August 23-24, 2024
Carnegie Mellon University
Carnegie Mellon University
| Home | Registration | Program | Posters | Flash Talks |
The CMU INFORMS Student Chapter is excited to host the 7th YinzOR Conference. This student conference provides an opportunity for students whose research is in Operations Research, Management Science, Industrial Engineering, and other related fields to interact with each other. This year, we are hosting the conference at the Tepper School of Business at CMU on August 23rd-24th!
The INFORMS Student Chapter at Carnegie Mellon University hosted YinzOR 2024 on August 23-24 at Tepper School of Business, Carnegie Mellon University. This year’s conference was gently sponsored by Hexaly. The conference consisted of ten regular-track talks, a poster competition with 12 participants, a flash talk competition with 9 participants, a few interaction breaks and a happy hour. This year we had the honor of hosting more than 70 attendees. A nomination and voting process was used by the organizing committee to choose and invite excellent young researchers from various institutions. We were excited to have a diverse group of speakers from Carnegie Mellon University, Stanford University, Georgia Tech, Cornell University, Columbia University, and Hexaly. On Friday August 23, Laurence Ales, currently the Senior Associate Dean, Faculty and Research at Tepper School of Business, kicked off YinzOR with Opening Remarks. Dean’s opening remarks was followed by a featured talk by Mathieu Dahan from Georgia Tech. Professor Dahan presented his work on designing large-scale resilient relay logistics networks by locating logistics hubs to optimize resilience metrics. Following Professor Dahan’s featured talk, Woody Zhu from Carnegie Mellon University presented his work on decision rules for a quantifiable objective when there is an abundance of choices and information due to increased data availability. Following Professor Zhu’s talk, there was a short networking break in which participants had time to connect and talk about their research. After the break, Ziv Scully from Cornell University presented his work on the problem of scheduling jobs in a queueing system. After Professor Scully’s talk, Ellen Vitercik from Stanford University presented her work on online matching with graph neural networks. The flash-talk competition was held after Professor Vitercik’s talk. 9 participants gave 5 minute talks on their research. The flash talk competition had participants from Carnegie Mellon University, Cornell University, Columbia University, MIT, Georgia Institute of Technology, and University of Toronto. The panel of judges for the flash-talk competition consisted of three esteemed professors: Fatma Kilinc-Karzan, Emily Diana, Paul Gölz. Additionally, a fan favorite was voted by general attendees. The winners of the competition were Rakesh Allu (Cornell University), Man Yiu Tsang (Lehigh University), Yu Ma (MIT), and Yiling Xie (Georgia Tech). Congratulations to all winners! Continuing the tradition, we wrapped up the first day of YinzOR with a delicious dinner from Piada. The second day of YinzOR 2024, August 24, started with a healthy breakfast, and morning session speakers. In the first Saturday session of YinzOR, Rachitesh Kumar (Carnegie Mellon University), Tianyi Peng (Columbia University), and Paul Gölz (Cornell University) presented their work. Professor Kumar presented his work with the title “Strategically-Robust Bidding Algorithms for First-Price Auctions”. Professor Peng talked about his work on reevaluating A/B testing through reinforcement learning. Professor Gölz presented his work on generative social choice. Professor Gölz’s talk was followed by lunch in which attendees had the opportunity to explore restaurants around the area. After lunch, Founder and CEO of Hexaly, Fred Gardi gave a talk regarding Hexaly Optimizer, which is a new global optimization solver. We thank Gardi for his inspiring talk and contributions to the YinzOR conference! The poster competition was held after Gardi’s talk. There were 12 posters from Carnegie Mellon University, University of Pittsburgh, Columbia University, Georgia Institute of Technology, Harvard University, and