Lecturers

Senior Researcher, ETH Zurich, Switzerland

Short biography

Nino is a senior researcher at ETH Zurich, COSS group and visiting research associate at Courant Institute of Mathematical Sciences, NYU. He works at the interface of complexity and data science. His main interests include dynamical processes on networks, predictive analytics for FinTech (cryptocurrency & blockchain markets), machine learning, social network analysis and Monte-Carlo algorithms. He is a co-founder of Aisot GmbH, Prior to ETH Zurich, he worked at the Rudjer Boskovic Institute and Faculty of Electrical Engineering and Computing, Croatia and he was a visiting scientist at Robert Koch Institute, Berlin. He also works as Supervisor & Panel member of PhD Program in Data Science, Scuola Normale Superiore, Pisa. He worked on several EU projects: SoBigData --- “Social Mining & Big Data Ecosystems”, Multiplex --- “Foundational Research on MULTI-level comPLEX networks and systems”, FOC --- “Forecasting Financial Crisis” and eLico --- “An e-Laboratory for Interdisciplinary Collaborative Research in Data Mining and Data Intensive Science”.

Senior Researcher at the Centre de Physique Théorique, Marseille, France

Short biography

Alain Barrat is a Senior Researcher at the Centre de Physique Théorique (CPT), Marseille, France, deputy director of the CPT and head of the "Statistical physics and complex physics team" of the CPT.

He obtained his PhD in theoretical physics in 1996, on the topic of the out-of-equilibrium dynamics of spin glasses. He then spent two years at the Abdus Salam ICTP in Trieste, Italy, as a postdoctoral fellow. In 1998, he entered the National Council for Scientific Research (CNRS) of France with a permanent position as junior researcher. He spent 10 years at the Laboratoire de Physique Théorique at the University of Paris-Sud before moving to the Centre de Physique Théorique in Marseille. He has also been research scientist at the Institute for Scientific Interchange in Turin, Italy from 2006 to 2019 and he is Specially Appointed Professor at the Tokyo Tech World Research Hub Initiative (Tokyo, Japan) since April 2019. He is vice-president of the Complex Systems Society and board member of the NetSci Society.

His research focuses on complex networks and temporal networks, and in particular on processes on networks, with many interdisciplinary connections and collaborations. He is co-founder of the SocioPatterns collaboration and co-author of the book "Dynamical processes on complex networks"

Associate Professor in Computer Science, NTU, Singapore

Short biography

Xavier Bresson (PhD 2005, EPFL, Switzerland) is Associate Professor in Computer Science at NTU, Singapore. His research focuses on Graph Deep Learning, a new framework that combines graph theory and deep learning techniques to tackle complex data domains in computer vision, natural language processing, combinatorial optimization, quantum chemistry, physics, neuroscience, genetics and social networks. In 2016, he received the highly competitive Singaporean NRF Fellowship of US$2.5M to develop these new techniques. He was also awarded several research grants in the U.S. and Hong Kong.

As a leading researcher in the field, he has published more than 60 peer-reviewed papers in the leading journals and conference proceedings in machine learning, including articles in NeurIPS, ICML, ICLR, CVPR, JMLR. He has organized several international workshops and tutorials on graph deep learning in collaboration with Facebook, DeepMind, NYU and Imperial such as the 2021, 2019 and 2018 UCLA workshops (https://bit.ly/3gS2oFU, https://bit.ly/2N65idn, https://bit.ly/2TC0hug), the 2017 CVPR tutorial (https://bit.ly/2vJbRa0) and the 2017 NeurIPS tutorial (https://bit.ly/2YsFvOx). He was speaker at ICML'20 and ICLR'20 (https://bit.ly/3drhwYE, https://bit.ly/3eJt21z). He has been teaching undergraduate, graduate and industrial courses in AI and deep learning since 2014 at EPFL (Switzerland), NTU (Singapore) and UCLA (U.S.),

CSSI, Kellogg School of Management, Northwestern University, USA

Nima Dehmamy is a research assistant professor at Northwestern Institute for Complex Systems in Kellogg School of Management at Northwestern University, Evanston IL, USA. He is a physicist working on complex systems and theory of machine learning. His research involves AI in graph learning, using physics to understand optimization landscapes and neuroscience. He earned his PhD in physics with Eugene Stanley at Boston University in 2016 and he was a postdoctoral fellow at the Barabasi Lab at Northeastern University, Boston, MA.

Assistant Professor, Computer Science and Engineering, University of Michigan, USA

Danai Koutra is a Morris Wellman Assistant Professor in Computer Science and Engineering at the University of Michigan, where she leads the Graph Exploration and Mining at Scale (GEMS) Lab. Her research focuses on practical and scalable methods for large-scale real networks (including network summarization and multi-network analysis), and has applications in neuroscience, organizational analytics, and social sciences. She has won an NSF CAREER award, an ARO Young Investigator award, a Precision Health Investigator Award, the 2016 ACM SIGKDD Dissertation award, an honorable mention for the SCS Doctoral Dissertation Award (CMU), and various faculty research awards from Google, Amazon, Facebook and Adobe. She holds one "rate-1" patent and has six (pending) patents on bipartite graph alignment. She has multiple papers in top data mining conferences, including 6 award-winning papers, and her work has been covered by the popular press, such as the MIT Technology Review. She is the Program Director of the SIAG on Data Mining and Analytics, an Associate Editor of ACM TKDD, and has held several organizational roles at the leading data mining conferences. She earned her Ph.D. and M.S. in Computer Science from CMU in 2015 and her diploma in Electrical and Computer Engineering at the National Technical University of Athens in 2010.

