جست‌وجوی استاد و پژوهشگر

• Calculus of Variations
• Partial Differential Equations
• Optimal Transport
• +۴ مورد دیگر

Alessio Figalli is a renowned mathematician and Professor at the Department of Mathematics , ETH Zurich , where he also serves as Director of the Forschungsinstitut für Mathematik (FIM) . Born in Rome (Italy) in April 1984, he holds Italian citizenship and is a Swiss permanent resident. His academic journey includes a PhD from the Scuola Normale Superiore (Pisa) and École Normale Supérieure (Lyon), with postdoctoral roles at institutions like the University of Nice and École Polytechnique. Prior to ETH Zurich, he held professorships at the University of Texas at Austin (2009–2019). Figalli's research focuses on Calculus of Variations , Partial Differential Equations (PDEs) , Optimal Transport , and Free Boundary Problems . His work bridges pure and applied mathematics, with applications in geometry, probability, and physics. He is particularly known for contributions to optimal transport theory, regularity in free boundary problems, and stability estimates for geometric inequalities. Figalli has authored over 200 publications, including seminal papers on regularity of optimal transport maps , stability of geometric inequalities , and nonlocal PDEs . His invited lectures include plenary talks at the International Congress of Mathematicians (2018) and the Bourbaki seminar . He has supervised numerous PhD students and postdocs, fostering collaborations globally. Awards: Fields Medal (2018), EMS Prize (2012), Feltrinelli Prize (2017). Editorial Roles: Arch. Ration. Mech. Anal., Duke Math. J., and 10+ other journals . Grants: ERC Grant RSPDE , NSF awards, and the Swiss National Science Foundation. His ERC-funded project RSPDE explores regularity and stability in PDEs, while his mentorship has shaped the careers of over 30 students/postdocs. Figalli actively promotes mathematics through outreach, including public lectures and media engagements.

Kai Hormannمشاهده پروفایل

استاد

• Computational Sciences
• Geometric and Visual Computing
• Informatics
• +۳ مورد دیگر

Kai Hormann is a full professor in the Faculty of Informatics at Università della Svizzera italiana (USI Lugano). He earned his Ph.D. in computer science from the University of Erlangen-Nürnberg in 2002 and has held positions at Clausthal University of Technology, Caltech, and the CNR in Pisa. His research focuses on mathematical foundations of geometry processing, including barycentric coordinates, subdivision algorithms, and rational interpolation. Hormann has authored over 100 publications and serves as an associate editor for journals like Computer Aided Geometric Design and Dolomites Research Notes on Approximation . In 2024, he received the prestigious John A. Gregory Award for his contributions to geometric modeling. Education : Ph.D. in Computer Science, University of Erlangen-Nürnberg (2002) Diploma in Mathematics, University of Erlangen (1997) Abitur, Leibniz-Gymnasium Bad Schwartau (1992) Research Interests : Hormann’s work bridges computer science, applied mathematics, and engineering. His current projects include interactive shape deformations, time-dependent object processing, 3D visualization, and GPU-accelerated algorithms. His research areas encompass geometry processing, computational sciences, and numerical analysis. Publications : His recent articles focus on barycentric coordinates, subdivision schemes, and surface reconstruction. Themes include transfinite coordinates, curvature continuity, and efficient interpolation methods. Awards : John A. Gregory Award (2024) Chair of SIAM Activity Group on Geometric Design (2017–2018) Advising & Grants : He has advised numerous PhD students, including work on barycentric rational curves and subdivision schemes. He has organized conferences such as the SIAM GD and GMP series, reflecting his leadership in computational geometry. Labs & Teams : Hormann is affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI), collaborating on interdisciplinary projects in AI and geometry processing.

