Erik Sudderth is an Associate Professor of Computer Science and Statistics at the University of California, Irvine. He directs the HPI Research Center in Machine Learning and Data Science at UC Irvine, as well as the UC Irvine Center for Machine Learning and Intelligent Systems.  His Learning, Inference, and Vision research group studies probabilistic graphical models and probabilistic programming, nonparametric Bayesian methods for weakly supervised learning, and applications of statistical machine learning in computer vision and the sciences.  Erik was previously an Associate Professor of Computer Science at Brown University, and a postdoctoral scholar at UC Berkeley. He received the Bachelor's degree (summa cum laude, 1999) in Electrical Engineering from UC San Diego, and the Master's degree (2002) and Ph.D. degree (2006) in EECS from MIT. He received an NSF CAREER award, the ISBA Mitchell Prize, and was named one of "AI's 10 to Watch" by IEEE Intelligent Systems Magazine. 

Stephan Mandt is an Assistant Professor of Computer Science at the University of California, Irvine, working in the field of machine learning. His interests include deep learning and probabilistic modeling with a focus on variational inference, modeling sequential data such as video, and neural data compression. From 2016 until 2018, he was a Senior Researcher and head of the statistical machine learning group at Disney Research, first in Pittsburgh on CMU campus and later in Los Angeles. He held previous positions as a postdoc with David Blei at Columbia University and as a PCCM Postdoctoral Fellow at Princeton University. Stephan holds a PhD in Theoretical Physics from the University of Cologne. He held fellowships by the German National Merit Foundation (Studienstiftung) and the Kavli Foundation. Stephan regularly serves as an Area Chair or Senior Program Committee Member for NeurIPS, ICML, and AAAI. In Summer 2019, he held a visiting researcher position at Google Brain. He currently serves as a PI on a $1.6M DARPA grant on novelty detection, and is further supported by NSF and Qualcomm. 

Padhraic Smyth is a Chancellor's Professor in the Department of Computer Science at UC Irvine, with joint appointments in the Department of Statistics and in the Department of Education. His research interests include machine learning, artificial intelligence, pattern recognition, and applied statistics and he has published over 200 papers on these topics. He is an ACM Fellow (2013), a AAAI Fellow (2010), and a recipient of the ACM SIGKDD Innovation Award (2009). He is co-author of the text Modeling the Internet and the Web: Probabilistic Methods and Algorithms (Wiley, 2003) and Principles of Data Mining (MIT Press, 2001). He received a first class honors degree in Electronic Engineering from National University of Ireland (Galway) in 1984, and the MSEE and PhD degrees (in 1985 and 1988 respectively) in Electrical Engineering from the California Institute of Technology. From 1988 to 1996 he was a Technical Group Leader at the Jet Propulsion Laboratory, Pasadena, and has been on the faculty at UC Irvine since 1996. 

Michael Carey is a Bren Professor of Information and Computer Sciences and Distinguished Professor of Computer Science at UC Irvine and a Consulting Architect at Couchbase, Inc. His current research interests center around data-intensive computing and scalable data management (a.k.a. Big Data); he leads the AsterixDB project at UCI. Other interests include database management, information integration, middleware, parallel and distributed systems, and computer system performance evaluation. He received B.S. and M.S. degrees from Carnegie-Mellon University and a Ph.D. from the University of California, Berkeley. Before joining UCI in 2008, he worked at BEA Systems for seven years and led the development of their AquaLogic Data Services Platform product for virtual data integration. He also spent a dozen years at the University of Wisconsin-Madison, five years at the IBM Almaden Research Center working on object-relational databases, and a year and a half at e-commerce platform startup Propel Software during the infamous 2000-2001 Internet bubble. He is an ACM Fellow, an IEEE Fellow, a member of the National Academy of Engineering, and a recipient of the ACM SIGMOD E.F. Codd Innovations Award. 

Roy Fox is an Assistant Professor and founder of the Intelligent Dynamics Lab (indylab) in the Department of Computer Science in the Donald Bren School of Information & Computer Science at the University of California, Irvine. Until 2019, he was a postdoc in Berkeley AI Research (BAIR), where he collaborated on systems for distributed reinforcement learning and program synthesis in the RISELab, and on reinforcement and imitation learning for robotics in the AUTOLAB, in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His PhD research studied information-theoretic methods for reinforcement learning, in the School of Computer Science and Engineering at the Hebrew University. Roy's research interests include theory and applications of reinforcement learning, dynamical systems, information theory, and robotics. His current research focuses on structure, exploration, and optimization in deep reinforcement learning and imitation learning of robotic tasks. 

Alexander Ihler is a Professor in the Department of Computer Science at the University of California, Irvine. He received his Ph.D. in Electrical Engineering and Computer Science from MIT in 2005 and a B.S. with honors from Caltech in 1998. His research focuses on machine learning, graphical models, and algorithms for exact and approximate inference, with applications to areas such as sensor networks, computer vision, data mining, and computational biology. He is the recipient of an NSF CAREER award and several best paper awards at conferences including NIPS, IPSN, and AISTATS. 

Sharad Mehrotra is a Professor of Computer Science at the University of California, Irvine.   His primary research interests include scalable data analytics, data cleaning, big data, distributed systems, secure databases, privacy, and Internet of Things.  He currently leads a DARPA funded TIPPERS project that is building an IoT data management middleware that supports plug-n-play support for diverse privacy technologies and for scalable analytics on such data. He received his B.Tech at IIT Kanpur, India and an MS and PhD degree in Computer Science at the University of Texas, Austin, in 1993. He served as a Scientist at Matsushita Information Technology Laboratory from 1993-94 where he designed a Concurrent Text Indexing Engine. He was an Assistant Professor at the University of Illinois at Urbana Champaign from 1994-98 where he led the development of a database system, entitled MARS, that provided native support for content-based retrieval from images in databases.  He has received numerous awards and honors, including the 2011 SIGMOD Best Paper Award, 2007 DASFAA Best Paper Award, 2012 SIGMOD Test of Time award, DASFAA ten year best paper awards for 2013 and 2014, ACM ICMR best paper award for 2013, IEEE NCA Best paper award for 2019, a Dean’s Award for Research in 2016, and a CAREER Award in 1998 from the US National Science Foundation (NSF). 

Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. His group is working on black-box explanations, robustness to adversarial attacks, and interpretability of machine learning algorithms, along with neural-symbolic models that reason with text and structure for natural language processing. Sameer was a postdoctoral researcher at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs. He was selected as a DARPA Riser in 2015, and his work has received awards at ICML WS 2016, KDD 2016, ACL 2018, and EMNLP 2019. His group has received funding from Amazon, Allen Institute for AI, NSF, DARPA, Adobe Research, and FICO.