Home

2022 YinzOR Student Conference

The CMU INFORMS Student Chapter is excited to host the 5th 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 In-Person at the Tepper School of Business at CMU on August 26th-27th!

Information

Registration is FREE and now open for the In-Person YinzOR2022 conference!
To receive a free t-shirt please register by July 22. Otherwise register by August 15 to attend.

Flash Talk and Poster Competition deadline is extended to July 22. For accepted flash talks and posters, CMU INFORMS chapter will provide accommodations if needed to help with travel and printing costs. Please click on the individual poster and flash talk tabs for additional information and guidelines.

Poster Competition Winners!

1rd place - $500 - Nan Jiang - DFO: A Framework for Data-driven Decision-making with Endogenous Outliers
2rd place - $200 - Neha Sharma - Structuring online Question and Answer communities
3rd place - $100 - Billy Jin - Tight Robustness-Consistency Tradeoffs for Two-Stage Bipartite Matching with Advice
Fan Favorite - $100 - Siddharth Prasad - Learning to Cut in Integer Programming

Flash Talk Competition Winners!

Top 3 Presentations - $100 - (No Order)

Alyf Janmohamed - Charging Fleets of E-bikes and E-scooters
Neha Sharma - Should hosts list their assets early? An equilibrium analysis of advance asset-sharing platforms
Hansheng Jiang - Learning while Repositioning in On-demand Vehicle Sharing Networks


Fan Favorite - $100

Chenghuai Li - The Blockchain Newsvendor: Value of Freshness Transparency and Smart Contracts

Organizing Committee

CMU OR/OM/ChemE PhD Students

Anthony Karahalios (Co-Chair) (email: akarahal@andrew.cmu.edu)
Neha (Co-Chair) (email: neha2@andrew.cmu.edu)
Tom Krumpolc (Webmaster)
Yanhan (Savannah) Tang (Speaker Chair)
Nilsu Uzunlar (Flash Talk and Poster Session Co-Chair)
Tian Wang (Flash Talk and Poster Session Co-Chair)
Mik Zlatin (Marketing Chair)

* Please feel free to email either co-chair with any of your questions/comments!

Past Conferences

YinzOR 2021
YinzOR 2019
YinzOR 2018
YinzOR 2017

Conference Program

Friday, August 26th

1:15pm Registration Opens
Come pick up your name tag and t-shirt before the conference begins!
1:45pm-2:00pm Opening Remarks
Willem-Jan van Hoeve, Carnegie Bosch Professor of Operations Research and Senior Associate Dean, Tepper CMU
2:00pm-2:45pm Plenary Speaker - Estimating the Transition Probabilites of Discrete-Time Markov Chains from Partially Observed Aggregate Data
Jourdain Lamperski, Assistant Professor in the Department of Industrial Engineering at the University of Pittsburgh
We consider the inverse problem of estimating the transition probabilites of discrete-time markov chains (DTMCs) from partially observed aggregate data. Our motivation for studying this problem stems from its application to calibrating markov chain models of disease progression. Existing work in the public health literature tackles the problem with black box simulation techniques, leaving open a number of interesting research directions. For instance, more principled calibration algorithms have yet to be developed, and it is not known under what conditions the transition probabilities are identifiable. In this talk we leverage some classical results about homogeneous symmetric polynomials to make progress in these directions. Specifically, we establish how many time periods of aggregate data are needed to ensure that the transition probabilites are identifiable, and we present an algorithm that is guaranteed to recover the parameters when they are identifiable. To conclude, we outline a number of future research directions.
2:50pm-3:40pm Small Group Chats
We will break attendees up into small groups so that people can get a chance to know each other a little better!
3:45pm-4:10pm Inverse Mixed Integer Optimization: Polyhedral Insights and Trust Region Methods
Ian Zhu, UToronto
Inverse optimization — determining parameters of an optimization problem that render given solutions optimal — has received increasing attention in recent years. Although significant inverse optimization literature exists for convex optimization problems, there have been few advances for discrete problems, despite the ubiquity of applications that fundamentally rely on discrete decision making. In this talk, I will present a new set of theoretical insights and cutting plane algorithms for the general class of inverse mixed integer linear optimization problems. Through an extensive set of computational experiments, I will show that these new algorithms provide substantial improvements over existing methods in solving the largest and most difficult inverse optimization instances to date.
4:10pm-4:20pm Short Break
4:20pm-4:45pm A Theoretical and Computational Analysis of Full Strong-Branching
Yatharth Dubey, Georgia Tech
Full strong-branching is a well-known variable selection rule that is known experimentally to produce significantly smaller branch-and-bound trees in comparison to all other known variable selection rules. In this paper, we attempt an analysis of the performance of the strong-branching rule both from a theoretical and a computational perspective. On the positive side for strong-branching we identify vertex cover as a class of instances where this rule provably works well. In particular, for vertex cover we present an upper bound on the size of the branch-and-bound tree using strong-branching as a function of the additive integrality gap, show how the Nemhauser-Trotter property of persistency which can be used as a pre-solve technique for vertex cover is being recursively and consistently used throughout the strong-branching based branch-and-bound tree, and finally provide an example of a vertex cover instance where not using strong-branching leads to a tree that has at least exponentially more nodes than the branch-and-bound tree based on strong-branching. On the negative side for strong-branching, we identify another class of instances where strong-branching based branch-and-bound tree has exponentially larger tree in comparison to another branch-and-bound tree for solving these instances. On the computational side, we conduct experiments on various types of instances to understand how much larger is the size of the strong-branching based branch-and-bound tree in comparison to the optimal branch-and-bound tree. The main take-away from these experiments is that for all these instances, the size of the strong-branching based branch-and-bound tree is within a factor of two of the size of the optimal branch-and-bound tree.
5:00pm-6:30pm Poster Session
Students will setup posters in an open space. Winners will be determined by both formal judges and attendees popular votes!
6:30pm-9:00pm Dinner
All are welcome to a free dinner catered by a local Pittsburgh restaurant. We may go out for drinks after also.

