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Franck IutzelerView profile

Professor

• Numerical optimization
• Statistical learning
• Stochastic programming
• +5 more

Franck Iutzeler is a Professor of Applied Mathematics at Université de Toulouse, working within the Statistics & Optimization team of the Institut Mathématique de Toulouse and teaching in the Department of Mathematics. He previously served as an Assistant Professor at Université Grenoble Alpes from 2015 to 2023 and completed his Habilitation à Diriger des Recherches in 2021. His research focuses on the intersection of optimization, statistics, and optimal transport theory to develop robust data-driven models. Key areas include numerical optimization, statistical learning, stochastic programming, and optimal transport. He is particularly interested in distributionally robust optimization using Wasserstein metrics and has developed the skwdro Python library for implementing these methods. Iutzeler's recent publications demonstrate a strong focus on Wasserstein Distributionally Robust Optimization (WDRO), with multiple papers in top venues like NeurIPS and SIAM Journal on Optimization. His work bridges theoretical guarantees with practical implementation, particularly through the skwdro library which provides efficient code for WDRO in machine learning applications. ANR JCJC grant for project STROLL: Harnessing Structure in Optimization for Large-scale Learning Co-PI of ANITI chair on Trust and Responsibility in Artificial Intelligence led by JM. Loubes and J. Bolte Iutzeler actively supervises PhD students including Yu-Guan Hsieh (awarded Université Grenoble Alpes's PhD award), Gilles Bareilles, Waïss Azizian, and Victor Mercklé. He has secured research funding through the ANR (MAD project on Automatic Differentiation) and ANITI. His current research includes statistical fairness using optimal transport theory and automatic differentiation for stochastic optimization. He leads the development of the skwdro library for Wasserstein Distributionally Robust Optimization and is involved with ANITI (Toulouse's AI Cluster), where he also took responsibility for the 2nd year of the Master SID in Data Science & Engineering in September 2024.

Silke AdamView profile

Professor

• Political Communication
• Digital Media
• Conspiracy Theories
• +4 more

Professor Silke Adam serves as the Director of the Institute of Communication and Media Studies (IKMB) at the University of Bern, Switzerland. The institute operates within the Department of Social Sciences and focuses on political communication in digital environments. Professor Adam leads research examining how digitization transforms political communication, the influence of algorithms and artificial intelligence, and comparative analyses of communication in democratic and authoritarian contexts. Her primary research interests include political communication in digital environments, conspiracy theories, online information behavior, and right-wing populism. Professor Adam has pioneered research on how conspiracy theories spread during crises, particularly during the COVID-19 pandemic. Her work combines web tracking, survey research, and experimental methods to understand media effects on belief formation. She has made significant contributions to understanding how mainstream media can effectively debunk conspiracy theories without inadvertently reinforcing them. Professor Adam's research portfolio demonstrates a strong focus on methodological innovation in digital communication research, particularly in web tracking and computational content analysis. Her recent publications reveal increasing attention to algorithmic influences on information exposure, the spread of misinformation, and the relationship between political attitudes and selective information consumption. She has developed sophisticated approaches to studying online behavior through combined tracking, survey, and automated text classification methods. ICA Top Faculty-Award of the Mass Communication Division for 'Tracing Knowledge Gaps: Investigating the Influence of Education on News Exposure and Knowledge using Digital Trace Data' Professor Adam actively bridges academic research and practical journalism through workshops connecting media professionals with communication scholars. She has secured funding from the Multidisciplinary Center for Infectious Diseases for pandemic-related communication research. Her work emphasizes the importance of empirical evidence in guiding journalistic practices, particularly regarding how to address conspiracy theories without amplifying them. She supervises research projects examining political communication in digital environments and has mentored numerous researchers in the field. The Institute of Communication and Media Studies under Professor Adam's leadership maintains strong connections with the Multidisciplinary Center for Infectious Diseases and collaborates with media professionals through regular workshops. Her research team employs innovative methodologies including web tracking, algorithmic content analysis, and experimental designs to investigate contemporary challenges in political communication. The institute actively contributes to academic discourse through publications, conferences, and direct engagement with media practitioners to improve evidence-based journalism.

