Jiayue Wan

Jiayue Wan

PhD candidate in Operations Research

Cornell University

I’m a fifth year PhD student in Operations Research at Cornell University, where I am fortunate to be advised by Professor Peter Frazier. Prior to Cornell, I completed my undergraduate in Mathematics and Physics at Haverford College and earned my Master’s degree in Management Science & Engineering at Stanford University.

My research interests lie at the intersection of machine learning and operations research. My current research focuses on designing novel Bayesian optimization algorithms for drug discovery and materials design. More generally, I am interested in researching and utilizing data science, operations research and machine learning to engender substantial real-world impact.

In Summer 2022, I interned at Meta Core Data Science (CDS) in the Adaptive Experimentation team, mentored by Daniel Jiang. In Fall 2022, I will continue my work at Meta as a student researcher.

In April 2020 - May 2022, I worked with a fabulous COVID mathematical modeling team to model the spread of COVID-19 since pandemic began. Leveraging techniques from stochastic modeling, simulation and optimization, our model directly guided Cornell’s president and provost on reopening and intervention decisions, and has influenced policies at many other US universities. Our work has appeared in news media such as ABC news, the Wall Street Journal, the Asahi Shimbun and more.

In my spare time I like hiking, cooking, and trying something new.

Contact me at jw2529@cornell.edu.

Download my resume (last updated November 2022).

Interests
  • Bayesian Optimization
  • Machine Learning
  • Stochastic Modeling
  • Experimental Design
  • Simulation Optimization
Education
  • Ph.D. in Operations Research, Expected 2024

    Cornell University

  • M.S. in Management Science & Engineering, 2018

    Stanford University

  • B.S.in Mathematics and Physics, 2016

    Haverford College

Experience

 
 
 
 
 
Part-time Student Researcher
Aug 2022 – Present Remote
Core Data Science (Adaptive Experimentation Team)
 
 
 
 
 
Research Engineering Intern
May 2022 – Aug 2022 Menlo Park, CA
Core Data Science (Adaptive Experimentation Team)
 
 
 
 
 
Data Scientist, COVID-19 Pandemic Response
Apr 2020 – May 2022 Ithaca, NY
  • Developed a Python compartmental simulation model to predict 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 20-21 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 here.

 
 
 
 
 
Teaching Assistant
Cornell University
Aug 2018 – Dec 2019 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)
 
 
 
 
 
Algorithm Engineer Intern
Cardinal Operations
Jun 2017 – Sep 2017 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

(2022). Booster vaccination protection against SARS-CoV-2 infections in young adults during an Omicron-predominant period: a retrospective cohort study. To appear in PLOS Medicine.

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(2022). Routine Surveillance and Vaccination on a University Campus During the Spread of the SARS-CoV-2 Omicron Variant. In JAMA Network Open 5(5):e2212906.

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(2022). Correlation Improves Group Testing. Major revision at Management Science.

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(2022). Modeling for COVID-19 College Reopening Decisions: Cornell, A Case Study. In Proceedings of the National Academy of Sciences 119(2).

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(2020). Group Testing Enables Asymptomatic Screening for COVID-19 Mitigation: Feasibility and Optimal Pool Size Selection with Dilution Effects.

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Talks

Bayesian Optimization in Materials Design
Fighting COVID-19 at Cornell
COVID Modeling at Cornell