Charles-Albert Lehalle

Capital Fund Management (CFM), France and Imperial College, London

Currently Head of Data Analytics at Capital Fund Management (CFM, Paris) and visiting researcher at Imperial College (London), Charles-Albert Lehalle studied machine learning for stochastic control during his PhD 20 years ago.  He started his career being in charge of AI projects at the Renault research center and moved to the financial industry with the emergence of automated trading in 2005.  He became an expert in market microstructure and has been appointed Global Head of Quantitative Research at Crédit Agricole Cheuvreux, and Head of Quantitative Research on Market Microstructure in the Equity Brokerage and Derivative Department of Crédit Agricole Corporate Investment Bank after the crisis.  He provided research and expertise on these topics to investors and intermediaries, and is often heard by regulators and policy-makers like the European Commission, the French Senate, the UK Foresight Committee, etc.  He chairs the Index Advisory Group of Euronext, is a member of the Scientific Committee of the French regulator (AMF), and has been part of the Consultative Workgroup on Financial Innovation of the European Authority (ESMA).  

Petter Kolm

NYU Courant

Petter Kolm is the Director of the Mathematics in Finance Master’s Program and Clinical Professor at the Courant Institute of Mathematical Sciences, New York University and the Principal of the Heimdall Group, LLC. Previously, Petter worked in the Quantitative Strategies Group at Goldman Sachs Asset Management where his responsibilities included researching and developing new quantitative investment strategies for the group's hedge fund.  Petter has coauthored four books: Financial Modeling of the Equity Market: From CAPM to Cointegration (Wiley, 2006), Trends in Quantitative Finance (CFA Research Institute, 2006), Robust Portfolio Management and Optimization (Wiley, 2007), and Quantitative Equity Investing: Techniques and Strategies (Wiley, 2010). He holds a Ph.D. in Mathematics from Yale, an M.Phil. in Applied Mathematics from the Royal Institute of Technology, and an M.S. in Mathematics from ETH Zurich. 

Dirk Helbing

Computational Social Science, ETH Zurich

Dirk Helbing is Professor of Computational Social Science at the Department of Humanities, Social and Political Sciences and affiliate of the Computer Science Department at ETH Zurich. In January 2014 Prof. Helbing received an honorary PhD from Delft University of Technology (TU Delft). Since June 2015 he is affiliate professor at the faculty of Technology, Policy and Management at TU Delft, where he leads the PhD school in "Engineering Social Technologies for a Responsible Digital Future". Dirk Helbing is elected member of the German Academy of Sciences “Leopoldina” and is engaged in the study of computational social science, complex adaptive systems, and systemic risks, including epidemic spreading processes. 

The photo left is by Giulia Marthaler.

Ulrik Brandes

Social Networks Lab, ETH Zurich

Ulrik Brandes is professor for social networks at ETH Zürich. His background is in computer science with

a Diploma degree from RWTH Aachen and a PhD from the University of Konstanz. After his habilitation in 2002 he became associate professor at the University of Passau in the same year, and professor for algorithmics at the University of Konstanz one year later.  He is vice-president of the International Network for Social Network Analysis (INSNA), coordinating editor of Network Science,  and on the editorial boards of Social Networks, Journal of Mathematical Sociology, and the Journal of Graph Algorithms and Applications. His main interests are in network analysis and visualization, with application to social networks in particular. He is a co-author of the visone software for network analysis and the GraphML data format. Following a DFG-funded Reinhart Koselleck-Project on Social Network Algorithmics, he takes a shot at improving the methodological foundations of network science. As a principal investigator in the ERC Synergy Project NEXUS 1492 he was working on reconstructing archaeological networks from fragmented and heterogeneous observations.

Fabrizio Lillo

Dipartimento di Matematica, Università di Bologna

Fabrizio Lillo is Full Professor of Mathematical Methods for Economics and Finance at the University of Bologna (Italy).  Formerly he has been Associate Professor of Mathematical Finance at the Scuola Normale Superiore, Pisa (Italy) where he had directed for seven year the group of Quantitative Finance.  He has also been External Faculty (2005-2009) and Professor (2009-2012) at the Santa Fe Institute (USA).  He received the Master (laurea) in Physics and PhD in Physics at the University of Palermo (Italy).  He has been postdoc (1999-2001) and researcher (2001-2003) of the National Institute of the Physics of Matter, INFM.  After that he has been postdoc (2003) and member of the External Faculty (2004-2009) of the Santa Fe Institute.  He has been awarded the Young Scientist Award for Socio-and Econophysics at the German Physical Society in 2007.  He is the author of more than 120 referred scientific papers.

