Do medical students need to know how to code?
No. As a medical student, you bring the research question and clinical expertise. We match you with a Machine Learning fellow who handles all the coding.
01 // THE APPROACH
How It Works
Projects are organized into triads: a machine learning fellow who builds, a medical student who guides, and a physician who ensures it's clinically relevant.
Clinical Context
Medical Mentor
As a medical student, you lead the research team, guide your machine learning fellow, and frame results for a clinical audience.
You Build It
Machine Learning Fellow
You write the code and run the experiments, guided by a team of experienced advisors and surrounded by a cohort of equally-talented peers.
Quality Control
Senior Physician
As a practicing physician, you bring the research question, review methods, ensure the project is clinically valid, and open doors for publication.
ENABLED BY
Community Builder
Chapter Director
You lead Tensor Lab at your medical school, select the cohort, and manage the chapter's success.
02 // TIMELINE
Timeline
January – February
Medical Leadership
We select the Chapter Directors who will build the community and the Medical Mentors who will lead the projects.
March
Project Incubation
Mentors refine their hypotheses with senior faculty. We ensure every project is viable, high-impact, and ready for a technical partner.
March – April
Fellowship Selection
We open the doors to engineers. Medical students interview and select their own technical counterparts, building a team perfectly matched for the problem at hand.
April – June
The Onramp
Teams secure IRBs, data access, and credentials now so that when summer starts, you can focus 100% on building.
June – August
10-Week Research Sprint
Build the model, run experiments, analyze results, and write the paper. This is the core of the fellowship.
September
Present your work
Share your findings at the Tensor Lab Symposium and submit your paper for publication
03 // OUR TEAM
Leadership Team
Founding Directors
Matt Allen
Co-Founder & Executive Director
University of California, San FranciscoSchool of Medicine
Aaron Ge
Co-Founder & Technical Director
University of MarylandSchool of Medicine
Additional Directors
Chy Murali
Operational Director
University of MarylandSchool of Medicine
Gavin Shu
Strategic Director
University of California, San FranciscoSchool of Medicine
Angie Lee
Business Director
University of MarylandSchool of Medicine
04 // 2025 COHORT
Selected Works
From our Pilot Cohort
LLMs vs. Traditional ML
Compared the performance of LLM-based semantic models (using LLM embeddings) against traditional ML models (XGBoost) across different clinical prediction tasks.
Result: Text representation critically affects performance; traditional ML
excels at physiological data.
Oncology Knowledge Graph
Developed the OPMG prototype to accelerate clinical evidence synthesis using an ETL-First strategy and Knowledge Graph RAG system.
Result: Validated ETL-first strategy and end-to-end KG-RAG functionality.
Prostate Cancer & SDOH
Trained ML models using SEER registry data linked with Social Determinants of Health proxies to stratify prostate cancer patients.
Result: SDOH adds measurable predictive value, highlighting social context.
RFS Prediction via PET/CT
Investigated whether a simplified multimodal model using only PET/CT and clinical data could achieve comparable RFS prediction to expert-annotated models.
Result: Simpler PET/CT model achieved comparable performance and is more
generalizable.
LLM ER Triage Simulator
Developed a training simulator using LLMs to create interactive, data-grounded emergency triage encounters from MIMIC-IV data.
Result: Confirmed feasibility of using LLMs for realistic medical training
tools.
Predicting Readmissions
Developed an attention-based deep learning model (BioClinicalBERT) to predict 30-day readmissions from free-text discharge summaries.
Result: Achieved AUROC 0.730 with interpretable predictions via SHAP.
Deep Learning for CT Lung
Compared ResNet50, VGG16, and InceptionV3 for CT-based lung lesion classification using the IQ-OTH/NCCD dataset.
Result: InceptionV3 showed the most consistent performance (0.92 F1).
Fellow Stories
Tensor Lab showed me meaningful challenges that combine both clinical and technical domains. As I prepare for my PhD applications, this experience was invaluable in clearly defining my research direction.
Research FellowTensor Lab taught me to conceptualize and develop applications for LLMs that require significant domain knowledge.
Research FellowThe guidance from the faculty and mentors was exceptional... I wouldn’t have reached our final publication goals without their help.
Research FellowCollaborating with a global cohort... provided me with a network of contacts for future projects.
Research Fellow05 // FAQ
Common Questions
How much time do medical students spend per week?
About 2-4 hours per week. You'll meet with your fellow regularly to review progress and guide the direction of the research.
Do Machine Learning fellows need a medical background?
No. Your medical student partner will teach you everything you need to know about the clinical context. We're looking for strong Python/ML skills.
How much time do Machine Learning fellows spend per week?
Plan for 15-20 hours per week during the 10-week summer sprint. Think of it like a serious research internship.
Is this remote or in-person?
Mostly remote. You'll meet with your team over Zoom/Discord, but some chapters organize optional in-person sessions.
Is this paid?
Not yet. Right now, this is an unpaid fellowship focused on research experience and publication. We're working on funding for future cohorts.
Who owns the code and research?
We believe in open science. Code is open-sourced, and all team members share authorship. Patient data stays under the PI's institutional controls.
06 // OPEN ROLES
Join the Team
Lead a Chapter at Your Medical School
Want to lead Tensor Lab at your institution? We're looking for teams of 3+ students to lead new chapters. Below are the available chapter directorship positions.
Chapter Executive Director
Strategy & Institutional RelationsYou are the CEO of your chapter. You build the relationships with the medical school, recruit the best talent, and clear the path for your teams to innovate.
Chapter Technical Director
Technical Rigor & Quality ControlYou are the CTO. You ensure every project is technically sound, every dataset is ready, and every engineer is up to the challenge.
Chapter Operations Director
Logistics & SystemsYou are the COO. You design the systems that make the fellowship run, from the first application to the final publication.
For Medical Students: Lead a Research Project
MENTORSHIP ROLE • CO-FIRST AUTHORSHIPApplications open now. Turn your clinical insights into published research by leading an AI-powered project. We'll pair you with a talented Machine Learning fellow who builds the technical solution while you guide the clinical direction. If you don't have a faculty mentor yet, we'll match you with one. You bring the medical knowledge—they bring the machine learning expertise. Together, you'll co-author a paper that advances both fields.
What You'll Do
- Define a meaningful clinical problem that Machine Learning can address
- Interview and select a Machine Learning fellow to collaborate with
- Guide research direction through weekly strategy meetings
- Co-write the manuscript and submit for peer-reviewed publication
Who Should Apply
- Medical students with dedicated research time (summer block or gap year)
- Students passionate about applying AI to solve real clinical challenges
- Students ready to earn co-first authorship on an impactful publication
For Machine Learning Engineers: Join Waitlist
RESEARCH FELLOWSHIP • PUBLICATION OPPORTUNITYApplications open March 2026. Join the waitlist to get notified and browse cutting-edge clinical AI projects. You'll work with actual patient data, collaborate with medical students who guide the clinical direction, and publish research under renowned faculty mentors at top institutions. We provide the clinical expertise—you bring the technical skills to build solutions that matter.
What You'll Do
- Build production-grade ML models with real medical datasets
- Collaborate with medical students who guide the clinical direction
- Work under renowned faculty mentors at top institutions
- Co-author and submit papers for peer-reviewed publication
Who Should Apply
- Students with strong Python/ML experience (PyTorch, scikit-learn, TensorFlow)
- Students committed to 15 hrs/week during the 10-week summer program
- Students excited to apply ML to high-impact healthcare challenges