Vasudha Varadarajan

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Vasudha

PhD Candidate

I am on the academic job market for faculty and post-doc positions!

(Disclaimer: This new website is still under construction, please check back in about two weeks while I update all the information, thank you for your patience!)

I am Vasudha Varadarajan, a fourth year PhD student working in the areas of natural language processing and the relationships between cognition and language, especially in the context of social media. I work with Prof. H. Andrew Schwartz in the State University of New York at Stony Brook. I am interested in applying NLP/data science techniques to problems in computational social science to study social/psychological phenomena through language use at scale, active learning, and improving conversational AI for psychometric surveys. I am also interested in interpretability and explainability of language models, and exploring NLP for low-resource and code-mixed settings.

I am always looking for opportunities (currently internships) to work with teams with similar interests– preferably with a research component. Other than that, I have been trying to improve my science communication skills, and I love facilitating discussions and teaching!

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Interests

Natural Language Processing
Computational Social Science

Education

PhD in Computer Science
Stony Brook
MSc (Hons.) Physics
Birla Institute of Technology and Science (BITS) Pilani
BE (Hons.) Computer Science
Birla Institute of Technology and Science (BITS) Pilani

📚 My Research

I am a Ph.D. candidate working on modeling cognitive styles expressed through personal discourse using large language models. I am broadly interested in using NLP and data science to study social/psychological phenomena through language use, annotations and active learning, and improving conversational AI, especially for mental health. I am always looking for opportunities to work with teams with similar interests. Please reach out to collaborate 😃

Featured Publications

Mental Health, Assessments, Adaptive, Item Response Theory, Chatbot

ALBA: Adaptive Language-Based Assessments for Mental Health

This study introduces Adaptive Language-Based Assessment (ALBA) for mental health, using limited language responses to adaptively order questions and assess psychological traits. Two methods, ALIRT and Actor-Critic, outperformed non-adaptive baselines. ALIRT proved most effective, achieving high accuracy with fewer questions, demonstrating that adaptive assessments can maintain validity with smaller language samples and lower computational costs.

Jan 1, 2024

Reddit, Suicidality, Clpsych, Entropy, Archetypes, Llms

Archetypes and Entropy: Theory-Driven Extraction of Evidence for Suicide Risk

We combined theory-driven suicidal archetypes with language models and relative entropy to identify suicide risk in text. The combined approach outperformed individual methods, achieving high accuracy in shared task evaluations. Our findings suggest that integrating theoretical and data-driven methods is crucial for mental health analysis, outperforming prompt-based approaches.

Jan 1, 2024

Human-in-the-Loop, Active Learning, Large Language Models, Dissonance, Stance

Transfer and active learning for dissonance detection: Addressing the rare-class challenge

This study explores transfer- and active learning solutions for rare-class problems, focusing on detecting cognitive dissonance in social media. We propose a probability-of-rare-class (PRC) approach for selecting samples and evaluate various acquisition strategies. We find that PRC effectively guides annotations and improves model accuracy, while specific transfer-learning sequences enhance initial performance but don't benefit subsequent active learning iterations.

Jan 1, 2023

Dissonant Stance, Topic-Independent, Roberta

Detecting dissonant stance in social media: The role of topic exposure

Jan 1, 2022

Recent Publications

Vasudha Varadarajan, Sverker Sikström, Oscar Kjell, H Schwartz (2024). ALBA: Adaptive Language-Based Assessments for Mental Health. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers).

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Vasudha Varadarajan, Allison Lahnala, Adithya v Ganesan, Gourab Dey, Siddharth Mangalik, Ana-Maria Bucur, Nikita Soni, Rajath Rao, Kevin Lanning, Isabella Vallejo, Others (2024). Archetypes and Entropy: Theory-Driven Extraction of Evidence for Suicide Risk. Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024).

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Adithya v Ganesan, Siddharth Mangalik, Vasudha Varadarajan, Nikita Soni, Swanie Juhng, João Sedoc, H Andrew Schwartz, Salvatore Giorgi, Ryan L Boyd (2024). From Text to Context: Contextualizing Language with Humans, Groups, and Communities for Socially Aware NLP. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts).

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Salvatore Giorgi, David M Markowitz, Nikita Soni, Vasudha Varadarajan, Siddharth Mangalik, H Andrew Schwartz (2023). \" I Slept Like a Baby\": Using Human Traits To Characterize Deceptive ChatGPT and Human Text.. IACT@ SIGIR.

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Swanie Juhng, Matthew Matero, Vasudha Varadarajan, Johannes Eichstaedt, Adithya v Ganesan, H Andrew Schwartz (2023). Discourse-level representations can improve prediction of degree of anxiety. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers).

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Recent & Upcoming Talks