I am a third year Ph.D. student in the Operations Research & Information Engineering Department at Cornell University. I am co-advised by Prof. Jim Dai and Prof. Manxi Wu. My research focuses on the optimal control of stochastic systems with multiple agents with tools from (deep) reinforcement learning, mathematical programming, and game theory. Before I came to Cornell, I completed my master's degree in Statistics at Columbia University. I obtained my undergraduate degree from the University of Washington with a double major in Computer Science and Statistics.
Zhanhao Zhang
Ph.D. student, Cornell University
Preprints & Working Papers:
Mixed-Type Courier Dispatch For Online Food Delivery Platforms (Submitted to Transportation Science)
Junlin Chen, Manxi Wu, Chiwei Yan, Zhanhao Zhang.
Designing high-occupancy toll lanes: A game-theoretic analysis (Submitted to Transportation Science)
Zhanhao Zhang, Ruifan Yang, Manxi Wu.
Atomic proximal policy optimization for electric robo-taxi dispatch and charger allocation (Preparing for Submission to Transportation Science)
Jim Dai, Manxi Wu, Zhanhao Zhang.
Optimal Batched Scheduling of Stochastic Processing Networks Using Atomic Action Decomposition (Working Paper)
Jim Dai, Manxi Wu, Zhanhao Zhang.
Generative Market Equilibrium Models with Stable Adversarial Learning via Reinforcement Link (Preparing for Submission to Quantitative Finance)
Anastasis Kratsios, Xiaofei Shi, Qiang Sun, Zhanhao Zhang.
Selected Publications:
Rest-Activity Rhythms are Associated with Prevalent Cardiovascular Disease, Hypertension, Obesity, and Central Adiposity in a Nationally Representative Sample of US Adults Nov 2023
Nour Makarem, Charles A. German, Zhanhao Zhang, Keith Diaz, Priya Palta, Dustin Duncan, Cecilia Castro-Diehl, Ari Shechter.
Journal of the American Heart Association 13 (2024). https://doi.org/10.1161/JAHA.122.032073
Capacity allocation and pricing of high occupancy toll lane systems with heterogeneous travelers Jul 2023
Haripriya Pulyassary, Ruifan Yang, Zhanhao Zhang, Manxi Wu.
Accepted in the 62nd IEEE Conference on Decision and Control, 2023.
Deep Learning Algorithms for Hedging With Frictions Mar 2023
Xiaofei Shi, Daran Xu, Zhanhao Zhang.
Digital Finance 5, 113–147 (2023). https://doi.org/10.1007/s42521-023-00075-z
Instructor:
ORIE 5270/6125: Big Data Technologies Spring 2024, Spring 2025
Cornell University, Operations Research and Information Engineering
Teaching Assistant:
ORIE 4580/5580/5581: Simulation Modeling and Analysis Fall 2022
Cornell University, Operations Research and Information Engineering
GR 5241: Statistical Machine Learning Winter 2022
Columbia University, Department of Statistics
GU 3105: Applied Statistical Methods Fall 2021
Columbia University, Department of Statistics
CSE 446/546: Machine Learning Spring 2020
University of Washington, Paul G. Allen School of Computer Science
CSE 312: Foundations of Computing II Fall 2019, Winter 2020
University of Washington, Paul G. Allen School of Computer Science
CSE 344: Introduction of Database Management Summer 2019
University of Washington, Paul G. Allen School of Computer Science
Grader:
GU 4001: Probability & Statistical Inference Winter 2021
Columbia University, Department of Statistics
STAT 302: Statistical Software & Applications Fall 2019, Winter 2020, Spring 2020
University of Washington, Department of Statistics
Aetna at CVS Health:
Data Scientist Jan 2022 - Jul 2022
- Design and implement the Monte Carlo simulation pipeline to obtain range estimates of member disenrollment upon product updates. Enhance efficiency through parallelization and vectorization. Complete 100K+ simulations on datasets with millions of rows within an hour on a distributed system.
- Investigate optimal campaign test design and calculate the efficient allocation of sample sizes (with given power and significance level) using quadratic programming.
