About

Daniel Freund is an Associate Professor of Operations Management at the MIT Sloan School of Management. His research applies techniques from optimization, stochastic modeling, and revenue management to problems in transportation, online platforms, and humanitarian immigration among others. Freund's work has been recognized with several awards, including the PSOR Best Paper Award (2024), the MSOM Best OM Paper in OR Award (2023), the Applied Probability Society Best Publication Award (2021), the George B. Dantzig Dissertation Award (2018), the Daniel H. Wagner Prize for Excellence in Operations Research and Analytics (2018), and a Best Paper award at the ACM SIGCAS COMPASS conference (2018). He holds a BSc in mathematics from the University of Warwick and an MS and PhD in applied mathematics from Cornell University, where he was advised by David B. Shmoys. Prior to joining MIT, Freund was a postdoctoral researcher in Lyft's Marketplace Labs team.

Research topics

• Computer Security
• Computer Science
• Automotive engineering
• Transport engineering
• Operations research
• Engineering
• Embedded system

Selected publications

• The Value of a Little Flexibility in Stable Matching

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
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• Regulating Wait-Driven Requests in Queues

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Regulating Wait-Driven Requests in Queues

  2025-07-02

  articleOpen access1st authorCorresponding

  The study of rational queueing has a long and distinguished history focused on individuals' preference to avoid waiting. Surprisingly, there are settings in which some potential arrivals (which we also refer to as requests) derive utility from waiting and disutility from service. Our primary example is the U.S. affirmative asylum process. In this context, applicants obtain a work permit while waiting for an asylum interview; hence, if the (expected) wait is long enough, then even an applicant who knows that their application will be denied and lead to deportation proceedings, may find it in their interest to apply and thus benefit from legally working during the wait. Similar dynamics could occur in other settings like content moderation in social networks.

  PublisherOA PDFDOI

• Pricing fast and slow: Limitations of dynamic pricing mechanisms in ride-hailing

  Transportation Research Part C Emerging Technologies · 2025-10-23

  article1st authorCorresponding
  PublisherDOI

• On the Supply of Autonomous Vehicles in Platforms

  Manufacturing & Service Operations Management · 2025-10-21 · 2 citations

  article1st authorCorresponding

  Problem definition: The ongoing large-scale deployment of autonomous vehicle (AV) technology has the potential to fundamentally change the transportation landscape. Due to the high cost of AV hardware, the most likely path to widespread AV use is via platforms that can sustain high utilization, such as ride-hailing and delivery services. Our work studies the incentives of stakeholders in such deployments, possible misalignments, and contracting solutions to overcome them. Methodology/results: We consider four potential operational models to commercialize AVs, which we model as a supply chain game between a platform, an AV supplier, and human individual contractors (ICs), including (1) an open platform in which the AV supplier and ICs bring their own vehicles to the system, (2) an AV-supplier operated platform without ICs, (3) a platform that sources AVs through leasing contracts, and (4) an integrated supply chain. We find that except for the AV-only platform, all deployment models are subject to a risk of AV underutilization due to the need to maintain the ICs’ utilization sufficiently high to ensure ICs remain engaged. As AV underutilization propagates in a nonintegrated supply chain, an open platform can become arbitrarily worse than supply chain integration, and careful usage commitments are needed to overcome the efficiency loss. Managerial implications: This paper identifies a new kind of supply chain misalignment that is likely to emerge as AVs become deployed technology, which both platform operators and AV suppliers should be mindful of. Moreover, we demonstrate the value of usage commitment in the deployment of AVs through an open platform, which makes it more efficient than AV-only/AV leasing platforms. History: This paper was selected as part of the 1RR initiative between the M&SOM Journal and the MSOM Society. This particular paper was part of the 2024 MSOM Service Operations SIG Conference. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1175 .

