Alessio Alessio

Alessio

Executive In Residence in the Engineering Graduate and Professional Programs

Dr. Alessio Brini is a Postdoctoral Researcher at Duke University's Pratt School of Engineering, specializing in the Digital Asset Research & Engineering Collaborative (DAREC). Alessio addresses financial challenges in digital assets and decentralized finance (DeFi) with a focus on applied machine learning and data science methodologies. His proficiency spans all the diverse machine learning paradigms, including supervised, unsupervised, and reinforcement learning.

Dr. Brini earned his Ph.D. in Mathematical Finance from Scuola Normale Superiore, where he concentrated on implementing value-based and policy-based reinforcement learning algorithms to solve various economic and financial issues. His thesis, "Reinforcement learning for sequential decision-making: a data-driven approach for finance," reflects his deep commitment to the intersection of finance and technology.

At Duke University, Dr. Brini has been instrumental in delivering coursework on Machine Learning for Fintech, Data Wrangling and Visualization with Python, and Introduction to Statistics for the Master of Engineering in Financial Technology program. He has also overseen FinTech Capstone Projects and led summer research initiatives.

Dr. Brini's research contributions are well-recognized. His working papers and conference presentations further demonstrate his thought leadership in areas like cryptocurrency market stability and deep reinforcement learning for continuous financial trading.

In addition to his research and teaching roles, Dr. Brini actively contributes to the academic community through referee services. He has also been involved in conference organizations and graduate student advising.

Appointments and Affiliations

  • Executive In Residence in the Engineering Graduate and Professional Programs

Contact Information

Education

  • Ph.D. Scuola Normale Supiore (Italy), 2022

Research Interests

Dr. Brini's research focus encompasses the intersection of finance, machine learning, and economics, with a particular emphasis on reinforcement learning algorithms to address complex financial challenges. This includes sequential decision-making using data-driven approaches and the application of machine learning to financial stability, trading strategies, and decentralized finance (Defi) problems. 

Courses Taught

  • FINTECH 591: Special Readings in Financial Technology
  • FINTECH 590: Advanced Topics in Financial Technology
  • FINTECH 540: Machine Learning for FinTech
  • FINTECH 502: FinTech Capstone
  • ECE 590: Advanced Topics in Electrical and Computer Engineering

Representative Publications

  • Brini, A; Lenz, J, A comparison of cryptocurrency volatility-benchmarking new and mature asset classes, Financial Innovation, vol 10 no. 1 (2024) [10.1186/s40854-024-00646-y] [abs].
  • Brini, A; Lenz, J, Pricing cryptocurrency options with machine learning regression for handling market volatility, Economic Modelling, vol 136 (2024) [10.1016/j.econmod.2024.106752] [abs].
  • Brini, A; Tedeschi, G; Tantari, D, Reinforcement learning policy recommendation for interbank network stability, Journal of Financial Stability, vol 67 (2023) [10.1016/j.jfs.2023.101139] [abs].
  • Brini, A; Tantari, D, Deep reinforcement trading with predictable returns, Physica A: Statistical Mechanics and its Applications, vol 622 (2023) [10.1016/j.physa.2023.128901] [abs].
  • Brini, A; Lenz, J, Assessing the resiliency of investors against cryptocurrency market crashes through the leverage effect, Economics Letters, vol 220 (2022) [10.1016/j.econlet.2022.110885] [abs].