• MRI
• Brain Imaging (EEG, MRI)
• Neuroscience
• +4 more

Franceschiello Benedetta is an Associate Professor at HES-SO Valais-Wallis School of Engineering, specializing in Technical and IT disciplines. She holds a PhD in Mathematical Neuroscience from Université Pierre et Marie Curie (Paris). Her work bridges applied mathematics, computational neuroscience, and neuroimaging, with a focus on visual perception modeling, MRI techniques, and neural dynamics. Teaching: Linear Algebra courses across multiple engineering bachelor programs Expertise: Combines mathematical modeling with neuroscientific applications to study optical illusions, brain connectivity, and ophthalmic diagnostics Key Affiliations: ISMRM, Organization for Human Brain Mapping (OHBM), Association for Research in Vision and Ophthalmology (ARVO) Research Interests: Computational modeling of visual cortex mechanisms underlying geometric optical illusions Development of MRI-based methods for eye structure segmentation and axial length estimation Analysis of brain network reliability through standardized MRI protocols Optimization techniques for medical imaging reconstruction (e.g., weighted LASSO problems) Recent Work Trends: Focus on integrating psychophysical experiments with computational models to elucidate perceptual mechanisms, emphasizing synergistic interactions between physical stimulus parameters. Active in advancing MRI applications for both clinical diagnostics and fundamental neuroscience research.

• Natural Language Processing
• Machine Translation
• Speech Processing
• +3 more

Popescu-Belis Andrei serves as an Associate Professor at the University of Applied Sciences and Arts Western Switzerland (HES-SO), specifically within the Institute of Information and Communication Technologies (IICT) at HEIG-VD campus in Yverdon-les-Bains. His academic work spans teaching computer science fundamentals and advanced natural language processing concepts across both undergraduate and graduate programs. His research interests focus on Natural Language Processing , Machine Translation , and Human-Machine Dialogue systems, with particular emphasis on low-resource language scenarios. His work addresses challenges in speech-to-speech translation, subword tokenization, neural machine translation optimization, and creative applications like poem generation. Recent publications through 2025 demonstrate his ongoing contributions to the field, particularly in multilingual NMT training schedules and low-resource language processing pipelines. Analysis of his 15 most recent publications reveals consistent focus on practical solutions for language barriers, with increasing attention to reinforcement learning applications and creative language generation. His work on GPoeT demonstrates the intersection of traditional literary forms with modern language models, while his research on low-resource translation pipelines addresses real-world community interpreting needs. He has led significant research projects including Digital Lyric (2019-2020), a collaboration with UNIL (University of Lausanne) to develop systems for aiding poem generation for a public exhibition at Morges Castle. This project demonstrates his interdisciplinary approach bridging computational linguistics with creative applications. His academic contributions include: Development of specialized training pipelines for low-resource neural machine translation Innovative approaches to speech-to-speech translation for community interpreting Novel methods for constrained language model applications in creative writing Significant contributions to understanding subword tokenization mechanics Popescu-Belis maintains active research collaborations across institutions, as evidenced by his work with UNIL and various international conference publications. His teaching portfolio includes courses in natural language processing, data preparation, unsupervised machine learning, and computer science fundamentals, demonstrating his commitment to both theoretical and practical aspects of the field.

