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[Shih-Fu Chang]   [Cheng Deng]   [Rongrong Ji]   [Sanjiv Kumar]   [Ping Li]   [Wu-Jun Li]   [Wei Liu]   [Mingsheng Long]   [Yadong Mu] [Mohammad Norouzi]   [Chunhua Shen]   [Hen-Tao Shen]   [Fumin Shen]   [Jingkuan Song]   [Jingdong Wang]   [Felix X. Yu]  

Shih-Fu Chang is the Sr. Executive Vice Dean and the Richard Dicker Professor of The Fu Foundation School of Engineering and Applied Science at Columbia University. His research is focused on multimedia information retrieval, computer vision, machine learning, and signal processing. A primary goal of his work is to develop intelligent systems that can harness rich information from the vast amount of visual data such as those emerging on the Web, collected through pervasive sensing, or stored in gigantic content archives.

I am a Full Professor in School of Electronic Engineering at Xidian University. From 2012 to 2013, I was a Visiting Scholar at DVMM Lab with Prof. Shih-Fu Chang, Columbia University. Earlier, I received my Ph.D. degree under supervision of Prof. Xinbo Gao from School of Electronic Engineering, Xidian University in 2009.

My research interests are the broad area of computer vision, machine learning, and multimedia processing. I am particularly interested in multi-modal learning and applications, including cross-modality retrieval, 3D action recognition, hyperspectral image classification, image super-resolution, multimedia information security (image forensics and information hiding).

My research falls in the field of computer vision, multimedia, and machine learning. My scholarly work mainly focuses on leveraging big data to build computer systems to understand visual scenes and human behaviors, inferring the semantics and retrieving instances for various emerging applications. My recent interests include compact visual descriptor, social media sentiment analysis, and holistic scene understanding. I have published 70+ papers in tier-1 journal and conferences like PAMI, IJCV, TIP, CVPR, ICCV, IJCAI, AAAI and ACM Multimedia.

Research Interests:

Big Data, Large Scale Machine Learning, Computer Vision, Graphical Models, Medical Imaging, Robotics

Department of Statistics, Department of Computer Science

National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University

Research interests: machine learning, computer vision, big data, information retrieval, and optimization.

I am an assistant professor and Ph.D supervisor in the School of Software, Tsinghua University. My current research spans machine learning algorithms, systems, and applications, with specific focus on deep learning, transfer learning, predictive learning, adversarial learning and embedding learning.

I am leading the Machine Learning Group in the National Engineering Lab for Big Data Software. I am looking for highly motivated graduate or undergraduate students to work on machine learning and its applications in intelligent data analytics. Please email me with CV if you are interested.

I am leading the Machine Intelligence (MI) Lab at Institute of Computer Science & Technology, Peking Univeristy. Before joining Peking University, I have ever worked as research fellow at National University of Singapore (PI: Prof. Shuicheng Yan), research scientist at the DVMM lab of Columbia University (PI: Prof. Shih-Fu Chang), researcher at the data mining team of Huawei Noah's Ark Lab in Hong Kong (head: Dr. Wei FAN), and senior scientist at Multimedia Department of AT&T Labs, New Jersey, U.S.A. (head: Dr. Behzad Shahraray). I obtained both the B.S. and Ph.D. degrees from Peking University.

I have interest in broad research topics in computer vision and machine learning, particularly large-scale image and video computing (search, indexing, event detection etc), autonomous driving techniques, distributed / approximate large-scale machine learning, deep / reinforcement learning.

My research lies at the intersection of machine learning, computer vision, and natural language processing with an emphasis on neural networks and reinforcement learning. My current research focuses on

(1) Learning to produce images, sentences, and other structured objects.

(2) Advancing reinforcement learning and its applications.

(3) Connecting 1 & 2.

I graduated with a PhD in computer science from the University of Toronto in Dec 2015. My advisor was Prof. David Fleet, and my PhD thesis concerned scalable and efficient algorithms for processing, indexing, and searching digital media, to ease the use of web-scale datasets in machine learning. My PhD research was supported by a Google PhD fellowship.