Maryam Kordiمشاهده پروفایل

پژوهشگر ارشد

• Anthropometrics
• Historical Epidemiology

Maryam Kordi serves as a Postdoctoral Research Assistant in the Anthropometrics & Historical Epidemiology Group, holding a Dr. phil. doctoral degree. Her work bridges interdisciplinary research in human biological anthropology and disease history, operating from Room Y11 G68 with contactable Swiss phone access (+41 44 635 05 13). Her core research domains are Anthropometrics—the scientific study of human body measurements, proportions, and variations across populations—and Historical Epidemiology, which investigates long-term patterns of disease spread, mortality, and health determinants through historical records. These fields synergize to analyze how socio-environmental factors shape physical development and disease vulnerability across centuries, contributing to broader understandings of human adaptability and public health evolution. As a member of the Anthropometrics & Historical Epidemiology Group, she engages in collaborative projects examining anthropometric data (e.g., stature, BMI trends) alongside epidemiological reconstructions of past pandemics, famines, and nutritional transitions. This research environment fosters integration of archival analysis, statistical modeling, and comparative cross-cultural studies to decode health trajectories in historical contexts.

Rahel Brüggerمشاهده پروفایل

پژوهشگر

• Behavioral biology
• Primatology
• Evolution of morality
• +۵ مورد دیگر

Rahel Brügger is a behavioral biologist and primatologist working as a Researcher at the University of Zurich's Department of Anthropology within the Faculty of Arts and Social Sciences. She is currently part of the ERC Research Project ENGINE, investigating the consequences of interdependence in cooperatively breeding common marmosets to understand the uniqueness of human cognition. Her research combines experimental approaches with innovative non-invasive methods to study social cognition and behavior in primates. Dr. Brügger's research interests focus on the evolution of morality and theory of mind abilities, with particular emphasis on social evaluation and third-party understanding in common marmosets. She investigates marmoset vigilance behavior as an indicator of understanding conspecifics' attentional states Her work examines marmoset sensitivity to audience effects in social contexts She employs thermography as a non-invasive method to measure arousal levels during social evaluations Her research explores how cooperative breeding systems shape social cognition She applies advanced statistical methods using R for data analysis Her approach combines naturalistic observation with controlled experimental paradigms to gain insight into animal minds. Analyzing her publication record from 2018-2025 reveals consistent research output focused on marmoset social cognition, with recent work expanding into play behavior ontogeny, novelty exploration dynamics, and rhythmic coordination mechanisms. Her articles demonstrate an interdisciplinary approach bridging primatology, cognitive science, and evolutionary psychology, with a strong emphasis on methodological innovation through non-invasive techniques like thermography. The research shows increasing sophistication in experimental design and theoretical framing, particularly regarding how cooperative breeding systems may have shaped cognitive abilities relevant to human social evolution. Her current work as part of the ERC Research Project ENGINE represents a significant research commitment, investigating how interdependence in cooperative breeding systems influences cognitive development. While no specific students are mentioned in the available materials, her position suggests potential mentoring responsibilities within the research project. The ERC funding indicates substantial research support for her investigations into primate social cognition. Dr. Brügger's laboratory work appears centered around non-invasive experimental approaches for studying marmoset cognition, with particular expertise in thermographic measurement of physiological responses. Her research team likely includes collaborators across disciplines given the methodological diversity evident in her publications, combining expertise in primatology, physiology, and cognitive science to address complex questions about social cognition evolution.

• Medical Imaging
• Artificial Intelligence
• Image Processing
• +۳ مورد دیگر

Prof. Jérôme Schmid is a Full Professor (Professeur HES ordinaire) at the Haute école de santé - Genève, part of the Faculty of Health. His expertise spans medical image processing, artificial intelligence (AI), and their clinical applications. He leads innovative projects addressing challenges in diagnostics, surgery planning, and medical education. Key roles include Principal Investigator in grants funded by Swiss National Science Foundation, Swiss Innovation Agency, and others. Research Focus: Combines AI with medical imaging for applications such as Parkinson’s disease detection via SPECT, breast lesion analysis using ultrafast MRI, and AI-driven radiography training tools. Projects emphasize interdisciplinary collaboration with hospitals and industry partners. Projects: DeepDAT (2022–2025): AI for Parkinson’s diagnosis via SPECT imaging. SUBREAM (2022–2025): Rapid breast MRI protocols with AI integration. AIRx (2019–2020): AI-based radiography simulation for student training. MyHip (2012–2014): Patient-specific hip arthroplasty planning. Publications: Focus on AI-driven diagnostics, imaging techniques, and medical education. Recent works address drowning detection via post-mortem CT, multimodal AI fusion for breast cancer, and serious games in radiology training. Grants & Partnerships: Swiss National Science Foundation: FAI analysis via multi-modal imaging. Innosuisse: Low-cost X-ray detectors for developing countries (GlobalDiagnostiX). Swiss Cancer Research Foundation: Breast MRI advancements (SUBREAM). Labs/Teams: Collaborates with the Geneva University Hospitals, EPFL, and industry (e.g., Medacta International SA) on hardware and clinical AI solutions.

