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 an I.T. Distinguished Professor of Computer Science in the Department of Computer Science and Engineering at the University of Minnesota. He holds the William Norris Chair for Large-Scale Computing. He received his B.S. degree in mathematics from the University of Algiers. He then joined University of Grenoble for the doctoral program and obtained a junior doctorate, ‘Doctorat de troisieme cycle’ in 1974 and a higher doctorate, ‘Doctorat d’Etat’ in 1983. During the course of his academic career he has held various positions, including Research Scientist in the Computer Science Department at Yale University (1981–1983), Associate Professor in the University of Tizi-Ouzou in Algeria (1983–1984), Research Scientist in the Computer Science Department at Yale University (1984–1986), and Associate Professor in the Mathematics Department at University of Illinois at Urbana-Champaign (1986–1988). He also worked as a Senior Scientist in the Research Institute for Advanced Computer Science (RIACS) during 1980–1990. He is known for his contributions to the matrix computations, including the iterative methods for solving large sparse linear algebraic systems, eigenvalue problems, and parallel computing. Saad is listed as an ISI highly cited researcher in mathematics and is the author of the highly cited book Iterative Methods for Sparse Linear Systems. 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.

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.