Jiayue Wan
Mathematical Modeling Researcher
Quantitative Researcher at Susquehanna International Group
Ph.D., Operations Research, Cornell University
I am a quantitative researcher at Susquehanna International Group. I received my Ph.D. in Operations Research from Cornell University, where I was advised by Professor Peter Frazier. Before Cornell, I studied Mathematics and Physics at Haverford College and earned a master’s degree in Management Science & Engineering at Stanford University.
My research interests lie at the intersection of operations research and statistical learning. My doctoral work focused on designing novel grey-box Bayesian optimization algorithms. More broadly, I am interested in using operations research to create substantial real-world impact.
From April 2020 to May 2022, I worked with a fabulous COVID-19 mathematical modeling team to model the spread of COVID-19 from the start of the pandemic. Leveraging techniques from stochastic modeling, simulation and optimization, our model directly guided Cornell’s president and provost on reopening and intervention decisions, and influenced policies at many other US universities. Our work appeared in news media such as ABC News, the Wall Street Journal, the Asahi Shimbun and more.
In summer 2022, I interned at Meta Core Data Science on the Adaptive Experimentation team, mentored by Daniel Jiang, and continued there as a student researcher that fall.
In my spare time I like hiking, cooking, and trying something new.
Interests
- Bayesian Optimization
- Statistical Learning
- Stochastic Modeling
- Experimental Design
- Simulation Optimization
Education
- Ph.D. in Operations ResearchCornell University, 2024
- M.S. in Management Science & EngineeringStanford University, 2018
- B.S. in Mathematics and PhysicsHaverford College, 2016
Experience
Aug 2024 – Present
Quantitative Researcher
Susquehanna International Group · Bala Cynwyd, PA
Jun 2023 – Aug 2023
Quantitative Research Intern
Susquehanna International Group · Bala Cynwyd, PA
Aug 2022 – Jan 2023
May 2022 – Aug 2022
Apr 2020 – May 2022
Data Scientist, COVID-19 Pandemic Response
Cornell University · Ithaca, NY
- Developed a Python compartmental simulation model to forecast epidemiological outcomes in college environments
- Led housing capacity planning and risk analysis to communicate with stakeholders
- Led retrospective parameter estimation and model calibration analysis for the 2020–21 academic year
- Led analysis of the risk of infection during travel to support travel policy decisions and communication with stakeholders
All modeling reports are published online.
Aug 2018 – Dec 2019
Teaching Assistant
Cornell University · Ithaca, NY
- ENGRD 2700: Basic Engineering Probability and Statistics (Fall 2018)
- ORIE 3800: Information Systems and Analysis (Spring 2019)
- ORIE 4580/5580/5581: Simulation Modeling and Analysis (Fall 2019)
Jun 2017 – Sep 2017
Algorithm Engineer Intern
Cardinal Operations · Shanghai, China
- Led a consulting engagement with Budweiser, designing and implementing operations research software for managing warehouse operations
- Delivered business region partition, facility location and route planning solutions for SF Express, a large courier company
Publications
* Equal contribution
Correlation Improves Group Testing: Modeling Concentration-Dependent Test Errors
Management Science 72(7):5507–5527, 2026
PDF Code DOIAbstract
Population-wide screening is a powerful tool for controlling infectious diseases. Group testing enables such screening despite limited resources. Viral concentration of pooled samples are often positively correlated, either because prevalence and sample collection are influenced by location, or through intentional enhancement via pooling samples according to risk/household. Such correlation is known to improve efficiency under fixed test sensitivity. However, in reality, a test's sensitivity depends on the concentration of the analyte (e.g., viral RNA), as in the so-called dilution effect, where sensitivity decreases for larger pools. We show that concentration-dependent test error alters correlation's effect under the most widely-used group testing procedure, the two-stage Dorfman procedure. We prove that when test sensitivity increases with concentration, pooling correlated samples together (correlated pooling) achieves asymptotically higher sensitivity than independently pooling the samples (naive pooling). In contrast, in the concentration-independent case, correlation does not affect sensitivity. Moreover, with concentration-dependent errors, correlation can degrade test efficiency compared to naive pooling whereas under concentration-independent errors, correlation always improves efficiency. We propose an alternative measure of test resource usage, the number of positives found per test consumed, which we argue is better aligned with infection control, and show that correlated pooling outperforms naive pooling on this measure. In simulation, we show that the effect of correlation under realistic concentration-dependent test error meaningfully differs from correlation's effect assuming fixed sensitivity. Our findings underscore the importance for policy-makers of using models that incorporate naturally-occurring correlation and of considering ways of strengthening this correlation.
