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Jagrit Acharya

Jagrit Acharya

AI researcher & full‑stack developer — MSc Software Engineering, UCalgary

jag2025
Calgary, Alberta, Canada
Joined November 2025
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Summary

Applied AI and software-engineering researcher: Jagrit combines academic research (conference and arXiv papers) with applied engineering. His recent work focuses on improving bug-report quality using instruction-tuned LLMs and comparing graph neural networks versus LLMs for bug-priority/severity prediction. He publishes with the SHAKTI Lab / University of Calgary and presents at venues like PROMISE / EASE. arxiv+3
Full‑stack engineer and product builder: his portfolio and internships show hands‑on experience building web and mobile products (Django + React stacks, WhatsApp chatbots, eCommerce platforms, deployed services). He bridges research and product work by shipping prototypes (JobMatchify, NoteTakers, Next Stop Recommender) and open-source projects. jagrit+2
Academic teaching and mentorship: while pursuing his MSc at University of Calgary he has served as a teaching assistant and mentored in programs like GirlScript Summer of Code, as well as led competitive programming efforts during his undergraduate studies. jagrit+1
Privacy-preserving and decentralized systems interest: earlier roles (OpenMined) and projects reference DID, Hyperledger Aries, and privacy-aware ML — indicating an interest in decentralized identity and privacy-related tooling in AI. jagrit+1

Work

Education

Projects

Writing

Can We Enhance Bug Report Quality Using LLMs?: An Empirical Study of LLM-Based Bug Report Generation

January 1, 2025

Examines instruction‑fine‑tuned LLMs for transforming casual/unstructured bug reports into high-quality, structured bug reports; compares multiple open-source LLMs and ChatGPT across established metrics and datasets.

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BugsRepo: A Comprehensive Curated Dataset of Bug Reports, Comments and Contributors Information from Bugzilla

January 1, 2025

Dataset paper describing a curated dataset of bug reports and contributor metadata to support research in bug-triage and automated tooling.

Favicon imagearxiv.org

Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction

January 1, 2024

Conference paper comparing GNNs and LLM approaches for predicting bug-report priority and severity (Proceedings of the 20th Int'l Conference on Predictive Models in Software Engineering).

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AROhI: An Interactive Toolkit for Estimating ROI of Data Analytics

January 1, 2024

Toolkit and research work describing ROI estimation for data-analytics projects; tied to IEEE workshop/conference listings.

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Hobbies

Open-source contribution, mentoring students (GSSoC), and competitive programming / algorithmic problem solving. github+1