B.S./M.S. in Computer Science @ University of Washington
Robotics Research @ WEIRD Lab
Teaching Assistant @ Paul Allen School, UW
cleahw@cs.washington.edu
Hello! I'm a master's student in Computer Science at the University of Washington (B.S. completed Winter 2026, minor in Business Administration). I do research in reinforcement learning and robotics at the WEIRD Lab, advised by Prof. Abhishek Gupta. I'm excited about developing sample-efficient robot policies that can rapidly adapt to new environments by leveraging suboptimal data and human data. Previously, I've explored LLMs for clinical workflows and improving healthcare technologies for patients.
CoRL 2026
Pranav Teegavarapu*, Sriyash Poddar*, Rico Qi, Cleah Winston, et al. [show all]
ICML 2026
Sarvesh Patil*, Mitsuhiko Nakamoto*, Manan Agarwal†, Shashwat Saxena†, Jesse Zhang†, Giri Anantharaman, Cleah Winston, et al. [show all]
ICRA 2026
Kevin Huang*, Rosario Scalise*, Cleah Winston, et al. [show all]
GenAI4Health, NeurIPS 2024 Poster
Caleb Winston, Cleah Winston, et al. [show all]
IEEE International Conference on Healthcare Informatics, 2024
Caleb Winston, Chloe Winston, Cailin Winston, et al. [show all]
IEEE International Conference on Software Engineering, 2022
Cailin Winston, Caleb Winston, Chloe N Winston, et al. [show all]
IEEE Engineering in Medicine & Biology Society, 2021
Chloe N Winston, Cailin Winston, Caleb Winston, et al. [show all]
January 2025 - Present
Developed a pain logging app using Gemini models to process user inputs, display structured pain events, and visualize pain metrics in an intuitive UI. This platform allows users to simply enter in any event (pain levels, medication intake, diet, etc.) and the LLM will categorize events.
September 2022 – May 2024
Trained convolutional neural networks to predict steering angles to follow a road from simulated data from AirSim. Investigated whether robust training can be achieved with simulated data and less real data, collected from MuSHR cars.
September 2023 – February 2024
Developed a prototype for an AR-based system using the Meta Quest Pro that creates a more immersive and interactive form of doing at-home physical therapy. Created a business plan for the product and pitched at the 2024 Holloman Health Innovation Challenge, UW.
January 2021 – March 2021
Designed an ML-based system to predict distracted driving from EEG data. Collected data through a user study where subjects were placed in distracting situations while operating a virtual car. Trained regression models and neural network decoders.
November 2018 – May 2019
Developed an Android and iOS app for motivating scoliosis patients to complete Schroth therapy and daily bracing. The app was endorsed by Dr. Manuel Rigo (MD/PhD), the head of the Barcelona Scoliosis Physical Therapy School, and has reached over 100 therapists globally and many scoliosis patients.
November 2018 – May 2019
Designed a fruit ripeness sensor and a reinforcement-learning system that processes sales data and ripeness sensor data to suggest effective selling prices to minimize fruit wastage. Created a circuit system that detects the color of fruits based on photoresistor signals.
September 2017 – March 2018
Built a robotic car by programming an Arduino UNO board to understand the risk of cyberattacks on driverless cars. Designed and conducted experiments to investigate how cybersecurity is a real issue for autonomous vehicles.
I am a 10th time Allen school teaching assistant (TA) for machine learning, discrete math, and probability classes for computer science. As part of being a TA, I lead weekly ~30 student sections, assist students through office hours and 1-1 sessions, help with course logistics (e.g., running the website), host review sessions, and proctor exams.
I contributed to developing a platform that allows internal users to easily fine-tune LLMs. Using AWS S3 for data storage and AWS EC2 instances for training, we abstracted away the complications of data generation, training, and evaluation.
I developed and trained ML models that detect stroke in patient voice recordings with limited data.
At Banyan, I developed Banyan Processes, a feature that enables users to save Julia scripts to the cloud and schedule them for later execution. Additionally, I implemented features to allow users to manage their cloud spending.