Bayesian Optimization of Function Networks with Partial Evaluations
Proceedings of the 41st International Conference on Machine Learning, PMLR, 2024
PDF CodeAbstract
Bayesian optimization is a powerful framework for optimizing functions that are expensive or time-consuming to evaluate. Recent work has considered Bayesian optimization of function networks (BOFN), where the objective function is given by a network of functions, each taking as input the output of previous nodes in the network as well as additional parameters. Leveraging this network structure has been shown to yield significant performance improvements. Existing BOFN algorithms for general-purpose networks evaluate the full network at each iteration. However, many real-world applications allow for evaluating nodes individually. To exploit this, we propose a novel knowledge gradient acquisition function that chooses which node and corresponding inputs to evaluate in a cost-aware manner, thereby reducing query costs by evaluating only on a part of the network at each step. We provide an efficient approach to optimizing our acquisition function and show that it outperforms existing BOFN methods and other benchmarks across several synthetic and real-world problems. Our acquisition function is the first to enable cost-aware optimization of a broad class of function networks.
Booster Vaccination Protection Against SARS-CoV-2 Infections in Young Adults during an Omicron-predominant Period: A Retrospective Cohort Study
PLOS Medicine 20(1):e1004153, 2022
Routine Surveillance and Vaccination on a University Campus During the Spread of the SARS-CoV-2 Omicron Variant
JAMA Network Open 5(5):e2212906, 2022
Modeling for COVID-19 College Reopening Decisions: Cornell, A Case Study
Proceedings of the National Academy of Sciences 119(2), 2022
PDF CodeAbstract
We consider epidemiological modeling for the design of COVID-19 interventions in university populations, which have seen significant outbreaks during the pandemic. A central challenge is sensitivity of predictions to input parameters coupled with uncertainty about these parameters. Nearly two years into the pandemic, parameter uncertainty remains because of changes in vaccination efficacy, viral variants and mask mandates, and because universities' unique characteristics hinder translation from the general population: a high fraction of young people, who have higher rates of asymptomatic infection and social contact, as well as an enhanced ability to implement behavioral and testing interventions. We describe an epidemiological model that formed the basis for Cornell University's decision to reopen for in-person instruction in fall 2020 and supported the design of an asymptomatic screening program instituted concurrently to prevent viral spread. We demonstrate how the structure of these decisions allowed risk to be minimized despite parameter uncertainty leading to an inability to make accurate point estimates and how this generalizes to other university settings. Looking forward, we find that once-per-week asymptomatic screening of vaccinated undergraduate students provides substantial value, even if all students are vaccinated, and that more targeted testing of the most social vaccinated students provides further value.
Group Testing Enables Asymptomatic Screening for COVID-19 Mitigation: Feasibility and Optimal Pool Size Selection with Dilution Effects
arXiv:2008.06642, 2020
PDF CodeAbstract
Repeated asymptomatic screening for SARS-CoV-2 promises to control spread of the virus but would require too many resources to implement at scale. Group testing is promising for screening more people with fewer test resources: multiple samples tested together in one pool can be excluded with one negative test result. Existing approaches to group testing design for SARS-CoV-2 asymptomatic screening, however, do not consider dilution effects: that false negatives become more common with larger pools. As a consequence, they may recommend pool sizes that are too large or misestimate the benefits of screening. Modeling dilution effects, we derive closed-form expressions for the expected number of tests and false negative/positives per person screened under two popular group testing methods: the linear and square array methods. We find that test error correlation induced by a common viral load across an individual's samples results in many fewer false negatives than would be expected from less realistic but more widely assumed independent errors. This insight also suggests that false positives can be controlled through repeated tests without significantly increasing false negatives. Using these closed-form expressions to trace a Pareto frontier over error rates and tests, we design testing protocols for repeated asymptomatic screening of a large population. We minimize disease prevalence by optimizing a time-varying pool sizes and screening frequency constrained by daily test capacity and a false positive limit. This provides a testing protocol practitioners can use for mitigating COVID-19. In a case study, we demonstrate the effectiveness of this methodology in controlling spread.