About

Nikos Trichakis is the J.C. Penney Professor of Operations Management at the MIT Sloan School of Management and serves as the Associate Dean of Social and Ethical Responsibilities of Computing at the MIT Schwarzman College of Computing. His research spans several critical areas including fairness and ethics in analytics, data-driven and robust optimization, resource allocation, healthcare operations, supply chain management, revenue management, and the financing of operations. Trichakis has made significant contributions to the understanding of fairness in operations, developing models and frameworks that balance efficiency and equity in resource allocation and organ transplantation policies. His work integrates machine learning with ethical considerations to design inclusive and efficient systems, particularly in healthcare settings such as kidney and liver transplant allocation. Additionally, he has advanced the field of robust optimization and queuing theory, applying these methods to real-world problems like supply chain resilience and healthcare appointment scheduling. Trichakis is also recognized for his research at the interface of finance and operations, exploring the impacts of operating flexibility, debt, and blockchain adoption on supply chain transparency and revenue management. He has been honored with multiple awards from INFORMS and other professional societies, reflecting the impact and innovation of his research. Beyond research, he has contributed to education through teaching various operations management courses at MIT and Harvard Business School, and has developed case studies used in business education.

Research topics

• Computer Science
• Computer Security
• Political Science
• Business
• Industrial organization
• Finance
• Economics
• Nursing
• Statistics
• Geography
• Medicine
• Microeconomics
• Mathematics
• Marketing

Selected publications

• Modernizing the Design Process for US Organ Allocation Policy: Toward a Continuous Distribution Policy for Kidneys

  Transplantation Direct · 2025-08-22

  articleOpen accessSenior author

  Background: The allocation of a limited supply of donor organs remains a critical challenge for organ transplantation. The analytical tools that policymakers rely upon for improving allocation policy have seen little advancement since the introduction of computer simulation in 1995. In recent years, simulation has increasingly become a bottleneck in the policy design process. Partnering with the Organ Procurement and Transplantation Network Kidney Transplantation Committee, our team introduced new analytical techniques into the policy design process. Methods: A new simulation algorithm was developed that reduces the time required to simulate 1 y of allocation from >6 h down to about 15 s while using the same simulation model as the preexisting simulator used by the Organ Procurement and Transplantation Network. This improvement enabled the simulation of thousands of allocation policies, allowing the introduction of multiobjective optimization as a primary method for policy design. An interactive website was created for committee members to analyze results and perform policy optimization. Results: These techniques were applied to the development of new continuous distribution allocation policies for kidneys. We detail the policy design process, present graphical results from 50 000 policy simulations, and highlight 4 policies optimized to balance between multiple objectives differently. Conclusions: Advances in analytical tools offer a path to improving organ transplantation through more effective and equitable organ allocation policies.

  PublisherDOI

• On the impact of mass screening for SARS-CoV-2 through self-testing in Greece

  Frontiers in Public Health · 2024-03-06

  articleOpen accessSenior authorCorresponding

  Background: Screening programs that pre-emptively and routinely test population groups for disease at a massive scale were first implemented during the COVID-19 pandemic in a handful of countries. One of these countries was Greece, which implemented a mass self-testing program during 2021. In contrast to most other non-pharmaceutical interventions (NPIs), mass self-testing programs are particularly attractive for their relatively small financial and social burden, and it is therefore important to understand their effectiveness to inform policy makers and public health officials responding to future pandemics. This study aimed to estimate the number of deaths and hospitalizations averted by the program implemented in Greece and evaluate the impact of several operational decisions. Methods: Granular data from the mass self-testing program deployed by the Greek government between April and December 2021 were obtained. The data were used to fit a novel compartmental model that was developed to describe the dynamics of the COVID-19 pandemic in Greece in the presence of self-testing. The fitted model provided estimates on the effectiveness of the program in averting deaths and hospitalizations. Sensitivity analyses were used to evaluate the impact of operational decisions, including the scale of the program, targeting of sub-populations, and sensitivity (i.e., true positive rate) of tests. Results: Conservative estimates show that the program reduced the reproduction number by 4%, hospitalizations by 25%, and deaths by 20%, translating into approximately 20,000 averted hospitalizations and 2,000 averted deaths in Greece between April and December 2021. Conclusion: Mass self-testing programs are efficient NPIs with minimal social and financial burden; therefore, they are invaluable tools to be considered in pandemic preparedness and response.

