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. I have worked as a Research Assistant in the Data Mining and Knowledge Lab 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 and workshops like ACL’ 2023, UAI’ 2023, SIGKDD’ 2022, ESEC/FSE’2021 and workshops like RANLP’2021.

Publications

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!

Sep ‘24: I 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: I served as 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: Serving as a mentor for an undergraduate student at Iowa State University for the McNair program.

Oct ‘23: I have taught a graduate-level course at Iowa State University as a guest lecturer for COM S 571X (Responsible AI: Risk Management in Data Driven Discovery.) to teach the students about representation learning, transformer models, and future research directions to develop trustworthy machine learning methods for natural language processing.

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: I served as Review member at HCOMP 2022.

July ‘22: I served as 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: I served as 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: Present 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.