Index

Education:

Ph.D in Computer Science , Rutgers University , NJ, USA (Oct. 2008)

MS in Statistics, Data Mining track, Rutgers University, NJ, USA (May. 2005)

MS in Computer Science, Rutgers University, NJ, USA (Jan. 2004)

BS in Computer Science, Drexel University, PA, USA (Jun. 2001)

Associate in Electrical Engineering, known back then as St. Johns and St. Marys Institute of Technology, but now known as St. John's University Taipei, Taiwan, R.O.C (Jun. 1995)

Basic Information:

Pai-Hsi Huang, Ph.D
I will be joining force with Novartis in the end of July, 2008. My permanent email is gmail (same username, see below).
AKA Bae-Shi Huang (You pronounce my first name as pronoucing the words Bye-She)
Current Occupation: Teaching Assistant of the Computer Science Department of Rutgers University, the State University of New Jersy, USA
Office: Hill - 402
Office Hour:
Office Number: (732)445-6450 (X 9603) (See notes below)

Important Notes:

The best and easiest way to contact me is not through telephone, for I do not check my voice mail very often. If you want to contact me due to an emergency, send me an email. I gaurantee you that, during week days, I will check my email at least 3 to 4 times a day.
You can email me if you have a question with regard to your homework, project..etc. Nevertheless, keep in mind that, it usually takes a day or two, in the worst case, for me to reply to your email. Therefore, if you email me a question regarding your homework one day prior to its due date, please do not expect to get my response by the due date.

Research Interests:

My research interests are in Machine Learning/Data Mining and I am currently applying my knowledge of these subjects to a discipline known as BioInformatics. I focus on statistical learning methods that are known to be interpretable; these models tend to offer you more insights to the structure of the data and perhaps the underlying generation process.

I am currently working on protein homology detection, with a goal to develop a principled way of using a generative model (in this case, hidden Markov models) as a feature extractor and feed the features to an interpretable, discriminative model (for example, Logistic Regression model) to perform the classification task.
I am also interested in semi-supervised learning algorithms, in which one taps into the abundant unlabeled data in the hope to populate the training sets and thus lower the variance of the estimates.

Teaching:

(Fall 2001) 198:314 Principles of Programming Languages with Barbara Ryer
(Spring 2002) 198:314 Principles of Programming Languages with Thorne McCarty
(Summer 2002)198:170 Computer Applications for Bussiness with Carol Schwartz
(Fall 2002)CS 323 Numerical Analysis and Computing with William Steiger
(Spring 2002)CS 510 Numerical Analysis with William Steiger
(Fall 2003)CS 323 Numerical Analysis and Computing with Sylvia Halasz
(Spring 2004)CS 530 Principles of Artificial Intelligence with Vladimir Pavlovic
(Fall 2005)CS 530 Principles of Artificial Intelligence with Chung-Chieh (Ken) Shan
(Spring 2006) CS 323 Numerical Analysis and Computing with Sylvia Halasz
(Summer 2006) CS 170 Computer Applications for Business with Carol Schwartz
(Fall 2006) CS 440 Introduction to Artificial with Thorne McCarty
(Spring 2007) CS 205 Introduction to Discrete Structures with Thorne McCarty
(Fall 2007) CS 530 Principles of Artificial Intelligence with Chung-Chieh (Ken) Shan
(Spring 2008) CS 112 Data Structures with Sesh Venugopal

Personal:

Useful References and my Internet BookMarks
Some pictures that I took on a trip to California May, 2003.
Pictures that I took in Hong Kong while attending a conference in December 2006.
The paperless project
Campus Status. Is your class cancelled???
Disk space usage on /filer/tmp
CS research cluster
load of the president cluster
Quotes
Instruction for gradebooks