I have experince in probabilistic models for biological and financial systems under uncertainty. My work extends stochastic models for asset pricing, spatiotemporal transformers for ecological risk, and domain-generalizable inference for physiological signals.

• Quantitative Developer @ Dalhousie Investment Society
  Prototyping stochastic volatility models to stress-test a $20M simulated portfolio against non-stationary market conditions.

• ML Researcher @ MAPS Lab
  Working on probabilistic frameworks to model ballast-water bioinvasion risk across the North Atlantic.

• Founding President @ Dalhousie Machine Learning Society
  Orchestrating research initiatives and technical curriculum for 200+ members.

Quantitative Finance
Monte Carlo Simulations  •  Stochastic Calculus (SDEs)  •  Volatility Surface Modelling  •  Convex Optimization

Machine Learning
Bayesian Inference  •  Time Series (ARIMA/Transformers)  •  PyTorch  •  Computer Vision (YOLO/SAM2)

Data Engineering
Apache Spark  •  Databricks  •  Azure  •  ETL Pipelines  •  Docker

IEEE BigData 2025
Goal-Conditioned Reinforcement Learning for Data-Driven Maritime Navigation (Accepted)

RBC Borealis (Fellowship)
Modeled eelgrass trajectories using non-stationary Bayesian analysis.

TD Bank (Internship)
Worked on Multi-Agent LLM architectures (MCP) research for regulated financial environments.