Associate Professor of Network and Data Science at the Central European University, Budapest, Hungary

Short biography

Tiago de Paula Peixoto is a Brazilian physicist who works in the areas of Network Science, Statistical Physics, and Complex Systems. He is currently an Associate Professor of Network and Data Science at the Central European University and external researcher at the ISI Foundation.

Peixoto is mostly known for his work in statistical inference in networks. He developed and maintains the graph manipulation library graph-tool, which contains readily available implementations of the methods he proposes in his publications.

Peixoto graduated with a bachelor's degree in physics from the University of São Paulo in 2003. He earned a PhD in Physics from the same university in 2007, advised by Carmen Pimentel Cintra do Prado with a dissertation entitled "Dynamics of the epicenters of the Olami-Feder-Christensen model of earthquakes (OFC)". In 2017 he obtained his Habilitation in Theoretical Physics at the University of Bremen.

Peixoto worked as a post-doctoral fellow in Germany (2008 - 2016) at the Technische Universität Darmstadt and University of Bremen before becoming a Lecturer, in 2016, at the Department of Mathematics of the University of Bath. In 2019, he joined the faculty of the Central European University as an Associate Professor.

In 2019, Peixoto was awarded the prestigious Erdős–Rényi Prize in Network Science for his contributions for the statistical inference of network modules (aka communities), statistical analysis and network visualization. He was also the sixth recipient of the Zachary Karate Club CLUB prize.

Ecole Polytechnique Fédérale de Lausanne, Signal Processing Laboratory (LTS4)

Short biography

Hermina is a doctoral student in the Signal Processing Laboratory (LTS4) at EPFL, under the supervision of Professor Pascal Frossard. Her research interests include optimal transport, graph learning, network analysis, interpretability and machine learning in general. In 2020 she will defend her PhD thesis on graph learning and comparison. The focus of her thesis will be on multi-graph learning methods and the interpretability they offer, as well as network comparison through probabilistic distributions of data supported by the graphs. Her work has been published in major conferences and journals in the field (NeurIPS, IEEE TSIPN…) and has found interesting applications in Computational neuroscience. Prior to EPFL, she has completed two master’s programs at University of Zagreb, in Computer science and Mathematical Probability & Statistics. In 2019, she interned at Amazon as an applied scientist, looking into interpretability and explainability of deep learning methods for object detection.

Full Professor, Faculty of Mathematics and Natural Sciences, Bergische Universität Wuppertal, Germany

Short biography

Ingo Scholtes is a professor of data analytics in the Faculty of Mathematics and Natural Sciences at the University of Wuppertal, Germany and an SNSF Professor for Data Analytics at the Department of Informatics at the University of Zurich, Switzerland. He has a background in computer science and mathematics and received his doctorate degree from the University of Trier in Germany. At CERN, he developed a large-scale data processing framework that is currently used to monitor particle collision data within the Large Hadron Collider. After finishing his doctorate degree, he was a postdoctoral researcher at the Chair of Systems Design at ETH Zürich from 2011 till 2016. In 2016 he held an interim professorship for Applied Computer Science at the Karlsruhe Institute of Technology. In 2017 he returned to ETH Zürich as a senior assistant and lecturer. He is an associate editor of EPJ Data Science.

Ingo's research focuses on new data analytics methods for time series data on networks, with applications in information systems, empirical software engineering and computational social science. At the German Informatics Society (GI) he serves as founding co-chair of the Computational Social Science working group, as elected member of the presiding committee, and as a member of the Data Science Task Force. In 2014 he was awarded a Junior-Fellowship from the German Informatics Society.

In 2018 he received an SNSF Professorship Grant from the Swiss National Science Foundation. Since July 2019, he is a Full Professor of Data Analytics at the Faculty of Mathematics and Natural Sciences of Bergische Universität Wuppertalin Germany.

Senior Research Associate, Ruđer Bošković Institute, Zagreb, Croatia

Short biography

Vinko Zlatić is a senior research associate and a head of Condensed Matter and Statistical Physics group in the Division of Theoretical Physics Division at the Ruđer Bošković Institute in Zagreb, Croatia. He has a background in theoretical physics of complex systems and has received his doctorate degree from the University of Zagreb, Croatia. After finishing his doctorate degree, he was a postdoctoral researcher at the Sapineza university in Rome, Italy 2008-2010. In 2012 he became a research associate at the Ruđer Bošković Institute and since 2018 he is a senior research associate

Vinko's research focuses on probabilistic models of complex networks with special emphasis on topological connectivity and new types of percolations. He was also working on a number of applied problems, with special focus on systemic risk in financial systems. Besides coordinating Ruđer Bošković nodes on a number of FP7 and H2020 projects, he applied his research in risk in commercial projects with Croatian Banking Union and Croatian Depositary Agency.