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DTSTART;TZID=Asia/Singapore:20261112T100000
DTEND;TZID=Asia/Singapore:20261112T113000
DTSTAMP:20261106T012801Z
CREATED:20261106T012801Z
LAST-MODIFIED:20261106T012801Z
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SUMMARY:DAO-ISEM-IORA Seminar Series: Hummy Song
DESCRIPTION:Name of Speaker\n\n\nHummy Song\n\n\n\n\nSchedule \n\n12 Nov 2026\, 10am – 11:30am \n(60 min talk + 30 min Q&A)\n\n\n\nVenue \n\n\nBIZ1 0202\n\n\n\nLink to register \n(via Zoom)\n\nhttps://nus-sg.zoom.us/meeting/register/IaHHjTRFRmyNhiwFBUgbhQ\n\n\n\n\nTitle\n\n\nThe First Draft Problem: Productivity\, Quality\, and Physiological Strain in LLM-Assisted Writing\n\n\n\n\nAbstract \n\n\nThe use of large language models (LLMs) has grown rapidly over the past few years\, especially in health care\, where documentation burden remains a persistent problem for practicing clinicians. However\, despite the deployment of many ambient scribes for documentation\, it is still unclear how LLMs impact those who work with them. Across two preregistered studies\, we examine how this shift—from writing documentation to editing an LLM-generated draft—impacts workers. We study this “first draft problem”: eliminating the blank page does not remove the work of documentation but relocates it into revising and editing. In Study 1\, an online experiment with 16 clinicians producing documentation following a teletherapy patient encounter\, we establish the puzzle: counter to our hypotheses\, editing an LLM-generated draft of the documentation as opposed to writing it from scratch does not result in time savings or quality improvements on average. Instead\, while some clinicians experience no time savings but large improvements in quality after making substantial revisions\, others experience meaningful time savings and no change in quality after making limited revisions. In other words\, we observe significant heterogeneity in how clinicians engage with the same draft\, which leads to variation in productivity and quality. In Study 2\, a lab experiment with 100 university students summarizing video interviews\, we unpack the mechanisms underlying this heterogeneity using granular keystroke logs\, validated self-reports of workload\, and micro-level physiological and eye-tracking measurements. In this setting\, we find the expected productivity improvements: editing reduces task completion time by roughly 70%\, raises quality\, and lowers self-reported workload. Yet the physiological evidence tells a more nuanced story. Physiological markers indicate that fatigue accumulates faster when editing an LLM-generated draft than when writing from scratch. Thus\, we find that (a) time savings can be real\, but are setting dependent; (b) quality improvements are possible\, but the relationship between how heavily a draft is revised and the quality of the result differs sharply across our two settings; and (c) while editing an LLM draft lowers reported cognitive load\, the body still tires faster over time.\n \nJoint work with Xufei Liu and Christian Terwiesch\n\n\n\n\nAbout the Speaker\n\n\nHummy Song is an Associate Professor of Operations\, Information and Decisions at the Wharton School at the University of Pennsylvania. She also holds an appointment as Associate Professor of Health Care Management. She conducted her undergraduate\, master’s\, and PhD studies at Harvard University.\nProfessor Song’s research focuses on identifying ways to improve the performance of service systems\, with a particular emphasis on the health care sector. For her research\, Professor Song has worked with hospitals and health care delivery organizations in the U.S. and in developing countries. She is the recipient of the 2026 MSOM Young Scholar Prize\, the 2022 POMS Early Career Research Accomplishments Award\, and the M&SOM Service Management SIG Best Paper Award. She was also named by Poets & Quants as one of the Top 50 Undergraduate Business Professors. She currently serves as an Associate Editor of Management Science\, M&SOM\, Operations Research\, and Service Science.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-hummy-song/
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DTSTART;TZID=Asia/Singapore:20261113T100000
DTEND;TZID=Asia/Singapore:20261113T113000
DTSTAMP:20261106T021341Z
CREATED:20261106T021341Z
LAST-MODIFIED:20261106T021341Z
UID:28890-1794564000-1794569400@iora.nus.edu.sg
SUMMARY:DAO-ISEM-IORA Seminar Series: Nicholas G. Hall
DESCRIPTION:Name of Speaker\n\n\nNicholas G. Hall\n\n\n\n\nSchedule \n\n13 Nov 2026\, 10am – 11:30am \n(60 min talk + 30 min Q&A)\n\n\n\nVenue \n\n\nBIZ1 0202\n\n\n\nLink to register \n(via Zoom)\n\nhttps://nus-sg.zoom.us/meeting/register/vfxjtzRuTna0i-LY_dmrTg\n\n\n\n\nTitle\n\n\nVehicle Platooning\n\n\n\n\nAbstract \n\n\nVehicle platooning offers significant benefits\, including reduced energy consumption\, lower emissions\, improved road utilization\, enhanced safety\, and reduced driver fatigue. As intelligent driving technologies continue to advance\, platoon sizes are expected to increase substantially\, making the efficient sequencing and resequencing of vehicles increasingly important. We study the vehicle platoon sequencing and resequencing problem on road networks with varying segment lengths under two fundamental objectives: minimizing total energy consumption and minimizing the maximum energy consumption of any vehicle. For the typically encountered combinations of vehicle and road characteristics\, we provide a complete computational complexity classification\, either developing polynomial-time algorithms or proving computational intractability. For several intractable cases\, we design fully polynomial-time approximation schemes and polynomial-time heuristics with provable performance guarantees. A computational study demonstrates that the proposed heuristics achieve average solutions within 1% of optimal. We also consider settings in which only limited information about position-dependent energy savings is available and develop a heuristic with bounded worst-case performance. In addition\, we present an efficient algorithm for on-road vehicle resequencing when only limited position changes are permitted. Together\, these results provide a comprehensive algorithmic framework for energy-efficient vehicle platoon sequencing and resequencing.\n\n\n\n\nAbout the Speaker\n\n\nNicholas G. Hall is Berry Family Professor of Operations & Business Analytics\, Fisher College of Business\, The Ohio State University. He holds B.A.\, M.A. degrees in economics (University of Cambridge)\, and a Ph.D. in Management Science (U.C.\, Berkeley). His research and teaching interests are in project management\, scheduling\, and sports analytics. He has published over 100 articles in Operations Research\, Management Science\, Mathematics of Operations Research\, Mathematical Programming\, Games and Economic Behavior\, Interfaces\, and others. He has served for over 47 years on the editorial boards of Operations Research and Management Science\, and given over 450 academic presentations\, 200 invited presentations in 29 countries\, 21 conference keynote presentations\, and 10 INFORMS tutorials. In 2018\, as President of INFORMS\, he initiated a government outreach program yielding 30 briefings at The White House and over 500 on Capitol Hill. A 2021 bibliometric study published in Operations Research ranked him 1st in the field of scheduling (1952–2019). He has authored 8 of the 128 most cited articles in the h-index of Operations Research. He served as President of Manufacturing and Service Operations Management society during 1999-2000. He is a Fellow of INFORMS. He is serving as Editor of the multimedia publication INFORMS Analytics Collections (2021-2026). In 2022\, he published Supply Chain Scheduling (Springer\, 706 pp)\, with Zhi-Long Chen.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-nicholas-g-hall/
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