OBJECTIVE: seek summer internship opportunities
EDUCATION BACKGROUND
Ph.D in Computer Science, Stanford University Sep. 2001 - Feb. 2007 (expected)
* GPA: 4.3/4.3
Artificial Intelligence: Principles & Techniques (A+) Information Theory A (A+)Probabilistic Models in Artificial Intelligence (A+) Information Theory B (A+)Modern Applied Statistics: Learning (credit) Accelerated Spanish I (credit)Convex Optimization w/ Engineering Applications (A+)
M.S. in Ecology and Evolution, The University of Chicago Jan. 1999 - Sep. 2001
B.S. in Mathematics, Fudan University July 1995 - July 1998
* Completed fast-track 4-year Advanced Science Curriculum in 3 years
SELECTED AWARDS AND HONORS
Honor Award of Howard Hughes Predoctoral Fellowships in Biological Sciences, 1999
Gold Medallist, 36th International Mathematical Olympiad (IMO), Toronto, Canada, 1995
Silver Medallist, 35th International Mathematical Olympiad (IMO), Hong Kong, 1994
* Fifth person in China since 1985 to participate IMO twice.
LEADSHIP AND ORGANIZATIONAL EXPERIENCE
Director of Stanford Asia Technology Initiative (ATI), Stanford University, Oct. 2001 - Sep. 2002
ATI Aims to promote Silicon Valley entrepreneurship in Asia by establishing and coordinating summer internship and career opportunities in Shanghai, Tokyo, and Bangalore for Stanford students
* Established contact and liaised with companies and academic institutions in Shanghai
* Conducted interviews and screened potential candidates for opportunities in Shanghai
* Organized Speaker Series and various conferences on Stanford campus and in Shanghai; Successfully raised and managed event fundings (US$ 100,000)
ADDITIONAL SKILLS
* Fluent in Unix, C, C++, Perl, Matlab, and MySQL
* Fluent in Chinese and English
* Basic Spanish (an accelerate course at Stanford and 70 days in Mexico)
RESEARCH PROJECT
Study Protein Interactions by Probabilistic Graphical Model and Machine Learning in AI
* The goal is to extract patterns and learn relationships from the large quantity of genomic data (>200GB generated to date) available for protein interaction, sequence, structure, etc. by using a probabilistic model. The model has been successful in diverse fields of AI such as computer vision, natural language processing, and webpage classification.
* Discovery of mechanisms underlying protein interaction network will allow for meaningful predictions about the functions of cellular proteins, with possible applications to drug design.
SCIENTIFIC PUBLICATIONS
Wang H, Segal E, Ben-Hur A, Brutlag D, Koller D; Identifying protein-protein interaction sites on a genome-wide scale. Advances in Neural Information Processing Systems (NIPS) 17, 2004
Segal E, Wang H, Koller D; Discovering Molecular Pathways from Protein Interaction and Gene Expression Data, Bioinformatics, 2003; 19 (Suppl 1): 264-272
* Recipient of the Best Student Paper Award at the 11th International Conference on Intelligent Systems for Molecular Biology (ISMB)
Li WH, Gu Z, Wang H, Nekrutenko A; Evolutionary analyses of the human genome. Nature. 2001 Feb 15;409(6822):847-9
* This paper is part of the first publish of the completely sequenced human genome
Gu Z, Wang H, Nekrutenko A, Li WH; Densities, length proportions, and other distributional features of repetitive sequences in the human genome estimated from 430 megabases of genomic sequence. Gene. 2000 Dec 23;259(1-2):81-8