- Implement a nested parallelized multivariate risky cohort identifier completely from scratch using the Dask backend, catering to imbalanced datasets.
- Investigate drivers of member lapse using ad-hoc analysis and develop a member-churn data warehouse on Hive.
Data Scientist (Part-Time) Oct 2021 - Dec 2021
- Build supervised machine learning models to forecast sales call receptivities of Medicare enrollees.
- Automate and engineerize the data cleaning, modeling, hyper-parameter tuning, visualizations, and the report summary pipeline.
- Develop the ETL pipeline on Hive database to get prepared for modeling.
Data Scientist (Internship) Jun 2021 - Aug 2021
- Automate data collection pipeline from web portals using Javascript, saving 100+ analyst hours. Expedite the whole process by multi-threading using asynchronized code.
- Conduct n-gram analysis (TF-IDF), topic modeling (LDA), and sentiment analysis on thousands of sales call transcripts.
- Predict clients' dis-enrollment status by leveraging supervised machine learning models (logistic regression, k nearest neighbors, random forest, gradient boosting, support vector machine, adaboost, neural networks, etc.).
- Experiment audio data preprocessing scopes (ambient noise, volume, etc.) for call recording transcription.
- Generate business insights and actionable recommendations from modeling and analysis results.
Institute for Health Metrics and Evaluation:
Research Assistant Feb 2019 - Sep 2019
- Researched and executed Iterative Filtering, Least Square Method, and Particle MCMC in R to position parameters in differential equation systems. Conducted tests with 10+ algorithms to forecast traveling flows across territories within Equatorial Guinea. Accelerated data cleaning and model training process by vectorizations using linear algebra.
- Saved 80% storage space for repeated simulations by wrangling data output format. Implemented ETL to prepare data across different sources for model training. Employed statistical tests to expedite posterior inference to assess the model performance. Utilized ggplot to create plots to foresee model functionality. Used Raster and MapTools to plot commuting flows on a map.
Operations Research Graduate Students’ Association (ORGA), Cornell University
Co-President Aug 2023 - May 2024
- Facilitated the organization of department activities with ORGA officers.
- Reported concerns of PhD students in the department to faculties and staffs and discussed potential solutions.
Department of Statistics, Columbia University
Student Representative Sep 2020 - Dec 2021
- Gathered opinions from students in the Statistics Master's program and pinpointed their majority viewpoints to the department.
- Organized and facilitated events to connect students with their peers and career professionals.
Organization of Hua Classmates (OHCM), University of Washington
President Sep 2018 - Jun 2019
- Lead bi-weekly meetings with department leaders to pinpoint the priorities and brainstormed solutions to roadblocks.
- Held monthly meetings with OHCM officers to communicate short & long term goals.
Public Relation Department Leader Sep 2017 - Jun 2018
- Connected with potential sponsors and other student organizations for cooperation opportunities.
- Raised more than 10K dollars for club events and expenditures.
- Succeeded in convincing multiple sponsors to double their budgets through strategic negotiations.
- Trained new club members on effective negotiation skills for fund raising and connections establishments with other organizations.
IT Department Leader Sep 2016 - Jun 2019
- Initialized a technological revolution to automate repetitive tasks that were originally done manually.
- Led the development of OHCM websites and WeChat Mini-Programs to publicize club resources for international students.
- Guided IT officers without coding background to efficiently acquire the syntax of new programming languages.
Housing & Food Services (HFS), University of Washington
Representative of Budget Advisory Committee Sep 2017 - Jun 2018
- Collected the latest budgeting plans from the Housing and Food Service and updated the information to the residents on campus. Utilized common languages to explain policy terms and financial concepts and resolved any questions or confusions.
- Gathered opinions from a large number of residents and pinpointed the majority ones to the budgeting team at the Housing and Food Service. Shared personal perspectives when forming budgeting plans.
- Reported the 5% increase (2% in previous years) in the student housing prices to residents. Explained the exact utilizations of the funding and convinced most student residents that the exceptionally high increase in the housing prices was justified.