Luca de Alfaro

Luca de Alfaro


Computer Science and Engineering

University of California, Santa Cruz

Ph.D. Stanford University, 1998

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.

We have just released DivExplorer: a Python package, and an associated Web Application, that enables anyone to analyze a dataset, and identify data subgroups where a classifier behaves in a way that deviates from the average. This can be used, for instance, to identify who benefits, and who is disadvantages, from errors in machine-learning classifiers.

Short Video
Project Site
Python Package
Web App


How Divergent Is Your Data? E. Pastor, A. Gavgavian, E. Baralis, L. de Alfaro. In Proceedings of the 47th International Conference on Very Large Data Bases (VLDB), Demo Track, 2021.
Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence. E. Pastor, L. de Alfaro, E. Baralis. In Proceedings of the 2021 ACM SIGMOD Conference, 2021.

Reinforcement learning for networks

In current network protocols, much of the behavior is hardwired in rules that prescribe when to transmit packets, how frequently to send them, how to route them, and so forth. The project seeks to leverage reinforcement learning to obtain protocols that adapt to their environment and achieve both greater efficiency, and greater robustness via their adaptation capability. We have developed techniques for channel access in wireless networks that, by using reinforcement learning, enable network nodes to co-adapt and collaborate to achieve fair and efficient bandwidth sharing. We are now looking at channel access, routing, congestion control, and more.


Adaptive Policy Tree Algorithm to Approach Collision-Free Transmissions in Slotted ALOHA. M. Zhang, L. de Alfaro, M. Mosko, C. Funai, T. Upthegrove, B. Thapa, D. Javorsek, J.J. Garcia-Luna-Aceves. In Proceedings of the 17th International Conference on Mobile Ad-Hoc and Smart Systems (IEEE MASS), 2020.
Using Reinforcement Learning in Slotted Aloha for Ad-Hoc Networks. M. Zhang, L. de Alfaro, JJ. Garcia-Luna-Aceves. In Proceedings of the 23rd International ACM Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems (MSWiM), pages 245-252, 2020.
An Adaptive Tree Algorithm to Approach Collision-Free Transmission in Slotted ALOHA. M. Zhang, L. de Alfaro, J.J. Garcia-Luna-Aceves. In Proceedings of the ACM SIGCOMM 2020 Workshop on Network Meets AI & ML (NetAI), 2020.
Approaching Fair Collision-Free Channel Access with Slotted ALOHA Using Collaborative Policy-Based Reinforcement Learning. L. de Alfaro, M. Zhang, J.J. Garcia-Luna-Aceves. In Proceedings of the IFIP Networking 2020 Conference, 2020.