Christoph Stadtfeld

Social Networks Lab, ETH Zurich

Christoph Stadtfeld is an Associate Professor of Social Networks at ETH Zürich, Switzerland. He holds a PhD from Karlsruhe Institute of Technology and has been postdoctoral researcher and Marie-​Curie fellow at the University of Groningen, the Social Network Analysis Research Center in Lugano, and the MIT Media Lab. His research focuses on the development and application of theories and methods for social network dynamics.

Jan Nagler

Frankfurt School of Finance and Managment, ETH

Jan Nagler studied physics in Kiel, Germany, before he went for his PhD (on dynamical systems, in particular problems in chaotic astrophysics) to Bremen, Germany. His scientific postdoctoral positions include Boston University, USA (research on networks and econophysics), the Max-Planck-Institute for Dynamics and Self-Organization, Goettingen, Germany, and ETH Zurich & ETH Risk Center, Switzerland.
Since 2016 he is the Vice Chair of the Physics of Socio-economic Systems Division of the German Physical Society.
His research includes the understanding and control of networked stochastic systems, with applications at the interface between physics, biology, and socio-economic systems, in particular game theory and phase transitions, ergodicity breaking and risk and survival in uncertain environments.

Heinrich H. Nax

University of Zurich

Behavioral game theorist, educated in economics and philosophy, currently SNF Assistant Professor at UZH, Privatdozent at ETH Zurich and Visiting Professor at the Cowles Foundation for Research in Economics at Yale. Previously at London School of Economics, Oxford, École normale sup (Paris School of Economics) and Johns Hopkins, my research interests include (Experimental/Behavioral) Market Design and Learning in Games applied to Markets and Collective Goods.

Tiziano Squartini

IMTSchool for Advanced Studies Lucca

Tiziano Squartini is a theoretical physicist.  He holds a Master Degree in Physics (2008) and a PhD degree in Physics (2011) from the University of Siena (thesis title: “Information-theoretic approach to the analysis of complex networks”).  During the biennium 2012-2013 he was Postdoctoral Researcher at the Lorenthz-Institute for Theoretical Physics (Leiden, NL) under the supervision of Diego Garlaschelli.  From January 2014 to October 2015 he was Postdoctoral Researcher at the Institute for Complex Systems “UOS Sapienza” in Rome, under the supervision of Luciano Pietronero.  Since November 2015 he is Assistant Professor at the IMT School for Advanced Studies, in Lucca (within the NETWORKS Research Unit); since December 2018 he is a Tenure Track researcher within the same institute where he teaches the course “Advanced Topics in Network Theory”.  He is PhD Board Member of the “Data Science” joint doctorate (in collaboration with Scuola Normale Superiore, Sant’Anna School, University of Pisa and National Research Council) and visiting fellow at the Institute for Advanced Study (IAS-University of Amsterdam, NL).  His research interests lie at the intersection between statistical physics and graph theory, both on the theoretical and on the applied side: some of the research topics pursued by him concern network reconstruction, systemic risk estimation in financial networks, (mis)information spreading on social networks and functional brain networks analysis. 

Mario Lucic

Google Research (Brain team)

Mario Lucic is a senior research scientist at Google Research (Brain team) where he is pursuing fundamental challenges in machine learning and artificial intelligence. He received his Ph.D. in Computer Science from ETH Zurich (2017), a M.Sc. degree (cum laude) in Computer Science from Politecnico di Milano (Italy), and a B.Sc. degree in Computing from University of Zagreb (Croatia). During his PhD he was supported by an IBM Fellowship and was an Associated Fellow at the Max Planck ETH Center for Learning Systems. Before starting his doctoral studies he was working at IBM Research on ML models for predictive maintenance. His research on machine learning and artificial intelligence has received awards at several premier conferences, most notably the best paper award at the International Conference on Machine Learning and the best student paper award at the International Conference on Artificial Intelligence and Statistics.

