wei Chang

Kai-Wei Chang is an assistant professor in the Department of Computer Science at the University of California Los Angeles (UCLA). His research interests include designing robust machine learning methods for large and complex data and building fair, reliable, and accountable language processing technologies for social good applications. Dr. Chang has published broadly in natural language processing, machine learning, and artificial intelligence. His research has been covered by news media such as Wires, NPR, and MIT Tech Review. His awards include the Sloan Research Fellowship (2021), the EMNLP Best Long Paper Award (2017), the KDD Best Paper Award (2010), and the Okawa Research Grant Award (2018). Dr. Chang obtained his Ph.D. from the University of Illinois at Urbana-Champaign in 2015 and was a post-doctoral researcher at Microsoft Research in 2016. Additional information is available at http://kwchang.net

Transferable Representation in Natural Language Processing(Tutorial)

Dir. | Associate Prof.StarAI (Statistical and Relational Artificial Intelligence Lab) | UCLA

Guy Van den Broeck, PhD

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.

Reasoning About the Probabilistic Behavior of Classifiers(Talk)

Director | Research ProfessorCenter on Knowledge Graphs Research Group | Computer Science and Spatial Sciences, USC

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).

Director | Assistant ProfessorStatistical Machine Learning Lab | UCLA

Quanquan Gu, PhD

Quanquan Gu is an Assistant Professor of Computer Science at UCLA and the director of the statistical machine learning lab. His research is in the area of artificial intelligence and machine learning, with a focus on developing and analyzing nonconvex optimization algorithms for machine learning to understand large-scale, dynamic, complex, and heterogeneous data and building the theoretical foundations of deep learning and reinforcement learning. He received his Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign in 2014. He is a recipient of the Yahoo! Academic Career Enhancement Award, NSF CAREER Award, Simons Berkeley Research Fellowship among other industrial research awards. He leads a team at UCLA using machine learning to forecast the spread of COVID-19 (https://covid19.uclaml.org) and their model has been adopted by the U.S. Centers for Disease Control and Prevention and the California Department of Public Health.

Statistical Machine Learning(Talk)

DirectorIBM Research

Lisa Amini, PhD

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

What’s Next for Data Scientists? Auto ML+DO(Talk)

Senior Research ScientistNVIDIA Research

Arash Vahdat

Arash Vahdat is a senior research scientist at NVIDIA research specializing in machine learning and computer vision. Before joining NVIDIA, he was a research scientist at D-Wave Systems where he worked on deep generative learning and weakly supervised learning. Prior to D-Wave, Arash was a research faculty member at Simon Fraser University (SFU), where he led research on deep video analysis and taught graduate-level courses on big data analysis. Arash obtained his Ph.D. and MSc from SFU under Greg Mori’s supervision working on latent variable frameworks for visual analysis. His current areas of research include deep generative learning, weakly supervised learning, efficient neural networks, and probabilistic deep learning.

New Frontiers in Deep Generative Learning(Talk)

Research ScientistFacebook AI Research

Adriana Romero

Adriana Romero Soriano is a research scientist at Facebook AI Research and an adjunct professor at McGill University. Her research focuses on developing models and algorithms that are able to learn from multi-modal data, reason about conceptual relations, and leverage active acquisition strategies to mitigate their uncertainties. The playground of her research has been defined by problems that require inferring full observations from limited sensory data. She completed her postdoctoral studies at Mila, where she was advised by Prof. Yoshua Bengio. Her postdoctoral research revolved around deep learning techniques to tackle biomedical challenges, such as the ones posed by multi-modal data, high dimensional data, and graph-structured data. She received her Ph.D. from the University of Barcelona in 2015 with a thesis on assisting the training of deep neural networks, advised by Dr. Carlo Gatta.

Seeing the Unseen: Inferring Unobserved Information from Limited Sensory Data(Talk)

Chief Innovation Officer | Chief Executive OfficerUniversity of California | ACSILabs

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.

Assistant ProfessorETH Zürich

Fisher Yu

Fisher Yu is an Assistant Professor at ETH Zürich in Switzerland. He obtained his Ph.D. degree from Princeton University and became a postdoctoral researcher at UC Berkeley. He is now leading the Visual Intelligence and Systems (VIS) group at ETH Zürich. His goal is to build perceptual systems capable of performing complex tasks in complex environments. His research is at the junction of machine learning, computer vision and robotics. He currently works on closing the loop between vision and action. His works on image representation learning and large-scale datasets, especially dilated convolutions and the BDD100K dataset, have become essential parts of computer vision research. More info is available at https://www.yf.io

Large-Scale Video Analytics with Ease(Talk)

Data and Technology Global Lead ScientistWWF

Dave Thau, PhD

Dave Thau is WWF’s Data and Technology Global Lead Scientist with him over 30 years of software development and conservation experience. He is also a member of the IPBES Knowledge and Data taskforce. Prior to WWF, Dave worked at the California Academy of Sciences, the Kansas University Museum of Natural History, and Google where he helped launch Google Earth Engine. Dave’s work focuses on the fields of data management, sustainability, artificial intelligence, and remote sensing. He holds degrees from the University of California, Los Angeles, the University of Michigan, Ann Arbor, and a doctorate in computer science from the University of California, Davis. He also has an ant named in his honor – the charming Plectroctena thaui.

Artificial Intelligence for Conservation and Sustainability: From the Local to the Global(Talk)

ProfessorArtificial Intelligence Group, UCSD

Julian McAuley, PhD

Julian McAuley has been a professor in the Computer Science Department at the University of California, San Diego since 2014. Previously he was a postdoctoral scholar at Stanford University after receiving his PhD from the Australian National University in 2011. His research is concerned with developing predictive models of human behavior using large volumes of online activity data.

Personalized Machine Learning(Talk)

 


ODSC WEST 2022 - November 1st-3rd

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Active Research Focus Areas

Data science is a broad and expanding field with many areas of study. Here are some of the main areas that our presenting researchers will address:

  • Neural Networks

  • Machine Learning

  • Transfer Learning

  • Machine Vision

  • Natural Language Processing

  • Predictive Analytics

  • Pattern Recognition

  • Quantitative Finance

  • Speech Recognition

  • Time Series Analysis

  • Graph Theory

  • Network Analysis

  • Data Visualization

  • Anomaly Detection

You Will Meet

  • Experienced data scientists

  • Software engineers and architects

  • Business professionals interested in data science advancements

  • Experts from other domains looking to leverage data science

  • Researchers from academia and industry

  • Industry professionals

  • Technologists interested in new data science applications

  • Industry experts looking to access the impact of data science

Why Attend?

Hear from world-class researchers and academics about the top areas of active research

Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science

Be the first amongst your peers to grasp changes that will affect the field over the next few years

Take advantage of another 120 talks, tutorials, and workshops at ODSC West

Learn directly from top researchers what works and what doesn’t 

Connect and network with academics, research, and fellow professionals

Meet with peers and professionals looking to learn, connect, and collaborate

Get access to other focus area content, including ML / DL, Data Visualization, Quant Finance, and Open Data Science

ODSC WEST 2022 - November 1st-3rd

Register and save 70%

ODSC Newsletter

Stay current with the latest news and updates in open source data science. In addition, we’ll inform you about our many upcoming Virtual and in person events in Boston, NYC, Sao Paulo, San Francisco, and London. And keep a lookout for special discount codes, only available to our newsletter subscribers!


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