With increasing availability of data in various forms from images, audio, video, 3D models, motion capture, simulation results, to satellite imagery, representative samples of the various phenomena constituting the world around us bring new opportunities and research challenges. Such availability of data has led to recent advances in data-driven modeling. However, most of the existing example-based synthesis methods offer empirical models and data reconstruction that may not provide an insightful understanding of the underlying process or may be limited to a subset of observations.
In this talk, I present recent advances that integrate classical model-based methods and statistical learning techniques to tackle challenging problems that have not been previously addressed. These include flow reconstruction for traffic visualization, learning heterogeneous crowd behaviors from video, simultaneous estimation of deformation and elasticity parameters from images and video, and example-based multimodal display for VR systems. These approaches offer new insights for learning and understanding complex collective behaviors, developing better models for complex dynamical systems from captured data, delivering more effective medical diagnosis and treatment, as well as cyber-manufacturing of customized apparel. I conclude by discussing some possible future directions and challenges.
Ming C. Lin is currently Distinguished University Professor, Dr. Barry Mersky and Capital One E-Nnovate Endowed Professor, former Elizabeth Stevinson Iribe Chair of Computer Science at the University of Maryland College Park, and John R. & Louise S. Parker Distinguished Professor Emerita of Computer Science at the University of North Carolina (UNC), Chapel Hill. She is also an Amazon Scholar. She obtained her B.S., M.S., and Ph.D. in Electrical Engineering and Computer Science from the University of California, Berkeley. She received several honors and awards, including NSF Young Faculty Career Award, Honda Research Initiation Award, UNC/IBM Junior Faculty Development Award, UNC Hettleman Award for Scholarly Achievements, Beverly W. Long Distinguished Professorship, IEEE VGTC Virtual Reality Technical Achievement Award, and many best paper awards at international conferences. She is a Fellow of National Academy of Inventors, ACM, IEEE, Eurographics, ACM SIGGRAPH Academy, and IEEE VR Academy.
Her research interests include AI/ML, computational robotics, virtual reality, physically-based modeling, multimodal interaction, and geometric computing. She has (co-)authored more than 300 refereed publications in these areas and several books. She has served on hundreds of program committees of leading conferences and co-chaired dozens of international conferences and workshops. She is currently a member of Computing Research Association (CRA) and CRA-Widening Participation (CRA-WP) Board of Directors and Chair of IEEE Computer Society (CS) Goode Award Committee. She was a former chair of IEEE CS Computer Pioneer Award and IEEE CS Fellow committee, and the Founding Chair of ACM SIGGRAPH Outstanding Doctoral Dissertation Award. She is also a former member of IEEE CS Board of Governors, a former Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics (2011-2014), a former Chair of IEEE CS Transactions Operations Committee, and a member of several editorial boards. She also served on steering committees and advisory boards of several international conferences, government and industrial technical advisory committees.