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  <title>Alyssa Unell</title>

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          <!-- Intro Section -->
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                  <p style="text-align:center;">
                    <name>Alyssa Unell</name>
                  </p>
                  <p>
                    I'm Alyssa, a Computer Science PhD student at Stanford advised by
                    <a href="https://stairlab.stanford.edu/">Professor Sanmi Koyejo</a> and
                    <a href="https://shahlab.stanford.edu/">Professor Nigam H Shah</a>. My research generally focuses on trustworthy evaluations of AI for medical applications.
                    I previously worked with <a href="https://marvl.stanford.edu/">Professor Serena Yeung</a> on VLM generalization and biomedical dataset creation and with
                    <a href="https://hazyresearch.stanford.edu/">Professor Chis Ré</a> on the capabilities of language models to perform acts of long context retrieval.
                    Additionally, I have worked with Microsoft Research's <a href="https://www.microsoft.com/en-us/research/group/real-world-evidence/">Real World Evidence Group</a> to evaluate calibration methods for model capabilities in data-sparse settings.
                  </p>
                  <p>
                    Prior to beginning my PhD at Stanford, I graduated from MIT with a degree in Computation and Cognition. I was extremely fortunate to receive amazing mentorship throughout
                    my undergraduate experience. I worked with <a href="https://www.sinhalab.mit.edu/">Professor Pawan Sinha</a>,  <a href="http://www.kylekeane.com/">Dr. Kyle Keane</a>,
                    and <a href="http://web.mit.edu/xboix/www/index.html">Dr. Xavier Boix Boisch</a> within the MIT Quest for Intelligence.
                    <br><br>
                    I worked with <a href="https://people.epfl.ch/martin.jaggi">Professor Martin Jaggi</a> and
                    <a href="https://www.yale-light.org/projects-cv">Dr. Annie Hartley</a> in the
                    <a href="https://www.epfl.ch/labs/mlo/">Machine Learning Optimization Lab</a> where we explored the implementation of federated learning
                    architecture for secure medical information sharing.
                    <br><br>
                    I have also had the privilege to work with
                    <a href="https://people.csail.mit.edu/polina/">Professor Polina Golland</a> on projects relating to the use of generative AI for improving
                    MRI acquisitions. Additionally, I have worked as a Machine Learning Intern for
                    <a href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/overview.html#gs.rfxorb">Intel</a> serving to improve their optimization
                    software.
                  </p>
                  <p style="text-align:center;">
                    <a href="mailto:aunell@stanford.edu">aunell@stanford.edu</a> &nbsp;/&nbsp;
                    <a href="data/AlyssaUnell_ResumeGrad.pdf">CV</a> &nbsp;/&nbsp;
                    <a href="https://www.linkedin.com/in/alyssa-unell-a8a9b81a9/">LinkedIn</a> &nbsp;/&nbsp;
                    <a href="https://github.com/aunell/">Github</a> &nbsp;/&nbsp;
                    <a href="https://twitter.com/AlyssaUnell">Twitter</a>
                  </p>
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                <td style="padding:2.5%; width:40%; max-width:40%;">
                  <a href="images/alyssaImage.png">
                    <img style="width:100%; max-width:100%;" alt="profile photo" src="images/alyssaImage.png" class="hoverZoomLink">
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          <!-- Research Section -->
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                  <heading style="margin-bottom:0;">Research</heading>
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          <ol style="padding-left:0; list-style-position:inside; font-family:sans-serif;">

            <li style="padding:10px 20px;">
              <a href="https://arxiv.org/abs/2505.23802">
                <papertitle>Holistic Evaluation of Large Language Models for Medical Tasks with MedHELM</papertitle>
              </a><br>
              (α-β) Suhana Bedi*, Hejie Cui*, Miguel Fuentes*, <strong>Alyssa Unell*</strong>,... Percy Liang, Mike Pfeffer, Nigam H Shah<br>
              <em>Nature Medicine</em>, 2025
            </li>

            <li style="padding:10px 20px;">
              <a href="https://arxiv.org/abs/2509.07325">
                <papertitle>CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimation</papertitle>
              </a><br>
              <strong>Alyssa Unell</strong> ... Matthew Lungren, Hoifung Poon<br>
              <em>ML4H Proceedings 2025. (Presented at NeurIPS 2025 Workshop on GenAI for Health.)</em>
            </li>

            <li style="padding:10px 20px;">
              <a href="data/judge_sampling.pdf">
                <papertitle>Smarter Sampling for LLM Judges: Reliable Evaluation on a Budget</papertitle>
              </a><br>
              <strong>Alyssa Unell*</strong>, Natalie Dullerud*, Nils Kasper, Nigam Shah, Sanmi Koyejo<br>
              <em> NeurIPS 2025 Workshop LLM-Eval</em>, 2025
            </li>

