PolyNet

Anna joined the SDSC as a Senior Data Scientist in 2020. She is a statistician by training with a Master’s degree with Honors in Mathematical Statistics from Lomonosov Moscow State University. Anna has graduated with a PhD from ETH Zurich in 2018, where she worked on causal structure learning for protein signaling pathways. During her studies she did an internship at Facebook AI Research in New York working on discovery of hierarchies from data using hyperbolic geometry. Later she joined Facebook AI Research in Paris as a postdoctoral researcher, where she worked on a problem of out-of-distribution prediction of unseen drug combinations. Broadly her research interests are in unsupervised and self-supervised learning, domain adaptation and generalisation.

Ekaterina received her PhD in Computer Science from Moscow Institute for Physics and Technology, Russia. Afterwards, she worked as a researcher at the Institute for Information Transmission Problems in Moscow and later as a postdoctoral researcher in the Stochastic Group at the Faculty of Mathematics at University Duisburg-Essen, Germany. She has experience with various applied projects on signal processing, predictive modelling, macroeconomic modelling and forecasting, and social network analysis. She joined the SDSC in November 2019. Her interests include machine learning, non-parametric statistical estimation, structural adaptive inference, and Bayesian modelling.

Fernando Perez-Cruz received a PhD. in Electrical Engineering from the Technical University of Madrid. He is Titular Professor in the Computer Science Department at ETH Zurich and Head of Machine Learning Research and AI at Spiden. He has been a member of the technical staff at Bell Labs and a Machine Learning Research Scientist at Amazon. Fernando has been a visiting professor at Princeton University under a Marie Curie Fellowship and an associate professor at University Carlos III in Madrid. He held positions at the Gatsby Unit (London), Max Planck Institute for Biological Cybernetics (Tuebingen), and BioWulf Technologies (New York). Fernando Perez-Cruz has served as Chief Data Scientist at the SDSC from 2018 to 2023, and Deputy Executive Director of the SDSC from 2022 to 2023

Guillaume Obozinski graduated with a PhD in Statistics from UC Berkeley in 2009. He did his postdoc and held until 2012 a researcher position in the Willow and Sierra teams at INRIA and Ecole Normale Supérieure in Paris. He was then Research Faculty at Ecole des Ponts ParisTech until 2018. Guillaume has broad interests in statistics and machine learning and worked over time on sparse modeling, optimization for large scale learning, graphical models, relational learning and semantic embeddings, with applications in various domains from computational biology to computer vision.

Izabela holds a PhD degree in Computer Science from University of Rennes 1, France and the National French Institute for Research in Computer Science and Automatics (INRIA), France. Before joining the SDSC, she was a postdoctoral researcher at the Chair of Computational Social Science at ETH Zurich and a lecturer for the “Data Science in Techno-Socio-Economic Systems” course at ETH Zurich. Her main research focus is on big data analytics, tools and platforms, machine learning and data mining, large scale network analysis, in the particular setting of social data mining.

Victor has joined the SDSC in 2020 to design solutions for data-driven optimization problems. His research interests lie at the crossroad of machine learning and decision-making. This contains several topics such as stochastic optimization, reinforcement learning, combinatorial optimization, and probabilistic graphical models. Victor received a PhD in operations research and machine learning from Ecole des Ponts Paristech in 2020. Before that, he completed a master degree in Operation Research and Machine learning at Ecole des Ponts Paristech and a bachelor degree in Applied Mathematics and Computer Sciences.