Prof. Dr. Michael Gerfin is a Professor of Public Economics at the University of Bern, affiliated with the Center for Regional Economic Development (CRED). His research focuses on health economics, labor economics, and tax-benefit models. He holds an office at A315 and can be reached via email. His work examines topics such as healthcare demand, physician incentives, and policy design, with recent contributions analyzing drug pricing, pandemic response strategies, and educational interventions in developing countries. Key themes in his publications include understanding how financial mechanisms influence provider behavior and designing effective social policies. Gerfin's research spans empirical studies on healthcare utilization, wage dynamics, and household decision-making. His analysis often integrates microsimulation techniques to evaluate policy impacts, such as in-work benefits and active labor market policies. Collaborations with Swiss statistical agencies highlight his engagement with real-world data, such as studies on adverse healthcare events and nursing staff ratios. His work bridges theoretical economic models with practical policy applications, emphasizing evidence-based solutions for public welfare challenges. Labs/Teams: Core member of the Center for Regional Economic Development (CRED), contributing to interdisciplinary projects on regional economic trends and public policy evaluation.
Vincent Vargas is a French mathematician and Associate Professor at the University of Geneva, where he joined in 2021 after holding a research position at CNRS. He completed his PhD in mathematics at Paris-Diderot University under the supervision of Francis Comets. His primary research interests include: Probability Mathematical Physics Statistical Mechanics Quantum Field Theory Gaussian Multiplicative Chaos Liouville Quantum Gravity Vargas has made significant contributions to the rigorous probabilistic construction of Liouville field theory and the proof of the DOZZ formula, work that was featured in Quanta Magazine. His research bridges mathematics and theoretical physics through probabilistic methods applied to quantum gravity. Analysis of his recent publications reveals a strong focus on mathematical structures underlying conformal field theory, with particular attention to Liouville quantum gravity across various geometries and the connections between probability and quantum physics. His notable scientific achievements have been recognized with prestigious awards: Marc Yor Prize (2019) George Pólya Prize (2022) Vincent Vargas has mentored several PhD students including Romain Allez, Yichao Huang, Guillaume Rémy, and Tunan Zhu. He has been actively involved in the academic community through organizing conferences and workshops, including a trimester at the Institut Henri Poincaré in 2015 and a conference on 'Probability and quantum field theory' in 2019. His professional activities extend to industry applications through his previous consultancy with Capital Fund Management (2007-2013) and his current role on the board of their research foundation.
Jana Mareckova is an Assistant Professor of Econometrics at the Swiss Institute for Empirical Economic Research (SIEW), part of the School of Economics and Political Science (SEPS) at the University of St. Gallen. She joined the university in 2020 after completing a postdoc at SEW-HSG following her PhD from the University of Konstanz (2019). Her research focuses on causal machine learning, shrinkage methods, regularization techniques, and labor economics. She explores applications in labor market outcomes and fairness, leveraging econometric tools to address real-world economic questions. Education: PhD in Econometrics, University of Konstanz (2019); Postdoc at SEW-HSG (pre-2020). Research interests include shrinkage estimation for categorical regressors, causal inference via machine learning, and predicting economic outcomes using noncognitive skills. Her work bridges statistical theory with practical policy analysis, as seen in her 2021 Journal of Econometrics publication on shrinkage methods. Recent projects emphasize causal forests and comprehensive frameworks for policy evaluation. No scientific awards are listed, though her contributions to causal ML and econometric methods are notable. She has no documented advising or grant information. Her research is affiliated with SIEW, focusing on empirical economic research.
Beat Rechsteiner is a Senior Lecturer and Postdoctoral Researcher at the Institute of Education, University of Zurich, specializing in educational processes within schools. He serves as Project Lead for the SNSF Research Project R2 (Regulation of Routines in Teaching Development) and contributes to theoretical and empirical research on teacher collaboration, school improvement, and social network analysis. His work emphasizes self-regulated learning, instructional capacity, and adaptive strategies for educational challenges. Doctoral Program in Education (University of Zurich, 2018–2022) Master’s in Educational Science (University of Zurich, 2014–2018) Secondary School Teacher Training (Zurich University of Teacher Education, 2005–2009) His research focuses on: Teacher collaboration networks and their impact on school improvement Professional development dynamics through experience sampling Brokerage mechanisms in educational change Adaptation of routines during crises (e.g., pandemic effects on math competencies) Recent publications highlight trends in social network analysis, school reform, and collective regulation. Key themes include boundary-crossing activities, data-driven school development, and stress management in collaborative environments. Awards include the GRC Travel Grant (2022). He actively reviews for journals like Teaching and Teacher Education and Journal of Educational Change , participates in international exchanges (University of Antwerp), and teaches graduate courses on systematic reviews, school improvement routines, and educational research.
