Welcome to Mohna Chakraborty’s homepage

I am a post-doctoral fellow at the University of Michigan (Michigan Institute for Data Science) under the guidance of Dr. David Jurgens and Dr. Lu Wang. I finished my Ph.D. in Computer Science from Iowa State University under my advisor Dr. Qi Li. I have also worked as a Data Science intern at The Home Depot, Epsilon, and as a Data Analytics intern at Delaware North. My research interests are in the domain of data mining, natural language processing, and machine learning. Through my research, I have contributed several key methods in top conferences like ACL, UAI, SIGKDD, PAKDD, SIAM, ESEC/FSE, journal like TKDD and workshops like ICLR, ICML, WWW, PAKDD, RANLP.

Publications

2025
Mohna Chakraborty, Adithya Kulkarni, and Qi Li. Modeling Data Diversity for Joint Instance and Verbalizer Selection in Cold-Start Scenarios, PAKDD, 2025 [paper]

Mohna Chakraborty, Adithya Kulkarni, and Qi Li. Empirical Evaluation of Prompting Strategies for Fact Verification Tasks, SIAM, 2025 [paper]

Adithya Kulkarni, Mohna Chakraborty. Blue Sky: Reducing Performance Gap between Commercial and Open-Source LLMs, WWW, 2025 [paper]

Adithya Kulkarni, Mohna Chakraborty, Yonas Sium, Sai Charishma Valluri, Wei Le and Qi Li. FROM PSEUDO-CODE TO SOURCE CODE: A SELFSUPERVISED SEARCH APPROACH, ICLR Workshop, 2025 [paper]

2023
Mohna Chakraborty , Adithya Kulkarni, and Qi Li. Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts, ACL, 2023 [paper]

Adithya Kulkarni, Mohna Chakraborty and Qi Li. Optimal Budget Allocation for Crowdsourcing Labels for Graphs, UAI, 2023 [paper]

2022
Mohna Chakraborty, Adithya Kulkarni, and Qi Li. Open-Domain Aspect-Opinion Co-Mining with Double-Layer Span Extraction, SIGKDD, 2022 [paper]

2021
Richard D Jiles, Mohna Chakraborty. [Re] Domain Generalization using Causal Matching, ML Reproducibility Challenge, 2021: [paper]

Abhishek Kumar Mishra*, Mohna Chakraborty*. Does local pruning offer task-specific models to learn effectively?, Proceedings of the Student Research Workshop Associated with RANLP, 2021: [paper]

Mohna Chakraborty. Does reusing pre-trained NLP model propagate bugs?, ESEC/FSE, 2021: [paper]

Recent News!

June ‘25: Our journal paper on “Weakly Supervised Open-Domain Aspect-based Sentiment Analysis.” has been accepted at Transactions on Knowledge Discovery from Data, TKDD, 2024.

June ‘25: Our paper on “Self-Imputation and Cross-Variable Learning Improve Water Quality Prediction with Sparse Data” has been accepted at ICML Workshop on Foundation Models for Structured Data, 2025.

May ‘25: Our paper on “Is Large Language Model Performance on Reasoning Tasks Impacted by Different Ways Questions Are Asked?” has been accepted at ACL Findings 2025.

May ‘25: Visited Jackson Medical School as part of Data and AI Intensive Research with Rigor and Reproducibility (DAIR3) program at Jackson, Mississippi.

May ‘25: Our paper on “Budget Allocation Exploiting Label Correlation between Instances” has been accepted at UAI 2025.

May ‘25: Our paper on “Reducing Performance Gap between Commercial and Open-Source LLMs” has been has been awarded the third Place Best Vision Paper at the Blue Sky Track, SDM Conference 2025.

March ‘25: Visited University of Bonn and HumanCLAIM Workshop at Gottingen as part of DAAD AInet fellowship.

March ‘25: Our paper on “Beyond Single Parsers: An Empirical Analysis of Dependency Parse Tree Aggregation.” has been accepted at Research and Applications of Foundation Models for Data Mining and Affective Computing (RAFDA), PAKDD 25.

