🔬 Scientific Machine Learning → Physics‑Informed Models
- Inverse problems & Tikhonov regularization
- Ensemble & time‑series models with uncertainty quantification
- Physics-constrained reconstruction of geophysical fields
🌌 Space Physics & Space Weather → Model the Geospace
- Real-time IMEF & Cross Polar Cap Potential (CPCP) prediction
- Electric-field reconstruction via field-line mapping & inversion
- Geomagnetically induced current (GIC) modeling
⏱ Time Series & Forecasting → Nonstationary Systems
- LSTM, TCN, ARIMA & Prophet models
- Feature engineering & noise-robust preprocessing
- Storm-time forecasting of geophysical dynamics
🛠 ML Engineering → From Research to Deployment
- End-to-end ML pipelines & experiment tracking (MLflow)
- Automated data ingestion & scheduled inference
- Deployment via Docker, systemd & GitHub Actions
- GPU acceleration & performance benchmarking
⚙ Physics & Computational Modeling
- FORTRAN/Python simulators & finite-difference methods
- PDE-based modeling of electromagnetic systems
- Nonlinear optics & second-harmonic generation
- High-performance scientific computing
🎓 Teaching & Mentorship
- Instructor/TA at UNH Physics Department
- Guided problem-solving & structured learning design
- Mentorship in numerical modeling & ML
| Skills | Technologies |
|---|---|
| ML Engineering | |
| Deep Learning | |
| Machine Learning | |
| Cloud Platforms | |
| Time Series Analysis | |
| Pkg / Env Managers | |
| Python | |
| Git | |
| Visualization | |
| Editors |