Lenka Zdeborová is an Associate Professor at EPFL, jointly affiliated with the School of Basic Sciences and School of Computer and Communication Sciences. She leads the Laboratory of Statistical Physics of Computational Systems, where her research bridges statistical physics, machine learning, and computational biology. Education: PhD in Physics, Université Paris-Cité (2012) MSc in Fundamental Physics, École Normale Supérieure (2009) BSc in Physics, École Normale Supérieure de Lyon (2007) Her work focuses on phase transitions in learning algorithms, high-dimensional statistics, and neural network theory. Current projects investigate fundamental limits of machine learning, dynamics of graph neural networks, and applications to biological systems. Recent publications explore attention mechanisms in transformers, neural network depth advantages, and Bayes-optimal learning. Methodological innovations include cavity methods for hypergraphs and analysis of high-dimensional inference problems. Supervises doctoral students researching statistical physics approaches to machine learning and optimization. Teaches graduate courses in data science and machine learning for physicists.
Rachel Grange is a Full Professor in the Department of Physics at ETH Zurich and Head of the FIRST Center for Micro- and Nanoscience. Her research focuses on nanoscale material investigations, particularly using metal-oxides like lithium niobate and barium titanate for classical and quantum photonic devices. Education: Ph.D. in Ultrafast Laser Physics from ETH Zurich (2006). Career: Postdoctoral work at EPFL (2007–2010), group leader at Friedrich Schiller University in Jena (2011–2014), and progressive roles at ETH Zurich since 2015 (Assistant Professor, Associate Professor, Full Professor from 2025). Her recent work emphasizes integrated photonic platforms for quantum computing, nonlinear optics, and miniaturized electro-optic spectrometers. She explores both top-down and bottom-up fabrication techniques for nanophotonic structures, with applications in neuromorphic computing and mid-infrared communication. Grange leads the FIRST Center, advancing micro- and nanoscience technologies. She teaches courses like Nanomaterials for Photonic Devices and contributes to the development of scalable photonic systems for next-generation computing and quantum technologies.
Manos Athanassoulis is an Associate Professor in the Department of Computer Science at the College of Arts and Sciences, Boston University. He is the Founder and Director of the BU Data-intensive Systems and Computing (DiSC) lab and a member of the BU MiDAS group. His research focuses on data systems, particularly cloud data management, hybrid transactional/analytical workloads, and integration with emerging hardware such as non-volatile memory and heterogeneous computing. His educational background includes a PhD from EPFL (2014), an MSc in Computer Systems Technology, and a BSc in Informatics and Telecommunications from the University of Athens, Greece. Prior to BU, he was a Postdoctoral Researcher and Research Associate at Harvard University, supported by a SNSF Postdoc Mobility Fellowship. His research interests span data systems, database architectures, LSM trees, indexing, storage systems, and performance optimization. He explores how novel hardware can be leveraged to improve data management efficiency and scalability, especially in cloud environments. His recent publications (2021–2025) predominantly focus on LSM trees, covering topics such as compaction policies, Bloom filter tuning, DPU offloading, adversarial resilience, and sustainable caching. Earlier works include foundational contributions on access methods (RUM Conjecture) and optimal key-value stores (Monkey). The trend shows a consistent focus on data system efficiency, adaptability, and robustness under varying workloads and hardware constraints. Scientific Awards: NSF CAREER Award (2022) Facebook Faculty Research Award (2020) NSF CRII Award (2019) Best of VLDB 2017 and Best of SIGMOD 2017 SIGMOD Most Reproducible Paper Award (2017) Multiple ACM SIGMOD Distinguished PC Member recognitions (2018–2025) VLDB 2023 Best Demo Award RedHat Collaboratory Research Incubation Awards (multiple, 2021–2023) SNSF Postdoc Mobility Fellowship (2015–16) IBM PhD Fellowship (2011–12) Dr. Athanassoulis has advised numerous students and collaborators, evident from his co-authorship on works with researchers such as Niv Dayan, Stratos Idreos, and A. Ailamaki. His grants include major awards from NSF, Facebook, and RedHat, supporting research in robust data systems, hardware-software co-design, and learned cost models. He has also been recognized for teaching excellence at Harvard University. He leads the DiSC lab at Boston University, which focuses on data-intensive computing and systems research. The lab explores next-generation data architectures, particularly in cloud and hardware-aware environments. Collaborations with the BU MiDAS group enhance interdisciplinary research in data science and AI.
Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.
Xue-Mei Li is a Professor of Mathematics at Imperial College London and École Polytechnique Fédérale de Lausanne (EPFL). She holds chairs in Probability and Stochastic Analysis at both institutions. Her research focuses on stochastic analysis, geometric stochastic processes, and multi-scale systems, with contributions to areas like Malliavin calculus, fractional dynamics, and coarse curvature. Li has held positions at the University of Warwick, University of Connecticut, and others, supported by fellowships from the Alexander von Humboldt Foundation, Royal Society, and MSRI. Her work addresses fundamental questions in stochastic differential equations, geometric analysis, and their applications to complex systems. Education and Career: PhD in Mathematics, University of Warwick EPSRC Research Associate Faculty positions at the University of Connecticut (tenured Associate Professor) Research Interests: Her research spans stochastic differential equations (SDEs), stochastic partial differential equations (SPDEs), geometric stochastic analysis, and fractional dynamics. Notable contributions include the BEL formula, strict local martingales, and solutions to longstanding problems in strong completeness on non-compact manifolds. She explores interactions between stochastic processes and geometric structures, including coarse Ricci curvature and homogenization theory. Awards and Grants: Supported by NSF, EPSRC/UKRI, and Swiss NSF grants Awarded fellowships from Alexander von Humboldt Foundation, Royal Society, and MSRI Advising and Teams: PhD students: Johann Gehringer, Rhys Steel, Julian Sieber, and others Leading working groups on stochastic analysis and geometric dynamics
Prof. Daniel Kressner is a Professor at the École Polytechnique Fédérale de Lausanne (EPFL), holding positions in the School of Basic Sciences (SB), Mathematics Institute (MATH), and the Numerical Algorithms and High-Performance Computing (ANCHP) group. He also leads the SMA-ENS unit within the SB-SMA division. His research focuses on numerical linear algebra, high-performance computing, and tensor approximation methods, with applications in scientific computing and data science. Education details are not explicitly listed, but his career at EPFL includes leadership in key research groups and doctoral programs. He supervises multiple doctoral students, including Alice Cortinovis, Peter Effenberger, and others. Research interests emphasize low-rank methods, matrix equations, and efficient algorithms for large-scale problems. Recent work includes advancements in randomized algorithms, tensor networks, and preconditioning techniques for eigenvalue problems. His publications span high-impact journals like Siam Journal on Matrix Analysis and Applications and Numerical Linear Algebra with Applications, addressing topics such as compressed sensing, multigrid methods, and distributed signal processing. Prof. Kressner advises doctoral candidates and contributes to the Program doctoral Mathématiques (EDMA-GE) committee. His lab, ANCHP, develops software tools for hierarchical matrices and tensor computations, such as the hm-toolbox for HODLR and HSS matrices.
Andrew J. Patton is a Professor in the Department of Economics at Duke University's Trinity College of Arts & Sciences, with a secondary affiliation at Singapore Management University's School of Economics. His office is located at 213 Social Sciences Building, Box 90097, Durham, NC 27708-0204, United States, and his professional website is available at http://econ.duke.edu/~ap172/. Dr. Patton's research focuses on financial econometrics, with particular expertise in volatility modeling, high-frequency data analysis, and dependence structures in financial markets. His work spans several key areas including realized variance methods, copula modeling for financial time series, risk management techniques, and forecast evaluation methodologies. He has made significant contributions to understanding market microstructure, hedge fund risk exposures, and the dynamics of financial correlations. His publication record demonstrates consistent output in top finance and econometrics journals, with recent work exploring machine learning applications in volatility forecasting, granular risk measures, and the impact of high-frequency data on asset pricing models. The research shows a clear trajectory toward increasingly sophisticated modeling of financial dependencies and risk structures. Dr. Patton has received recognition for his scholarly contributions, ranking among SSRN's top 1,427 authors by total paper downloads and top 1,090 by total paper citations. His work is frequently cited in the field of financial econometrics, indicating substantial influence on contemporary research. He maintains active research collaborations with leading economists including Tim Bollerslev, Kevin Sheppard, Tarun Ramadorai, and Robert Engle, among others. These collaborations span multiple institutions and have produced influential work on volatility modeling, risk measurement, and financial market dynamics.
