AI, ML & Mathematical Modeling: 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, dynamical systems, ODE/PDE, graph theory, game theory, statistical mechanics.
Computational Biology & Simulation: 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).
Scientific Tools & Frameworks: PyTorch, TensorFlow, scikit-learn, XGBoost, SciPy, NumPy, JAX, pandas, matplotlib, OpenCV, Fiji/ImageJ, Virtual Cell, ITK, MATLAB (and toolkits), SageMath.
Programming Languages: Python, C/C++, Perl, Common Lisp, Scheme, assembly, LaTeX, Overleaf.
Cloud, Data & MLOps: GCP (BigQuery, CloudSQL), PostgreSQL, MySQL, MongoDB, NoSQL, Redis, Neo4j, distributed microservices, MLOps, Docker, Kubernetes, CI/CD, git (GitHub, GitLab), TDD, code review, Agile/Atlassian methodologies.
Leadership & Strategy: IP strategy, patent portfolio management, cross-functional & interdisciplinary team leadership, AI governance & risk mitigation, grant writing.