2018 ShanghaiTech Symposium on Information Science and Technology

Distinguished Academic Speakers

ShanghaiTech Symposium on Information and Science and Technology

Xiang Bai

Huazhong University of Science and Technology

Professor of Department of Electronics and Information Engineering

Speech details

Rama Chellappa

University of Maryland

Distinguished Professor and Chair

IEEE Fellow

Speech details

Irfan Essa

Georgia Institute of Technology

Professor and Associate Dean

IEEE Fellow

Speech details

Richard Hartley

Australian National University

Professor

IEEE/Australian Academy of Science Fellow

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Hong Jiang

University of Texas at Arlington

Professor, Department Chair of Computer Science & Engineering

IEEE Fellow

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Mark Johnson

Macquarie University

Professor, Department of Computing

ACL Fellow

Speech details

Patrick A. Naylor

Imperial College London

Professor of Electronic & Electrical Engineering

IET Fellow

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Ivan Edward Sutherland
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Ivan Edward Sutherland

Portland State University

Member of NAS and NAE

Turing Award (1988)

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Rene Vidal

Johns Hopkins University

Professor

IEEE Fellow

Speech details

Liang Wang

Chinese Academy of Sciences

Researcher

杰青

Speech details

Yongtian Wang

Beijing Institute of Technology

Professor

杰青,长江

Speech details

Dong Xu

The University of Sydney

Professor and Chair in Computer Enengeering

IEEE/IAPR Fellow

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Xiangyang Xue

Fudan University

Professor, Dean

上海市科技进步一等奖

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Ming-Hsuan Yang

UC Merced

Professor

NSF CAREER Award

Speech details

Qiang Yang

HKUST

Chair Professor and Department Head

ACM/IEEE Fellow

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Xiaokang Yang

Shanghai Jiao Tong University

Professor, Associate Dean

杰青

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Alan L. Yuille

Johns Hopkins University

Bloomberg Distinguished Professor of Cognitive Science and Computer Science

IEEE Fellow

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Rui Zheng

ShanghaiTech University

Assistant Professor, School of Information Science and Technology

Speech details

Jie Zhou

Tsinghua University

Professor, Dean

杰青

Speech details

Kun Zhou

Zhejiang University

Professor, Director of the State Key Lab of CAD&CG

IEEE Fellow

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Distinguished Industrial Speakers

ShanghaiTech Symposium on Information and Science and Technology

Gang Hua

Microsoft

Principal Researcher

IAPR Fellow

Speech details

Jiaya Jia

Tencent

Distinguished AI Scientist

IEEE Fellow

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Dinggang Shen

United Imaging Intelligence

CO-CEO

IEEE Fellow

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Tony Tang

ABB Engineering(Shanghai) Ltd.

Head of Robotics R&D

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Jing Xiao

Pingan

Chief Scientist

国家千人

Speech details

Shuicheng Yan

Qihoo 360

VP, AI Institute Director

IEEE/IAPR Fellow

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ShanghaiTech Symposium on Information and Science and Technology

Speakers and Speeches Information

Rama Chellappa

University of Maryland

Title: Deep Representations, Adversarial Learning and Domain Adaptation for Some Computer Vision Problems

Abstract:  Recent developments in deep representation-based methods for many computer vision problems have knocked down many research themes pursued over the last four decades. In this talk, I will discuss methods based on deep representations for designing robust computer vision systems with applications in unconstrained face and action verification and recognition, expression recognition, subject clustering and attribute extraction. The face and action recognition system being built at UMD is based on fusing multiple deep convolutional neural networks (DCNNs) trained using publicly available still and video face data sets and task appropriate loss functions. I will then discuss some new results on generative adversarial learning and domain adaptation for improving the robustness of computer vision systems.

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.

Patrick A. Naylor

Imperial College London

Title: Modulation-domain Multichannel Kalman Filtering for Speech Enhancement

Abstract:  In space-time multichannel signal processing, there are opportunities to exploit simultaneously the spatial structure of the signals captured by multiple microphones and also the temporal structure of speech signals. However, many existing speech enhancement methods neglect the temporal structure and generally rely only on the spatial information from multichannel observations in, for example, beamforming. It is well-known that a speech signal can be modelled as an autoregressive process, and based on linear prediction (LP), single-channel Kalman filtering (KF) based speech enhancement algorithms have been developed. In this talk, a multichannel Kalman filter (MKF) for speech enhancement is derived to consider jointly the multichannel spatial information and the temporal correlation of speech. The temporal evolution of speech is modelled in the modulation domain, and by integrating the spatial information, an optimal MKF gain is derived in the short-time Fourier transform (STFT) domain. It is also shown that the proposed MKF reduces to the conventional multichannel Wiener filter (MWF) if the LP information is discarded. Experimental simulation results demonstrate the effectiveness of the proposed method.

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.

Rene Vidal

Johns Hopkins University

Title: Automatic Methods for the Interpretation of Biomedical Data

Abstract:  In this talk, I will overview our recent work on the development of automatic methods for the interpretation of biomedical data from multiple modalities and scales. At the cellular scale, I will present a structured matrix factorization method for segmenting neurons and finding their spiking patterns in calcium imaging videos, and a shape analysis method for classifying embryonic cardiomyocytes in optical imaging videos. At the organ scale, I will present a Riemannian framework for processing diffusion magnetic resonance images of the brain, and a stochastic tracking method for detecting Purkinje fibers in cardiac MRI. At the patient scale, I will present dynamical system and machine learning methods for recognizing surgical gestures and assessing surgeon skill in medical robotic motion and video data.

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.

Dong Xu

The University of Sydney

Title: Visual Domain Adaptation

Abstract:  In many computer vision applications, the domain of interest (i.e., the target domain) contains very few or even no labelled samples, while an existing domain (i.e., the auxiliary/source domain) is often available with a large number of labelled examples. For example, millions of loosely labelled Flickr photos or YouTube videos can be readily obtained by using keywords based search. On the other hand, users may be interested in retrieving and organizing their own multimedia collections of images and videos at the semantic level, but may be reluctant to put forth the effort to annotate their photos and videos by themselves. This problem becomes furthermore challenging because the feature distributions of training samples from the web domain and consumer domain may differ tremendously in statistical properties. To explicitly cope with the feature distribution mismatch for the samples from different domains, in this talk I will describe several our recent works for domain adaptation under different settings as well as their interesting applications in computer vision.

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.