Giacomo De Giorgi is Full Professor of Economics at the Institute of Economics and Econometrics, Geneva School of Economics and Management (GSEM), University of Geneva, and holds visiting appointments at UC Irvine. An alumnus of University College London (Ph.D.), he previously served as assistant professor at Stanford University (2006–2013), visiting professor at UC Berkeley and Columbia University, research professor at ICREA-MOVE/Barcelona GSE, and senior economist at the Federal Reserve Bank of New York (2014–2016). He is an associate editor of the Journal of the European Economic Association and a research fellow of CEPR and BREAD. Education Ph.D. in Economics, University College London Research Interests De Giorgi’s research integrates applied micro-econometrics with policy-relevant questions in development, labor, and household finance. His work explores how social networks shape economic decisions, the distributional impacts of cash and in-kind transfer programs, lifecycle inequality across racial and migrant groups, and the determinants of micro-enterprise formalization. Methodologically, he leverages large-scale randomized controlled trials, quasi-experimental designs, and cutting-edge network econometrics. Recent projects investigate: • Consumption and inequality : quantifying racial gaps in income and consumption dynamics over the lifecycle; • Financial inclusion : analyzing credit market imperfections, subprime borrowing, and the effects of business literacy interventions; • Migration and integration : comparing economic trajectories of migrants and natives in Denmark and the impact of refugee inflows on child poverty. Publications & Impact With more than 25 peer-reviewed articles in top journals—including the American Economic Review , Review of Economic Studies , Economic Journal , and Journal of Public Economics —De Giorgi’s scholarship has advanced understanding of social spillovers, program evaluation, and macro-financial linkages. His 2020 Review of Economic Studies paper on “Consumption Networks Effects” (with Frederiksen & Pistaferri) has become a benchmark for identifying neighborhood and family influences on household spending. Scientific Awards 2020 Banamex Prize for the best article published in American Economic Journal: Economic Policy Seminar & Grant Activity De Giorgi co-founded and co-organizes the Virtual Development Economics Seminar (VDEV), a joint initiative of BREAD, CEPR, and VDEV that hosts weekly online seminars reaching a global audience. He has delivered over 150 invited talks at institutions such as the World Bank, NBER, LACEA, and universities across five continents. His research has been supported by grants from the World Bank, CEPR, and private foundations. Teaching & Programs At Geneva, he teaches Development Economics, Labor Economics, and Advanced Econometrics at both graduate and executive levels. He co-founded the IEE Honors’ Program, an innovative selective track that immerses top undergraduate students in frontier research and policy engagement.
Simon Weber is a researcher affiliated with the ETH Zurich (Department of Computer Science). His work focuses on Unique Sink Orientations (USOs) , a combinatorial abstraction of optimization problems like Linear and Quadratic Programming. Simon's research spans three areas: (1) Structure of USOs and their links to Oriented Matroids; (2) Constructions of high-dimensional USOs to analyze algorithm complexity; and (3) Algorithmic improvements for sink-finding. He also explores topics in graph compression, neural networks, and ∃R-complete problems. Key Publications: PhD thesis on USO reductions, ∃R-completeness in neural training, and USO phase analysis. Scientific Contributions: Advances in USO complexity, FPT algorithms for MaxCut, and recognition of geometric hypergraphs and nerves of convex sets. He has supervised multiple theses at ETH Zurich, including topics on USO visualization, MaxCut algorithms, and necklace splitting. His teaching experience includes being a Head Assistant for courses like Geometry: Combinatorics & Algorithms and Topological Data Analysis . Simon's work has been recognized with Best Paper and Best Student Paper Finalist awards at SC19.