  PublisherDOI

• On the Power of Delayed Flexibility: Balls, Bins, and a Few Opaque Promotions

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Good Prophets Know When the End Is Near

  Management Science · 2024-09-23 · 10 citations

  articleSenior author

  We consider a class of online decision-making problems with exchangeable actions, where in each period a controller is presented an input type drawn from some stochastic arrival process. The controller must choose an action, and the final objective depends only on the aggregate type-action counts. Such a framework encapsulates many online stochastic variants of common optimization problems with knapsack, bin packing, and generalized assignment as canonical examples. In such settings, we study a natural model-predictive control algorithm. We introduce general conditions under which this algorithm obtains uniform additive loss (independent of the horizon) compared with an optimal solution with full knowledge of arrivals. Our condition builds on the recently introduced compensated coupling technique, providing a unified view of how uniform additive loss arises as a consequence of the geometry of the underlying decision-making problem. Our characterization allows us to derive uniform loss algorithms for several new settings, including the first such algorithm for online stochastic bin packing. It also lets us study the effect of other modeling assumptions, including choice of horizon, batched decisions, and limited computation. In particular, we show that our condition is fulfilled by the above-mentioned problems when the end of the time horizon is known sufficiently long before the end. In contrast, if at a late stage, there is still uncertainty about the end of the time horizon we show that such uniform loss guarantees are impossible to achieve. We use real and synthetic data to illustrate our findings. This paper was accepted by J. George Shanthikumar, data science. Funding: This work was supported by the Division of Mathematical Sciences [Grant 1839346]; the Division of Electrical, Communications and Cyber Systems [Grant 1847393]; and the Army Research Laboratory [Grant W911NF-17-1-0094]. The authors also gratefully acknowledge support from the NSF [Grants ECCS-1847393, CNS-195599, and DMS-1839346], AFOSR [Grant FA9550-23-1-0068], an ARL award [Grant W911NF-17-1-0094], and ARO MURI [Grant W911NF-19-1-0217]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.04307 .

  PublisherDOI

• The Dedicated Docket in U.S. Immigration Courts: An analysis of fairness and efficiency properties

  2024-07-08 · 1 citations

  article1st authorCorresponding

  The dedicated docket was introduced by the Biden Administration to expedite the processing of asylum claims. It creates a separate queue for immigration proceedings where judges are supposed to issue a decision for each asylum case within a target timeframe. The administration announced the docket with the goals of speed, accuracy, and fairness. Though the program meets its first goal, legal advocacy groups report that this comes at the expense of the last. Referring to it as a "Denial of justice", they find that cases on the dedicated docket routinely fail to access legal representation, and have a much lower asylum grant rate. We aim to understand the operational implication of the dedicated docket. In our stylized queueing model, a policy maker (PM) routes asylees to either the regular or the dedicated docket, and sets a delay target for the dedicated one. Constrained by the target, the court allocates its limited capacity to minimize the average delay. Immigration lawyers schedule their time between dockets to maximize the rate of successful asylum cases.

  PublisherDOI

• Halo effect of faces and bodies: Cross-cultural similarities and differences between German and Japanese observers

  Personality Science · 2024-04-01 · 5 citations

  articleOpen access

  According to the halo effect, person perceptions are globally biased by specific traits or characteristics. Attractive people are attributed positive traits like prosociality, health, and dominance. Due to a strong focus on facial stimuli it remains unclear whether this effect can also be found for bodies. Furthermore, most studies involved observers from individualistic cultures. This preregistered study explored the consistency of halo effects for men’s faces and bodies for individualistic and collectivistic observers. Facial photos and 3D body scans of 165 German men were judged separately for attractiveness, prosociality, health, and physical dominance by 123 German and 100 Japanese observers. Results were mostly consistent between both observer groups and revealed strong attractiveness halo effects for faces and bodies, and a physical dominance halo effect for bodies. This study provides new insights on consistent halo effect biases in person perception for faces and bodies for observers with different cultural backgrounds.

  PublisherDOI

• The Dedicated Docket in U.S. Immigration Courts: An Analysis of Fairness and Efficiency Properties

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• David B. Shmoys

  Cornell University

  30 shared

• David P. Williamson

  25 shared

• Alice Paul

  Brown University

  23 shared

• Aaron Ferber

  21 shared

• Thodoris Lykouris

  Massachusetts Institute of Technology

  11 shared

• Siddhartha Banerjee

  Cornell University

  10 shared

• Shane G. Henderson

  Cornell University

  9 shared

• Şahin Albayrak

  7 shared

Awards & honors

• PSOR Best Paper Award (2024)
• MSOM Best OM Paper in OR Award (2023)
• Applied Probability Society Best Publication Award (2021)
• George B. Dantzig Dissertation Award (2018)
• Daniel H. Wagner Prize for Excellence in Operations Research…