  PublisherOA PDFDOI

• Targeted Broader Sharing for Liver Continuous Distribution

  Transplantation · 2024-09-09 · 6 citations

  articleOpen access

  BACKGROUND: In recent years, changes to US organ allocation have aimed to improve equity and accessibility across regions. The Organ Procurement and Transplantation Network plans to adopt continuous liver distribution, prioritizing candidates based on a weighted composite allocation score (CAS) incorporating proximity, ABO types, medical urgency, and pediatric priority. The Liver Committee has requested research on CAS variations that account for geographical heterogenicity. METHODS: We describe a method for designing a geographically heterogeneous CAS with targeted broader sharing (CAS-TBS) to balance the highly variable geographic distributions of liver transplant listings and liver donations. CAS-TBS assigns each donor hospital to either broader sharing or nearby sharing, adjusting donor-candidate distance allocation points accordingly. RESULTS: We found that to reduce geographic disparity in the median Model for End-stage Liver Disease at transplant (MMaT), >75% of livers recovered in regions 2 and 10 should be distributed with broader sharing, whereas 95% of livers recovered in regions 5 and 1 should be distributed with nearby sharing. In a 3-y simulation of liver allocation, CAS-TBS decreased MMaT by 2.1 points in high-MMaT areas such as region 5 while increasing MMaT only by 0.65 points in low-MMaT areas such as region 3. CAS-TBS significantly decreased median transport distance from 202 to 167 nautical miles under acuity circles and decreased waitlist deaths. CONCLUSIONS: Our CAS-TBS design methodology could be applied to design geographically heterogeneous allocation scores that reflect transplant community values and priorities within the continuous distribution project of the Organ Procurement and Transplantation Network. In our simulations, the incremental benefit of CAS-TBS over CAS was modest.

  PublisherOA PDFDOI

• Applying Analytics to Design Lung Transplant Allocation Policy

  INFORMS Journal on Applied Analytics · 2023-09-01 · 2 citations

  articleSenior author

  In 2019, the United Network for Sharing (UNOS), which has been operating the Organ Procurement and Transplantation Network (OPTN) in the United States since 1984, was seeking to design a new national lung transplant allocation policy. The goal was to develop a point system that would prioritize candidates on the waiting list in a way that would yield more efficient and equitable outcomes. Our joint Massachusetts Institute of Technology (MIT)/UNOS team joined forces with the OPTN Lung Transplantation Committee in these policy design efforts. We discuss how our team applied a novel analytical framework, which was developed at MIT and utilizes optimization, regression, and simulation techniques, to illuminate salient trade-offs among outcomes and guide the choice of how to weigh different point attributes in the allocation formula. The committee selected for the allocation formula weights that were highlighted in the team’s analysis. The team’s proposal was implemented as the national lung allocation policy on March 9, 2023 across the United States. History: This paper has been accepted for the INFORMS Journal on Applied Analytics Special Issue—2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research.

  PublisherDOI

• Reshaping National Organ Allocation Policy

  Operations Research · 2023-11-20 · 17 citations

  articleSenior author

  Working with U.S. policymakers to redesign national organ allocation The Organ Procurement & Transplantation Network (OPTN), which manages transplantation activities in the United States, recently partnered with the MIT Operations Research Center to design and implement novel organ allocation policies that are more equitable, efficient, and inclusive. National organ allocation policies need to strike a delicate balance between efficiency and fairness in multiple objectives, reconciling often disparate value judgments and priorities from many different stakeholders. In “Reshaping National Organ Allocation Policy,” T. Papalexopoulos, J. Alcorn, and D. Bertsimas et al. introduced a novel optimization- and machine learning-based framework to aid policy design and navigate challenging fairness-efficiency tradeoffs. The authors collaborated with the OPTN to apply the framework to the design of a new national allocation policy for lungs, which was implemented in March 2023 and is anticipated to reduce waitlist mortality by approximately 20%. Based on this success, the authors are now working toward the redesign of the entire U.S. organ allocation system, including kidneys, pancreata, hearts, and livers.

  PublisherDOI

• On the Impact of Mass Screening for SARS-CoV-2 through Self-Testing in Greece

  medRxiv · 2023-02-16

  preprintOpen accessSenior authorCorresponding

  The emergence of COVID-19 stressed country health systems up to the point of triggering compulsory public health interventions to flatten the epidemic curve. Most of the interventions during the first year of the pandemic were non-pharmaceutical and aimed to reduce the contact rate of the people, which reduced the transmission rate of all respiratory pathogens, but had a large social and financial burden. SARS-CoV-2 specific interventions included screening, that is testing of asymptomatic people, which was largely facilitated by the availability of self-testing lateral flow antigen detection devices. The importance of self-testing interventions in controlling COVID-19 epidemic is not well-documented. We study as a paradigm-model the self-testing COVID-19 mass screening program that was implemented in Greece, involving large, susceptible populations taking tests routinely and pre-emptively so as to enable early detection of infections. Using a novel compartmental model we quantify the effectiveness of the program in curbing the COVID-19 pandemic. Conservative estimates indicate that the program reduced the reproductive number by 4%, hospital admissions by 25% and deaths by 20%, which translated into approximately 20,000 averted hospitalizations and 2,000 averted deaths between April-December 2021. Self-testing mass screening programs are efficient interventions with minimal social and financial burden, thus they are invaluable tools to be considered in pandemic preparedness.

