About Me

I am a Postdoctoral Scholar in Prof. Somayeh Sojoudi's research group, in the Electrical Engineering and Computer Sciences department at the University of California, Berkeley. I received a Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania in 2020, advised by Prof. Alejandro Ribeiro. I received an M. A. in Statistics from the Wharton School in 2017, and an Electronic Engineering degree from the School of Engineering of the University of Buenos Aires, Argentina in 2013. I have been a visiting researcher at TU Delft in 2017, and a research intern at Facebook Artificial Intelligence Research in 2018. I was awarded with a Fulbright scholarship for international students for 2014-2016.

My research interests currently lie on the field of machine learning for network data. More specifically, I am interested in developing collaborative intelligence. The fundamental objective is for a group of entities (modeled as nodes in a graph; could be a team of autonomous agents, sensors in a network, sources in a power grid, vehicles in a transportation network) to learn, from data, how to collaboratively accomplish a certain task. The challenge is that the nodes have access only to partial, local information acquired through exchanges with neighboring nodes, but need to coordinate a global solution for the entire team.

To tackle this problem, we have been developing tools within the context of graph neural networks (GNNs). We have been focusing on solutions that can be implemented locally on a given graph, exploiting the fact that nodes have computational capabilities. Our main focus is on characterizing the representation space of GNNs, that is, to understand what functions can be learned when using graph neural networks. This entails obtaining properties that all functions in this representation space have. For example, we have proved permutation equivariance and stability to perturbations which, together help explain the observed scalability and transferability of graph neural networks on homogeneous teams. While we keep investigating different characterizations of the representation space of graph neural networks, we have also been exploring diverse applications including control of teams of autonomous agents, power grids and wireless networks.

News

  • 5 March 2021: Invited talk "Graph Neural Networks" as part of the ComsoStat Day on Machine Learning in Astrophysics, Laboratoire CosmoStat, IRFU, CEA-Saclay, France. [Remote delivery]

  • 2 February 2021: Invited talk "Graph Neural Networks" as part of a five-class course on "Machine Learning on Graphs" at the University of the Republic, Montevideo, Uruguay. [Remote delivery]

  • 11 December 2020: Invited talk "Graph Neural Networks for Distributed Control" at the University of Buenos Aires, Argentina. [Remote delivery]

  • 18 September 2020: Invited talk "Graph Neural Networks" at the University of Texas, Austin, TX. [Remote delivery]

  • 15 September 2020: Start as a Postdoctoral Scholar, working with Prof. Somayeh Sojoudi, at the University of California, Berkeley.

  • 8 September 2020: Special session "Theoretical Foundations of Graph Neural Networks" proposal accepted at 46th IEEE ICASSP 2021.

  • 26 May 2020: Invited Talk "Graph Neural Networks" at Delft University of Technology, Delft, the Netherlands. [Remote delivery]

  • 4 May 2020: Presenter of the tutorial "Graph Neural Networks" at 45th IEEE ICASSP 2020, Barcelona, Spain. [Remote delivery]

  • 30 March 2020: Invited talk "Graph Neural Networks" at Dataminr, New York, NY. [Remote delivery]

  • 18 March 2020: Invited talk "Graph Neural Networks and Collaborative Intelligent Systems" at the University of Colorado, Boulder, CO. [Remote delivery]

  • 20 February 2020: Invited talk "Graph Neural Networks and Collaborative Intelligent Systems" at Johns Hopkins University, Baltimore, MD.

  • 14 November 2019: Invited talk "Graph Neural Networks" at Blackstone, New York, NY.

  • 18 October 2019: Awarded Neural Information Processing Systems travel award for attending NeurIPS 2019, Vancouver, BC.

  • 4 September 2019: Best student paper award at the 27th EUSIPCO 2019, A Coruña, Spain.

  • 22 July 2019: Awarded NSF student travel grant for attending 27th EUSIPCO 2019, A Coruña, Spain.

  • 5 July 2019: Invited talk "Graph Neural Networks" at Satellogic, Buenos Aires, Argentina.

  • 2 July 2019: Invited talk "Graph Neural Networks" at the National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina.

  • 23 May 2019: Awarded NSF student travel grant for attending IEEE DSW 2019, Minnesota, MN.