IORA Seminar Series – He Wang

He Wang is an Assistant Professor and Colonel John B. Day Early Career Professor in the School of Industrial and Systems Engineering at Georgia Tech. His research interests include pricing and revenue management, supply chain, transportation, and machine learning. His works have received 1st place in INFORMS Junior Faculty Interest Group paper competition, Best Paper […]

IORA Seminar Series – Mika Sumida

Mika Sumida is an Assistant Professor of Data Sciences and Operations in the Marshall School of Business at the University of Southern California. Her research focuses on developing efficient, provably good algorithms for revenue management and resource allocation problems, with applications in the sharing economy, online marketplaces, and delivery systems. She holds a Ph.D. in […]

DAO-IORA joint seminar: Ethan X. Fang

Ethan X. Fang is an Assistant Professor of Biostatistics & Bioinformatics at Duke Medical School and affiliated with Decision Sciences of Fuqua Business School and Rhodes Information Initiative at Duke University. He works on different data science problems from computational and statistical perspectives. Before joining Duke, he was an assistant professor of Statistics at Penn […]

IORA Seminar Series – Saif Benjaafar

Saif Benjaafar is McKnight Presidential Endowed Professor and Distinguished McKnight University Professor at the University of Minnesota. He is Head of the Department of Industrial & Systems Engineering at the University of Minnesota, where he also directs the Initiative on the Sharing Economy. He is a founding member of the Singapore University of Technology and […]

IORA Seminar Series – Timothy Chan

Timothy Chan is the Associate Vice-President and Vice-Provost, Strategic Initiatives at the University of Toronto, the Canada Research Chair in Novel Optimization and Analytics in Health, a Professor in the department of Mechanical and Industrial Engineering, and a Senior Fellow of Massey College. His primary research interests are in operations research, optimization, and applied machine learning, with applications […]