Rajarshi Das (রাজর্ষি দাশ)

I'm a postdoctoral researcher at the University of Washington where I work with Prof. Hannaneh Hajishirzi. Previously, I earned my Ph.D. from UMass Amherst, where I worked with Prof. Andrew McCallum. I earned my Masters from the Language Technologies Institute at Carnegie Mellon University, where I worked with Prof. Chris Dyer .

My research is on developing nonparametric, contextual reasoning models for program synthesis and question answering on knowledge graphs and text. These models are accurate, controllable (debuggable), offer interpretable predictions and can seamlessly reason with newly arriving information. Recently, I have been working on neuro-symbolic advancements to an old nonparametric framework initially proposed in classical AI - Case-based Reasoning. In a CBR framework, the reasoning pattern required to solve a problem are derived from the reasoning patterns of other similar problems. A CBR framework provides a natural way of extending K-nearest neighbor approaches for classification to more complex problems such as program synthesis and question answering. My research interests also include developing models for open-domain QA, building procedural knowledge graphs from text and common-sense reasoning.

Contact: first_name@cs.washington.edu


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One of the most rewarding parts of my Ph.D. journey have been mentoring and working with undergraduate and MS students at UMass. I had the pleasure of working with several talented students.


May 2022: Talk at CMU
February 2022: Talk at Stanford NLP seminar series
June 2021: Talk at University of Washington (H2Lab)


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


Email: first_name@cs.washington.edu