Nikos Trichakis


Nikos Trichakis

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Nikolaos (Nikos) Trichakis is the J.C. Penney Associate Professor of Management 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.


Trichakis and co-authors win first prize

Trichakis wins best paper award

Trichakis wins 2020 Koopman Prize

Trichakis honored for best working paper

Trichakis wins 2021 digital teaching award

Young Researchers Prize awarded to Trichakis

MIT authors win second prize


"Applying Analytics to Design Lung Transplant Allocation Policy."

Trichakis, Nikolaos, Theodore Papalexopoulos, Dimitris Bertsimas, James Alcorn, R.R. Goff, and D.E. Stewart. INFORMS Journal on Applied Analytics. Forthcoming.

"​Platform Tokenization: Financing, Governance, and Moral Hazard."

Chod, Jiri, Nikolaos Trichakis, and S. Alex Yang. Management Science. Forthcoming. Download Paper .

"On the Learning Benefits of Resource Flexibility."

Chod, Jiri, Mihalis Markakis, and Nikos Trichakis. Management Science Vol. 67, No. 10 (2021): 6513-6528. Download Paper.

"Pareto Adjustable Robust Optimality via a Fourier-Motzkin Elimination Lens."

Bertsimas, Dimitris, Stefan ten Eikelder, Dick den Hertog, and Nikolaos Trichakis, MIT Sloan Working Paper 6285-20. Cambridge, MA: MIT Sloan School of Management, July 2021.

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Researchers use data analytics for national lung transplant allocation

Dimitris Bertsimas and Nikolaos Trichakis modeled a points-based framework called continuous distribution (CD) based on AI and machine learning to aid in allocating lung transplants.

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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.

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