Distinguished Academic Speakers
Ivan Edward Sutherland View homepage
Ivan Edward Sutherland
Portland State University
Member of NAS and NAE
Turing Award (1988)
Distinguished Industrial Speakers
Bio: Prof. Rama Chellappa is a Distinguished University Professor, a Minta Martin Professor of Engineering and Chair of the ECE department at the University of Maryland. His current research interests span many areas in image processing, computer vision, machine learning and pattern recognition. Prof. Chellappa is a recipient of an NSF Presidential Young Investigator Award and four IBM Faculty Development Awards. He received the K.S. Fu Prize from the International Association of Pattern Recognition (IAPR). He is a recipient of the Society, Technical Achievement and Meritorious Service Awards from the IEEE Signal Processing Society. He also received the Technical Achievement and Meritorious Service Awards from the IEEE Computer Society. Recently, he received the inaugural Leadership Award from the IEEE Biometrics Council. At UMD, he received college and university level recognitions for research, teaching, innovation and mentoring of undergraduate students. In 2010, he was recognized as an Outstanding ECE by Purdue University. He received the Distinguished Alumni Award from the Indian Institute of Science in 2016. Prof. Chellappa served as the Editor-in-Chief of PAMI. He is a Golden Core Member of the IEEE Computer Society, served as a Distinguished Lecturer of the IEEE Signal Processing Society and as the President of IEEE Biometrics Council. He is a Fellow of IEEE, IAPR, OSA, AAAS, ACM and AAAI and holds six patents.
Bio: Patrick Naylor is a member of academic staff in the Department of Electrical and Electronic Engineering at Imperial College London. He received the BEng degree in Electronic and Electrical Engineering from the University of Sheffield, UK, and the Ph.D. degree from Imperial College London, UK. His research interests are in the areas of speech, audio and acoustic signal processing. He has worked in particular on adaptive signal processing for dereverberation, blind multichannel system identification and equalization, acoustic echo control, speech quality estimation and classification, single and multi-channel speech enhancement and speech production modelling with a particular focus on the analysis of the voice source signal. In addition to his academic research, he enjoys several fruitful links with industry in the UK, USA and in Europe. He is the past-Chair of the IEEE Signal Processing Society Technical Committee on Audio and Acoustic Signal Processing, director and president-elect of the European Association for Signal Processing (EURASIP) and Senior Area Editor of IEEE Transactions on Audio Speech and Language Processing.
Bio: Rene Vidal is a Professor of Biomedical Engineering and the Innaugural Director of the Mathematical Institute for Data Science at The Johns Hopkins University. His research focuses on the development of theory and algorithms for the analysis of complex high-dimensional datasets such as images, videos, time-series and biomedical data. His current major research focus is understanding the mathematical foundations of deep learning and its applications in computer vision and biomedical data science. He has pioneered the development of methods for dimensionality reduction and clustering, such as Generalized Principal Component Analysis and Sparse Subspace Clustering, and their applications to face recognition, object recognition, motion segmentation and action recognition. He has also created new technologies for a variety of biomedical applications, including detection, classification and tracking of blood cells in holographic images, classification of embryonic cardio-myocytes in optical images, and assessment of surgical skill in surgical videos. Dr. Vidal is recipient of numerous awards for his work, including the Jean D'Alembert Faculty Fellowship (2017), IAPR Fellowship (2016), IEEE Fellowship (2014), J.K. Aggarwal Prize (2012), ONR Young Investigator Award (2009), Sloan Fellowship (2009), NSF CAREER Award (2004), as well as best paper awards for his work in machine learning, computer vision, medical imaging, and controls.
Bio: Dong Xu is Chair in Computer Engineering at the School of Electrical and Information Engineering, The University of Sydney, Australia. He received the B.Eng. and PhD degrees from University of Science and Technology of China, in 2001 and 2005, respectively. While pursuing the PhD degree, he worked at Microsoft Research Asia and The Chinese University of Hong Kong for more than two years. He also worked as a postdoctoral research scientist at Columbia University from 2006 to 2007 and a faculty member at Nanyang Technological University from 2007 to 2015. His current research interests include computer vision, multimedia and machine learning. He has published more than 100 papers in IEEE Transactions and top tier conferences. His co-authored work (with his former PhD student Lixin Duan) received the Best Student Paper Award in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) in 2010. His co-authored work (with his former PhD student Lin Chen) won the IEEE Transactions on Multimedia Prize Paper Award in 2014. He was awarded the IEEE Computational Intelligence Society Outstanding Early Career Award in 2017. He is/was on the editorial boards of T-PAMI, T-TIP, T-NNLS, T-MM and T-CSVT. He also served as a guest editor of seven special issues in T-NNLS, T-CYB, T-CSVT, IJCV, ACM TOMM, CVIU and IEEE Multimedia. Moreover, he served as a steering committee member of ICME (2016-2017), a program co-chair of ICME 2014, as well as an area chair of CVPR 2012, ECCV 2016 and ICCV 2017. He is a Fellow of the IEEE and the IAPR.