I am currently a researcher at AWS AI Labs, where I work on building retrieval and generative models over structured, unstructured, and multi-modal data. Previously, I was a postdoc at the wonderful H2lab at the University of Washington working with Prof. Hanna Hajishirzi. Before that, I completed my Ph.D. advised by Prof. Andrew McCallum as a part of the wonderful IESL lab at UMass Amherst. My Ph.D. thesis was on building neuro-symbolic models of reasoning over knowledge, primarily motivated by case-based reasoning. My research interest lies in building semiparametric models of reasoning applied to structured (graphs, databases, tables), unstructured (text), and multimodal (images, UX widgets) data. I am interested in how new knowledge can be introduced (via nonparametric memories), used/manipulated (via parametric models), as well as synthesized/discovered (via reasoning). Contact: dasrajar [at] amazon [dot] com

Selected Works

For a full list, check Google Scholar

1. Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA
   Dhruv Agarwal, Rajarshi Das, Sopan Khosla, Rashmi Gangadharaiah NAACL 2024
   [code], [Slides]
2. When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
   Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, Hannaneh Hajishirzi ACL 2023
   [code]
3. Nonparametric Contextual Reasoning for Question Answering over Large Knowledge Bases
   Rajarshi Das Ph.D. Thesis 2022
4. Case-based Reasoning for Natural Language Queries over Knowledge Bases
   Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay-Yoon Lee, Lizhen Tan, Lazaros Polymenakos, Andrew McCallum EMNLP 2021
5. A Simple Approach to Case-Based Reasoning in Knowledge Bases
   Rajarshi Das, Ameya Godbole, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum AKBC 2020 Best Paper Runner-up
   [code], [Talk]
6. Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering
   Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum ICLR 2019
   [code]
7. Go for a Walk and Arrive at the Answer -- Reasoning over Paths in Knowledge Bases using Reinforcement Learning
   Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum ICLR 2018
   [code]

Mentoring / Interns

I had the pleasure of mentoring and working with several graduate and undergraduate students during my PhD. I am grateful that I get to continue to do that in industry by hosting interns.

• Ting-Yun (Charlotte) Chang (Intern host for Summer 2024)
• Dhruv Agarwal (Intern host for Summer 2023, 2024)
• Mudit Chaudhary MS'23 → J&J Innovative Medicine
• Ankita Naik MS'22 → IBM Research
• Ameya Godbole MS'20 → Research Fellow at IESL → Ph.D. student at USC
• Daivik Swarup MS'20 → Google
• Ahsaas Bajaj MS'20 → Walmart Labs
• Apurva Bhandari MS'20 → Uber
• Derek Tam (w/ Nick Monath) MS'19 → Ph.D. student at UNC Chapel Hill
• Ishita Ankit MS'19 → Microsoft
• Shehzaad Dhuliawala MS'18 → Microsoft Research → Ph.D. student at ETH Zurich
• Aaron Traylor (w/ Nick Monath) U.grad'18 → Ph.D. student at Brown U.

Invited Talks

Apr 2024: Talk at Georgia Tech on Semiparametric Reasoning over Structured Data
May 2022: Talk at CMU
February 2022: Talk at Stanford NLP seminar series on Nonparametric Contextual Reasoning for Question Answering over Knowledge Bases
June 2021: Talk at University of Washington (H2Lab)

Service

Along with Tegan, I serve as a managing editor for Journal of Machine Learning Research (JMLR)
I had a great time co-organizing the weekly Machine Learning and Friends Lunch for 3 years. Please consider giving a talk!
I have co-organized the following workshops

• Semiparametric Methods in NLP: Decoupling Logic from Knowledge at ACL 2022 (with Patrick Lewis (Meta AI), Sewon Min (UW), June Thai (UMass Amherst) and Manzil Zaheer (DeepMind))
• Unstructured and Structured KBs at AKBC 2021 (with Bhuwan Dhingra (Duke), Nicholas Fitzgerald (Google), Sewon Min (UW), Aleksandra Piktus (Meta AI), Siamak Shakeri (Google), Pat Verga (Google))
• Unstructured and Structured KBs at AKBC 2020 (with Danqi Chen (Princeton), Angela Fan (MetaAI), Sewon Min (UW), Siva Reddy (Mila), Pat Verga (Google))
• Neural+Symbolic Representation and Reasoning at AKBC 2019 (with Danqi Chen (Princeton), Jay Pujara (USC), Sebastian Riedel (UCL/Meta AI), Ivan Titov (U. Edinburgh))
• Reasoning for Complex Question Answering at AAAI 2019 (with Kartik Talamadupula (IBM), Pavan Kapanipathi (IBM))

I review for almost all of major NLP (ACL, EMNLP, NAACL, EACL) and ML conferences (Neurips, ICML, ICLR) and journal (TMLR) every year.