            <li style="padding:10px 20px;">
              <a href="https://arxiv.org/abs/2503.04176">
                <papertitle>TIMER: Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records</papertitle>
              </a><br>
              Hejie Cui*, <strong>Alyssa Unell*</strong>, Bowen Chen, Jason Alan Fries, Emily Alsentzer, Sanmi Koyejo, Nigam H Shah<br>
              <em>NPJ Digital Medicine</em> 2025. (Presented at ICLR 2025 Workshop on Synthetic Data.)
            </li>

            <li style="padding:10px 20px;">
              <a href="https://www.medrxiv.org/content/10.1101/2025.05.02.25326781v1">
                <papertitle>Real-World Usage Patterns of Large Language Models in Healthcare</papertitle>
              </a><br>
              <strong>Alyssa Unell*</strong>, Mehr Kashyap*, Michael Pfeffer, Nigam H Shah<br>
              <em>MedRxiv</em>, 2025
            </li>

            <li style="padding:10px 20px;">
              <a href="https://arxiv.org/pdf/2405.18415">
                <papertitle>Why are Visually-Grounded Language Models Bad at Image Classification?</papertitle>
              </a><br>
              Yuhui Zhang, <strong>Alyssa Unell</strong>, Xiaohan Wang, Dhruba Ghosh, Yuchang Su, Ludwig Schmidt, Serena Yeung-Levy<br>
              <em>Conference on Neural Information Processing Systems</em>, 2024
            </li>

            <li style="padding:10px 20px;">
              <a href="https://arxiv.org/pdf/2407.01791">
                <papertitle>µ-BENCH: VISION-LANGUAGE BENCHMARK FOR MICROSCOPY UNDERSTANDING</papertitle>
              </a><br>
              Alejandro Lozano, Jeffrey Nirschl, James Burgess, Sanket Rajan Gupte, Yuhui Zhang, <strong>Alyssa Unell</strong>, Serena Yeung-Levy<br>
              <em>Conference on Neural Information Processing Systems Datasets and Benchmarks Track</em>, 2024
            </li>

            <li style="padding:10px 20px;">
              <a href="https://openreview.net/pdf?id=sm9Udj2c6u">
                <papertitle>Feasibility of Automatically Detecting Practice of Race-Based Medicine by Large Language Models</papertitle>
              </a><br>
              Akshay Swaminathan, Sid Salvi, Philip Chung, Alison Callahan, Suhana Bedi, <strong>Alyssa Unell</strong>, Mehr Kashyap, Roxana Daneshjou, Nigam H Shah, Dev Dash<br>
              <em>AAAI 2024 Spring Symposium on Clinical Foundation Models</em>
            </li>

            <li style="padding:10px 20px;">
              <a href="https://www.visionsciences.org/presentation/?id=449">
                <papertitle>From Clear to Noise: Investigating Neural Noise Progression in Visual System Robustness</papertitle>
              </a><br>
              Hojin Jang, <strong>Alyssa Unell</strong>, Suayb Arslan, Walt Dixon, Michael Fux, Matt Groth, Joydeep Munshi & Pawan Sinha<br>
              <em>Vision Sciences Society Poster Session</em>, 2024
            </li>

            <li style="padding:10px 20px;">
              <a href="https://iclr2021generalization.github.io/papers/">
                <papertitle>Transformation Tolerance of Machine-based Face Recognition Systems</papertitle>
              </a><br>
              Ashika Verma, Kyle Keane, <strong>Alyssa Unell</strong>, Anna Musser & Pawan Sinha<br>
              <em>ICLR Generalization Beyond the Training Distribution in Brains and Machines Workshop</em>, 2021
            </li>

            <li style="padding:10px 20px;">
              <a href="https://www.nature.com/articles/s41598-021-96876-6">
                <papertitle>Influence of Visual Feedback Persistence on Visuo-Motor Skill Improvement</papertitle>
              </a><br>
              <strong>Alyssa Unell</strong>, Zachary M. Eisenstat, Ainsley Braun, Abhinav Gandhi, Sharon Gilad-Gutnick, Shlomit Ben-Ami & Pawan Sinha<br>
              <em>Nature Scientific Reports</em>, 2021
            </li>
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          <!-- Open-Source Contributions -->
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                  <heading style="margin-bottom:0;">Open-Source Contributions</heading>
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          <ol style="padding-left:0; list-style-position:inside; font-family:sans-serif;">
            <li style="padding:20px;">
              <a href="https://epfml.github.io/disco/#/">
                <papertitle>Distributed Collaborative Learning (DisCo)</papertitle>
              </a><br>
              Added security guarantees to the DisCo platform that allows clients to securely train models in a decentralized fashion.
            </li>
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