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DTSTART;TZID=Asia/Singapore:20261014T100000
DTEND;TZID=Asia/Singapore:20261014T113000
DTSTAMP:20261005T083149Z
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SUMMARY:DAO-ISEM-IORA Seminar Series: Jing Dong
DESCRIPTION:Name of Speaker\n\n\nJing Dong\n\n\n\n\nSchedule \n\n14 Oct 2026\, 10am – 11.30am \n(60 min talk + 30 min Q&A)\n\n\n\nVenue \n\n\nBIZ1 0305\n\n\n\nLink to register \n(via Zoom)\n\nhttps://nus-sg.zoom.us/meeting/register/pewK2LZ4SZeJWbzoUudBlg\n\n\n\n\nTitle\n\n\nAI for Scalable and Practical Queueing Network Modeling and Control\n\n\n\n\nAbstract \n\n\nQueueing network models are powerful mathematical modeling tools for managing congestion across various applications\, yet broader adoption remains limited due to challenges in accessibility and scalability. This talk explores how recent advances in AI can enable more accessible and practical applications of this modeling tool. We first present a model-based reinforcement learning framework\, called differentiable discrete event simulation\, to solve general queueing network control problems. Our approach enables efficient and scalable policy gradient evaluation using auto-differentiation software. We then present a new data-driven modeling framework based on autoregressive sequence models. This framework allows us to utilize the rich operational data to automatically learn and represent system dynamics. Together\, these methods demonstrate how AI may help enhance the reach and usability of queueing models\, helping address operational challenges across diverse domains.\n\n\n\n\nAbout the Speaker\n\n\nJing Dong is the DeRosa Family Associate Professor of Business in the Decision\, Risk\, and Operations Division at Columbia Business School and a visiting faculty member at the Antai College of Economics and Management at Shanghai Jiao Tong University. Her research lies at the intersection of applied probability and service operations management\, with a particular focus on patient flow management in healthcare delivery systems. She serves as an associate editor for Operations Research\, Mathematics of Operations Research\, Management Science\, Manufacturing & Service Operations Management\, and the INFORMS Journal on Computing\, and as vice chair of the INFORMS Applied Probability Society. She received her Ph.D. in Operations Research from Columbia University and was on the faculty at Northwestern University before joining Columbia Business School.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-jing-dong/
CATEGORIES:IORA Seminar Series
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DTSTART;TZID=Asia/Singapore:20261016T100000
DTEND;TZID=Asia/Singapore:20261016T113000
DTSTAMP:20261013T040745Z
CREATED:20261013T040745Z
LAST-MODIFIED:20261013T040745Z
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SUMMARY:DAO-ISEM-IORA Seminar Series: Gregor Schubert
DESCRIPTION:Name of Speaker\n\n\nGregor Schubert\n\n\n\n\nSchedule \n\n\n16 Oct 2026\, 10am – 11.30am \n (60 min talk + 30 min Q&A) \n\n\n\n\nVenue \n\n\nBIZ1 0202\n\n\n\n\nLink to register \n(via Zoom) \n\n\nhttps://nus-sg.zoom.us/meeting/register/de8USvKzShCe7R8is-bjlw\n\n\n\n\nTitle\n\n\nOrganizational Technology Ladders: Remote Work and Generative AI Adoption\n\n\n\n\nAbstract \n\n\nThis study proposes that firms move along an ”organizational technology ladder”: adopting one technology transforms hiring and work processes and builds skills and organizational capital that change the cost of adopting subsequent technologies. I study how firms’ adoption of remote work technology during the COVID-19 period shaped later uptake of generative AI. Using U.S. job-posting data and an instrumental-variables strategy based on predicted differences in labor-market pressure to offer remote work\, I estimate that a 10 percentage point increase in remote hiring in 2021–-2022 increases the share of job postings mentioning generative AI in 2023–-2024 by 0.4 percentage points across firms and 0.7 percentage points across occupations within firms. I provide evidence on mechanisms consistent with a technology-ladder channel: remote work adoption shifts hiring toward technical and managerial capabilities that predict faster conversion of generative AI exposure into adoption. Firms with return-to-office mandates–interpreted as revealing low remote productivity–exhibit a substantially larger response of generative AI adoption to remote work\, consistent with an organizational frictions channel. I formalize the technology-ladder mechanism using a task-based model of sequential technology investments where remote work changes the costs and benefits of generative AI automation.\n\n\n\n\nAbout the Speaker\n\n\nGregor Schubert is an applied microeconomist with a background in real estate\, labor economics and finance. His research focuses on new technologies\, including Generative AI and robotics\, and their impact on firms\, labor markets and the economy. He also develops new AI-based tools and methodologies for research in finance and economics\, and studies housing markets and real estate technology. At UCLA\, he has developed and taught several courses for managers on how to successfully deploy artificial intelligence and machine learning projects\, and how to manage organizational change in response to technology shocks. Before academia\, he worked as a strategy consultant at The Boston Consulting Group on projects implementing data analytics\, surveys\, and technology transformations in industrial and financial services companies.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-gregor-schubert/
CATEGORIES:IORA Seminar Series
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