Avishek Anand

L3S Research Center, Hannover

Avishek Anand is an Assistant Professor in the Leibniz University of Hannover.  His research broadly falls in the intersection of Machine learning on Web and information retrieval problems. Specifically, he has worked on designing scalable algorithms to improve search and graph representation algorithms for the Web.  Recently, he became interested in interpretability of retrieval models. That is, how can we better understand the rationale behind predictions of a black-box retrieval model ? His research is supported by Amazon research awards and he has been a visiting scholar in Amazon Search. He holds a PhD in computer science from the Max Planck Insitute for Informatics, Saarbrücken. 

Nino Antulov-Fantulin

COSS, ETH Zurich

Nino is a senior researcher at ETH Zurich, COSS group and visiting research associate at Courant Institute of Mathematical Sciences, NYC.

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 Ecosystem", Multiplex−“Foundational Research on MULTI-level comPLEX networks and systems”, FOC−“Forecasting Financial Crisis” and e-Lico− “An e-Laboratory for Interdisciplinary Collaborative Research in Data Mining and Data-Intensive Science”.

Mireille El Gheche


Mireille El Gheche received the Master degree in Radio-communication from SupElec (France) in 2010.  She received the Ph.D. degree in signal and image processing from the Université Paris-Est 

Marne-la-Vallée (France) in 2014. From Jan. 2015 un􀆟l Aug. 2017, she was a Postdoctoral Researcher

at the IMS and IMB laboratories, Université de Bordeaux (France), where she was working on the topic of super-resolution of texture images, texture volume for Computed Tomography applications, and Horizon volumes reconstruction from seismic data. Since Nov. 2017, she is a Postdoctoral Researcher at École Polytechnique Fédérale de Lausanne (Switzerland), where she is working Multi-layer graph clustering, Graph signal embedding and Graph comparison.  Her research interests focus on optimization applied to image processing, machine learning, optimal transport, graph signal processing and foresighted applications.

Luka Rimanic


Luka Rimanic earned his PhD in 2018 in the mathematics department at University of Bristol, after completing Part III at the University of Cambridge.  Back in the days, he was exploring the field of additive combinatorics, whilst using a number of other fields in conjunction.  Upon completing his PhD degree, Luka transitioned to industry, working as a consultant in machine learning where he implemented several real-world applications.  In October 2019, Luka joined the DS3Lab, part of the Systems Group at ETH, where he works on several projects concerning the usability of machine learning systems and, in particular, the theory behind such systems.

Peter Nystrup

Lund University and Technical University of Denmark

Peter Nystrup is a Postdoctoral Fellow in the Division of Mathematical Statistics at Lund University in Sweden and in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark.  He has previously been a visiting researcher at Stanford and New York University.  He has worked in equity sales at Nordea Markets, in the investment department at pension fund Sampension, at startup quant hedge fund Annox, and as an external consultant on advanced analytics at energy company Ørsted.

Dr. Nystrup earned his B.Sc. in Engineering degree in Mathematics and Technology from the Technical University of Denmark (DTU) in 2012, followed by a M,Sc. (Hons.) in Engineering degree in Mathematical Modeling and Computation in 2014.  In 2018, he was awarded the Ph.D. degree in Engineering from DTU upon completion of a research project on dynamic asset allocation and identification of regime shifts in financial time series.  His research has been published in leading journals covering topics fro quantitative finance and portfolio management to forecasting, data science, optimization, and operations research.

Lucas Böttcher


Lucas Böttcher is a research scientist and fellow of the Swiss National Fund at the Depts. of Computational Medicine and Mathematics at UCLA. My current research work combines

aspects from applied mathematics, control theory, and machine learning to tackle real-world problems including epidemic spreading, political polarization, and the rapid rise of antibiotic resistance.

Tian Guo

Systematic Equity Research, RAM Active Investments

Tian Guo is now a senior data scientist with RAM Active Investments, a Geneva based asset management boutique.  He is leading the research efforts on natural language processing and machine learning techniques tailored for systematic investments and working with portfolio managers to design strategies.  Previously he was a post-doc researcher at ETH Zurich and got his Ph.D. in computer science from EPFL.  Tian’s research interest is to design statistical and deep learning models that convert heterogeneous and multi-source data into actionable and interpretable decisions.  His research led to the publication in several top-tier venues of machine learning and data mining, e.g. ICML, ICLR, IJCAI, ECML, ICDM, etc. and won SIGMOD MobiDE Best Paper Award, 2011.

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