Xi Chen is a professor and Andre-Meyer Faculty Fellow at Stern School of Business, New York University, who is also an affiliated professor at Computer Science and Center for Data Science. Before that, he was a Postdoc in the group of Prof. Michael Jordan at UC Berkeley and obtained his Ph.D. from the Machine Learning Department at Carnegie Mellon University. He studies high-dimensional machine learning, online learning, large-scale stochastic optimization, and applications to operations management and FinTech. Recently, he started a new research line on blockchain technology and decentralized finance. He is an ASA Fellow, IMS Fellow, recipient of COPSS Leadership Award, NSF Career Award, the World's Best 40 under 40 MBA Professor by Poets & Quants, and Forbes 30 under 30 in Science.

Title: LLM Alignment Techniques:  Stochastic Optimizations in LLM Post-training and Reasoning

Abstract: This talk explores approaches to improving large language model (LLM) post-training and reasoning through stochastic optimization techniques. The first part introduces ComPO, a preference alignment method using comparison oracles in stochastic optimization. The work addresses likelihood displacement issues in traditional direct preference optimization. The second part proposes the spectral policy optimization, a framework that overcomes GRPO's limitations with all-negative-sample groups by introducing response diversity with AI feedback. Both approaches demonstrate significant improvements across various model sizes and benchmarks, representing important advances in LLM post-training via stochastic optimization. This is a joint work with Peter Chen, Xiaopeng Li, Ziniu Li, Wotao Yin, and Tianyi Lin.

Moderator: Weiwei Chen (Rutgers University)
Date: December 15, 2025 10:00 AM-11:00 AM EST
Zoom link: https://rutgers.zoom.us/j/95804877123?pwd=bipsDDJRbaULzgkgTBA0Wa1NaiaPhQ.1&from=addon
Meeting ID: 958 0487 7123
Password: 095884

Tinglong Dai is the Bernard T. Ferrari Professor at the Johns Hopkins Carey Business School, specializing in Operations Management and Business Analytics. He holds a joint faculty appointment at the Johns Hopkins School of Nursing. He is a member of the Johns Hopkins University Council and serves on the leadership team of the Hopkins Business of Health Initiative. He also co-leads the University’s Bloomberg Distinguished Professorship Cluster on Global Advances in Medical Artificial Intelligence. As a co-chair of the Johns Hopkins Workgroup on AI and Healthcare, his current work focuses on integrating AI into clinical workflows and improving productivity, access, and equity in healthcare delivery.

Title: Would You Trust a Doctor Who Uses ChatGPT?

Introduction: We’ll go beyond the hype to examine how AI is transforming healthcare operations and decisions, not just diagnostics. From triaging patients and AI scribes to clinical decision support and hospital productivity, we’ll discuss how AI is being embedded into real workflows, where people and machines must learn to trust each other.

What we’ll talk about:

• When does AI make doctors better decision-makers, and when does it undermine trust?

• Can AI boost productivity in hospitals without harming patient care?

• How do we prevent AI from widening inequities in access and outcomes?

• What does the future of healthcare operations look like in an AI-first world?
  
  

Moderator: Tom Tan (Southern Methodist University)
Date: November 7, 2025 10:00 AM-11:00 AM EST

Michael Pinedo is the Julius Schlesinger Professor of Operations Management in the Department of Technology, Operations, and Statistics at New York University Leonard N. Stern School of Business. Professor Pinedo's research focuses on the modeling of production and service systems, more specifically, on the planning and scheduling of these systems. Recently, his research has focused on operational risk in financial services. He has both authored and co-authored numerous technical papers on these topics. In this interview-style session, Professor Pinedo will share reflections from his distinguished academic career and offer practical advice on topics that matter to scholars at every stage, including navigating the publication process, succeeding in the academic job market, balancing research, teaching, and service, transitioning into editorial and administrative roles, and building a long-term and fulfilling academic career.

Moderator: Muge Yayla-Kullu (The University of Texas at San Antonio)
Date: October 15, 2025, 02:00 PM-3:00 PM EST
Recording on YouTube: https://www.youtube.com/watch?v=hto7VUc6Esw
Recording on Bilibili: https://www.bilibili.com/video/BV1Su1HBRE7m/

Opher Baron is a Distinguished Professor of Operations Management at the Rotman School of Management, University of Toronto, and a cofounder and CEO of SiMLQ. On the theory side, Opher’s research interests include queueing, business analytics, service operations (such as healthcare), autonomous vehicles, and supply chain management.

Abstract: We will start by defining management analytics along descriptive, predictive, comparative, i.e., comparing performance indicators under different interventions, and prescriptive analytics dimensions. We would then shortly discuss how ML&AI can help create digital twins of queueing systems. We will finish with a short demo of SiMLQ, see www.SiMLQ.com, a software that automates the visualization, Simulation, and optimization of Queueing processes.