Saturday, August 27th

9:00am Registration Opens
Come pick up your name tag and t-shirt before the second day of the conference begins!
9:00am-9:35am Breakfast
All are welcome to a free breakfast and coffee to start the day
9:40am-11:00am Industry Problems Session
This session will be hosted by 2-3 sponsor companies. Each company will have roughly 20 minutes to present a key business problem that is solved using Optimization / Data Analytics. This session will be interactive and show some current solutions. Companies may also advertise internships and full-time positions for those interested!
11:00am-11:15am Coffee Break
11:15am-12:00pm Plenary Speaker - Incentive-Aware Machine Learning for Decision Making
Chara Podimata, Postdoctoral researcher UC Berkeley & Assistant Professor of Operations Research and Statistics at MIT Sloan (starting July 2023)
As machine learning algorithms are increasingly being deployed for consequential decision-making (e.g., loan approvals, college admissions, probation decisions, etc.) humans are trying to strategically change the data they feed to these algorithms in an effort to obtain better decisions for themselves. If the deployed algorithms do not take these incentives into account they risk creating policy decisions that are incompatible with the original policy’s goal.
In this talk, I will give an overview of my work on Incentive-Aware Machine Learning for Decision Making, which studies the effects of strategic behavior both on institutions and society as a whole and proposes ways to robustify machine learning algorithms to strategic individuals. I will look at the problem from a societal lens and discuss the tension that arises between having decision-making algorithms that are fully transparent and incentive-aware.
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-3:00pm 5-Minute Flash Talk Competition
Selected students will present 5-Minute Flash Talks in a competition judged by the attendees with cash prizes!
3:00pm-3:20pm Coffee Break
3:20pm-3:45pm Competitive analysis and low regret in online control
Karan Singh, Microsoft Research and CMU
Regret and competitive ratio are two widely used performance measures for online algorithms, and have inspired practical algorithms with qualitatively different characters -- the former espouses near-competitiveness against a restricted policy class; the latter aspires to a more forgiving multiplicatively suboptimal guarantee against the stronger baseline of an (unrestricted) offline-optimal policy. In typical settings, good performance in one of these dimensions happens at the exclusion of the other. For the problem of online (nonstochastic) control, we show how these two objectives may be achieved simultaneously. On the path to establishing this, we, perhaps surprisingly, demonstrate any (appropriately qualified) low-regret learner attains a near-optimal competitive ratio. The talk discusses further implications of this result.
3:45pm-3:55pm Short Break
3:55pm-4:40pm Plenary Speaker - Reducing Marketplace Interference Bias Via Shadow Prices
Arthur Delarue, Assistant Professor at Georgia Tech in Industrial & Systems Engineering
Marketplace companies rely heavily on experimentation when making changes to the design or operation of their platforms. The workhorse of experimentation is the randomized controlled trial (RCT), or A/B test, in which users are randomly assigned to treatment or control groups. However, marketplace interference causes the Stable Unit Treatment Value Assumption (SUTVA) to be violated, leading to bias in the standard RCT metric. In this work, we propose a technique for platforms to run standard RCTs and still obtain meaningful estimates despite the presence of marketplace interference. We specifically consider a matching setting, in which the platform explicitly matches supply with demand via a matching algorithm. Our proposed technique is quite simple: instead of comparing the total value accrued by the treatment and control groups, we instead compare each group’s average shadow price in the matching linear program. We prove that, in the fluid limit, our proposed technique corresponds to the correct first-order approximation (in a Taylor series sense) of the value function of interest. We then use this result to prove that, under reasonable assumptions, our estimator is less biased than the RCT estimator. At the heart of our result is the idea that it is relatively easy to model interference in matching-driven marketplaces since, in such markets, the platform intermediates the spillover.
4:40pm-5:15pm Closing Remarks and Award Ceremony
Fatma Kilinc-Karzan, Associate Professor of Operations Research and CMU INFORMS Faculty Advisor
5:15pm-7:30pm Happy Hour
Free refreshments to celebrate a wonderful conference. The party may continue past 7:30pm outside of the building.