Suryanarayana Sankagiri is a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, affiliated with the Information and Network Dynamics (INDY1) group under Professor Matthias Grossglauser. Previously, he earned his Ph.D. in Electrical & Computer Engineering (2018-2022) from the University of Illinois at Urbana-Champaign , where he was supervised by Bruce Hajek and participated in the Coordinated Science Lab . He also holds an M.S. in Electrical & Computer Engineering from the University of Illinois (2016-2018) and a B.Tech. in Electrical Engineering from the Indian Institute of Technology Bombay (2012-2016). Education : Ph.D., Electrical & Computer Engineering, University of Illinois (2018-2022) M.S., Electrical & Computer Engineering, University of Illinois (2016-2018) B.Tech., Electrical Engineering, IIT Bombay (2012-2016) Suryanarayana's research focuses on discrete choice models and their application to recommendation systems , with a particular emphasis on learning from choice data and developing novel models for human decision-making. His broader interests include blockchain security under adverse network conditions, network dynamics , probabilistic modeling , and algorithm design . Recent work explores nonconvex matrix factorization and contextual dueling bandits for recommendation systems. His publications span theoretical and applied domains, including high-impact venues like ICML , Stochastic Systems , and IEEE Transactions on Networking . Themes include blockchain efficiency , hidden community detection in preferential attachment graphs, and temporal analysis of Indian classical music. Current projects involve refining recommendation systems through sparse comparison data and designing protocols for resilient blockchain networks. Scientific Awards : zkCapital Paper of the Week (2021) Rambus Fellowship (2021) Mavis Future Faculty Fellowship (2019) List of Teachers Ranked as Excellent (2019) Nomination for IIT Bombay Undergraduate Colloquium (2016) Best Poster Award, IIT Bombay Undergraduate Research Symposium (2013) Suryanarayana has advised no students listed in the provided materials. His work has been supported by fellowships such as the Mavis Future Faculty Fellowship and Rambus Fellowship . He contributes to the INDY1 group at EPFL, which investigates information and network dynamics through interdisciplinary approaches combining probability , network theory , and algorithmic design .
Leon Bungert is a Professor of Mathematics of Machine Learning at the University of Würzburg, working in applied analysis and numerics with a particular focus on data science and machine learning. His research investigates PDEs and variational models on graphs, adversarial robustness of machine learning, variational regularization, and nonlinear optimization. Dr. Bungert serves as a guest editor for the European Journal of Applied Mathematics, an associate editor for Advances in Continuous and Discrete Models: Theory and Applications, and is a member of the program committee at SSVM 2025. He is also an ELLIS member and actively organizes conferences and workshops, including "MIA'25" at IHP in Paris (January 13-15, 2025), "Synergies of Machine Learning and Numerics" in Osaka (March 11-13, 2025), and "Mathematical Analysis of Adversarial Machine Learning" in Oaxaca (August 17-22, 2025). Research Interests Dr. Bungert's primary research areas include: PDEs on graphs Adversarial robustness in machine learning Inverse problems Optimization Variational problems in L-infinity Nonlinear eigenvalue problems Image reconstruction with structural priors His work bridges theoretical mathematics with practical applications in machine learning, particularly focusing on the mathematical foundations of deep learning and developing robust algorithms that can withstand adversarial attacks. He has made significant contributions to understanding the connections between partial differential equations and machine learning algorithms. Research Trends Analysis of Dr. Bungert's recent publications reveals a strong focus on the intersection of machine learning and mathematical analysis. A key theme is the application of variational methods and partial differential equations to machine learning problems, particularly in understanding and improving the robustness of neural networks against adversarial examples. His work on Lipschitz learning on graphs has established important theoretical foundations for graph-based semi-supervised learning. Additionally, his research on the infinity Laplacian and p-Laplacian equations provides deep insights into the mathematical structure of machine learning algorithms. The development of Bregman learning frameworks for sparse neural networks represents a significant contribution to efficient deep learning model training. Professional Activities Dr. Bungert is actively involved in the academic community through editorial roles and conference organization. His current professional activities include: Guest editor for the European Journal of Applied Mathematics Associate editor for Advances in Continuous and Discrete Models: Theory and Applications Member of the program committee at SSVM 2025 ELLIS member Co-organizer of multiple international conferences and workshops Technical Contributions Dr. Bungert has developed several open-source software packages that implement his theoretical contributions, including: Code for convergence rates of Lipschitz learning on graphs A Bregman training framework for sparse neural networks CLIP: Cheap Lipschitz Training of Neural Networks Nonlinear Power Method for Proximal Operators and Neural Networks Robust Image Reconstruction with Misaligned Structural Information These implementations are primarily in Python and MATLAB, demonstrating his commitment to making theoretical advances accessible for practical applications.