  PublisherOA PDFDOI

• Dynamic Project Expediting: A Stochastic Shortest-Path Approach

  Management Science · 2023-08-03 · 7 citations

  articleOpen accessSenior author

  We deal with the problem of managing a project or a complex operational process by controlling the execution pace of the activities it comprises. We consider a setting in which these activities are clearly defined, are subject to precedence constraints, and progress randomly. We formulate a discrete-time, infinite-horizon Markov decision process in which the manager reviews progress in each period and decides which activities to expedite to balance expediting costs with delay costs. We derive structural properties for this dynamic project expediting problem. These enable us then to devise exact solution methods that we show to reduce computational burden significantly. We illustrate how our method generalizes and can be used to tackle a wide range of so-called stochastic shortest-path problems that are characterized by an intuitive property and can capture other applications, including medical decision-making and disease-modeling problems. Moreover, we also deal with the state identification issue for our problem, which is a challenging task in and of itself, owing to precedence constraints. We complement our analytical results with numerical experiments, demonstrating that both our solution and state identification methods significantly outperform extant methods for a supply chain example and for various randomly generated instances. This paper was accepted by Chung Piaw Teo, optimization. Funding: R. Mogre acknowledges support from the U.S.-UK Fulbright Commission and the Lloyd’s Tercentenary Research Foundation through the Fulbright-Lloyd’s Scholar Award, which allowed him to spend an extended period of time at Massachusetts Institute of Technology. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4876 .

  PublisherOA PDFDOI

• Pareto Adaptive Robust Optimality via a Fourier–Motzkin Elimination lens

  Mathematical Programming · 2023-06-30 · 6 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Ethics-by-design: efficient, fair and inclusive resource allocation using machine learning

  Journal of Law and the Biosciences · 2022-01-01 · 23 citations

  articleOpen accessSenior author

  The distribution of crucial medical goods and services in conditions of scarcity is among the most important, albeit contested, areas of public policy development. Policymakers must strike a balance between multiple efficiency and fairness objectives, while reconciling disparate value judgments from a diverse set of stakeholders. We present a general framework for combining ethical theory, data modeling, and stakeholder input in this process and illustrate through a case study on designing organ transplant allocation policies. We develop a novel analytical tool, based on machine learning and optimization, designed to facilitate efficient and wide-ranging exploration of policy outcomes across multiple objectives. Such a tool enables all stakeholders, regardless of their technical expertise, to more effectively engage in the policymaking process by developing evidence-based value judgments based on relevant tradeoffs.

  PublisherOA PDFDOI

• Platform Tokenization: Financing, Governance, and Moral Hazard

  Management Science · 2022 · 118 citations

  • Computer Science
  • Computer Security
  • Business

  This paper highlights two channels through which blockchain-enabled tokenization can alleviate moral hazard frictions between founders, investors, and users of a platform: token financing and decentralized governance. We consider an entrepreneur who uses outside financing and exerts private effort to build a platform and users who decide whether to join in response to the platform’s dynamic transaction fee policy. We first show that raising capital by issuing tokens rather than equity mitigates effort under-provision because the payoff to equity investors depends on profit, whereas the payoff to token investors depends on transaction volume, which is less sensitive to effort. Second, we show that decentralized governance associated with tokenization eliminates a potential holdup of platform users, which in turn alleviates the need to provide users with incentives to join, reducing the entrepreneur’s financing burden. The downside of tokenization is that it puts a cap on how much capital the entrepreneur can raise. Namely, if tokens are highly liquid, that is, they change hands many times per unit of time, their market capitalization is small relative to the net present value (NPV) of the platform profits, limiting how much money one can raise by issuing tokens rather than equity. If building the platform is expensive, this can distort the capacity investment. The resulting tradeoff between the benefits and costs of tokenization leads to several predictions regarding adoption. This paper was accepted by Vishal Gaur, operations management.

  PublisherDOI

Frequent coauthors

• Gerry Tsoukalas

  25 shared

• Jiri Chod

  Boston College

  21 shared

• Dan A. Iancu

  Stanford University

  14 shared

• Dimitris Bertsimas

  14 shared

• Parsia A. Vagefi

  The University of Texas Southwestern Medical Center

  6 shared

• Thomas A Trikalinos

  Providence College

  4 shared

• Vivek F. Farias

  Massachusetts Institute of Technology

  4 shared

• Yiwei Chen

  Guangdong University of Foreign Studies

  4 shared

Awards & honors

• 2023 Sanjay and Panna Mehrotra Research Excellence Award fro…
• 2021 Teaching with Digital Technology Award from MIT
• 2021 Manufacturing & Service Operations Management (M&SOM) I…
• 2018 INFORMS MSOM iFORM Best Paper Award
• 2020 INFORMS Koopman Prize