Host: LaToya Gourdine
IEEE POWER & ENERGY SOCIETY
IEEE Power & Energy Society Live Online Learning
Topic: Constrained-aware Machine Learning
Presented by: Ferdinando Fioretto, Assistant Professor, Syracuse University
In this webinar we will delve into the need for constraint-aware ML. We will present how to integrate key constrained optimization principles within the training process of deep learning models, endowing them with the capability of handling hard constraints and physical principles. The resulting models will bring a new level of accuracy and efficiency to hard decision tasks, which will be showcased on energy and scheduling problems. We will then introduce a powerful integration of constrained optimization as neural network layers, resulting in ML models that are able to enforce structure in the outputs of learned embeddings. This integration will provide ML models with enhanced expressiveness and modeling ability, which will be showcased through the certification fairness in learning to rank tasks and the assembly of high-quality ensemble models. Finally, we will discuss a number of grand challenges that I plan to address to develop a potentially transformative technology for both optimization and machine learning.
Ferdinando Fioretto is an assistant professor at Syracuse University. He works on machine learning, optimization, differential privacy, and fairness. His recent work focuses on (1) the integration of constrained optimization and machine learning to enhance the expressive ability of machine learning models and (2) understanding the interplay among privacy, equity, robustness, and performance in machine learning models and decision tasks.
He is a recipient of the 2022 Amazon Research Award, the 2022 NSF CAREER award, the 2022 Google Research Scholar Award, the 2022 Caspar Bowden PET award, the 2021 ISSNAF Mario Gerla Young Investigator Award, the 2021 ACP Early Career Researcher Award, the 2017 AI*AI Best AI dissertation award, and several best paper awards. He is also actively involved in the organization of several workshops, including the Algorithmic Fairness through the lens of Causality and Privacy at NeurIPS, the Privacy-Preserving Artificial Intelligence workshop at AAAI, and the Optimization and Learning in multiagent systems workshop at AAMAS.