Peter ScheiblechnerView profile

Lecturer

• Statistics
• Data Analysis
• Machine Learning
• +5 more

Peter Scheiblechner is a Lecturer at Lucerne University of Applied Sciences and Arts' School of Engineering and Architecture, within the Department of Natural and Humanities Sciences (ING). He holds a PhD in Mathematics from the University of Paderborn (2007) and has held academic positions including Visiting Assistant Professor at Purdue University (2010-2011) and Postdoc at the Hausdorff Center for Mathematics (2011-2012). His business experience includes software development roles at companies like ClassWare GmbH and UBS in Switzerland. Education: PhD in Mathematics, University of Paderborn (2007) Master's in Mathematics (minor: Physics), Albert-Ludwigs University Freiburg (1997) Bachelor's in Mathematics (minor: Physics), Philipps-University Marburg (1993) High School Diploma, Martin-Luther-Schule Marburg (1991) Research Interests: Focus on applying statistics, data analysis, and machine learning to real-world problems; computational algebra/geometry/topology with complexity theory; algebraic and classical complexity theory. Active in projects like ENFLATE (flexibility markets), COSMOS Data Cockpit (personalized medicine), and topological data analysis. Publications: Over 10 peer-reviewed articles in journals like Journal of Symbolic Computation , Foundations of Computational Mathematics , and Communications in Contemporary Mathematics , with focuses on algorithmic algebraic geometry, complexity analysis, and topological computations. Awards: DFG fellowship (2008-2010), 3rd place in German Mathematics Competition (1991), and regional championship in Hessen (1985/86). Teaching: Teaches mathematics, physics, statistics, and numerical methods at bachelor and master levels, including courses on differential equations, linear algebra, stochastic processes, and engineering applications.

Jörg NickView profile

Researcher

• Partial Differential Equations
• Wave Phenomena
• Numerical Analysis
• +3 more

Jörg Nick is a Postdoctoral researcher at the Seminar for Applied Mathematics at ETH Zurich, Switzerland. His academic journey includes a Doctorate degree in Mathematics from the University of Tübingen (2023) under Prof. Christian Lubich and an MSc in Mathematics from the same institution (2018). He previously held a postdoctoral position at the University of Tübingen (2023) and was a doctoral researcher there from 2018 to 2023, associated with the Deutsche Forschungsgemeinschaft (DFG) project Wave phenomena: analysis and numerics , specifically Project A2 on numerical methods for wave problems with nontrivial boundary conditions. His research focuses on the numerical analysis of partial differential equations (PDEs), particularly wave scattering and metamaterials. Key areas include integral equations for PDEs, dynamical parametric approximation, numerical methods for time-dependent metamaterials, and inverse problems in wave phenomena. His work often integrates the Boundary Element Method (BEM) and Convolution Quadrature (CQ) to analyze wave scattering and subwavelength resonators. Notable contributions include studies on electromagnetic scattering from dispersive materials, time-periodic coefficients in wave equations, and regularized parametric approximation techniques. His research bridges theoretical developments with practical applications in metamaterials and medical imaging (e.g., anomaly detection in capsule endoscopy using ensemble learning). His academic affiliations include the Collaborative Research Center (CRC) 1173 at Tübingen and current participation in ETH Zurich’s mathematical and computational initiatives. While no formal awards are listed, his prolific publication record reflects sustained contributions to numerical mathematics and applied physics.

• Quantitative Finance
• Portfolio Optimization
• Covariance Matrix Estimation
• +3 more

Gianluca De Nard serves as a Senior Research Associate at the University of Zurich's Department of Economics, Research Fellow at New York University's Volatility and Risk Institute, and Head of Quantitative Research at OLZ AG in Zurich. His career bridges academic research and practical finance applications, with significant contributions across multiple institutions. Dr. De Nard specializes in quantitative finance with expertise in portfolio optimization, covariance matrix estimation, and financial econometrics. His research focuses on developing robust statistical methods for handling large-dimensional financial data, with particular emphasis on shrinkage techniques and factor models. His work addresses fundamental challenges in modern portfolio theory, including nonstandard errors in financial research, climate risk integration, and AI applications in investment management. Analysis of his recent publications reveals a clear evolution toward integrating machine learning with traditional financial econometrics. His 2024-2025 work shows increasing sophistication in applying AI techniques to portfolio construction while maintaining rigorous statistical foundations. The recurring themes across his research include improving covariance matrix estimation, developing factor models for portfolio selection, and addressing methodological challenges in financial research. Dr. De Nard has established collaborations with leading researchers including Nobel laureate Robert F. Engle, Olivier Ledoit, and Michael Wolf. His work appears in top finance journals including the Journal of Finance, Journal of Financial Econometrics, and Journal of Banking and Finance, with his 2024 paper 'Nonstandard Errors' accumulating over 17,000 downloads. At OLZ AG, he applies his academic research to practical investment challenges as Head of Quantitative Research, demonstrating his ability to translate theoretical advances into real-world solutions. His dual academic-industry roles position him at the forefront of quantitative finance research and application.

