Nikos Trichakis

Faculty

Nikos Trichakis

About

Nikolaos (Nikos) Trichakis is the Zenon Zannetos (1955) Career Development Professor and an Associate Professor of Operations Management at the MIT Sloan School of Management.

His research interests include optimization under uncertainty, data-driven optimization and analytics, with application in healthcare, supply chain management, and finance. Trichakis is also interested in the interplay of fairness and efficiency in resource allocation problems and operations, and the inherent tradeoffs that arise in balancing these objectives.

Before his doctoral studies, Trichakis worked in quantitative finance at SunGard APT in London. Prior to joining MIT, Trichakis was on the faculty of Harvard Business School.

He received his BS degree from Aristotle University (Greece), and MS degrees from Stanford University and Imperial College (UK), all in electrical and computer engineering. He holds a PhD in operations research from MIT.

Honors

Young Researchers Prize awarded to Trichakis

Trichakis wins best paper award

Trichakis honored for best working paper

MIT authors win second prize

Publications

"Data-driven Appointment Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center."

Mandelbaum, Avishai, Petar Momčilovič, Nikolaos Trichakis, Sarah Kadish, Ryan Leib, and Craig A. Bunnell. Management Science. Forthcoming.

"Development and Validation of an Optimized Prediction of Mortality (OPOM) for Candidates Awaiting Liver Transplantation."

Bertsimas, Dimitris, Jerry Kung, Nikolaos Trichakis, Yuchen Wang, Ryutaro Hirose, and Parsia A. Vagefi. American Journal of Transplantation. Forthcoming. Supplementary Materials.

"Loyalty Program Liabilities and Point Values."

Chun, So Yeon, Dan A. Iancu, and Nikolaos Trichakis. Manufacturing & Service Operations Management. Forthcoming.

"On the Efficacy of Static Prices for Revenue Management in the Face of Strategic Customers."

Chen, Yeiwei, Vivek F. Farias, and Nikolaos Trichakis. Management Science. Forthcoming.

Load More

Recent Insights

Ideas Made to Matter

How an analytics-based predictive model can improve kidney transplant survival rates

Doctors need to trust their intuition, but a decision support tool can help them make hard choices.

Read Article
Press

MIT Sloan professors design model to improve decision-making process for kidney transplantation

MIT Sloan Profs. Dimitris Bertsimas and Prof. Nikos Trichakis created a data-based model to improve the kidney transplant decision-making process.

Read Article
Load More

Media Highlights