Project
Research Project
GPTutor
We prototyped a jupyter lab widget using LLM to provide hints for programmers to debug both functional and performance issues of their code. We aim to facilitate a learning environment that encourages users to value iteration, experimentation, and feedback as essential components of the problem-solving process.
Ongoing
Collaborated with Shivansh Shekhar
Advised by David McDonald, Colin Clement (Microsoft)
Research Project
Measuring Affective and Cognitive Trust in AI
As a first step towards designing for AI systems that build appropriate trust through affective and cognitive routes, we seek to develop a valid and generalizable set of scales for this 2-dimensional construct of trust. Through a survey over 32 scenarios across 5 dimension and an exploratory factor analysis on the data collected, we established the scale with 27 items and demonstrated its validity. We then conducted another survey study, using the scale to explore a conversational agent's capability to build affective trust when the user is seeking for emotional support.
Ongoing
Advised by Gary Hsieh, Chirag Shah
Research Project
Chatbot-assisted Collaboration
Designing a chatbot to help strangers get familiarized with each other and studying the effects on collaboration performance.
Feb 2022 - Jan 2023
Collaborated with Donghoon Shin, Soomin Kim
Advised by Gary Hsieh, Joonhwan Lee
UXR Summer Internship Projectat TruEra
How data scientists diagnose ML models
With the support from designers and ML engineers in the company, I conducted internal research and interview study with data scientists to understand how people approach performance debugging of ML models in the field. Based on the interview findings, I challenged and validated the team’s internal assumptions, identified needs and difficulties people have with debugging ML models, and proposed ways to redesign and automate features of the product.
Jul 2022 - Sep 2022
Advised by Mantas Lilis, Joshua Noble, Justin Lawyer
UXR Summer Internship Projectat TruEra
Value Sensitive Design for Recommender Systems
We adopted value sensitive design approach to explore the research question: How can designers of Recommender Systems adopt a Value-Sensitive approach? We conducted conceptual and technical investigation, analyzing the how everyday recommender systems uphold or violate stateholders’ values. We proposed design recommendations with respect to algorithmic awareness, profiling transparency, and user control.
Winter 2022
Collaborated with Mrudali Birla, Sourojit Ghosh, Lubna Razaq
Advised by Batya Friedman