I am 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. I visited the Simons Institute for the Theory of Computing as a visiting graduate student in Fall 2021. I received my MS in statistical and economic modeling under Prof. Jerry Reiter from Duke University in 2017, my BS in Economics from Renmin University of China in 2015.
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.
Updates:
I will join the Wharton School of Business as an assistant professor of Operations, Information and Decisions, starting in summer of 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 presented Detection and recovery thresholds for graph matching based on our recent papers at INFORMS 2021.
I visited Simons Institute in Fall 2021 to participate in the program Computational Complexity of Statistical Inference.
I presented the paper "Settling the sharp reconstruction threshold of random graph matching" at IEEE ISIT conference, July 2021.
In the summer of 2021, I led a Data+ project, see student project presentation here: Detecting and Matching Similar Networks.
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
Under major revision at Management Science, 2023
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
Under minor revision at Operations Research, 2024
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)
Differentially private verification of regression predictions from synthetic data
Haoyang Yu and Jerome P. Reiter
Accepted to Transactions on Data Privacy 11 (2018) 279–297
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
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