teaching

Graduate- and professional-level courses in machine learning, data science, and statistics for financial technology.

Duke University · Pratt School of Engineering

FINTECH 540: Machine Learning for FinTech

  • Format: Graduate course on machine learning applications across the fintech industry.
  • Highlights: Explored how supervised, unsupervised, and reinforcement learning techniques address rapidly evolving challenges in digital finance through extensive coding sessions that use real financial data.

FINTECH/ECE 590: Data Wrangling and Visualization with Python

  • Format: Graduate special topics course cross-listed in Electrical & Computer Engineering.
  • Highlights: Focused on end-to-end data pipelines for analytics, covering Python-based querying, cleaning, manipulation, and visualization with tools such as SQL, web scraping workflows, and modern data-collection practices.

FINTECH 502: FinTech Capstones

  • Format: Experiential learning capstone for FinTech MEng students.
  • Highlights: Guided student consulting teams as they delivered industry-sponsored projects that translate quantitative finance and machine learning research into deployable solutions for partner organizations.

FINTECH 520: Introduction to Statistics

  • Format: Core quantitative foundations course for incoming FinTech graduate students.
  • Highlights: Built the statistical toolkit—probability, inference, and modeling—required for advanced financial technology coursework and applied research.

University of Florence · School of Economics and Management

Python for Data Science (March–May & September–December 2020)

  • Format: Professional training program for students and practitioners.
  • Highlights: Introduced Python fundamentals alongside the scientific stack (NumPy, pandas, matplotlib, and related libraries) to support data analysis and the extraction of actionable insights.