• Medical Imaging
• Artificial Intelligence
• Image Processing
• +۵ مورد دیگر

Prof. Jérôme Schmid is a Professor at the Geneva School of Health Sciences (Haute école de santé - Genève) in the Department of Medical Radiology Techniques. He specializes in medical image processing, artificial intelligence applications in healthcare, and computer-aided diagnosis systems. His work focuses on improving diagnostic accuracy through machine learning, deformable models, and multimodal imaging fusion. Education includes a Master of Science (MSc) in Health Sciences from HES-SO/UNIL and a Bachelor of Science (BSc) in Medical Radiology Techniques from HES-SO. He also contributed to advanced education programs in 3D technologies in orthopedics and sports medicine through collaborations with the Geneva University Medical Faculty. Research interests revolve around AI-driven medical imaging analysis, specifically in breast lesion detection via MRI, Parkinson's disease diagnosis through SPECT imaging, and fracture recognition in radiographs. He leads projects addressing global healthcare challenges such as affordable imaging systems for low-resource countries and AI integration into radiology education through platforms like AIRx and ImmeRgaMe. Current projects (2022-2025) include DeepDAT (AI for Parkinson's diagnosis using SPECT) and SUBREAM (ultrafast breast MRI protocols). Completed projects span hip arthroplasty planning (MyHip), thermal-tolerant X-ray detectors, and FAI impingement modeling. His work is funded by Swiss National Science Foundation, Innosuisse, and private sector partnerships. Publications span 2015-2025, focusing on image segmentation, AI integration in diagnosis, and medical education innovations. He collaborates with academic partners like Hôpitaux Universitaires de Genève and industry leaders such as Medacta International.

Naima Chabouniمشاهده پروفایل

پژوهشگر ارشد

• Energy Economics
• Energy Systems Modeling
• Renewable Energy Integration
• +۳ مورد دیگر

Naima Chabouni serves as a Postdoctoral Fellow at Switzerland's Paul Scherrer Institute (PSI), embedded within the Energy Economics Group of the Laboratory for Energy Systems Analysis under the PSI Center for Nuclear Engineering and Sciences. Her work forms part of the SCENE project, a Center of Excellence dedicated to net-zero emissions research and cross-institutional collaboration. Her academic credentials include: Dr.-Ing. from the National School of Statistics and Applied Economics (ENSSEA) M.Sc. from the National School of Management (ENSM) Chabouni specializes in energy economics, modeling/optimization, and statistical analysis with emphasis on integrating variable renewable energy technologies into energy systems. Her research advances the Swiss TIMES model through enhanced temporal/spatial resolution and refined flexibility measures for supply/demand-side management. She particularly addresses energy transition challenges in hydrocarbon-dependent economies like Algeria, examining policy pathways for sustainable development. Her publication trends reveal consistent focus on Algeria's energy landscape through TIMES-based modeling, covering shale gas strategies, renewable integration, green hydrogen potential, and climate impacts on electricity demand. These studies employ rigorous econometric and statistical frameworks to evaluate decarbonization scenarios while balancing energy security and economic constraints. As an active contributor to the SCENE project and reviewer for organizations like the Mediterranean Energy Observatory and IRENA, Chabouni engages in shaping regional energy perspectives. While no formal student advising or specific grants are documented, her collaborative work across PSI, Mines Paris-PSL, and Algerian institutions demonstrates significant involvement in international energy research networks. She operates within PSI's Energy Economics Group, which concentrates on developing and applying energy system models to inform evidence-based policy decisions for sustainable energy transitions, particularly in European and North African contexts.