University of Washington. The panel of judges for the poster competition consisted of three esteemed professors: Alan Scheller-Wolf, Ellen Vitercik, and Tianyi Peng. Additionally, a fan favorite was also voted by general attendees. The winners of the competition were Priyank Agrawal (Columbia University), Tianjiao Li (Georgia Tech), Qing Ye (Georgia Tech), and Qiqi Hao (University of Pittsburgh). Congratulations to all winners! Congratulations to all winners! After the poster competition, Jiaoyang Li (Carnegie Mellon University) talked about her research on large-scale multi-robot coordination. Following Professor Li’s talk, Bryan Wilder (Carnegie Mellon University) presented his work on the increasing role of machine learning on public health, specifically allocating limited resources under uncertainty. Following Professor Wilder’s talk, we had the awards ceremony for poster and flash talk competition winners. Professor Fatma Kılınç-Karzan announced the winners and handed in their award certificates. After the closing remarks, the conference ended with the most important event of all: Happy Hour. It was held in the biggest balcony of the Tepper Quad, where the participants enjoyed a nice view of the beautiful city of Pittsburgh. As the INFORMS Student Chapter, we thank all the speakers and attendees for attending the YinzOR Conference 2024. We look forward to seeing you all next year! YinzOR 2024 team of organizers: Vrishabh Patil (Co-Chair), Jody Zhu (Co-Chair), H. Satyam Verma (Webmaster Chair), Lin An (Speaker Co-Chair), Siyue Liu (Speaker Co- Chair), Macarena Navarro (Marketing Co-Chair), Sebastian Vasquez (Marketing Co- Chair), Olha Silina (Flash Talk and Poster Session Co-Chair), Wenbin Zhou (Flash Talk and Poster Session Co-Chair), Anthony Karahalios (Sponsor Chair).
Registration is now open for the YinzOR2024 conference!
Register by August 15 to attend! If you want a t-shirt, make sure to register by July 15.
Be sure to checkout the Poster Session and 10-Minute Flash Talk Contest, both of which have cash prizes and accepted applicants can receive complimentary housing.
We are thrilled to announce an exceptional lineup of speakers for this year’s YinzOR conference. Each of these experts brings a wealth of knowledge and innovative research from the fields of operations research, machine learning, and more:
Stay tuned for further announcements and additions to our speaker list! We invite all participants to join us for what promises to be an enriching and engaging series of discussions and presentations. Stay tuned for more updates, including session schedules and topics!
1rd place - $200 - Priyank Agrawal, Columbia University - Optimistic Q-learning for average reward and episodic reinforcement learning
2rd place - $150 - Tianjiao Li, Georgia Institute of Technology - Accelerated stochastic approximation with state-dependent noise
3rd place - $100 - Qing Ye, Georgia Institute of Technology - Distributionally Fair Stochastic Optimization using Wasserstein Distance
Fan Favorite - $50 - Qiqi Hao, University of Pittsburgh - Managing Trade-offs Between Revenue, Access and Market Balance on Online Dating Platforms
1rd place - $200 - Rakesh Allu, Cornell University - Ranking Quality and User Engagement on an Online B2B Platform
2rd place - $150 - Man Yiu Tsang, Lehigh University - On the Trade-Off Between Distributional Belief and Ambiguity: Conservatism, Finite-Sample Guarantees, and Asymptotic Properties
3rd place - $150 - Yu Ma, MIT - Integrative Artificial Intelligence for System Medicine: a Multimodal, Multitask Framework
Fan Favorite - $50 - Yiling Xie, Georgia Institute of Technology - Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning
YinzOR 2023
YinzOR 2022
YinzOR 2021
YinzOR 2019
YinzOR 2018
YinzOR 2017
Vrishabh Patil (Co-Chair) (Email)
Jody Zhu (Co-Chair) (Email)
H. Satyam Verma (Webmaster Chair)
Lin An (Speaker Co-Chair)
Siyue Liu (Speaker Co-Chair)
Macarena Navarro (Marketing Co-Chair)
Sebastian Vasquez (Marketing Co-Chair)
Olha Silina (Flash Talk and Poster Session Co-Chair)
Wenbin Zhou (Flash Talk and Poster Session Co-Chair)
Anthony Karahalios (Sponsor Chair)
* Please feel free to email either co-chair with any of your questions/comments!