Chunhua Shen is a Professor at School of Computer Science, University of Adelaide. He is a Project Leader and Chief Investigator at the Australian Research Council Centre of Excellence for Robotic Vision (ACRV), for which he leads the project on machine learning for robotic vision. Before he moved to Adelaide as a Senior Lecturer, he was with the computer vision program at NICTA (National ICT Australia), Canberra Research Laboratory for about six years. His research interests are in the intersection of computer vision and statistical machine learning. Recent work has been on large-scale image retrieval and classification, object detection and pixel labelling using deep learning. He studied at Nanjing University, at Australian National University, and received his PhD degree from the University of Adelaide. From 2012 to 2016, he holds an Australian Research Council Future Fellowship. He served as Associate Editor of IEEE Transactions on Neural Networks and Learning Systems.

Heng Tao is currently a Professor of National "Thousand Talents Plan", the Dean of School of Computer Science and Engineering, and the Director of Center for Future Media at the University of Electronic Science and Technology of China (UESTC). He obtained his BSc with 1st class Honours and PhD from Department of Computer Science, National University of Singapore in 2000 and 2004 respectively. He then joined the University of Queensland as a Lecturer, Senior Lecturer, Reader, and became a Professor in late 2011. His research interests mainly include Multimedia Search, Computer Vision, Artificial Intelligence, and Big Data Management. Heng Tao has published 200+ papers, most of which appeared in prestigious publication venues of interests, such as ACM Multimedia, CVPR, ICCV, AAAI, IJCAI, SIGMOD, VLDB, ICDE, TOIS, TIP, TPAMI, TKDE, VLDB Journal, etc. He has received 7 Best Paper Awards from international conferences, including the Best Paper Award from ACM Multimedia 2017 and Best Paper Award - Honorable Mention from ACM SIGIR 2017. He got the Chris Wallace Award for outstanding Research Contribution in 2010 conferred by Computing Research and Education Association, Australasia, and the Future Fellowship from Australia Research Council in 2012. He has served as a PC Co-Chair for ACM Multimedia 2015 and currently is an Associate Editor of IEEE Transactions on Knowledge and Data Engineering (TKDE). He is an Honorary Professor at the University of Queensland, and holds a position of Visiting Professor at Nagoya University and National University of Singapore.

My research focuses on computer vision and machine learning, especially learning based hashing algorithms and their applications in visual retrieval and recognition problems. I have published 80+ papers in CVPR, ICCV, MM, SIGIR, TPAMI, TIP, TMM, TCSVT, etc. I have served as a regular PC member for ICCV, ACM MM, AAAI, ICMR, ICPR, MMM, journal reviewer for IEEE TIP, TNNLS, TKDE, TMM, TCYB, TCSVT, guest editor for Neurocomputing, PRL, NPL, special issue chair for ACPR'17 and special session organizer for MMM'16, ICMICS'16. I am the recipient of the Best Paper Award Honorable Mention at ACM SIGIR 2016 & ACM SIGIR 2017 and the World's FIRST 10K Best Paper Award - Platinum Award at IEEE ICME 2017.

Jingkuan Song is a full professor with University of Electronic Science and Technology of China (UESTC). He joined Columbia University as a Postdoc Research Scientist (2016-2017), and University of Trento as a Research Fellow (2014-2016). He obtained his PhD degree in 2014 from The University of Queensland (UQ), Australia (advised by Prof. Heng Tao Shen). His research interest includes large-scale multimedia retrieval, image/video segmentation and image/video understading using hashing, graph learning and deep learning techniques. He was the winner of the Best Paper Award in ICPR (2016, Mexico), Best Student Paper Award in Australian Database Conference (2017, Australia), and Best Paper Honorable Mention Award (2017, Japan). He is Guest Editor of TMM, WWWJ and he is PC member of CVPR'18, MM'18, IJCAI'18, etc.

I am a Senior Researcher at the Visual Computing Group, Microsoft Research Asia. My areas of interest include CNN architecture design, large-scale indexing, human understanding, and person re-identification. I will serve/have served as an Associate Editor of IEEE TMM, and an area chair/SPC of AAAI 2018, ICCV 2017, CVPR 2017, ECCV 2016 and ACM Multimedia 2015.

Felix X. Yu is a Research Scientist at Google, New York. He is currently working on large-scale machine learning for/on mobile devices. Felix received his Ph.D from Dept. of Electrical Engineering, Columbia University, in 2015, and his B.S. from Dept. of Electronic Engineering, Tsinghua University, China, in 2010. Google Research Google Scholar LinkedIn.