Research application
FAIRS Roulette Player
Research application for roulette training and inference experiments. It includes a DQN agent, a PyTorch training pipeline, a FastAPI backend, a React frontend, and a Tauri desktop shell.
Open-source machine learning
I build open-source tools across clinical AI, reinforcement learning, scientific computing, and LLM tooling.
01
Three projects that show the main areas of my work.
Research application
Research application for roulette training and inference experiments. It includes a DQN agent, a PyTorch training pipeline, a FastAPI backend, a React frontend, and a Tauri desktop shell.
Clinical workflow
Client-server application for generating draft radiological reports from X-ray images. It supports dataset preparation, model training, validation, and report generation.
Scientific computing
Application for collecting, managing, and modeling adsorption data. It fits theoretical models to empirical isotherms and works with NIST and ARPA-E datasets.
02
What I am building, learning, and looking for.
Open-source ML tools for clinical research, medical imaging, reinforcement learning, computational chemistry, and LLM workflows.
Front-end development and productionizing ML with Rust.
Collaborations involving biotechnology, healthcare, and machine learning.
Reinforcement learning, medical AI, or moving from biotechnology research into machine learning.
03
A concise view of the path behind the projects.
Applied AI tools for clinical and scientific workflows.
Worked on CheckPack, developing micro-sensors to detect food spoilage and monitor quality in real time.
Training in biotechnology, data analysis, and experimental research.
04
Technologies used across the projects above.
Python
Java
JavaScript
SQL
Bash
PyTorch
TensorFlow
Transformers
Hugging Face
LangChain
Docker
Kubernetes
Linux
REST APIs
PostgreSQL
RDKit
NumPy / SciPy
Pandas
NIST databases
ARPA-E
Git
GitHub Actions
Jupyter
VS Code
CI/CD
Biotechnology
Engineering Sciences
Experimental research
Data analysis
Open source