YinzOR 2023
YinzOR 2022
YinzOR 2021
YinzOR 2019
YinzOR 2018
YinzOR 2017
|
| 12:30pm-1:00pm Registration |
| Come pick up your name tag before the conference begins! |
|
1:00pm-1:20pm
Room-TEP3801 |
Opening Remarks |
| Laurence Ales, Professor of Economics at Tepper School of Business, Carnegie Mellon University |
|
1:20pm-2:00pm
Room-TEP3801 |
Topology-Based Resilient Logistics Network Design |
| Mathieu Dahan, Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech | |
| We consider the problem of designing large-scale resilient relay logistics networks by locating logistics hubs to optimize resilience metrics based on demand-weighted k shortest (edge-disjoint) path lengths between each origin-destination pair. We derive mixed-integer programming formulations using path and edge decisions, and leverage their structures to devise two tailored scalable solution methodologies based on Benders decomposition and branch-and-price. We showcase the applicability of our methodology in designing large-scale resilient relay networks for a China-based parcel delivery partner. Our results demonstrate that the designed networks significantly outperform traditional lean network designs when confronted with stochastic hub disruptions, with only marginal impact on operating costs in nominal situations. This work is joint with Onkar Kulkarni and Benoit Montreuil. |
|
2:00pm-2:40pm
Room-TEP3801 |
Balancing Optimality and Diversity: Enhancing Human Decision-Making through Generative Curation |
| Woody Zhu, Assistant Professor in Heinz College of Information Systems and Public Policy, Carnegie Mellon University | |
| The rapid increase in data availability has overwhelmed decision-makers with an abundance of choices and information. In response, there has been considerable work in creating optimal decision rules for a quantifiable objective. However, in many practical settings, human decision-makers must consider both explicit quantitative and implicit qualitative factors to make the final call. We introduce a general framework, termed generative curation, which generates optimal recommendations that account for both quantitative and qualitative objectives. We show that a consideration of implicit qualitative factors naturally leads to a metric that measures the diversity of the generated solutions, transforming the problem into balancing between quantitative optimality and qualitative diversity. Our proposed algorithm efficiently solves this optimization problem by generating a finite number of diverse and near-optimal solutions. We validate our approach with real-world datasets, showcasing its potential to enhance decision-making processes in complex decision-making settings. |
| 2:40pm-3:05pm | Break |
|
3:05pm-3:45pm
Room-TEP3801 |
Strongly Tail-Optimal Scheduling in the Light-Tailed M/G/1 |
| Ziv Scully, Assistant Professor at Cornell ORIE (Operations Research and Information Engineering) | |
| We study the problem of scheduling jobs in a queueing system, specifically an M/G/1 with light-tailed job sizes, to asymptotically optimize the response time tail. For some time, the best known policy was First-Come First-Served (FCFS), which has an asymptotically exponential tail. FCFS achieves the optimal exponential decay rate, but its leading constant is suboptimal. Designing a policy that minimizes this leading constant is a long-standing open problem. We solve this open problem with a new scheduling policy called 𝛾-Boost. Roughly speaking, 𝛾-Boost operates similarly to FCFS, but it pretends that small jobs arrive earlier than their true arrival times. This reduces the response time of small jobs without unduly delaying large jobs. We prove 𝛾-Boost's asymptotic tail optimality, and we show via simulation that 𝛾-Boost has excellent practical performance. The 𝛾-Boost policy as described above requires knowledge of job sizes. In preliminary work, we generalize 𝛾-Boost to work with unknown job sizes, proving an analogous asymptotic optimality result in the unknown-size setting. Our generalization reveals that 𝛾-Boost is a type of Gittins index policy, but with an unusual feature: it uses a negative discount rate. Joint work with George Yu (Cornell) and Amit Harlev (Cornell). Paper links: [1] [2]. |
|
3:45pm-4:25pm
Room-TEP3801 |
Online matching with graph neural networks |
| Ellen Vitercik, Assistant Professor at Management Science and Engineering and Computer Science, Stanford University | |
|
Online Bayesian bipartite matching is a central problem in digital marketplaces and exchanges, including advertising, crowdsourcing, ridesharing, and kidney exchange. We introduce a graph neural network (GNN) approach that emulates the problem's combinatorially complex optimal online algorithm, which selects actions (e.g., which nodes to match) by computing each action's value-to-go (VTG)—the expected weight of the final matching if the algorithm takes that action, then acts optimally in the future. We train a GNN to estimate VTG and show empirically that this GNN returns high-weight matchings across a variety of tasks. Moreover, we identify a common family of graph distributions in spatial crowdsourcing applications, such as rideshare, under which VTG can be efficiently approximated by aggregating information within local neighborhoods in the graphs. This structure matches the local behavior of GNNs, providing theoretical justification for our approach.
