KHIPU 2021

event series in

Artificial Intelligence

Khipu 2021 Event Series are monthly online meetings to support the advancement of AI talent in Latinamerica. Each event will feature two sessions covering: Conversations on AI and Applications on AI

OCTOBER:

Graph Neural Networks

October event speakers

Alejandro

Ribeiro

Alejandro Ribeiro received the B.Sc. degree in electrical engineering from the Universidad de la Republica Oriental del Uruguay, Montevideo, in 1998 and the M.Sc. and Ph.D. degree in electrical engineering from the Department of Electrical and Computer Engineering, the University of Minnesota, Minneapolis in 2005 and 2007. From 1998 to 2003, he was a member of the technical staff at Bellsouth Montevideo. After his M.Sc. and Ph.D studies, in 2008 he joined the University of Pennsylvania (Penn), Philadelphia, where he is currently Professor of Electrical and Systems Engineering. His research interests are in the applications of statistical signal processing to collaborative intelligent systems. His specific interests are in wireless autonomous networks, machine learning on network data and distributed collaborative learning. Papers coauthored by Dr. Ribeiro received the 2014 O. Hugo Schuck best paper award, and paper awards at CDC 2017, SSP Workshop 2016, SAM Workshop 2016, Asilomar SSC Conference 2015, ACC 2013, ICASSP 2006, and ICASSP 2005. His teaching has been recognized with the 2017 Lindback award for distinguished teaching and the 2012 S. Reid Warren, Jr. Award presented by Penn’s undergraduate student body for outstanding teaching. Dr. Ribeiro is a Fulbright scholar class of 2003 and a Penn Fellow class of 2015.

Joan

Bruna

Joan Bruna is an Associate Professor at Courant Institute, New York University (NYU), in the Department of Computer Science, Department of Mathematics (affiliated) and the Center for Data Science. He belongs to the CILVR group and to the Math and Data groups. From 2015 to 2016, he was Assistant Professor of Statistics at UC Berkeley and part of BAIR (Berkeley AI Research). Before that, he worked at FAIR (Facebook AI Research) in New York. Prior to that, he was a postdoctoral researcher at Courant Institute, NYU. He completed his PhD in 2013 at Ecole Polytechnique, France. Before his PhD he was a Research Engineer at a semi-conductor company, developing real-time video processing algorithms. Even before that, he did a MsC at Ecole Normale Superieure de Cachan in Applied Mathematics (MVA) and a BA and MS at UPC (Universitat Politecnica de Catalunya, Barcelona) in both Mathematics and Telecommunication Engineering. For his research contributions, he has been awarded a Sloan Research Fellowship (2018), a NSF CAREER Award (2019), a best paper award at ICMLA (2018) and the IAA Outstanding Paper Award.

Paola

Bermolen

Paola Bermolen received in 2004 the degree in Mathematics from the Universidad de la República, Uruguay, and her PhD in Computer Science and Networks in 2010 from Telecom ParisTech, France. Currently holds a position as Associate Professor with the Mathematical Department at the School of Engineering, Universidad de la República, where she joined in 1998. She works in Probability and Statistics. Her research interests are related to the stochastic modeling of networks in telecommunications and other applications domains such as ecology. Recently she is devoted to the performance analysis of wireless networks, including stochastic geometry, random graphs models, and large deviations theory. Currently, she is co-responsible for the five years project “CICADA Interdisciplinary Center in Data Science and Machine Learning”. Bermolen is a researcher with the Basic Sciences Development Program – PEDECIBA Mathematics and member of the National Researchers System in Uruguay.