Thank you for your interest in joining our group. We are looking for motivated researchers passionate about Trustworthy AI, Explainability, and Safety of frontier models.
We are always looking for motivated Ph.D. students to join our lab. If you are applying for Ph.D. positions at the UVA School of Data Science and interested in working with our lab, please fill out the interest form below.
If you are a current Ph.D. student at UVA interested in working at the intersection of Multimodal AI, Explainable AI, Mechanistic Interpretability, and AI Safety and Alignment for frontier models and agentic systems, email Prof. Agarwal directly with the subject line "Ph.D. Student at UVA" and include a brief description of your past and current research interests along with your CV.
We are actively recruiting undergraduate and Master's students affiliated with UVA who are excited about thesis research in trustworthy machine learning.
Undergraduate thesis students: email Prof. Agarwal with the subject line "Undergraduate Thesis Student at UVA", including a description of the topics you wish to pursue and your CV.
Master's thesis students: email Prof. Agarwal with the subject line "Master's Thesis Student at UVA", including a brief description of the topics you're interested in and your CV.
We are open to collaborating with students and researchers already thinking about problems related to Trustworthy Machine Learning — including research at the training, inference, and evaluation pipeline of large-scale models. See the Research page for focus areas.
Please send an email to Prof. Agarwal with the subject line "Interested in Collaboration", along with your CV and a brief description of your research interests.
We expect collaborating students and researchers to work with the group for at least 5 months and have some basic experience in ML/AI (e.g. coursework, demo projects, or online courses). Prior experience in trustworthy ML is not required — Prof. Agarwal believes in ground-up mentoring.
Students typically excel in some unique research skills. As a PI, Prof. Agarwal provides ample opportunities to flourish in those areas while encouraging and helping bridge the gaps where needed.
He follows a hands-on approach to advising that helps students overcome the initial barrier to entering new research areas and form a solid advisor-advisee relationship.
Regular project meetings structure one-on-one and group feedback sessions to track progress and provide clarity on short- and long-term research goals. Group brainstorming and open discussion build a culture of shared learning and innovation.
Lab members have opportunities to mentor juniors, lead sub-projects, and participate in decision-making — cultivating ownership and leadership.