I offer end-to-end machine learning solutions, from problem formulation through production deployment. My core capabilities include:
Data Ingestion & Preprocessing: Aggregating, cleaning, and transforming raw data into high-quality features optimized for model training.
Model Architecture Design: Crafting custom neural and hybrid models—LSTMs, attention modules, GCNs, or transformers—tailored to your problem.
Training, Tuning & Evaluation: Running scalable experiments with systematic hyperparameter searches and rigorous metrics (RMSE, ADE/FDE, MAE) to maximize accuracy.
Scalable Implementation & Frameworks: Developing modular, GPU-accelerated code in PyTorch, TensorFlow, or scikit-learn for reproducible, high-performance workflows.
Deployment & Monitoring: Packaging models with ONNX/TorchScript and deploying via REST APIs or containers, with real-time drift detection and retraining pipelines.
Technical Communication: Delivering clear documentation, reports, and publications that demystify complex methods and highlight key results.