Liala ConsoliView profile

Researcher

• Poverty
• Vulnerability
• Migration and Mobility
• +5 more

Liala Consoli is a Scientific Collaborator and Associate Postdoctoral Researcher at the University of Geneva , affiliated with the Institute of Sociological Research, the Swiss Center of Expertise in Life Course Research (LIVES Center UNIGE), and the Institute of Environmental Governance and Territorial Development (IGEDT – UNIGE). She also contributes to research and teaching at the Geneva School of Social Work (HETS-GE HES-SO) . PhD in Sociology (2024), University of Geneva Master of Arts in Socioeconomics (2018), University of Geneva Complementary Certificate in Economic and Social Demography (2016), University of Geneva Bachelor of Arts in Socio-economics (2014), University of Geneva Research Interests focus on poverty, migration, aspirations, life course transitions, and urban sociology, with methodological expertise in qualitative longitudinal and mixed-methods research. Her recent work examines regularization policies for undocumented migrants, mental health, and social policy evaluations during the Covid-19 pandemic . Scientific Contributions include studies on migrant health disparities, regularization impacts, and longitudinal analyses of social integration. She has been awarded a LIVES Young Scholar Grant for her methodological work.

Maryam KamgarpourView profile

Assistant Professor

• Control Theory
• Game Theory
• Mechanism Design
• +6 more

Maryam Kamgarpour is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), School of Engineering. She previously held faculty positions at the University of British Columbia and ETH Zürich. Her work bridges stochastic control , multiagent learning , and game theory , focusing on safety-critical systems. Education: PhD in Engineering from UC Berkeley, BSc in Applied Science from University of Waterloo. Research Interests: Control under uncertainty, game theory, mechanism design, mixed-integer optimization, and applications to transportation, robotics, power grids, and healthcare. Her recent publications emphasize safe reinforcement learning , multirobot coordination , and stochastic trajectory planning , with applications to aircraft navigation and energy systems. She has received the European Union ERC Starting Grant, NASA High Potential Individual Award, and IEEE Transactions on Control of Network Systems Outstanding Paper Award. Scientific Awards: ERC Starting Grant (2016-2021) NASA High Potential Individual Award (2010) NASA Excellence in Publication Award IEEE Outstanding Paper Award (2022) PhD Students: Jordan Philip Christopher Maddux Anna Maria Ni Tingting Ren Kai Salizzoni Giulio Schlaginhaufen Andreas Vaishampayan Saurabh Dilip Vallat Gabriel Rémi Former EPFL student: Guo Baiwei

Alin AchimView profile

Professor

• Statistical Signal Processing
• Inverse Problems
• Computational Imaging
• +7 more

