I am a machine learning-focused data scientist with a Ph.D. in Earth Science. My background combines research-heavy signal processing with production AI delivery across NLP, forecasting, computer vision, and multimodal sensing.

What I work on

Applied AI systems that need both rigor and pragmatism.

  • Enterprise NLP classification, retrieval workflows, and LLM-based tooling
  • Multivariate forecasting, pricing decision support, and anomaly detection
  • Remote sensing, object detection, and multimodal sensor fusion
  • Production pipelines using Databricks, MLflow, Kubernetes, and distributed data tooling

How I work

  • Frame the delivery problem before selecting the model.
  • Design evaluation loops that expose failure modes early.
  • Build pipelines that other engineers and analysts can actually run.
  • Balance research ambition with operational constraints and timelines.

Core tools

Python PyTorch Transformers XGBoost Scikit-learn PySpark Databricks MLflow Kubernetes Docker FastAPI Elasticsearch Neo4j Kubeflow

Education

  • University of Ottawa - Ph.D., Earth Science
  • China University of Petroleum (Beijing) - M.Eng., Geological Engineering
  • Chengdu University of Technology - B.Sc., Geophysics