I am a M.S. student in Human-Computer Interaction at University of Rochester, advised by Dr. Yukang Yan. My research explores how artificial intelligence can be integrated into human-centered systems to support diverse user needs in education, healthcare, and everyday life. I aim to develop AI-driven assistive technologies that enhance accessibility, empower underrepresented communities, and promote ethical, transparent design practices.

My research spans several interconnected directions, including applying generative AI for social good, designing AR/VR assistants for neurodivergent individuals, and creating AI-integrated tools to support healthcare professionals. I’m especially interested in understanding how users interact with intelligent systems and how technology can better adapt to their behaviors, challenges, and goals. Through these projects, I strive to bridge the gap between technical innovation and human empathy, ensuring that AI remains a tool for inclusion rather than exclusion.

Before starting my M.S. studies, I worked on projects in computer vision, psychology, and digital design that deepened my appreciation for interdisciplinary collaboration. My background in digital arts, creative writing, and programming helps me approach technology from both a technical and humanistic perspective. Through my research, I hope to build frameworks for trustworthy and accessible AI systems that make technology more equitable for everyone.

Education

2025-Present

University of Rochester

M.S. in Computer Science

2020-2024

University of Rochester

B.S. in Computer Science, minor in Mathematics

Publications

Submitted to IEEE Big Data 2025 2025

Assessing Historical Structural Oppression Worldwide via Rule-Guided Prompting of Large Language Models

Sreejato Chatterjee, Linh Tran, Quoc Duy Nguyen, Roni Kirson, Drue Hamlin, Harvest Aquino, Hanjia Lyu, Jiebo Luo, Timothy Dye

Using large language models to create a new way to measure historical oppression across countries by analyzing self-identified ethnicities and generating context-aware, interpretable scores of oppression.

Research Experience

Feb 2025–Present

Research Assistant ROC-HCI BEAR Lab

Advisor: Dr. Yukang Yan

Research an AR assistant integrating real-time object recognition to support ADHD users in managing household tasks.

Sep 2024–Present

Data Science Research Assistant Dye Lab, University of Rochester Medical Center

Advisor: Dr. Tim Dye

Developed an LLM-based system to detect systemic oppression patterns in global COVID-19 survey data.

Jan 2024–Mar 2025

Research Assistant kLab, University of Rochester

Advisor: Dr. Christopher Kanan

Built bias evaluation frameworks for vision-language models, reducing demographic bias while maintaining performance.

Work Experience

Summer 2025

Machine Learning/AI Developer Intern Bryan R. Harrison, PhD Psychologist, PC

Built an AI-powered clinical tool that generated structured reports from speech to improve workflow efficiency.

Summer 2023

Data Science Intern VinBigData

Enhanced LLM question classification and deployed multilingual NLP models to improve Q&A accuracy and speed.

Spring 2021

Web Development Intern & Frontend Team Leader HADTech Joint Stock Company

Led frontend design and development of web and mobile interfaces focused on usability and visual consistency.

Portfolio

AR Housework Assistant for Individuals with Executive Dysfunction

C#UnitySentisPython

Enabled context-aware task prompting for individuals with ADHD by integrating real-time object recognition into an Meta Quest 3S to reduce executive dysfunction during household chores.

URHungry - DandyHack

PythonTaipyCSSpandas

A web platform developed by Python and Taipy, enabling students to merge orders and collectively meet minimum price requirements for free shipping from popular grocery stores.

DoubletDetection - GIDS Biomedical Data Science Hackathon

PythonDoubletDetectionnumpypandasmatplotlibseaborn
🏆 First place Undergraduate Division

Implementation and optimization of DoubletDetection to predict doublet cells in single cell sequencing data, achieving a MCC score of 0.556.

MRC with Increased Negative Samples

PythonHuggingfaceTransformerTensorFlowPyTorchnumpypandasmatplotlibseaborn

Analysis of DeBERTa v3's performance on SQuAD 2.0 dataset, with and without generated negative samples.

GoodSoup - DandyHack

PythonAPInumpypandas
🏆 Winner of Community Track

A project that analyzes students’ allergies and food restrictions on University of Rochester's Dining Service’s daily recipes.

Rocky Road - CSC 214 Hackathon

Swift (iOS)Figma
🏆 Second place - Silver Joker prize

An endless running game inspired by University of Rochester's mascot Rocky.