This is joint work with Alexandre Hayderi, Amin Saberi, and Anders Wikum. |
| 4:25pm-4:50pm | Break |
|
4:50pm-6:30pm
Room-TEP3801 |
10-Minute Flash Talk Competition |
| Selected students will present 10-Minute Flash Talks in a competition judged by the attendees with cash prizes! |
|
6:30pm-9:00pm
Room-TEP3808 |
Dinner |
| Everyone is welcome to join us for a free dinner catered by a local Pittsburgh restaurant. We might go out for drinks afterward as well. |
|
9:00am-10:00am Breakfast
Room-TEP3801 |
| All are welcome to a free breakfast and coffee to start the day |
|
10:00am-10:40am
Room-TEP3801 |
Strategically-Robust Bidding Algorithms for First-Price Auctions |
| Rachitesh Kumar, Incoming Assistant Professor at Tepper School of Business, Carnegie Mellon University | |
|
Learning to bid in repeated first-price auctions is a fundamental problem at the interface of game theory and machine learning, which has seen a recent surge in interest due to the transition of display advertising to first-price auctions. Past work has predominantly treated it as an online learning problem and focused on developing regret-minimizing algorithms. However, such an approach ignores the strategic nature of the market in which such algorithms are deployed---sellers and buyers can manipulate these algorithms for their own benefit. In this work, we go beyond regret minimization and develop bidding algorithms that are robust to manipulations by strategic market participants. We propose a novel concave formulation for pure-strategy bidding in first-price auctions and use it to analyze Gradient Ascent. Unlike previously-proposed algorithms, which attain optimal regret but are manipulable, we show that Gradient Ascent attains optimal regret while being robust to strategic manipulations by the buyer and the seller.
Based on https://arxiv.org/abs/2402.07363. |
|
10:40am-11:20am
Room-TEP3801 |
When A/B Testing Platforms Meet Reinforcement Learning |
| Tianyi Peng, Incoming Assistant Professor at Columbia University | |
| In the internet era, A/B testing has been regarded as the gold standard for evaluating algorithm effectiveness. However, the problem of interference—where different experimental units affect each other—remains a fundamental challenge in A/B testing. To overcome this issue, we propose an innovative solution based on the reinforcement learning framework to reevaluate A/B testing. This method estimates the treatment effect by solving the difference in Q-values in reinforcement learning, hence named the "Difference-in-Q" (DQ) estimator. Theoretically, we find that DQ performs excellently in the bias-variance trade-off: on the one hand, DQ’s bias is second-order compared to traditional estimators; on the other hand, DQ's variance can be exponentially reduced compared to any unbiased estimator. We collaborated with Douyin to apply DQ in a large-scale commercial setting, where it reduced the mean squared error by over 99% in our tests. Additionally, DQ also demonstrated outstanding performance in a commercial-grade shared vehicle simulator. |
|
11:20am-12:00pm
Room-TEP3801 |
Generative Social Choice |
| Paul Gölz, Incoming Assistant Professor at Cornell ORIE (Operations Research and Information Engineering) | |
| Traditionally, social choice theory has only been applicable to choices among a few predetermined alternatives but not to more complex decisions such as collectively selecting a textual statement. We introduce generative social choice, a framework that combines the mathematical rigor of social choice theory with the capability of large language models to generate text and extrapolate preferences. This framework divides the design of AI-augmented democratic processes into two components: first, proving that the process satisfies rigorous representation guarantees when given access to oracle queries; second, empirically validating that these queries can be approximately implemented using a large language model. We apply this framework to the problem of generating a slate of statements that is representative of opinions expressed as free-form text; specifically, we develop a democratic process with representation guarantees and use this process to represent the opinions of participants in a survey about chatbot personalization. We find that 93 out of 100 participants feel ‘‘mostly’’ or ‘‘perfectly’’ represented by the slate of five statements we extracted. |
| 12:00pm-1:30pm | Lunch |
| Lunch on your own! We welcome you to explore restaurants around the area. Please ask us if you would like any recommendations! |
|
1:30pm-2:10pm
Room-TEP3801 |
Hexaly, a new kind of global optimization solver |
| Fred Gardi, Founder & CEO at Hexaly | |
|
Hexaly Optimizer is a new kind of global optimization solver. Its modeling interface is nonlinear and set-oriented. It also supports user-coded functions, thus enabling black-box optimization and, more particularly, simulation optimization. In a sense, Hexaly APIs unify modeling concepts from mixed-linear programming, nonlinear programming, and constraint programming. Under the hood, Hexaly combines various exact and heuristic optimization methods: spatial branch-and-bound, simplex methods, interior-point methods, augmented Lagrangian methods, automatic Dantzig-Wolfe reformulation, column and row generation, propagation methods, local search, direct search, population-based methods, and surrogate modeling techniques for black-box optimization.
