Scholar
Sanjeev Khudanpur
Google Scholar ID: K-BdgNwAAAAJ
The Johns Hopkins University
Citations & Impact
Citations
29,732
H-index
52
i10-index
130
Publications
20
Co-authors
65
list available
Publications
Resume (English only)
Academic Achievements
- Published multiple peer-reviewed journal articles, including:
- - A. Kanlis, S. Khudanpur and P. Narayan, “Typicality of a Good Rate-Distortion Code,” in Problemy Peredachi Informatsii (Problems of Information Transmission), 32(1):96-103, 1996.
- - M. Riley, W. Byrne, M. Finke, S. Khudanpur, A. Ljolje, J. McDonough, H. Nock, M. Saraclar, C. Wooters and G. Zavaliagkos, “Stochastic Pronunciation Modeling from Hand-Labelled Phonetic Corpora,” in Speech Communication, 29(2-4):209-224, 1999.
- - M. Saraclar, H. Nock and S. Khudanpur, “Pronunciation Modeling by Sharing Gaussian Densities Across Phonetic Models,” in Computer Speech and Language, 14(2):137-160, 2000.
- - S. Khudanpur and J. Wu, “Maximum Entropy Techniques for Exploiting Syntactic, Semantic and Collocational Dependencies in Language Modeling,” in Computer Speech and Language, 14(4):355-372, 2000.
Research Experience
- On the faculty of Johns Hopkins University since 1996. From July 2001 to June 2008, served as an Assistant Professor in the Department of Electrical and Computer Engineering and the Department of Computer Science. Became an Associate Professor in July 2008. Held a visiting appointment at the Institute for Mathematics and its Applications (IMA), University of Minnesota, Minneapolis, MN, in Fall 2000, and organized two IMA workshops on the role of mathematics in multimedia.
Education
- Received B.Tech. degree in Electrical Engineering from Indian Institute of Technology, Bombay, in 1988, and Ph.D. degree in Electrical and Computer Engineering from University of Maryland, College Park, in 1997, under the supervision of Prof. Prakash Narayan.
Background
- Research interests include the application of information theoretic methods to human language technologies such as automatic speech recognition, machine translation, and natural language processing. Interested in understanding the structure of statistical models and estimating their parameters from data.
