Artificial Intelligence: Present, Past, and Future (II)

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20/10/2021 @ 18:00 19:30 CEST

Artificial Intelligence techniques are increasingly present in all scientific fields and have driven great advances in recent years, becoming indispensable tools. Consequently, the Rafael del Pino Scientists Club, with the aim of promoting innovation, leadership, and social commitment among Spanish researchers, wants to bring together the community doing research in topics related to Artificial Intelligence

With that goal in mind, before the summer holidays, we hosted the first in a series of meetings on Artificial Intelligence and were fortunate to have Antonio Torralba (MIT) as speaker. 

This time, we will host two talks that we hope will be of interest to you: 

  1. “Sequential Decision Making in Uncertainty Environments by Carlos Riquelme 
  2. “Causal Inference Methodology” by Juan Gamella 

Some of the topics we will cover are the following: 

  • What have been the most important recent advances in your field of interest? 
  • What is the current state of the field? 
  • What is your vision for the future of your field? 
  • What have been your biggest personal successes and failures? 

We hope to see you all! 

*Carlos holds a Licenciatura in Mathematics and Computer Science degrees from Universidad Autónoma de Madrid. After completing his masters in Mathematics at the University of Oxford as a Caja-Madrid Scholar, Carlos did a PhD at Stanford University, mainly focused on statistical machine learning. Following brief stays at Twitter and Facebook, he joined Google Brain as a research scientist, first in Mountain View, California, and currently in Zurich, Switzerland. His research interests are twofold. First, he studied efficient solutions to the exploration-exploitation dilemma in sequential decision making and reinforcement learning. Recently, Carlos has focused on conditional computation: massive deep learning models that activate only a small subset of the network in an input-dependent fashion. This unlocked the training of some of the largest machine learning models to date. 

* Juan has a degree in Mathematics and Computer Science from La Universidad Politécnica de Madrid. After a Master’s degree at the ETH in Zürich as a “la Caixa” fellow, he began his doctorate at the Seminar for Statistics at the same university, under the supervision of Peter Bühlmann, Jonas Peters and Christina Heinze-Deml. His area of ​​research is Causality: the study of cause and effect relationships, and their relationship to the scientific process, statistical modeling and data analysis.