Ajinkya Kokandakar


June 8, 2024

Ajinkya Kokandakar

Ph.D. Candidate in Statistics | Statistical Consultant
The University of Wisconsin–Madison

Current: I am working as a Data Science Intern at Mathematica Inc (Summer 2024).

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.

Background: 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 in India.

Preprints and Working Projects

Adolescent sports participation and health in early adulthood: An observational study (2024+)
Ajinkya H. Kokandakar, Yuzhou Lin, Steven Jin, Jordan Weiss, Amanda R. Rabinowitz, Reuben A. Buford May, Dylan Small, Sameer K. Deshpande
[preprint] [code]

A Comparison of Regression Methods for Inferring Near-Surface NO2 with Satellite Data (Jan 2024)
Eliot J. Kim, Tracey Holloway, Ajinkya H. Kokandakar, Monica Harkey, Stephanie Elkins, Daniel L. Goldberg, Colleen Heck. In review


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.

Work Experience

Mathematica Inc.
Data Science Intern (June 2024 - Present)

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

Duke University
Research Assistant

Birla Institute of Technology and Science, Pilani

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