Letter of Recommendation

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{\large\bfseries\color{darkblue} Department of Computer Science}\\[2pt]
{\normalsize Stanford University}\\
{\small Gates Computer Science Building, Room 482\\
353 Jane Stanford Way\\
Stanford, CA 94305\\[6pt]
Phone: (650) 725-3748\\
Email: [email protected]}
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\today

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To the Graduate Admissions Committee,

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I am writing to offer my strongest possible recommendation for \textbf{Priya Raghavan} in support of her application to your doctoral program in Computer Science. I have known Priya for three years, first as her instructor in CS 229 (Machine Learning) and CS 330 (Deep Multi-Task and Meta-Learning), and subsequently as her research advisor on two published projects. In my eighteen years on the Stanford faculty, during which I have mentored over forty doctoral students and written recommendation letters for more than two hundred applicants, Priya stands out as one of the top three most talented and driven students I have encountered.

Priya first came to my attention when she achieved the highest score in my CS 229 course (enrollment of 623 students) in Autumn 2023. Her final project, which developed a novel approach to few-shot medical image classification using prototypical networks with learned task-specific distance metrics, was of publishable quality. What distinguished Priya from other high-performing students was not merely technical skill but the intellectual maturity and creativity she brought to problem formulation. Where others applied standard techniques, Priya identified fundamental limitations in existing approaches and proposed principled solutions grounded in both theoretical understanding and practical intuition.

Following CS 229, Priya joined my research group as an undergraduate researcher. Her first project investigated meta-learning algorithms for cross-domain generalization in clinical NLP. Within three months, Priya had produced results that exceeded what I had expected from a full-year effort. She independently identified a connection between our meta-learning framework and optimal transport theory that no one in the group had considered, leading to a new algorithm that improved cross-domain accuracy by 14\% over the state of the art. This work was published at AAAI 2025, with Priya as first author --- a remarkable achievement for an undergraduate.

Her second project, currently under review at NeurIPS, addresses the challenging problem of distribution shift in medical AI systems deployed across different hospital networks. Priya developed a theoretically grounded framework for continuous model adaptation that maintains performance guarantees while adapting to new patient populations. The mathematical analysis required for this work, involving PAC-Bayes bounds and information-geometric methods, is at a level that would be impressive for an advanced doctoral student, let alone an undergraduate. Her advisor at the Medical AI Lab, Dr.\ James Liu, has told me independently that Priya is the strongest research collaborator he has worked with among students at any level.

Beyond her technical brilliance, Priya possesses the qualities that distinguish truly exceptional researchers. She is an outstanding communicator, able to explain complex ideas with clarity and precision to both technical and non-technical audiences. She presented our AAAI paper at the conference and handled challenging questions from senior researchers with poise and depth. She is a generous collaborator who actively mentors junior students in our lab; two undergraduates she has guided have gone on to submit their own first-author papers. She demonstrates remarkable resilience and persistence when facing difficult problems, maintaining focus and optimism through months of challenging research.

Priya also has a deep commitment to the broader impact of her work. She co-founded the Stanford AI for Healthcare Equity initiative, which partners with community health centers in underserved areas to develop and deploy machine learning tools. She organized a workshop that brought together clinicians, patients, and technologists to discuss responsible AI deployment, attracting over 150 participants. This combination of technical excellence and social consciousness is rare and precisely what the field needs.

I recommend Priya Raghavan without any reservation whatsoever. She has the intellectual ability, research experience, communication skills, and personal qualities to thrive in a top doctoral program and to become a leading researcher in machine learning and its applications to healthcare. I am confident she will make transformative contributions to the field. Any program would be fortunate to have her.

Please do not hesitate to contact me if you require any additional information.

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Sincerely,

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\textbf{Professor Emily Zhang, PhD}\\
Associate Professor of Computer Science\\
Stanford University\\[4pt]
Associate Director, Stanford Institute for Human-Centered AI\\
Co-Director, Clinical Machine Learning Group\\[6pt]
{\small\textit{Fellow, Association for Computing Machinery (ACM)\\
Fellow, Association for the Advancement of Artificial Intelligence (AAAI)}}

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