AI/ML Models: physics-informed AI, physics-informed neural networks (PINNs), graph neural networks (GNNs), AI surrogate models, foundation models (time series, geometry, manufacturing), generative AI, large language models (LLMs), computer vision (CV), natural language processing (NLP), deep learning (DL), reinforcement learning (RL), supervised/unsupervised learning, causal inference, MLOps.
Mathematical Modeling: dynamical systems, ODE/PDE, graph theory, game theory, statistical mechanics.
Computational Modeling: whole-cell systems modeling, Gillespie algorithm (SSA), Fokker-Planck & Langevin equations, agent-based modeling (ABM), cellular Potts modeling (CPM), center-based modeling (CBM), coarse-grained (CG) simulations, flux balance analysis (FBA), cellular automata (CA) modeling, finite element modeling (FEM).
IP & Leadership: IP strategy, patent portfolio management, cross-functional & interdisciplinary team leadership, AI governance & risk mitigation, grant writing.
Data Science & Scripting: Python, Perl.
Functional & Symbolic AI: Common Lisp, Scheme.
Systems & Low-Level: C/C++, assembly.
Typesetting & Publishing: LaTeX, Overleaf.
AI & Scientific Computing: PyTorch, TensorFlow, scikit-learn, XGBoost, SciPy, NumPy, JAX, pandas, matplotlib, OpenCV, Fiji/ImageJ, Virtual Cell, ITK, MATLAB (and many toolkits), SageMath.
Data & Cloud: GCP (BigQuery, CloudSQL), PostgreSQL, MySQL, MongoDB, NoSQL, Redis, Neo4j, distributed microservices.
Software Engineering: git (GitHub, GitLab), CI/CD, TDD, code review, Docker, Kubernetes, Agile/Atlassian methodologies.