Alin Achim is a Professor of Computational Imaging at the School of Computer Science, University of Bristol, with a focus on statistical signal processing and inverse problems. His research bridges model-based and data-driven approaches in machine learning, particularly for computational imaging applications. Education: B.Sc. and M.Sc. from the University of Bucharest, Ph.D. from the University of Patras. His work emphasizes sparse representations in signal/image processing, enabling non-linear algorithms for image denoising, fusion, segmentation, super-resolution , and novel penalty functions for deep learning architectures. Applications span Synthetic Aperture Radar (SAR) imaging for Earth Observation and biomedical imaging (e.g., lung ultrasound, OCT). Recent publications highlight trends in computational medical imaging (2023), SAR for ocean surface modeling (2022), and non-convex optimization methods (2020). His research integrates statistical sparsity with classical sparsity constraints. Scientific Awards: EUSIPCO Best Student Paper Award (2009) Senior Member IEEE (2009) He has supervised multiple students and leads active projects like Next Generation Quantitative Acoustic Microscopy (NIH-funded) and AssenSAR for naval SAR applications. He contributes to open-access datasets (e.g., OCT2Confocal, 3D OCT Images of Murine Uveitis) and serves on editorial boards for journals like IEEE Transactions on Image Processing .

• Strategic Management
• Identity Management
• Organizational Ambidexterity
• +6 more

Nicole Rosenkranz serves as an Associate Professor at EHL Hospitality Business School in Lausanne, Switzerland. Her academic career combines rigorous theoretical research with practical applications in hospitality and business education. Her research interests focus on the complex interplay between individual identity and organizational strategy, with particular emphasis on role identity management, organizational ambidexterity, and strategic decision-making processes. Dr. Rosenkranz examines how individuals navigate multiple role identities in organizational contexts, how strategic change affects employee identification with organizations, and how hybrid organizations manage competing identities to gain competitive advantages. Analysis of her publication record reveals consistent contributions to understanding organizational behavior through qualitative methodologies. Her work spans multiple organizational levels from individual role transitions to firm-level strategic change, with recurring themes of identity management, ambidexterity, and strategic decision-making. Recent publications increasingly address digital transformation challenges and hybrid organizational forms. Dr. Rosenkranz has published in top-tier management journals including the Journal of Management and European Management Journal, with notable contributions to the Handbook of Research Methods on Creativity. Her research demonstrates strong methodological rigor through both qualitative case studies and survey-based approaches. Doctoral thesis completed at the University of St.Gallen (2012) Extensive publication record spanning over a decade Regular presenter at prestigious conferences including Academy of Management and EGOS

• Computer Science
• Machine Learning
• Artificial Intelligence
• +3 more

Yongtao Wu is a Doctoral Assistant and student in the doctoral program in Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL). He is affiliated with the Laboratory for Information and Inference Systems (LIONS), part of the School of Engineering (STI) and the Institute of Electrical Engineering (IEM). His research focuses on machine learning, adversarial learning, computer vision, and deep learning, with applications in vision-language models, robustness analysis, and optimization algorithms. His work explores topics such as diffusion-based purification for vision-language models, adversarial attacks on large models, and parameter-efficient fine-tuning techniques. He contributes to advancing foundational AI models and their robustness in real-world scenarios. His research also intersects with cybersecurity, structural engineering predictions, and quantum computing methodologies. Yongtao is actively involved in the LIONS lab, collaborating on projects that bridge theoretical advancements with practical applications in computer science and engineering. His doctoral studies emphasize interdisciplinary approaches to solving complex problems in AI and machine learning.

• Stochastic Methods
• Fluid Dynamics
• Multiphase Flow
• +7 more

PD Dr. Daniel Werner Meyer-Massetti is a Privatdozent (Part-Time Lecturer) at the Department of Mechanical and Process Engineering , ETH Zürich. His research focuses on stochastic methods for fluid dynamics and multiphase transport problems in complex systems. Primary Affiliation: ETH Zürich, Department of Mechanical and Process Engineering Email: meyerda@ethz.ch His work bridges theoretical and applied research in turbulence, porous media, and combustion. Key contributions include: Stochastic particle-based frameworks for fractured subsurface flows Turbulence modulation in droplet-laden flows Uncertainty quantification in heterogeneous reservoirs Computational tools like the Netflow Python library His recent publications demonstrate methodological advancements in: Modeling inertial particle clustering in turbulence Simulating evaporation dynamics in reactive flows Developing non-local transport formulations Quantifying dispersion mechanisms in porous media Validating kinematic turbulence models Creating adaptive simulation strategies He collaborates with research groups including the Coletti Group , Jenny Group , and Supponen Group , while maintaining connections to the Haller Group and Noiray People as a former member.

