About Myself

I am an assistant professor of Operations, Information and Decisions at the Wharton School of Business. I was a Postdoctoral Scholar under Prof. Itai Ashlagi and Prof. Amin Saberi, at Management Science and Engineering, Stanford University. I received my PhD in Decision Sciences from the Fuqua School of Business at Duke University, under Prof. Jiaming Xu and Prof. Yehua Wei.

My research interests focus on data analysis, algorithm design, and performance evaluation in large-scale networks and stochastic systems. My works draw inspiration from real-world business, engineering, and natural sciences problems that can be modeled into large and complex networks. I have explored a range of topics from the fundamental limits and efficient algorithms on graph matching to online platform policy design with bounded regret and data confidentiality protection.

News

• Our paper From signaling to interviews in random matching markets is accepted at STOC, 2025.

• Our paper Random graph matching at Otter's threshold via counting chandeliers is accepted at Operations Research, 2025.

• Our paper Stochastic online metric matching: adversarial is no harder than stochastic is accepted at WINE, 2024.

• I received Thomas M. Cover Dessertation Award from IEEE Information Theory Society, 2024.

• I will give a tutorial on Matching in Networks: Fundamental Limits and Efficient Algorithms with Prof. Jiaming Xu at ACM SIGMETRICS, 2024.

• My dissertation Matching in Networks: Fundamental Limits and Efficient Algorithms received Best Dissertation Award from Fuqua.

• Our paper Constant regret primal-dual policy for multi-way dynamic matching is accepted to SIGMETRICS 2023.

• Our paper Random graph matching at Otter's threshold via counting chandeliers is accepted to STOC 2023.

• Our paper Testing network correlation efficiently via counting trees is accepted to Annals of Statistics.

• Our paper Testing network correlation efficiently via counting trees is selected as a finalist for "George Nicholson Student Paper Competition", INFORMS 2022.

• Our paper Settling the sharp reconstruction thresholds of random graph matching is accepted to IEEE Transactions on Information Theory.

• I am teaching EGRMGMT-580 Decision Modeling (Graduate course) at Pratt school of engineering, Spring 2022.

• Our paper Testing correlation of unlabeled random graphs is accepted to Annals of Applied Probability.

• I visited Simons Institute in Fall 2021 to participate in the program Computational Complexity of Statistical Inference.

• In the summer of 2021, I led a Data+ project, see student project presentation here: Detecting and Matching Similar Networks.

Preprint and Publications

• From signaling to interviews in random matching markets

  Maxwell Allman, Itai Ashlagi, Amin Saberi, and Sophie H. Yu

  The conference version is accepted at ACM Symposium on Theory of Computing (STOC), 2025

• Dynamic resource allocation without re-solving: the effectiveness of primal-dual policies

  Siqi He, Yehua Wei, Jiaming Xu and Sophie H. Yu

• Stochastic online metric matching: adversarial is no harder than stochastic

  Amin Saberi, Mingwei Yang and Sophie H. Yu

  The conference version is accepted at The 20th Conference on Web and Internet Economics (WINE), 2024

• Constant regret primal-dual policy for multi-way dynamic matching

  Yehua Wei, Jiaming Xu and Sophie H. Yu

  The conference version is accepted in ACM SIGMETRICS, 2023

  Major revision at Management Science, resubmitted, 2024

• Random graph matching at Otter's threshold via counting chandeliers

  Cheng Mao, Yihong Wu, Jiaming Xu, and Sophie H. Yu

  The conference version is accepted in ACM Symposium on Theory of Computing (STOC), 2023

  Accepted to Operations Research, 2025

• Testing network correlation efficiently via counting trees

  Cheng Mao, Yihong Wu,  Jiaming Xu, and Sophie H. Yu

  Accepted to Annals of Statistics, 2023

  George Nicholson Student Paper Competition finalist, INFORMS 2022

  Algorithm available here

• Testing correlation of unlabeled random graphs

  Yihong Wu, Jiaming Xu, and Sophie H. Yu

  Accepted to Annals of Applied Probability, 2022

• Settling the sharp reconstruction thresholds of random graph matching

  Yihong Wu, Jiaming Xu, and Sophie H. Yu

  Accepted to IEEE Transactions on Information Theory, 2022

  A short version appears at 2021 IEEE International Symposium on Information Theory (ISIT), 2021

• Differentially private verification of regression predictions from synthetic data

  Haoyang Yu and Jerome P. Reiter

  Accepted to Transactions on Data Privacy, 2018

Selected Talks

• University of UT Austin, IEOR Seminar, Jan, 2025

• University of Toronto, Rotman Young Scholar Seminar, Dec, 2024

• University of Pennsylvania, Operations Seminar, Dec, 2024

• INFORMS Annual Meeting, Oct, 2024

• Stanford University, Rain Seminar, Apr, 2024

• London School of Business, Operations Management Seminar, Feb, 2024

• Shanghai Jiao Tong University, Antai College of Economics, Jan, 2024

• INFORMS Annual Meeting, Oct, 2023

• INFORMS Applied Probability Society Conference, June, 2023

• ACM SIGMETRICS, June, 2023

• The 55th ACM Symposium on Theory of Computing (STOC), June, 2023

• MIT FODSI Computational Complexity of Statistical Problems Workshop, June, 2023.

• University of California, Davis, Probability Seminar, May, 2023.

• Duke University, Department of Computer Science, CS Theory Seminar, Jan, 2023

• INFORMS Annual Meeting, Session TB41, Oct, 2022

• INFORMS Annual Meeting, George Nicholson Student Paper Competition, Oct, 2022

• Cornell University, ORIE Young Researchers Workshop, Oct, 2022

• Duke University, Fuqua School of Business, Interdisciplinary Seminar, Jan, 2022

• University of California, Berkeley, Simons Institute, CCSI Student Seminar, Nov, 2021

• INFORMS Annual Meetings, Session WC08, Oct, 2021

• IEEE International Symposium on Information Theory Conference, Jul, 2021

Teaching

• Instructor, EGRMGMT-580 Decision Modeling (Graduate) at Pratt school of engineering, Duke University, Spring 2022

  (Instructor Rating: 4.82/5.00, Response rate: 18/21, Department mean: 4.38/5.00)

• Teaching Assistant, PhD BA 915/ STA 715/ MATH 742: Stochastic Models, Duke University, Fall 2022

• Teaching Assistant, Weekend MBA Decision 611: Decision Models, Duke University, Fall 2022

• Teaching Assistant, PhD BA 990/ ECE 590: Statistical Inference on Graphs, Duke University, Spring 2022

• Teaching Assistant, PhD BA 910/Statistics 502: Bayesian Inference, Duke University, Spring 2021

• Teaching Assistant, MBA Decision 516: Quantitative Business Analysis, Duke University, Fall 2020

• Teaching Assistant, MBA Decision 521Q: Quantitative Business Analysis, Duke University, Fall 2020

• Teaching Assistant, MQM Decision 518Q: Applied Probability and Statistics, Duke University, Fall 2019

• Teaching Assistant, MQM Decision 521Q: Decision Analytics and Modeling, Duke University, Spring 2019

• Teaching Assistant, Economics 618: Advanced Econometrics, Duke University, Fall 2016