Summer School

6 July, AM Deep Learning and Its Applications to Computer Vision by Kai Yu
6 July, PM Deep Speech Recognition by Li Deng
7 July, AM Sparse Learning by Tong Zhang
7 July, PM Low-rank Matrix Completion and Dictionary Learning by Wei Dai

Kai Yu came back to China in April, 2012. Now he serves as a deputy engineering director of Baidu, managing the company's multimedia department. His team innovates search technologies and products every day, by making better use of speech, images, videos, and music. Before April 2012, he led the media analytics department of NEC Labs in northern California, developing intelligent systems involving machine learning, image recognition, multimedia search, data mining, and human-computer interface. The systems took the first place for many times in global competitions. Before joining NEC, he was a senior research scientist at Siemens. He obtained Bachelor degree in Nanjing University and PhD in Computer Science at University of Munich. He has published more than 100 papers in the research area of Machine Learning, Data Mining and Computer Vision and owned more than 30 patents. In 2011, he taught a class CS121 ˇ°Introduction to Artificial Intelligenceˇ± at Stanford University. He also served as Area Chair of NIPS and ICML for a couple of times.

Li Deng (IEEE SM'92;F'04) received the Bachelor degree from the University of Science and Technology of China, and received the Master and Ph.D. degrees from the University of Wisconsin-Madison. He was an assistant professor (1989-1992), tenured associate professor (1992-1996), and tenured Full Professor (1996-1999) at the University of Waterloo, Ontario, Canada. In 1999, he joined Microsoft Research, Redmond, WA as a Senior Researcher, where he is currently a Principal Researcher. Since 2000, he has also been an Affiliate Full Professor and graduate committee member at the University of Washington, Seattle. Prior to MSR, he also worked or taught at Massachusetts Institute of Technology, ATR Interpreting Telecom. Research Lab. (Kyoto, Japan), and HKUST during sabbaticals. He has been granted over 60 US or international patents in acoustics/audio, speech/language technology, and machine learning. He received numerous awards/honors bestowed by IEEE, ISCA, ASA, Microsoft, and other organizations.
His current (and past) research activities include deep learning and machine intelligence, deep/recurrent neural networks for speech and related information processing, automatic speech and speaker recognition, spoken language identification and understanding, speech-to-speech translation, machine translation, language modeling, information retrieval, web search, neural information processing, dynamic systems, machine learning and optimization, parallel and distributed computing, graphical models, audio and acoustic signal processing, image analysis and recognition, compressive sensing, statistical signal processing, digital communication, human speech production and perception, acoustic phonetics, auditory speech processing, auditory physiology and modeling, noise robust speech processing, speech synthesis and enhancement, multimedia signal processing, and multimodal human-computer interactions.
In the general areas of audio/speech/language technology and science, machine learning, signal/information processing, and computer science, he has published over 300 refereed papers in leading journals and conferences and 4 books. He is a Fellow of the Acoustical Society of America, a Fellow of the IEEE, and a Fellow of ISCA. He served on the Board of Governors of the IEEE Signal Processing Society (2008-2010). More recently, he served as Editor-in-Chief for the IEEE Signal Processing Magazine (2009-2011), which earned the highest impact factor in 2010 and 2011 among all IEEE publications and for which he received the 2012 IEEE SPS Meritorious Service Award. He recently served as General Chair of the IEEE ICASSP-2013, and currently serves as Editor-in-Chief for the IEEE Transactions on Audio, Speech and Language Processing. His technical work since 2009 on and the leadership in industry-scale deep learning with colleagues and academic collaborators have created significant impact in speech recognition (e.g., CD-DNN/CNN/T-DSN/K-DSN) and other areas of signal/information processing.

Tong Zhang received his PhD degree from Stanford Computer Science. After graduation, he worked at IBM T.J. Watson Research Center in Yorktown Heights, New York, and Yahoo! Research in New York City. He is currently with the statistics department, Rutgers University. His research interests are machine learning, statistical and numerical computation, as well as the design and theoretical analysis of statistical algorithms. He also has extensive large-scale data-analysis and statistical modeling experience, especially in text mining, natural language processing, web and image applications. He has published more than 100 papers in his research area. He once served as Area Chair in NIPS and ICML, and chairman in COLT. He also took editor of Machine Learning Journal, Journal of Machine Learning Research.

Wei Dai is currently a Lecturer in the Electrical and Electronic Engineering Department at Imperial College London. He received his Ph.D. degree in Electrical and Computer Engineering from the University of Colorado at Boulder in 2007. From 2007 to 2010, he was a Postdoctoral Research Associate in the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign. His interdisciplinary research interests include sparse signal processing, wireless communications, random matrix theory, applications of information theory and signal processing to biology. Dr. Dai was involved in the development of the first compressive sensing DNA microarray prototype. One of his publications in 2009 on compressive sensing reconstruction has been cited more than 400 times up to now (according to Google scholar).