Luca de Alfaro

Luca de Alfaro


Computer Science and Engineering

University of California, Santa Cruz

Ph.D. Stanford University, 1998

Office: Bld E2, Rm 339A. Office hours: Tuesdays 2-3pm.

I am a professor in the Computer Science and Engineering department at UCSC, where I have been a faculty member since 2001. Prior to that, I was an undergraduate student in Torino, Italy, then a Ph.D. student at Stanford, and then a postdoc at UC Berkeley. I am currently department vice-chair.

I have worked at the foundations of formal verification, contributing to the connection between game theory, logic, automata theory, and system modeling and verification, and these topics continue to be an interest of mine. Of late, I have become fascinated with the application of game theory and machine learning to the study of interaction, be it among humans, or animals, or nodes in a network. I call such field computational ecology.

I like to have an applied streak. I teach and I have contributed to the open-source web framework py4web, and I have been active in entrepreneurship, and I have co-founded two startups: SSB Progetti, lately acquired by Deloitte, and Camio, a pioneering ML-for-video company.

I love the outdoors and I am an avid mountain biker, hiker, skier, and bird-watcher.

Current Research

My current work is in computational ecology, a field that I define as the study of interaction, done with computational means. We use ideas from mathematics, game theory, and computer science, along with the most recent tools developed in ML, reinforcement learning, and GPU processing, to model and study the interaction of people, animals, and more. Here are some of the things I am working on:

  • Bird habitats and conservation. We are applying ML, parallel computation, and GPU processing to model the dynamics of animal habitats, with a particular focus on birds. The goal is to obtain computational models that can inform conservation and climate efforts. Joint project with Natalia Ocampo-PeƱuela, Tyler Sorensen and others.
  • Reinforcement-learning models of human behavior. We are studying how some human behaviors arise, modeling human experience with reinforcement learning.
  • Fairness in machine learning. The goal of this project is to develop the theory and the tools that enable people, from data scientists to non-specialists, to analyze datasets, and identify anomalous behavior that has potential fairness implications. Joint project with Elena Baralis and Eliana Pastor.


Two classes I created and I am teaching regularly are Programming Abstractions in Python, and Web Applications. The material for both classes is available to all; head over to my teaching page for more information.

In Fall 2023, I plan to teach a new graduate class on Computational Ecology.