5-Minute Flash Talk Competition

Optimal Feature-Based Market Segmentation and Pricing
Titing Cui
Learning while Repositioning in On-demand Vehicle Sharing Networks
Hansheng Jiang
ALSO-X and ALSO-X+: Better Convex Approximations for Chance Constrained Programs
Nan Jiang
The Blockchain Newsvendor: Value of Freshness Transparency and Smart Contracts
Chenghuai Li
Application of Quantum Computing in Discrete Portfolio Optimization
Justus Shunza
Emergency Care Access vs. Quality: Uncovering Hidden Consequences of Fast-Track Routing Decisions
Shuai Hao
Should hosts list their assets early? An equilibrium analysis of advance asset-sharing platforms
Neha Sharma

Saturday, August 27th 1:30pm-3:00pm

Register here for the 5-minute flash talk competition!

DEADLINE extended to July 22.


Prizes, Rules, Judges, and Logistics

Prizes: Top 3 places will receive $100 each. These are determined by the judges. One fan favorite will win $100, voted on by the students.

Rules: Accepted students will be given a list of words that they cannot use during their presentation. How well can you explain your research without using the keywords?

Judges: Michael Hamilton, Peter Zhang, Chara Podimata. The fan favorite will be voted on by students.

Logistics: Provide powerpoint slides beforehand. Minimum one week before the conference.
For accepted flash talks, CMU INFORMS chapter will provide accommodation if needed where the hotel room will be shared with a person of the same gender!

Poster Competition

Tight Robustness-Consistency Tradeoffs for Two-Stage Bipartite Matching with Advice
Billy Jin
Analyzing the Accessibility and Equity of the Smart Mobile Lockers in tandem with City Buses in the Last Mile Delivery
Si Liu
Investigating Customer Retention in Subscriptions via Multi-state Survival Model
Jiannan Xu
Learning to Cut in Integer Programming
Siddharth Prasad
High Probability Complexity Bounds for Adaptive Step Search Based on Stochastic Oracles
Miaolan Xie
Asymptotic Optimality of Semi-Open-Loop Policies in Markov Decision Processes with Large Lead Times
Xingyu Bai
DFO: A Framework for Data-driven Decision-making with Endogenous Outliers
Nan Jiang
Min-max-min Optimization with Smooth and Strongly Convex Objectives
Luca Wrabetz
Analyzing Capital Riot with Artificial Intelligence
Zhi-Qi Cheng
Structuring online Question and Answer communities
Neha Sharma

Friday, August 26th 5:00pm-6:30pm

Register here for the poster competition!

DEADLINE extended to July 22.


Prizes, Rules, Judges, and Logistics

Prizes:

Rules: Accepted students will present a standard sized poster. They will have ~5 minutes to present their poster to the judges.

Judges: Alan Scheller-Wolf, Fatma Kilinc-Karzan, Michael Hamilton. Also a fan favorite will be voted on by students.

Logistics: We will reimburse the cost of printing your poster up to $60. You will have to get it printed nearby - we can provide recommendations of where to do this if needed (see organizer emails on the Home page).
For accepted posters, CMU INFORMS chapter will provide accommodation if needed where the hotel room will be shared with a person of the same gender!

© CMU INFORMS