16 November 2015

CUHK Statistics PhD Student Wins Best Student Research Paper Award at INFORMS
First Winner From Asia Pacific Region



Pun Chi-seng, a PhD student in the Statistics programme, The Chinese University of Hong Kong (CUHK) recently won the 2015 Best Student Research Paper Award for a paper he presented at the 2015 Institute for Operations Research and the Management Sciences (INFORMS) Annual Meeting in Philadelphia, the USA. This is the first time ever a PhD student from the Asia Pacific region has won this international competition. Pun Chi-seng’s award winning paper entitled “Combined Estimation-Optimization (CEO) Approach for High Dimensional Portfolio Selection”. 

INFORMS has been sponsoring this student research contest since 2004. The contest invites postgraduate students from tertiary institutions all over the world to compete against one another in terms of research paper quality and presentation. The best four finalists are asked to present the papers at the INFORMS Annual Meeting. The first place winner receives a cash award of US$500 and an award certificate. Previous winners include PhD students from Stanford University, MIT, Columbia University, the University of Illinois at Urbana Champaign and Carnegie Mellon University. 

INFORMS is the largest society in the world for professionals in the field of operations research, management science, and analytics. INFORMS has held academic seminars and publishes internationally recognized academic journals, such as “Management Science”, “Organization Science” and “Information Systems Research”, to provide an academic exchange platform for professionals and scholars. 

Learn more about the 2015 Best Student Research Paper Award of INFORMS at https://www.informs.org/Community/Finance

Pun Chi-seng shows his award certificate for his research paper “Combined Estimation-Optimization (CEO) Approach for High Dimensional Portfolio Selection”.
Pun Chi-seng shows his award certificate for his research paper “Combined Estimation-Optimization (CEO) Approach for High Dimensional Portfolio Selection”.