Speaker |
Francesco Quinzan |
|---|---|
Affiliation |
University of Oxford |
Date |
Friday, 15 March 2024 |
Time |
12:00-13:00 |
Location |
Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH |
Link |
https://ucl.zoom.us/j/97245943682 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
Recent successes of AI and Machine Learning have ignited a fast transfer of technology from research into products and government services. This phenomenon has created a range of problems, which can be broadly attributed to the interaction between technology and society. Examples of these problems are bias and unfairness, lack of robustness, and lack of transparency. In this talk, I will discuss some of the main challenges in Trustworthy AI, focusing on various applications, including data-driven health care and offline RL. I will argue that it is possible to design AI systems that are robust and capable of generalizing effectively, by uncovering the causal mechanisms of the underlying data generating process. I will also discuss how state-of-the-art generative models can be used on top of these techniques, to further enhance generalization performance. I will illustrate recent advancements in this field, and discuss possible future directions. |
Biography |
Francesco is an associate researcher at the CS Department at the University of Oxford, hosted by Marta Kwiatkowska. He is also an ELSA Research Associate. Previously, Francesco was a Postdoc at the Division of Decision and Control Systems at KTH, where he worked with Stefan Bauer and Cristian Rojas. He obtained his Ph.D. in Computer Science from the Hasso Plattner Institute in Germany. Francesco visited various institutes and research groups, including the Max Plank Institute for Intelligent Systems, where he was hosted by Bernhard Schölkopf, and the Learning & Adaptive Systems Group at ETH. Francesco studied mathematics at the University of Roma Tre, where he graduated with honours. |