Presented By: Industrial & Operations Engineering
IOE 899: Daniel Freund, MIT CANCELED
Scheduling Considerations in the US Asylum System: LIFO, FIFO, and the Dedicated Docket
THIS EVENT HAS BEEN CANCELED DUE TO AN UNEXCEPTED ILLNESS
About the speaker
Daniel Freund is an Assistant 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. His work has been recognized with the George B. Dantzig Dissertation Award (2018) and the Daniel H. Wagner Prize for Excellence in Operations Research and Analytics (2018), as well as best paper awards from APS (2021) MSOM (2023), Service Science (2024), and PSOR (runner-up, 2024). Daniel is an AE for Operations Research and Transportation Science and frequently serves on ACM program committees (e.g., EC, EAAMO, TheWebConf). Before joining MIT, Daniel received a PhD from Cornell University and was a postdoc in Lyft’s Marketplace Labs.
Abstract:
In this talk, I will give an overview of my recent work at the intersection of humanitarian immigration and operations, focusing on two scheduling applications: the dedicated docket in immigration courts for defensive asylum cases and USCIS’s prioritization policy for the scheduling of affirmative asylum interviews. Both settings involve service systems designed to evaluate promptly whether or not an applicant should get asylum, and both settings deviate from traditional FIFO policies. However, in contrast to traditional service systems, both settings involve the benefits of waiting. I will discuss how these benefits can be modeled in both applications to capture key performance indicators, and highlight the impact of scheduling policies on the efficiency and fairness of both service systems.
This talk is based on joint work with my PhD student, Wentao Weng. A preprint of the first paper can be found here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4785713. This work received recognition as the 2024 Best Service Science Paper on DEIJ and as the runner-up for the 2024 Public Sector OR Best Paper Award.
About the speaker
Daniel Freund is an Assistant 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. His work has been recognized with the George B. Dantzig Dissertation Award (2018) and the Daniel H. Wagner Prize for Excellence in Operations Research and Analytics (2018), as well as best paper awards from APS (2021) MSOM (2023), Service Science (2024), and PSOR (runner-up, 2024). Daniel is an AE for Operations Research and Transportation Science and frequently serves on ACM program committees (e.g., EC, EAAMO, TheWebConf). Before joining MIT, Daniel received a PhD from Cornell University and was a postdoc in Lyft’s Marketplace Labs.
Abstract:
In this talk, I will give an overview of my recent work at the intersection of humanitarian immigration and operations, focusing on two scheduling applications: the dedicated docket in immigration courts for defensive asylum cases and USCIS’s prioritization policy for the scheduling of affirmative asylum interviews. Both settings involve service systems designed to evaluate promptly whether or not an applicant should get asylum, and both settings deviate from traditional FIFO policies. However, in contrast to traditional service systems, both settings involve the benefits of waiting. I will discuss how these benefits can be modeled in both applications to capture key performance indicators, and highlight the impact of scheduling policies on the efficiency and fairness of both service systems.
This talk is based on joint work with my PhD student, Wentao Weng. A preprint of the first paper can be found here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4785713. This work received recognition as the 2024 Best Service Science Paper on DEIJ and as the runner-up for the 2024 Public Sector OR Best Paper Award.
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