Munich AI Lectures: Alexei (A.) Efros

On a monthly basis, we invite top-level AI researchers to give us a glimpse into their work and the future of AI. Our lectures consist of a short presentation followed by a Q&A to enable a lively discussion with our speakers. We encourage our viewers to post their questions into the chat - the speaker will be happy to answer them after the presentation.
We are very happy to welcome Prof. Alexei (A.) Efros from UC Berkeley, who will give a lecture on "We are (still!) not giving data enough credit!".
Abstract:
[Coming Soon]
Bio:
Alexei (Alyosha) Efros is professor at UC Berkeley and affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR). Prior to that, he was for a decade on the faculty of Carnegie Mellon University, and has also been affiliated with École Normale Supérieure/INRIA and University of Oxford. His research is in the area of computer vision and computer graphics, especially at the intersection of the two. He is particularly interested in using data-driven techniques to tackle problems where large quantities of unlabeled visual data are readily available. He has been honored with numerous prizes, including the following: ACM Prize in Computing (2016), three PAMI Helmholtz Test-of-Time Prizes (1999,2003,2005), PAMI Thomas S. Huang Memorial Prize (2023).
About:
The Munich AI Lectures are a joint initiative of baiosphere, Bavarian Academy of Sciences and Humanities (BAdW), Helmholtz Munich, Ludwig-Maximilians-Universität München (LMU), Technische Universität München (TUM), AI-HUB@LMU, ELLIS Chapter Munich, Konrad Zuse School of Excellence in Reliable AI (relAI), Munich Center for Machine Learning (MCML), Munich Data Science Institute (MDSI) at TUM and Munich Institute of Robotics and Machine Intelligence (MIRMI), coordinated by the Bavarian AI Agency. The aim is to attract renowned AI researchers to Munich. To keep up to date with the latest Munich AI Lectures, follow us on LinkedIn or visit the lecture series’ website at munichlectures.ai

[@GerardSans]

Agreed. Data-centric AI should already be the focus and not brute force scaling or iterative optimising.