Skills & Technologies

Description

# Skills & Technologies ## Machine Learning / Deep Learning - **Generative Adversarial Networks (GANs)** — Conditional WGAN-GP architecture with gradient penalty for stable training - **Physics-Informed Machine Learning** — Custom monotonic distance-attenuation penalty encoding seismological prior knowledge into the loss function - **PyTorch** — Model definition (nn.Module), custom training loop, autograd for gradient penalty computation, GPU acceleration - **Residual Networks** — Pre-activation residual blocks with LayerNorm for both Generator and Critic - **Learned Embeddings** — Shared period embedding MLP mapping continuous spectral period to a higher-dimensional representation ## Data Engineering - **Pandas** — Loading, cleaning, and reshaping (~10K records x 48 columns) from wide to long format (255K samples) - **Feature Engineering** — Log transforms (Rrup, Vs30, Period, SA), PGA replacement for log-domain compatibility - **scikit-learn** — StandardScaler for feature normalization, train/test splitting, regression metrics (RMSE, MAE) - **Serialization** — Model checkpointing with `torch.save`, scaler persistence with `joblib` ## Domain Knowledge - **Earthquake Engineering** — Ground Motion Models, Spectral Acceleration, NGA-Subduction database - **Seismological Parameters** — Moment magnitude (Mw), rupture distance (Rrup), depth to top of rupture (Ztor), site shear-wave velocity (Vs30) - **Physical Constraints** — Distance-attenuation relationship (SA decreases with increasing Rrup) - **Response Spectra Analysis** — Per-event evaluation across 25 spectral periods (PGA to T=10s) ## Evaluation & Visualization - **Matplotlib** — Loss curves, real-vs-predicted scatter plots, residual analysis, per-event response spectra - **Diagnostic Plots** — Residuals vs spectral period for period-dependent bias detection - **Regression Metrics** — RMSE and MAE on held-out test set in log(SA) space ## Development Environment - **Google Colab** — Primary development environment with CUDA GPU - **Jupyter Notebooks** — Iterative experimentation with inline visualization - **Git / GitHub** — Version control and project hosting

Security Status