Matthias Studerمشاهده پروفایل

دانشیار

• Quantitative Methods
• Sequence Analysis
• Life Course Analysis
• +۵ مورد دیگر

Matthias Studer is an Associate Professor of quantitative methods for social sciences at the LIVES center and Institute of Demography and Socioeconomics of the Faculty of Social Sciences at the University of Geneva. His work focuses on developing and applying advanced statistical methods for analyzing life course trajectories and social inequalities. With a PhD in Socioeconomics from the University of Geneva, he has established himself as a leading expert in sequence analysis methodology. Studer's research interests center on quantitative methods for longitudinal data analysis, sequence analysis, gendered career inequalities, and labor market and social policy evaluation. His work bridges methodological innovation with substantive applications in sociology, demography, and public health. He has made significant contributions to the development of sequence analysis techniques, particularly through his work on the TraMineR package for R, which has become a standard tool in life course research. His recent publications (2023-2025) demonstrate the breadth of his research, spanning applications in cognitive aging, women's empowerment, labor market transitions, and cancer risk. These works consistently employ sophisticated sequence analysis techniques to examine complex life trajectories across different domains. His methodological contributions include advancements in validating sequence typologies, handling missing data in multichannel sequence analysis, and developing feature selection approaches to identify critical aspects of trajectories. Studer actively collaborates with researchers across multiple disciplines and institutions, contributing to interdisciplinary projects that examine life course vulnerabilities, social inequalities, and health outcomes. His work has significant implications for understanding how early life conditions shape later outcomes and how social policies might mitigate vulnerabilities across the life course.

Robert Feldmannمشاهده پروفایل

استاد

• Galaxy Formation
• Cosmology
• Data Science
• +۳ مورد دیگر

Robert Feldmann is a Professor of Astrophysics at the University of Zurich's Department of Astrophysics within the Faculty of Science. His research combines computational astrophysics with data science to investigate galaxy formation and evolution across cosmic history. As a key contributor to the Feedback in Realistic Environments (FIRE) project and leader of MassiveFIRE, he develops sophisticated cosmological simulations to understand how galaxies form stars, grow, and evolve within the cosmic web. Feldmann's research interests focus on understanding how galaxies and their properties evolve over cosmic time, particularly examining the processes that determine galaxy sizes, regulate star formation rates, and shape morphology. His work bridges observational astronomy with theoretical modeling, leveraging the exponential growth in computing power and data science techniques to tackle complex astrophysical problems. He investigates the role of stellar feedback, cosmological starvation, and dark matter halo properties in shaping galaxy evolution, with particular emphasis on massive galaxies during the cosmic noon period (redshifts z~1.5-3). His publication record shows consistent output in leading astrophysics journals, with recent work focusing on applying machine learning to cosmological simulations (EMBER framework), studying submillimeter-bright galaxies, and analyzing quiescent galaxy formation. Feldmann has developed significant open-source tools including LEO-Py for statistical analysis of astronomical data and ZEBRA for photometric redshift determination, demonstrating his dual expertise in astrophysics and computational methods. Feldmann actively collaborates with researchers worldwide, including Phil Hopkins, Rachel Bezanson, and Eliot Quataert, and participates in major international projects. He regularly presents at conferences, including organizing the 2024 Ascona conference on 'Observing and Simulating Galaxy Evolution in the Era of JWST.' His work has been featured in media outlets like Sueddeutsche Zeitung, highlighting the public impact of his research on galaxy morphology. As an educator, Feldmann mentors Master's students from both University of Zurich and ETH Zurich, offering research projects in astrophysics and data science. His computational approach to galaxy evolution provides students with valuable experience in high-performance computing and data analysis techniques applicable across scientific disciplines.