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The following announcement is from Ruishan Liu. Please contact them directly if you have any questions.
Dear all,
We are looking for motivated UG students to join our AI for health research projects this summer (May – August), with the goal of producing impactful research publications. This is a great opportunity to gain hands-on experience in interdisciplinary projects at the intersection of AI and healthcare.
1. Deep Learning for Medical Imaging in Radiation Oncology
A collaboration with the Department of Radiation Oncology at Keck School of Medicine.
Work includes image segmentation and dose prediction using deep learning.
Requirements: coursework or research experience in computer vision. No medical background needed.
2. Large Language Models for Mental Health Diagnosis and Counseling
Develop trustworthy LLMs for mental health diagnosis and create tools to evaluate AI-supported counseling.
Requirements: coursework or experience in NLP, large language models, or deep learning. No medical background needed — we work with psychiatrists.
3. Synthetic Medical Data Generation with Foundation Models
Focus on generating high-quality, multimodal synthetic medical data using foundation models.
Requirements: coursework or experience in deep learning. No medical background required.
Note: We are a part of the USC Undergraduate Research Associates Program (URAP) 2025–2026. If the summer collaboration goes well, selected students will have the opportunity to continue in our lab under URAP during the academic year.
What you’ll gain:
- Hands-on experience in cutting-edge AI for health
- Opportunities to contribute to impactful research publications
- Collaboration with clinical experts and researchers across disciplines
We are looking for students who:
- Are passionate about applying AI to real-world health challenges
- Have research experience in deep learning, computer vision, or NLP
- Are committed and collaborative
If you’re interested, please fill out this form: https://forms.gle/FhRpLPaDa2BbMHiV7
No separate email is needed; we’ll contact you if it’s a good fit.
Looking forward to working together!
Best,
Ruishan Liu