Ajinkya Kokandakar
Ph.D. Candidate in Statistics | Statistical Consultant
The University of Wisconsin–Madison
ajinkya@stat.wisc.edu
Research: I am a Ph.D. candidate in Statistics at the UW-Madison advised by Dr. Sameer Deshpande, pursuing research in causal inference and Bayesian methods. Currently, my work focuses on heterogeneous treatment effect estimation, and causal inference when the exposure is not very well defined.
Consulting: I work as a consultant in the Statistical Consulting Group at UW-Madison (formerly the CALS Consulting Lab), assisting clients from life sciences with experimental design and data analysis.
Open Source Code: I have experience writing software packages and modules in R/C++. I also occasionally contribute to the Julia programming language libraries and tooling.
Background: I worked as a Data Scientist intern at Mathematica Inc from June - Dec 2024. Prior to joining UW—Madison, I graduated with a MS in Economics and Computation at Duke University (2020). I got my undergraduate dual-degree in Computer Science and Economics from Birla Institute of Technology and Science, Pilani and worked as a Software Engineer at Infosys Ltd. in India
Publications
Ajinkya H. Kokandakar, Yuzhou Lin, Steven Jin, Jordan Weiss, Amanda R. Rabinowitz, Reuben A. Buford May, Dylan Small, Sameer K. Deshpande
Adolescent sports participation and health in early adulthood: An observational study (2025)
Youth & Society
[paper] [preprint] [code]
Eliot J. Kim, Tracey Holloway, Ajinkya H. Kokandakar, Monica Harkey, Stephanie Elkins, Daniel L. Goldberg, Colleen Heck, (2024)
A Comparison of Regression Methods for Inferring Near-Surface NO2 with Satellite Data
Journal of Geophysical Research: Atmospheres, 129, e2024JD040906
[paper] [preprint]
Ajinkya H. Kokandakar, Yuzhou Lin, Steven Jin, Jordan Weiss, Amanda R. Rabinowitz, Reuben A. Buford May, Sameer K. Deshpande, Dylan Small, (2024).
“Pre-analysis protocol for an observational study on the effects of adolescent sports participation on health in early adulthood.”
Observational Studies 10(1), 11-35
[paper] [preprint] [code]
Ajinkya H. Kokandakar, Hyunseung Kang, Sameer K. Deshpande, (2023).
“Bayesian causal forests and the 2022 ACIC Data Challenge: scalability and sensitivity.” Observational Studies, 9(3), 29-41.
[paper] [preprint] [code]
Jagat Sesh Challa, Poonam Goyal, Ajinkya Kokandakar, Dhananjay Mantri, Pranet Verma, Sundar Balasubramaniam & Navneet Goyal (2022).
“Anytime clustering of data streams while handling noise and concept drift.”
Journal of Experimental & Theoretical Artificial Intelligence, 34(3), 399-429.
[paper]
Work Experience
Mathematica Inc.
Data Science Intern (June - Aug 2024 full-time, Aug - Dec 2024 part-time)
- Developing and using Bayesian non-parametric methods for subgroup analysis in a large scale healthcare impact evaluation
University of Wisconsin – Madison
Project Assistant, Statistical Consulting Group (Aug 2023 - May 2024)
- Assisted more than 15 clients (graduate students and postdocs) from the College of Agriculture and Life Sciences with experimental design and analysis data obtained from experiments
Infosys Ltd
Specialist Programmer (July 2017 - May 2018)
- Designed and developed the telemetry and data analytics module for the company’s internal learning platform
Research Experience
University of Wisconsin – Madison
Research Assistant
- Department of Statistics,
Advisors: Prof Sameer Deshpande and Prof Keith Levin (May 2022 – Aug 2022) - Department of Biostatistics and Medical Informatics,
Advisors: Prof Menggang Yu and Prof Guanhua Chen (June 2020 – Dec 2021)
Duke University
Research Assistant
- The Fuqua School of Business
Advisor: Prof Giuseppe Lopomo (June 2019 - May 2020) - Department of Economics
Advisor: Prof Matt Masten (June 2019 - May 2020) - Department of Economics
Advisor: Prof Arjada Bardhi (Jan 2019 - May 2019)
Birla Institute of Technology and Science, Pilani
- Undergraduate Scholar,
Advisor: Prof S. Balasubramaniam, ADAPT Lab, BITS Pilani (Jan 2017 - Dec 2017)
Teaching Experience
University of Wisconsin – Madison
Teaching Assistant, Department of Statistics
- STAT 451: Introduction to Machine Learning and Statistical Pattern Classification (Fall 2023)
- STAT 240: Data Science Modeling I (Fall 2022)
- STAT 371: Introductory Applied Statistics for the Life Sciences (Spring 2022)
Duke University
Teaching Assistant, Department of Computer Science
- COMPSCI 370: Introduction to Artificial Intelligence (Spring 2020)
- COMPSCI 201: Algorithms and Data Structures (Spring 2019)
Birla Institute of Technology and Science, Pilani
Undergraduate Teaching Assistant
- ECON F211: Principles of Economics
- ECON F212: Fundamentals of Finance and Accounting
- ECON F412: Securities Analysis and Portfolio Management
- CS F211: Data Structures and Algorithms