• Theoretical Computer Science
• Automata Theory
• Formal Verification
• +7 more

Ulrich Ultes-Nitsche is a Professor in the Department of Informatics at the University of Fribourg, where he also serves as Dean of the Faculty of Science and Medicine. His research spans theoretical computer science, formal methods, and cybersecurity, with a strong focus on automata theory, model checking, and secure network systems. Institution: University of Fribourg School: Faculty of Science and Medicine Department: Department of Informatics Role: Professor and Dean His research interests include theoretical computer science, automata theory, formal verification, temporal logic, and cybersecurity. He has made significant contributions to the understanding of Büchi automata, liveness properties, and secure authentication mechanisms using Trusted Platform Modules. His interdisciplinary work extends to secure medical data access and ecological network analysis. The recent publications highlight a clear trend: a deep and sustained focus on automata theory, particularly Büchi automata, with increasing integration of machine learning techniques like graph neural networks for analysis. His earlier work laid foundational contributions in formal verification, model checking under fairness, and secure network protocols, especially in firewall and EAP-TLS authentication. The research consistently bridges theoretical computer science with practical security applications. Carolin Latze InSeon Yoo Dominik Jungo David Buchmann Thierry Nicola Michael Hayoz Christoph Ehret Nayla Sokhn Ulrich Ultes-Nitsche has led and co-organized numerous international workshops (e.g., NCMA, MSVVEIS, VCL) on formal methods and verification, demonstrating a strong commitment to the academic community. His work has been supported by research in formal verification, network security, and trusted computing, often involving collaborations across institutions. He has also contributed to projects involving secure e-commerce, medical data access, and enterprise information systems. He has been a key figure in organizing and editing proceedings for the International Workshop on Modelling, Simulation, Verification and Validation of Enterprise Information Systems (MSVVEIS) and the ACM Sigplan Workshop on Verification and Computational Logic (VCL), fostering research communities in formal methods and verification.

• Migration Health
• Health Inequalities
• Public Health
• +5 more

Dr. Yves Jackson is an Assistant Professor at the University of Geneva's Faculty of Medicine, working within the LIVES Centre (Interdisciplinary Centre for Research on Life Course and Vulnerabilities). His research focuses on vulnerable populations, particularly undocumented migrants in Switzerland, examining the intersection of legal status, health outcomes, and social determinants of health. His educational background includes medical training (MD), which informs his public health research approach. Dr. Jackson has established himself as a leading researcher on migrant health in Switzerland, with particular expertise in the health impacts of regularization processes for undocumented workers. Dr. Jackson's research portfolio demonstrates consistent productivity with numerous publications annually, primarily examining health inequalities among undocumented migrants. His work often employs longitudinal methodologies to track changes in health status following regularization, with particular attention to mental health outcomes, access to healthcare services, and social determinants of health. He has also conducted significant research on Chagas disease among Latin American migrant populations in Switzerland. His publication record shows a strong emphasis on policy-relevant research, with many studies directly addressing Swiss migration and health policies. The articles demonstrate interdisciplinary approaches, combining medical, sociological, and public health perspectives to understand complex health challenges faced by vulnerable populations. Dr. Jackson frequently collaborates with Claudine Burton-Jeangros, Jan-Erik Refle, and other researchers at the University of Geneva, contributing to a robust research program on migration and health. His work has been published in high-impact journals across public health, migration studies, and medical disciplines. His research has practical implications for health policy, particularly regarding equitable access to healthcare services for undocumented populations and the health consequences of migration status regularization. Dr. Jackson's work contributes significantly to understanding how legal frameworks impact health outcomes for vulnerable populations in Switzerland.