March ‘25: Selected for the Data and AI Intensive Research with Rigor and Reproducibility (DAIR3) with full scholarship.

March ‘25: Our paper on “From Pseudo-Code to Source Code: A Self-Supervised Search Approach.” has been accepted at Deep Learning for Code (DL4C) Workshop, ICLR 25.

March ‘25: Served as a PC member at ICLR 2025.

Jan ‘25: Our paper on “Modeling Data Diversity for Joint Instance and Verbalizer Selection in Cold-Start Scenarios.” has been accepted at Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025.

Jan ‘25: Our paper on “Empirical Evaluation of Prompting Strategies for Fact Verification Tasks.” has been accepted at PromptEng Workshop at the ACM WebConf, WWW 25.

Jan ‘25: Our paper on “Reducing Performance Gap between Commercial and Open-Source LLMs.” has been accepted at SIAM 2025.

Dec ‘24: Served as a PC member at AAAI 2025.

Dec ‘24: Served as a PC member at WWW 2025.

Oct ‘24: Selected for the prestigious DAAD AInet fellowship.

Oct ‘24: Served as a PC member at SDM 2025.

Sep ‘24: Joined The University of Michigan as a Post-doctoral fellow (MIDAS) under the guidance of Prof. David Jurgens and Prof. Lu Wang.

July ‘24: Defended my Ph.D. Final Oral Exam on Analysis of Textual-based Reviews with Minimal Supervision.

Nov ‘23: Served as a Review member at EACL 2023.

Oct ‘23: Awarded Best Research Poster Award for my paper “Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts” at MINK WIC (Missouri, Iowa, Nebraska, Kansas Women in Computing) Conference.

Oct ‘23: Selected to represent Iowa State University at MINK WIC (An ACM celebration of Women in Computing) Conference.

Oct ‘23: Defended my Preliminary Exam on Analysis of Textual-based Reviews with Minimal Supervision.

Oct ‘23: Invited to teach a graduate-level course at Iowa State University as a guest lecturer for COM S 571X (Responsible AI: Risk Management in Data Driven Discovery.).

May ‘23: Joined The Home Depot as a Data Science intern.

May ‘23: Our paper on “Optimal Budget Allocation for Crowdsourcing Labels for Graphs” has been accepted at UAI 2023.

May ‘23: Our paper on “Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts” has been accepted at ACL 2023.

March ‘23: Awarded 2nd position for 7th Annual Research Competition at Iowa State University.

Sep ‘22: Selected to represent Iowa State University for the prestigious and competitive Grace Hopper Celebration.

Aug ‘21: Presented our paper and poster “Open-Domain Aspect-Opinion Co-Mining with Double-Layer Span Extraction”, at the SIGKDD, 2022 conference in Washington D.C.

Aug ‘22: Awarded Student Travel Award for SIGKDD 2022.

July ‘22: Served as a Review member at HCOMP 2022.

July ‘22: Served as a Review member at EMNLP 2022.

May ‘22: Our paper on “Open-Domain Aspect-Opinion Co-Mining with Double-Layer Span Extraction” has been accepted at SIGKDD 2022.

May ‘22: Joined Epsilon as a PhD intern.

April ‘22: Defended my Research Proficiency Exam on Weakly Supervised Review Analysis Based on Task Correlation.

March ‘22: Awarded 1st position for 6th Annual Research Competition at Iowa State University.

March ‘22: Our paper on “[Re] Domain Generalization using Causal Matching” has been accepted at ML Reproducibility Challenge 2021 (Fall Edition).

Dec ‘21: Served as a Review member at PAKDD 2021.

Sep ‘21: Our paper on “Does local pruning offer task-specific models to learn effectively?” has been accepted at RANLP 2021.

Aug ‘21: Presented SRC paper “Does reusing pre-trained NLP model propagate bugs?”, ESEC/FSE, 2021.

June ‘21: Our paper on “Does reusing pre-trained NLP model propagate bugs?” has been accepted at ESEC/FSE, SRC 2021.

May ‘21: Joined Epsilon as a PhD intern.

Aug ‘20: Joined Ph.D. program at the Department of Computer Science at Iowa State University.