Fwd: RI PhD Thesis Proposal - Xinyu (Rachel) Li

[Artur Dubrawski]

Team,

Please come and witness Rachel giving her excellent thesis proposal
presentation on Monday next week!

Cheers,
Artur

---------- Forwarded message ---------
From: RI PhD Program Manager <ri-phd-manager at andrew.cmu.edu>
Date: Tue, Apr 7, 2026 at 2:45 PM
Subject: RI PhD Thesis Proposal - Xinyu (Rachel) Li
To: RI People <ri-people at andrew.cmu.edu>


*RI CALENDAR EVENT
<https://www.ri.cmu.edu/event/ri-phd-thesis-proposal-xinyu-rachel-li/>*




*Date: April 13, 2026Time: 03:15 PM (ET) Location: GHC 6121Zoom Link
<https://cmu.zoom.us/j/96249846505?pwd=aZalDPGdL4JsUbJR0M0YQOPta8pFZX.1>*

*Type: Ph.D. Thesis Proposal*
*
<https://cmu.zoom.us/j/96249846505?pwd=aZalDPGdL4JsUbJR0M0YQOPta8pFZX.1>Who:
Xinyu (Rachel) Li*
*Title: Towards Accessible AI Agents*

*Abstract:*
Empowered by large language models (LLMs), AI agents have shown strong
potential across tasks such as general-purpose assistance, software coding,
and scientific research. However, their practical utility in applications
involving consequential decisions such as healthcare, remains constrained
by three major challenges.

*Evaluation.* Existing agent evaluations often focus on well-structured
tasks and final outcomes, failing to fully capture the complexity of
real-world workflows. We propose evaluation frameworks grounded in
realistic machine learning engineering workflows, providing skill-based,
multi-artifact, and holistic assessments that systematically evaluate the
practical utility of AI agents.

*Learning.* Improving LLMs for agentic use typically relies on
reinforcement learning with large amounts of high-quality labeled data,
which are costly and difficult to obtain in expert domains including
healthcare. To address this limitation, we aim to develop learning
frameworks that require minimal external supervision, improving the
scalability and efficiency of agent learning.

*Specialization.* AI agents typically follow a one-size-fits-all paradigm
at the time of deployment, lacking mechanisms to account for task-specific
or user-specific requirements. We propose methods that enable agent
specialization for downstream tasks and users, expanding their
applicability across heterogeneous deployment settings.

This thesis aims to make AI agents more broadly accessible and impactful in
important real-world applications by enhancing their practical utility,
making them more measurable, more capable, and better tailored to the needs
of their users and applications.

*Link to thesis*
<https://drive.google.com/drive/folders/1mPlZXQ3WLa42e1LFKyMkjuHy_diDc6yy?usp=sharing>

*Thesis committee members:*
Artur Dubrawski (Chair)
Andrea Bajcsy
Barnabás Póczos
Daniel McDuff (Google)
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