Michael Fatemi

Michael Fatemi

Let's connect! Would love to chat about anything.
michael [at] michaelfatemi.com
Or connect with me on LinkedIn

I'm Michael Fatemi, a software developer and machine learning enthusiast.

I am researching robotic intelligence and bioinformatics.

Previously, I was a student researcher at Dartmouth's Emerging Diagnostic and Investigative Technologies lab, where I used deep computer vision and statistical techniques to analyze colorectal cancer data.

I also developed robotic path planning techniques using deep reinforcement learning with graph neural networks at the US Army Research Lab.

Additionally, worked as a contractor with Kyron Learning, where I developed methods to improve the factual accuracy and tutoring capabilities of language models.

I am currently an undergraduate at the University of Virginia, graduating in 2026. I am majoring in computer science and mathematics. I went to the Thomas Jefferson High School for Science and Technology.



Deceptive Path Planning via Reinforcement Learning with Graph Neural Networks. Michael Y. Fatemi, Wesley A. Suttle, Brian M. Sadler. Under review.


Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis. Zarif Azher, Michael Y. Fatemi, Yunrui Lu, Gokul Srinivasan, Alos Diallo, Brock Christensen, Lucas Salas, Fred Kolling IV, Laurent Perrard, Scott Palisoul, Louis Vaickus, Joshua Levy. Pacific Symposium on Biocomputing, 2024. (Paper)

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining. Gokul Srinivasan, Matthew Davis, Matthew LeBoeuf, Michael Y. Fatemi, Zarif Azher, Yunrui Lu, Alos Diallo, Marietta Saldias Montivero, Fred Kolling IV, Laurent Perrard, Lucas Salas, Brock Christensen, Thomas Palys, Margaret Karagas, Scott Palisoul, Gregory Tsongalis, Louis Vaickus, Sarah Preum, Joshua Levy. Pacific Symposium on Biocomputing, 2024. (Paper)

The Overlooked Role of Specimen Preparation in Bolstering Deep Learning-Enhanced Spatial Transcriptomics Workflows. Michael Y. Fatemi, Yunrui Lu, Alos B. Diallo, Gokul Srinivasan, Zarif L. Azher, Brock C. Christensen, Lucas A. Salas, Gregory J. Tsongalis, Scott M. Palisoul, Laurent Perreard, Fred W. Kolling IV, Louis J. Vaickus, Joshua J. Levy. Submitted for review. August 2023. (Preprint)

Inferring Spatially-Resolved Transcriptomics from Cell Detections. Michael Y. Fatemi, Yunrui Lu, Cyril Sharma, Eric Feng, Zarif L. Azher, Alos B. Diallo, Gokul Srinivasan, Grace M. Rosner, Kelli B. Pointer, Brock C. Christensen, Lucas A. Salas, Gregory J. Tsongalis, Scott M. Palisoul, Laurent Perreard, Fred W. Kolling IV, Louis J. Vaickus, Joshua J. Levy Submitted for review. August 2023. (Preprint)

Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot study. Michael Y. Fatemi, Eric Feng, Cyril Sharma, Zarif Azher, Tarushii Goel, Ojas Ramwala, Scott M. Palisoul, Rachael E. Barney, Laurent Perreard, Fred W. Kolling, Lucas A. Salas, Brock C. Christensen, Gregory J. Tsongalis, Louis J. Vaickus, Joshua J. Levy. March 2023.Journal of Pathology Informatics. (Paper, Video)


Cactus Courseware

This is an open-source educational content website that my friends and I built. Educational content can be imported from GitHub and is updated automatically whenever contributors make changes. It includes animations and interactive content to cater to different learning styles. Uses React and Express as its frontend and backend frameworks, and MongoDB for data persistence.


StreetSweep is an app that my friend and I created for a hackathon with the goal of helping policymakers and volunteer organizations by creating a heatmap of the prevalence and type of trash at each location. It uses class-agnostic object detection with a model adapted from a ResNet classifier to automatically detect garbage in photos of street litter.


An app to simplify carpooling: students can easily find people to carpool with for after-school events. 300+ users. Worked with school administration and sports coaches to get schoolwide adoption. Helps students who live far away from a school to have the same access to after-school activities as people who live nearby, especially if they are unable to drive themselves. Stack used: Frontend: Typescript React Backend: Node.js/Express, Prisma ORM, PostgreSQL database Hosting: DigitalOcean droplets, VPC for database security


An app that enables the disabled to control their computer mouse with their eyes: after a quick calibration step, the computer would detect which section of the screen they were looking at with 90% accuracy and move the mouse there. Then, via voice recognition provided by Google Cloud, they could click on those parts of the screen, type, search the web, and perform other tasks. Won 1st Place at the HooHacks 2020 Hackathon, an official hackathon organized by students at the University of Virginia. Utilized Haar cascades, DLib facial recognition, a custom iris detection algorithm made with OpenCV, and voice recognition from Google Cloud.

Clubs and Project Teams

University of Virginia Autonomous Racing

We are competing in the Indy Autonomous Challenge, where teams from around the world (UVA, MIT, CMU, KAIST, TUM) put their algorithms head to head in an autonomous Indy 500. We're using LiDAR sensors, cameras, and GPS to navigate the track. I am implementing algorithms to accurately detect other cars and plan methods to overtake opponents in real-time.

University of Virginia Solar Car Team

Working to develop a solar-powered car. Recently, I contributed to the development of a regenerative braking system to improve the car's battery efficiency. I mostly developed embedded software using Mbed OS and C++. I also gained experience using Docker as a simulated build environment.

Virginia Entrepreneurship Organization

I help organize events and workshops to help maintain a strong and dense community of builders at UVA.