Kanishka Misra

Kanishka Misra

PhD Student

Purdue University

I am a third year PhD Student at Purdue University, where I work on Natural Language Understanding with Dr. Julia Taylor Rayz at the AKRaNLU Lab. I am particularly interested in relating natural language processing algorithms and models from the perspective of ideas in cognitive science (specifically, the study of concepts and categories). I work closely with Dr. Allyson Ettinger of UChicago. I am also affiliated with CERIAS, Purdue’s center for research and education in areas of information security.

In 2018, I was fortunate to be a Purdue Research Foundation fellow. I then taught database fundamentals to sophomore level undergraduates for three semesters. I am currently funded by an NSF-EAGER grant focused on integrating cybersecurity education through Computational Humor.

My email is [my-first-name] @ purdue [dot] edu.[why is it like that?]

Interests

  • Language Understanding
  • Lexical Semantics
  • Categorization
  • Typicality and Vagueness
  • Induction
  • Reasoning

Collaborators

  • Julia Rayz, Purdue (Advisor)
  • Allyson Ettinger, UChicago
  • Hemanth Devarapalli, Purdue
  • Tatiana Ringenberg, Purdue
  • Geetanjali Bihani, Purdue

Education

  • PhD, Natural Language Understanding, Current

    Purdue University

  • MS in Computer Information Technology, 2020

    Purdue University

  • BS in Computer Information Technology, 2018

    Purdue University

Recent News

SEE ALL

  • July 2021: Presenting my paper at CogSci 2021!

  • May 2021: Our NAFIPS 2021 paper received an honorable mention for the Best Student Paper award.

  • April 2021: Paper about typicality ratings and language models with Allyson and Julia accepted accepted as a talk to CogSci 2021!

  • March 2021: Paper accepted to NAFIPS 2021!

  • February 2021: Submitted papers to ACL-IJCNLP 2021 and CogSci 2021.

  • October 2020: Passed my MS thesis defense!

Recent Publications

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Do language models learn typicality judgments from text?

Investigating manifestation of category typicality effects in predictive models of language processing. Presented at CogSci 2021

Finding fuzziness in Neural Network Models of Language Processing (Forthcoming)

Probing an NLI model for its handling of fuzzy concepts such as temperature. To be presented at NAFIPS 2021

Exploring BERT’s Sensitivity to Lexical Cues using Tests from Semantic Priming

Using semantic priming to investigate how BERT utilizes lexical relations to inform word probabilities in context. Presented at Blackbox NLP 2021.

Recent Posts

Introducing $\texttt{minicons}$: Running large scale behavioral analyses on transformer language models

In this post, I showcase my new python library that implements simple computations to facilitate large-scale evaluation of transformer language models.

Implementing a Neural Network from scratch in R using rrays

Summary In this post, I want to briefly demonstrate how one can implement a simple Neural Network architecture in R using a new package for array/tensor computation called rray along with the R6 object oriented programming system which will be used to modularize the various elements requried for anyone to build a neural network architecture.

My first few open source contributions: Authorship Attribution of the Federalist Papers

Background During the last semester of my undergraduate education at Purdue, I was engaged in a research project that analyzed conversation between two participants, and delivered some insight regarding the two participants’ future interaction(this will be expanded further in a blog post maybe).

Population growth and Doubling times with tidyverse

Roses are red, violets are blue This is a forced rhyme, here’s blog post two! Background Ever since I worked on data about populations at my internship at Perscio, a healthcare data analysis firm in Indianapolis, as well as worked with a Professor of Demography and Social Policy on a paper about demographic data, I have gained interest in population problems - mostly through readings.

Attitudes of employees towards mental health in the tech workplace

Understanding and accepting mental health as an issue at the workplace has become ever so crucial in recent times. Mental illnesses like depression and anxiety can have a significant economic impact; the estimated cost to the global economy is US$ 1 trillion per year in lost productivity (source).

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