Cat Zhang-Larson
Cat Zhang-Larson is a first-year student in the Master of Science in Biomedical Data Science and AI (MDSAI) at the Icahn School of Medicine at Mount Sinai.
In this Q&A, she explains how Mount Sinai’s investment in AI and data science inspired her to apply, and how the mentorship and resources at Mount Sinai are enabling her to pursue her clinical career goals.
What is your academic and career background?
I am a recent graduate of the University of Michigan (Class of 2025), where I completed dual Bachelor of Science degrees in Biomedical Engineering and Computer Science Engineering on a pre-medical track. As I progressed through my coursework and research, I found myself drawn to applying computational methods to clinically relevant problems, particularly in medical imaging. These experiences ultimately led to my current interests in surgical robotics and in integrating pre- and post-operative imaging to improve surgical outcomes.
What first attracted you to this field?
I developed a strong interest in coding and working with clinical data during my undergraduate coursework. At the same time, I started to notice how much untapped potential exists in clinical data. There is an overwhelming amount of patient information—electronic health records, imaging, and biosignals—but limited translation into meaningful, clinically actionable insights. This realization drew me toward machine learning and AI in medicine, where I became interested in applying data-driven approaches to support clinical decision-making. More recently, my interests have centered on surgical robotics, where I can integrate imaging and predictive modeling to guide procedures and improve patient outcomes.
Why did you choose to study at Mount Sinai?
As a native New Yorker, I was initially drawn to Mount Sinai because of its proximity to home, but I quickly came to appreciate both the institution’s guiding principles and the structure of the MDSAI program. I was interested in joining an institution actively investing in AI and data science, and Mount Sinai has demonstrated a clear commitment to this vision. I was also particularly drawn to this program’s flexibility, which allows students to pursue a wide range of research and professional directions. This structure fosters a diverse cohort, with students pursuing paths in industry, PhD programs, and medicine, and it has also broadened my perspective on how AI and data science can be applied across disciplines.
Who are your mentors, and what is the focus of your research?
My primary mentor is Xueyan Mei, PhD, and my research focuses on developing imaging-based machine learning models to improve patient outcomes. I have worked with large-scale imaging datasets, including RadImageNet—a dataset of over 5 million labeled images spanning PET, CT, ultrasound, and MRI—to build predictive and classification models that help clinicians better anticipate patient outcomes.
My personal research interests build on this foundation but are more focused, combining my engineering background with machine learning and medical imaging. Specifically, I am interested in surgical robotics, with the goal of developing models that enhance clinicians’ understanding and trust of robotic-assisted procedures. I am currently working on a project that analyzes arthroscopic video data to improve patient recovery time and procedural accuracy. Additionally, one idea I am exploring for my MDSAI capstone is a machine learning model that uses preoperative imaging to generate 3D anatomical reconstructions and guide robotic procedures. This direction was inspired by prior research on models that guide pedicle screw placement by generating 3D renderings of the spine from preoperative scans to improve alignment and accuracy.
What has been your greatest accomplishment in the program so far?
I think my greatest accomplishment in the program so far has been taking advantage of everything Mount Sinai has to offer outside the classroom. In addition to my coursework and research, I volunteer with the East Harlem Health Outreach Partnership and serve as the Master’s Year 1 Representative-at-Large on the Student Council. These experiences have allowed me to build meaningful relationships across the Mount Sinai community and have enriched my growth in the program beyond academics.
How have the resources at Mount Sinai contributed to your success in the program?
Access to both academic and clinical resources has played a significant role in my success. I feel confident navigating my coursework and research because my mentors and program leadership are consistently accessible and supportive. In addition, being part of a hospital-centered institution allows me to directly observe the clinical context behind my work. For example, in preparation for an upcoming study in orthopedics, I observed multiple knee arthroscopy procedures in the operating room, standing alongside surgeons and closely following intraoperative workflows. This direct exposure gave me a clearer, more grounded understanding of how imaging data is generated and how my research can be applied in practice.
What are your plans after you complete your MDSAI degree?
After completing my MDSAI degree, I plan to attend medical school. This program has given me the opportunity to further my academic, research, and clinical work in ways that are closely aligned with my long-term goals. Through this work, I have become increasingly motivated to pursue a career where I can directly contribute to patient care while also advancing the tools that support it. Moving forward, I hope to carry my background in AI and machine learning into my medical training, with a focus on surgical innovation. I am especially interested in continuing to explore how data-driven models and imaging technologies can enhance surgical robotics by improving procedural accuracy and making care more precise, personalized, and accessible for patients.