Integrating Artificial Intelligence, Data Science, and Multiomics for Biological Systems Discovery. Focus on Knowledge Graphs, Explainable Models, and Large-Scale Biomedical Data Integration.
- Languages: Python, Elixir, SQL, Shell Scripting, LaTeX, Julia, Nim, Clojure
- Data Science: Polars, Spark, Numpy, Matplotlib, Sqlite, Postgres, DuckDB
- AI/ML: PyTorch, LightGBM, Scikit-learn, LangChain, Snorkel, Transformers, PEFT, ChromaDB
- Knowledge Graphs: Graph Databases, Ontology, KGX, Centrality, Graph Neural Networks
- NLP: spaCy, NLTK, Embeddings, Text Classification, TF-IDF
- DevOps: Nix, Docker, Setuptools, Mix, Pytest, Kubernetes
- Biology: Heterogenous Multiomic Data, Shotgun Metagenomics, Polygenic Risk Scores, ELISA
- Chemistry: DFT, Mass Spectrometry, Proton NMR, FTIR, Organic Synthesis, Equillibreum
- Hobbies: Cooking, Skiing, Geometry Dash, Gardening, Travel, Hiking