Professor Kristen Grauman

Kristen Grauman is a Professor in the Department of Computer Science at the University of Texas at Austin and a Research Scientist in Facebook AI Research (FAIR).  Her research in computer vision and machine learning focuses on video, visual recognition, and action for perception or embodied AI.  Before joining UT-Austin in 2007, she received her Ph.D. at MIT.  She is an IEEE Fellow, AAAI Fellow, Sloan Fellow, a Microsoft Research New Faculty Fellow, and a recipient of NSF CAREER and ONR Young Investigator awards, the PAMI Young Researcher Award in 2013, the 2013 Computers and Thought Award from the International Joint Conference on Artificial Intelligence (IJCAI), the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2013.  She was inducted into the UT Academy of Distinguished Teachers in 2017.  She and her collaborators have been recognized with several Best Paper awards in computer vision, including a 2011 Marr Prize and a 2017 Helmholtz Prize (test of time award).  She currently serves as an Associate Editor-in-Chief for the Transactions on Pattern Analysis and Machine Intelligence (PAMI) and as an Editorial Board member for the International Journal of Computer Vision (IJCV).  She previously served as a Program Chair of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 and a Program Chair of Neural Information Processing Systems (NeurIPS) 2018 and will serve as a Program Chair of the IEEE International Conference on Computer Vision (ICCV) 2023.

Professor Yousef Saad

Yousef  Saad   is  a   College  of   Science  and   Engineering  (CSE) distinguished professor  with the  department of computer  science and engineering at the University of Minnesota.  He received the “Doctorat d’Etat” from the University of Grenoble (France) in  1983.  He joined the University of Minnesota in 1990 as a Professor of computer science and a Fellow of the Minnesota Supercomputer Institute.  He was head of the department of  Computer Science and Engineering  from January 1997 to June 2000, and became a  CSE distinguished professor in 2005.  From 1981 to 1990, he held  positions at  the University of  California at Berkeley, Yale, the University of Illinois, and the Research Institute for Advanced Computer Science (RIACS).  His current research interests include:  numerical   linear  algebra, sparse   matrix  computations, iterative   methods,  parallel   computing,   numerical  methods   for electronic structure, and  linear algebra methods in  data mining.  He is the author of two monographs  and over 190 journal articles.  He is also the  developer or co-developer  of several software  packages for solving  sparse linear  systems of  equations and eigenvalue problems including  SPARSKIT, pARMS,  ITSOL, and  EVSL. Yousef  Saad is  a SIAM
fellow (class of 2010) and a fellow of the AAAS (2011).

Professor Ravi Ramamoorthi

Ravi Ramamoorthi is a Professor at the University of California, San Diego, where he holds the Ronald L. Graham Chair of Computer Science. He is also the founding director of the UC San Diego Center for Visual Computing. He received the BS degree in engineering and applied science and MS degrees in computer science and physics from the California Institute of Technology, in 1998, and the PhD degree in computer science from the Stanford University Computer Graphics Laboratory, in 2002, upon which he joined the Columbia University Computer Science Department. Prior to UC San Diego he was on the UC Berkeley EECS faculty from 2009-2014. His research interests cover many areas of computer vision and graphics. His research has been recognized with a number of awards, including the 2007 ACM SIGGRAPH Significant New Researcher Award in computer graphics, and by the white house with a Presidential Early Career Award for Scientists and Engineers in 2008 for his work on physics-based computer vision. Most recently, he was named an IEEE and ACM Fellow in 2017, and inducted into the SIGGRAPH Academy in 2019. He has advised more than 20 Postdoctoral, PhD and MS students, many of whom have gone on to leading positions in industry and academia; and he has taught the first open online course in computer graphics on the edX platform in fall 2012, with more than 100,000 students enrolled in that and subsequent iterations. He was a finalist for the inaugural 2016 edX Prize for exceptional contributions in online teaching and learning, and again in 2017. (Based on document published on 30 July 2019).

Professor Matthew Turk

Matthew Turk is the President of Toyota Technological Institute at Chicago (TTIC) and a professor emeritus and former department chair of the Department of Computer Science and the Media Arts and Technology Program at the University of California, Santa Barbara. He was named a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2013 for his contributions to computer vision and perceptual interfaces. In 2014, Prof. Turk was also named a Fellow of the International Association for Pattern Recognition (IAPR) for his contributions to computer vision and vision based interaction. In January 2021, he was named a Fellow of the Association for Computing Machinery (ACM) for contributions to face recognition, computer vision, and multimodal interaction. He earned a PhD from the Massachusetts Institute of Technology, an MS from Carnegie Mellon University, and a BS from Virginia Tech. His research interests are in computer vision and human-computer interaction, largely concerned with using computer vision as an input modality.

Professor Shang-Hong Lai

Shang-Hong Lai received the Ph.D. degree from University of Florida, Gainesville, USA in 1995. He worked at Siemens Corporate Research in Princeton, New Jersey, USA, as a member of technical staff during 1995-1999. Since 1999, he joined the Department of Computer Science, National Tsing Hua University, Taiwan, where he is now a professor there. Since the summer of 2018, Dr. Lai has been on leave from NTHU to join Microsoft AI R&D Center, Taiwan. He is currently a principal researcher at Microsoft AI R&D Center and leads a science team focusing on computer vision research for face related applications. Dr. Lai’s research interests are mainly focused on computer vision, image processing, and machine learning. He has authored more than 300 papers published in refereed international journals and conferences in these areas. In addition, he has been awarded around 30 patents on his research on computer vision. He has involved in the organization for a number of international conferences in computer vision and related areas, including ICCV, CVPR, ECCV, ACCV, ICIP, etc. Furthermore, he has served as an associate editor for Pattern Recognition and Journal of Signal Processing Systems.

Panel Lead Dr. Subhro Das

Subhro Das is a Research Staff Member in AI Algorithms at the MIT-IBM Watson AI Lab, IBM Research, Cambridge MA. He is a Research Affiliate at MIT, co-leading IBM’s engagement in the Bridge pillar of MIT Quest for Intelligence. He serve as the Co-Chair of the AI Learning Professional Interest Community (PIC) at IBM Research. His research interests are in distributed learning over multi-agent networks, dynamical systems, multi-agent reinforcement learning, accelerated & adaptive optimization methods, and online learning in non-stationary environments – broadly in the areas of AI, machine learning, and statistical signal processing with applications in healthcare and social good. Before moving to Cambridge, he was a Research Scientist at IBM T.J. Watson Research Center, New York. Therein, he worked on developing signal processing and machine learning based predictive algorithms for a broad variety of biomedical and healthcare applications. He received MS and PhD degrees in Electrical and Computer Engineering from Carnegie Mellon University in 2014 and 2016, respectively. His dissertation research was in distributed filtering and prediction of time-varying random fields and he was advised by Prof. José M. F. Moura. He completed his Bachelors (B.Tech.) degree in Electronics & Electrical Communication Engineering from Indian Institute of Technology Kharagpur in 2011. During the summers of 2009, 2010 and 2015, he was an intern at Ulm University (Germany), Gwangju Institute of Science & Technology (South Korea), and, Bosch Research (Palo Alto, CA), respectively.

Keynote, Plenary, & Tutorial Speaker Recordings: All keynote, plenary and tutorials are required by the Signal Processing Society to be recorded and included in the SPS Resource Center. Because of this, IEEE Copyright and Consent Forms must be completed by each of these speakers before they can be confirmed by the conference. Organizers are responsible for collecting the recording file and forms, and any recording costs should be planned and budgeted for. See the SPS Conference Organizer Guidelines for more detail.