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Basics
| Name | Kenneth Zhang |
| Label | Quantitative Researcher |
| kzhang138@gmail.com | |
| Phone | +1 289 925 8185 |
| Url | https://kennethZhangML.github.io |
| Summary | A Canadian-born Quantitative Researcher, specializing in volatility modelling, statistical arbitrage, and statistical machine learning. |
Work
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2025.01 - 2025.08 New York, NY
Quantitative Researcher, Systematic Volatility
Squarepoint Capital
Options Market-Making, Futures Microstructure, Volatility Forecasting
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2024.01 - 2024.05 Toronto, Ontario
Quantitative Research Intern, Multi-Asset Strategies
Mackenzie Investments
Volatility Risk Premium, Systematic Equity Options, FX Carry
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2023.05 - 2023.08 Toronto, Ontario
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2020.06 - 2022.08 Cambridge, MA
Research Assistant to the Director of Biostatistics
Harvard T.H. Chan School of Public Health
Changepoint, lag-time modeling
Education
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2022.09 - 2026.04 Waterloo, Ontario
Bachelor's of Computer Science
University of Waterloo, David R. Cheriton School of Computer Science
Computer Science, Data Science Specialization
Awards
- 2021.08.31
Star-Friedman Scholars - Harvard University
Harvard T.H. Chan School of Public Health
Awarded the Star-Friedman Challenge for Promising Scientific Research alongside Professor Tanujit Dey and Professor Francesca Dominici for promising research in statistical time series modelling and public health research.
Publications
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2022.06.01 A Review on the Biological, Epidemiological, and Statistical Relevance of COVID-19 paired with Air Pollution
Environmental Advances
This narrative review critically evaluates recent studies on the associations between various air pollutants—CO, NO2, O3, PM2.5, PM10, and SO2—and COVID-19 outcomes, examining their individual and combined effects across different regions and exposure periods, while also exploring the biological mechanisms underlying these associations.
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2021.08.13 Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United States
Patterns: Cell Press, Harvard University
The research focused on using a data-driven search algorithm to detect change points in COVID-19 case and death trajectories, correlating these changes with state-level policy implementations.
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2021.04.21 Association of temporary Environmental Protection Agency regulation suspension with industrial economic viability and local air quality in California, United States
Environmental Sciences Europe
The study used machine learning models to predict weekly employment data and t-tests to assess the economic impact of the EPA's 2020 enforcement regulation rollbacks on oil and manufacturing industries, as well as on air quality in California, ultimately finding no economic growth and continued pollution despite the suspensions.
Languages
| English | |
| Native Speaker |
| Mandarin | |
| Fluent |