Moderator: Guangwen Kong (Temple)
Date: September 22, 2025 10:00 AM-11:00 AM EST
Zoom link: https://temple.zoom.us/j/99724478192

Dennis Zhang is a full professor of Operations Management as well as Marketing at the Olin Business School, Washington University in St. Louis. His research focuses on operations in innovative marketplaces and in the public sector. He built theoretical models to extract reliable insights from data and use data to improve existing models. Dennis is an Area Editor of Operations Research in the Machine Learning and Data Science Department.

Title: Personalized Policy Learning through Discrete Experimentation: Theory and Empirical Evidence

Abstract: Randomized Controlled Trials (RCTs), or A/B testing, have become the gold standard for optimizing various operational policies on online platforms. However, RCTs on these platforms typically cover a limited number of discrete treatment levels, while the platforms increasingly face complex operational challenges involving optimizing continuous variables, such as pricing and incentive programs. The current industry practice involves discretizing these continuous decision variables into several treatment levels and selecting the optimal discrete treatment level. This approach, however, often leads to suboptimal decisions as it cannot accurately extrapolate performance for untested treatment levels and fails to account for heterogeneity in treatment effects across user characteristics. This study addresses these limitations by developing a theoretically solid and empirically verified framework to learn personalized continuous policies based on high-dimensional user characteristics, using observations from an RCT with only a discrete set of treatment levels. Specifically, we introduce a deep learning for policy targeting (DLPT) framework that includes both personalized policy value estimation and personalized policy learning. We prove that our policy value estimators are asymptotically unbiased and consistent, and the learned policy achieves a √ n-regret bound. We empirically validate our methods in collaboration with a leading social media platform to optimize incentive levels for content creation. Results demonstrate that our DLPT framework significantly outperforms existing benchmarks, achieving substantial improvements in both evaluating the value of policies for each user group and identifying the optimal personalized policy.

Moderator: Renyu Zhang (CUHK)
Date: July 14, 2025 10:00 AM-11:00 AM EST
No Zoom Recording

Episode 3 of the INFORMS Service  Science Online Forum brings a dynamic fireside chat between Prof. Gad Allon (Wharton) and moderator Prof. Renyu Zhang (CUHK) on “Future‑Proofing Business Education in the Age of AI.” From rebooting undergraduate curricula for universal AI literacy to fast‑tracking executive MBAs’ tech acumen and re‑imagining PhD training, they will map the bold operating‑model shifts business schools need to outrun the technology curve. Join us via the Zoom link below to hear how AI is set to redefine classrooms, research agendas, and career paths for the next generation of service‑science leaders.

Gad Allon is the Jeffrey A. Keswin Professor and Professor of Operations, Information and Decisions at the University of Pennsylvania. His research interests include operations management in general, and service operations and operations strategy in particular.

Moderator: Renyu Zhang (CUHK)
Date: June 23, 2025 10:00 AM-11:00 AM EST
Recording on YouTube: https://www.youtube.com/watch?v=FnOadph21OM&list=PLCn8oCTLj5JEeIiA3_ATZp8gtlkWJCRpO
Recording on Bilibili: https://www.bilibili.com/video/BV1ZSgrzxEgZ/

Agostino Capponi is a professor of Industrial Engineering and Operations Research at Columbia University. His research interests are systemic risk and economic networks, financial technology, tokenomics, and market microstructure. Agostino is an Editor of Management Science in the Finance Department, and of Operations Research in the Financial Engineering Department.

Title: The Digital Future of Financial Services

Abstract: We examine the ongoing transformation of financial services, with a focus on new sources of risk and market design challenges posed by digital financial infrastructures. First, we discuss the challenges faced by robo-advisory firms in tailoring personalized financial  advice to meet individual client goals while enhancing their overall satisfaction. 

Next, we analyze the operational, systemic, and technological risks that arise when traditional lending and trading services are decentralized using blockchains, smart contracts, and digital assets. We balance these risks against the benefits of reduced market frictions, including faster settlement, lower intermediation costs, and greater transparency. Finally, we highlight how these digital innovations extend beyond the financial sector, offering potential to mitigate risk and improve efficiency across various types of marketplaces and supply chains.

Moderator: Yuqian Xu (UNC Chapel Hill)
Date: May 20, 2025 10:00 AM-11:00 AM EST
Recording on YouTube: https://www.youtube.com/watch?v=GxFDQYvO0rA
Recording on Bilibili: https://www.bilibili.com/video/BV1dmJ6z3EdY/