Purva Pruthi | UMass Amherst

About Me

I am a final year Ph.D. candidate at the Computer Science department at University of Massachusetts, Amherst, advised by David Jensen. My research combines causality, compositional reasoning and modular deep learning to develop efficient and scalable causal models for complex real-world systems such as databases, software programs, and manufacuring assembly system. I am also interested in out-of-distribution generalization, and understanding theoretical characteristics of modular models as compared to non-modular models in machine learning.

Previously, I was an AI resident at Google X, The Moonshot Factory, working on causal inference applications in agriculture and biology. I also interned at Amazon Cambridge (Summer 2018), working with Javier Gonzalez and Xiaoyu Lu on developing a transfer-learning framework for reinforcement learning. I have also worked with Madalina Fiterau at the intersection of causal reasoning and healthcare. Before my Ph.D., I spent three years at Goldman Sachs building statistical models for systematic trading and trade settlement analysis. Outside work, I enjoy writing prose poetry, reading books, playing piano, and doing yoga!

Publications

CLeaR

Compositional Models for Estimating Causal Effects

Purva Pruthi and David Jensen

Conference on Causal Learning and Reasoning (CLeaR), 2025.

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ICML

How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference

Amanda Gentzel, Purva Pruthi, and David Jensen

International Conference on Machine Learning (ICML), 2021.

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ICML Workshop

Structure Mapping for Transferability of Causal Models

Purva Pruthi, Javier González, Xiaoyu Lu, and Madalina Fiterau

ICML 2020 Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning

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News

[Jun. 2025] I’m starting a new position as Research Intern at Toyota Research Institute in Energy & Materials Division.
[May 2025] I presented our work titled “Compositional Models for Estimating Causal Effects” in the Causal Learning and Reasoning Conference held at Lausanne, Switzerland. This is joint work with my advisor, David Jensen.
[Jan. 2025] Our paper titled “Compositional Models for Estimating Causal Effects” has been accepted to the Conference on Causal Learning and Reasoning (CLeaR 2025)!
[Jun. 2024] Started as part-time AI consultant at Offline Studio. I am helping early-stage startups building AI products using large-language models (LLMs) and retreiver-augmented generation (RAG) systems.
[Feb. 2024] Awarded the CICS Dissertation Writing Fellowship for Spring 2024.
[May 2023] Worked as Data Science for Common Good (DS4CG) Fellow focusing on analysing energy consumption in Commonwealth buildings of Massachusetts.
[May 2022] Completed AI residency at Google X, The Moonshot Factory. Returned back to the PhD program.
[Sep. 2021] Started AI residency at Google X, The Moonshot Factory.
[May 2021] Passed the Ph.D. qualifying exam (portfolio) to achieve candidacy!
[May 2021] Our paper titled “How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference” has been accepted for presentation at the International Conference on Machine Learning 2021. [Paper]
[Jun. 2020] Our paper titled “Structure Mapping for Transferability of Causal Models” has been accepted for presentation at the Inductive Biases, Invariances and Generalization in Reinforcement Learning Workshop, ICML 2020. [Talk]
[May 2019] Started summer internship at Amazon, Cambridge, UK working with causal inference team.
[Sep. 2018] Started Ph.D. program in Computer Science at UMass Amherst.

Services and Outreach

Reviewer: CLeaR’25, AAAI’24, AISTATS’24, AAAI’23, AISTATS’23, AISTATS’22
Mentorship: Data Science Industry Mentor for Goldman Sachs (2024), Chan-Zuckerberg Institute (2023), EMBER Undergraduate Mentorship Program (2021), Ph.D. Applicant Support Program (2021
Social Chair: UMass Graduate CS Women Group (2019, 2020)