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sepehrmn/README.md

Typing SVG

Hi there 👋 I'm Sepehr.

Computational neuroscientist now building something cool in AI as the CTO of an early-stage startup.

Personal interests include spatial computing, healthcare, computational psychiatry, and applying AI in these areas.

Socials

The best way to reach me is via email or on X.

Sepehr Mahmoudian on LinkedIn Sepehr Mahmoudian on Substack Sepehr Mahmoudian on Google Scholar Sepehr Mahmoudian on X (Twitter) Sepehr Mahmoudian on Hugging Face

Expertise

I use Python and its ecosystem for AI/ML. For apps, Typescript, Javascript and React (+Native). Finally, I use C/C++ when performance is needed.

I've used many machine learning models over the years including reinforcement learning, different recurrent neural networks, Bayesian modeling, and various methods for clustering, classification, regression, and dimensionality reduction.

       
OS Linux Ubuntu MacOS Windows with WSL2 Nix and NixOS
Tools git icon GitHub icon Anaconda Icon Visual Studio Code Icon Xcode Icon Jupyter Icon Neovim icon
AI/ML Python icon PyTorch Icon Numpy icon Pandas Icon SciPy Icon Pydantic Hugging Face Icon OpenAI icon LangChain icon MLX Icon
Software 3.0 Python icon Streamlit icon Gradio icon Cursor icon Langflow icon CrewAI icon    
DevOps/MLOps Docker icon Rancher icon Kubernetes icon Terraform icon Azure icon GCP icon Poetry Jenkins icon Travis-CI icon
Backend C icon C++ icon Rust icon Python icon FastAPI icon SQLModel icon Neon icon postgreSQL icon
Frontend HTML Icon CSS Icon JavaScript Tailwind CSS Icon shadcn/ui icon TypeScript React icon NEXT.js icon
Testing Mocha icon Pytest icon
Visualization Matplotlib icon Seaborn icon Marimo icon Three.JS icon Bokeh icon LaTeX icon
Native React Native icon Expo for React Native icon Swift icon Expo for React Native icon Unity icon

My most important skills are adapting quickly, picking up new tech fast, and executing relentlessly with perseverance. I think these will be more important than ever with how fast technology will be advancing.

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    Partial Information Decomposition (PID) analysis of activation functions for PIDeepnets (PID deep neural networks)

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