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MathBio Seminar

September 30, 2020 - 9:00am

Title: Mathematicians for public good: Solving problems in government and public policy

Speakers: Ravi Goyal and John Hotchkiss

Ravi Goyal and John Hotchkiss are employees at Mathematica, an employee-owned consulting firm with a mission to improve public well-being by providing change-makers a rigorous, quantitative foundation on which to build strategies and make decisions. Ravi and John will share their combined 25+ years of experience in government and public policy through examples of recent work addressing return to learn strategies for universities and k-12 schools in the context of COVID. They will discuss how agent-based models can be used to evaluate how different interventions (such as structural changes, quarantining and testing) can mitigate the spread of COVID-19—and associated disruptions to school schedules. They will also discuss which skills are helpful to succeed at a public policy company.


Ravi Goyal (Ph.D., Biostatistics, Harvard University), a senior statistician at Mathematica, has more than 15 years of experience in applying data science techniques to deliver actionable insights to address public and social needs. Dr. Goyal, with Mr. Hotchkiss, has developed an agent-based, stochastic dynamic network-based COVID-19 model for modeling disease spread. The model is being used to assess alternative approaches to re-opening K-12 schools in Pennsylvania as well as by universities to investigate policies to mitigate the spread of COVID-19. Previously, he served as the technical lead in the design and development of an agent-based mathematical model to measure the cost-effectiveness of the Ryan White HIV/AIDS Program’s system of care. At Harvard School of Public Health, he played an integral part in the design of the HIV agent-based model used in the design of the Botswana Combination Prevention Program. Dr. Goyal’s dissertation research at Harvard University focused on integrating individual- and population-level data to accurately model HIV epidemic data. During his tenure as an applied mathematician at the National Security Agency, he gained field experience (deployed to Iraq) and experience with real-world complex data sets that included geospatial, longitudinal, and social network data.


John Hotchkiss (M.S., Data Analytics, Georgetown University), a data scientist at Mathematica, has been working in government or government consulting for 10 years. Mr. Hotchkiss is a programming and modeling expert specializing in high volume, complex data processing, simulation and machine learning methods, and translating results into actionable insights through visualization. Over the past three years, Mr. Hotchkiss, with Dr. Goyal, has developed computationally efficient and flexible agent-based, stochastic dynamic network-based infectious disease models. Prior to joining Mathematica, Mr. Hotchkiss gained experience as a data engineer for the Board of Governors of the Federal Reserve System managing the ingestion, cleaning, updating, and validating of bank financial filings.