Regarding performance benchmarks, Hexaly distinguishes itself against the leading solvers in the market, like Gurobi, IBM Cplex, and Google OR Tools, by delivering fast and scalable solutions to problems in the spaces of Supply Chain and Workforce Management like Routing, Scheduling, Packing, Clustering, and Location. For example, on notoriously hard problems like the Pickup and Delivery Problem with Time Windows or Flexible Job Shop Scheduling with Setup Times, Hexaly delivers solutions with a gap to the best solutions known in the literature smaller than 1% in a few minutes of running times on a basic computer. In addition to the Optimizer, we provide an innovative development platform called Hexaly Studio to model and solve rich Vehicle Routing and Job Shop Scheduling problems in a no-code fashion. The user can define its problem and data, run the Optimizer, visualize the solutions and key metrics through dashboards, and deploy the resulting app in the cloud – without coding. This web-based platform is particularly interesting for educational purposes; it is free for faculty and students. |
|
2:10pm-3:40pm
Room-TEP2001, TEP2002, TEP2003 |
Poster Session |
| Students will setup posters in an open space. Winners will be determined by both formal judges and attendees popular votes! |
|
3:40pm-4:20pm
Room-TEP3801 |
Layout Design for Large-Scale Multi-Robot Coordination |
| Jiaoyang Li, Assistant Professor at Robotics Institute Carnegie Mellon University | |
| Today, thousands of robots are navigating autonomously in warehouses, transporting goods from one location to another. While numerous planning algorithms are developed to coordinate robots more efficiently and robustly, warehouse layouts remain largely unchanged – they still adhere to the traditional pattern designed for human workers rather than robots. In this talk, I will share our recent progress in exploring layout design and optimization to enhance large-scale multi-robot coordination. First, I will introduce a direct layout optimization method based on stochastic optimization. Then, I will discuss a method to optimize layout generators instead of layouts. Last, I will extend these ideas to virtual layout design, which does not require changes to the physical world that robots navigate and thus has the potential for applications beyond automated warehouses. |
|
4:20pm-5:00pm
Room-TEP3801 |
Machine learning for public health decision making |
| Bryan Wilder, Assistant Professor in the Machine Learning Department at Carnegie Mellon University | |
| Many policy settings, such as public health, require allocating limited resources under uncertainty about which individuals need those resources the most. Machine learning plays an increasing role in such allocations, most commonly to provide predictions of individual-level need. In this talk, I will discuss two recent works that take a more expansive view of the role of ML in resource allocation. First, I will discuss methods that allow a decision maker to optimally balance the immediate goal of targeting high-need individuals and the long-term goal of estimating treatment effects (i.e., does the program help people?). Our proposed method provides a policy for resource allocation which minimizes the variance with which the average treatment effect is estimated, subject to a constraint that at least some fraction of in-need individuals receive the intervention. Second, I will introduce ML as a lens to study the fairness of discretionary allocations by human decision makers, such as a clinician's decision to prescribe a resource-limited drug. Here the challenge is that human decisions are likely to be confounded (based on unobserved variables); we provide nontrivial bounds on allocative equity even in this challenging setting. In both works, we give both strong guarantees for the statistical and computational properties of our proposed methods as well as applications to real-world data from health and social service settings. |
|
5:00pm-5:30pm
Room-TEP3801 |
Closing Remarks |
| Fatma Kilinc-Karzan, Associate Professor of Operations Research and CMU INFORMS Faculty Advisor |
|
5:30pm-7:30pm
Room-TEP2001, TEP2002, TEP2003 |
Happy Hour |
| Free refreshments to celebrate a wonderful conference. The party may continue past 7:30pm outside of the building. |
Registrations for the Flash Talk Competition are closed!
