ISyE Postdoctoral Fellowship Program

ISyE Postdoctoral Fellowship Program

The ISyE Postdoctoral Fellowship Program was established in 2020. Supported by generous donations from ISyE alumni and friends, this program provides full or partial funding for postdoctoral fellows in all research areas within ISyE, including advanced manufacturing; analytics and machine learning; applied probability and simulation; data science and statistics; economic decision analysis; energy and sustainable systems; health and humanitarian systems; optimization; supply chain engineering; and system informatics and control.

Postdoctoral Fellows in this program perform independent research at the cutting edge of their field in collaboration with one or more faculty mentors within the school. In addition, the program aims to prepare outstanding researchers for faculty careers that will contribute to diversity and equal opportunity through research, teaching, and service.

 

The Georgia Tech Community

Georgia Tech is a top-ranked public research university situated in the heart of Atlanta, a diverse and vibrant city with great economic and cultural strengths. The Institute is a member of the University System of Georgia, the Georgia Research Alliance, and the Association of American Universities. Georgia Tech prides itself in its high-quality  student body, faculty focus on excellence, outstanding staff, technology resources, interdisciplinary culture, and commitment to diversity, equity, and inclusion (DEI).

Diversity is one of Georgia Tech’s greatest strengths and one of the major priorities identified in our strategic plan. Georgia Tech is an equal education/employment opportunity institution dedicated to building a diverse community. We believe that diversity includes the individual differences among people from differing social, racial, or ethnic backgrounds, gender, sexual orientations, gender identities and expressions, economic circumstances, personal characteristics, philosophical outlooks, life experiences, perspectives, beliefs, expectations, physical abilities, and aspirations. Each of these characteristics, both singularly and in combination, contributes to the richness of the Georgia Tech community. 

2021 Cohort

Eugene Ndiaye

Eugene Ndiaye

Tennenbaum President's Postdoctoral Fellow

Eugene Ndiaye received his Ph.D. in applied mathematics from Telecom ParisTech. His research interests focus on the interplay between machine learning and optimization, mainly to understand how a statistical learning algorithm can select particular information from data and how this selection bias affects its prediction abilities. Among other long-term objectives, it aims to provide quantifiable and implementable guarantees on the performance and limits of artificial intelligence methods as well as their impacts when they are deployed in society.

Mentors: Xiaoming Huo and Pascal Van Hentenryck


Jingyan Wang

Jingyan Wang

Ronald J. and Carol T. Beerman President's Postdoctoral Fellow

Jingyan Wang received her Ph.D. in the School of Computer Science from Carnegie Mellon University in 2021, advised by Nihar Shah. She received her B.S. in Electrical Engineering and Computer Sciences with a minor in Mathematics from the University of California, Berkeley in 2015. Her research interests lie in understanding and mitigating biases in decision making problems such as admissions, peer grading and peer review, using tools from statistics and machine learning. She is the recipient of the Best Student Paper Award at AAMAS 2019.

Mentor: Ashwin Pananjady


Martin Zubeldia

Martin Zubeldia

Tennenbaum President's Postdoctoral Fellow

Martin Zubeldia received a B.S. degree in Electronics Engineering (2012) and a M.Sc. degree in Engineering (2014) from the Universidad ORT Uruguay, and a Ph.D. in Electrical Engineering (2019) from MIT. Before joining Georgia Tech, he was a postdoc at the Eindhoven University of Technology, and at the University of Amsterdam, in the Netherlands. His research primarily focuses on the modeling, analysis, and control of large-scale stochastic decision systems, inspired by applications in computer networks and other service systems. He is particularly interested in the fundamental tradeoffs between performance and efficiency in such systems, with an emphasis on the role that information plays in these tradeoffs. He was a finalist for the 2019 INFORMS APS Best Student Paper Award and for the 2016 INFORMS George E. Nicholson Student Paper Competition.

Mentor: Siva Theja Maguluri

First position after postdoc: Assistant Professor, Department of Industrial and Systems Engineering (ISyE), University of Minnesota, Twin-Cities