Prizes:
Rules: The competition has the following rules:
Judges: Fatma Kilinc-Karzan, Emily Diana, Paul Gölz.
Fan Ballot Link: Vote for your favorite talk!
Logistics: Provide PowerPoint slides beforehand. Minimum one week before the conference.
The CMU INFORMS chapter will provide accommodation for one night if needed for accepted flash talk presenters. Hotel rooms will be shared with one other person. We cannot provide travel reimbursements.
| The Whiplash Effect: Congestion Dissipation in a Disrupted Circulatory System |
| Chaoyu Zhang, University of Toronto |
| Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning |
| Yiling Xie, Georgia Institute of Technology |
| Integrative Artificial Intelligence for System Medicine: a Multimodal, Multitask Framework |
| Yu Ma, MIT |
| A Method for Diagnosing Vulnerability in Supply Chains that have High Uncertainty |
| Anthony Cheng, Carnegie Mellon University |
| From Submodular Flows to Planar Graphs: A Planarity Criterion Inspired by an OR Question |
| Yuchong Pan, MIT |
| On the Trade-Off Between Distributional Belief and Ambiguity: Conservatism, Finite-Sample Guarantees, and Asymptotic Properties |
| Man Yiu Tsang, Lehigh University |
| Ranking Quality and User Engagement on an Online B2B Platform |
| Rakesh Allu, Cornell University |
| Stochastic Interior-Point method for Inequality constrained optimization |
| Qi Wang, Lehigh University |
| Optimizer’s Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization |
| Tianyu Wang, Columbia University |
Registrations for the Poster Competition are closed!
Prizes:
Judges: Alan Scheller-Wolf, Ellen Vitercik, Tianyi Peng.
Fan Ballot Link: Vote for your favorite poster!
Logistics: The CMU INFORMS chapter will provide accommodation for one night if needed for accepted poster presenters. Hotel rooms will be shared with one other person. We will print posters for you in advance. We cannot provide travel reimbursements.
| Spatio-Temporal Compartmental Modeling for Customer-Level Power Outages |
| Shuyi Chen, Carnegie Mellon University |
| Hicksian Unit-Demand Pricing |
| Kasra Ganjizadeh Tari, University of Pittsburgh |
| Optimistic Q-learning for average reward and episodic reinforcement learning |
| Priyank Agrawal, Columbia University |
| Generative Dynamic Treatment Regimes |
| Yiwei Dong, Carnegie Mellon University |
| Accelerated stochastic approximation with state-dependent noise |
| Tianjiao Li, Georgia Institute of Technology |
| Towards Safe LLM-assisted Reward Generation in Restless Multi-Armed Bandit Problems |
| Shresth Verma, Harvard University |
| Hyperparameter tuning via trajectory predictions: Stochastic prox-linear methods in matrix sensing |
| Mengqi Lou, Georgia Institute of Technology |
| Can Price Discrimination be Progressive? |
| Enfeng Xing, University of Pittsburgh |
| Managing Trade-offs Between Revenue, Access and Market Balance on Online Dating Platforms |
| Qiqi Hao, University of Pittsburgh |
| Convergence Rates in Stochastic Stackelberg Games with Smooth Algorithmic Agents |
| Eric Frankel, University of Washington |
| Distributionally Fair Stochastic Optimization using Wasserstein Distance |
| Qing Ye, Georgia Institute of Technology |
| A Benders Decomposition Algorithm for Balancing Assembly Lines with Multi-Resource and Parallel Stations |
| Wencang Bao, Georgia Institute of Technology |
| © CMU INFORMS |