جستجوی استاد راهنمای دانشگاهی

• Digital Humanities
• Art History
• Urban History
• +۱۱ مورد دیگر

Isabella Di Lenardo is a Lecturer and Scientist at the Digital Humanities Institute (DHI) at École Polytechnique Fédérale de Lausanne (EPFL), where she also serves as the coordinator of the EPFL Time Machine Unit and the European Local Time Machines. She holds affiliations across multiple departments, including DHI-GE, SAR-ENS, SHS-ENS, and EDDH-ENS, reflecting her interdisciplinary role in teaching and research. Her educational background includes a PhD in Theories and Art History, with postdoctoral and faculty experience at institutions such as INHA (Paris), EPFL, and IUAV (Venice). Her research spans Digital Humanities, Art History, Urban History, and GIS , with a focus on digital urban reconstruction, historical cadastres, and AI applications in cultural heritage. She employs advanced computational methods including machine learning, 4D modeling, and semantic segmentation to analyze historical maps, cadastral records, and art archives. Her work bridges humanities scholarship with computer science, particularly in reconstructing urban evolution and analyzing visual patterns. The recent publications reveal a consistent trend in AI-powered historical data analysis , especially in processing non-standardized historical documents, reconstructing urban spaces, and developing open-source tools for digital heritage. Her work frequently involves large-scale datasets from Venice, Lausanne, Paris, and Jerusalem, demonstrating a transnational and interdisciplinary approach. She has contributed to significant collaborative projects such as the Venice Time Machine , Parcels of Venice , and Time Machine Organization , often acting as a principal investigator or project leader. Her role involves coordinating diverse teams of researchers, engineers, and cultural institutions. Scientific contributions include: Development of the Morphograph tool for visual pattern recognition in art archives Automatic vectorization and analysis of Napoleonic cadastres Creation of 4D models for historical cities AI-driven text and pattern extraction from historical maps Building discovery engines for digital art history She actively teaches ex cathedra courses in Digital Urban History and Art History at EPFL and internationally. Her work in grants and projects emphasizes open data, reproducibility, and interdisciplinary collaboration. She has led research funded by organizations supporting digital heritage innovation. She is a key member of the Digital Humanities Laboratory at EPFL and the Time Machine Organization , where she fosters collaboration between computer scientists, historians, and cultural institutions. Her work in the Replica Project and ARCHiVe center highlights her leadership in digitizing and making accessible large art historical archives.

David Dornمشاهده پروفایل

استاد

• Labor Economics
• International Trade
• Economic Geography
• +۶ مورد دیگر

David Dorn is the UBS Foundation Professor of Globalization and Labor Markets at the University of Zurich and Director of the university-wide interdisciplinary research priority program “Equality of Opportunity.” He is also an Affiliated Professor at the UBS International Center of Economics in Society and holds research fellowships at CEPR (London), IZA (Bonn), and CESifo (Munich). Previously, he was a tenured Associate Professor at CEMFI (Madrid) and a visiting professor at Harvard, UC Berkeley, MIT, Boston University, and the University of Chicago. Education Ph.D. in Economics, University of St. Gallen (Dissertation No. 3613, September 2009) Research Interests Professor Dorn’s work sits at the intersection of labor economics, international trade, economic geography, and macroeconomics . His research agenda focuses on understanding how globalization and technological change reshape labor markets, wage structures, and social inequality. He has produced seminal studies on the “China shock” —the labor-market impacts of rising Chinese import competition—and on the polarization of employment toward low-skill services and high-skill professions. His recent work extends to measuring multidimensional skills using LinkedIn data , analyzing superstar firms and the falling labor share , and examining public attitudes toward trade policy and their electoral consequences in the United States and Europe. Publications & Data Impact Dorn’s publications have appeared in top-tier journals such as the American Economic Review , Quarterly Journal of Economics , Journal of the European Economic Association , and Economic Journal . His datasets—covering occupation codes, task content, industry trade exposure, commuting-zone definitions, and political geography—are widely used across the economics profession. Awards & Honors Hermann Heinrich Gossen Prize, 2023 (top economist under 45 in German-speaking countries) Swiss National Science Foundation ERC Starting Grant Ranked among the 100 most-cited economists worldwide over the past decade Grants & Advising As Director of the URPP “Equality of Opportunity,” he leads an interdisciplinary team investigating the determinants and consequences of unequal life chances. He also serves on the editorial boards of the Review of Economic Studies and the Journal of the European Economic Association , shaping the direction of research in labor and international economics. Laboratory & Team His research group is embedded in the Department of Economics at the University of Zurich, housed at Schönberggasse 1, Zurich. Administrative support is provided by Anne Sander (anne.sander@econ.uzh.ch).

Kaspar Riesenمشاهده پروفایل

پژوهشگر

• Pattern Recognition
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

PD Dr. Kaspar Riesen is the Head of the Pattern Recognition Group at the Institute of Computer Science, University of Bern. His research focuses on graph-based methods for pattern recognition, with applications in document analysis, environmental modeling, and healthcare. Key interests include graph matching, neural networks, and spatio-temporal modeling. His work spans structural pattern recognition, graph embeddings, and keyword spotting in historical documents. Recent projects involve river network analysis using graph regression and hypoglycemia prediction via LSTM-GNN hybrid models. Publications emphasize graph theory advancements, such as normalized graph compression and geometric similarity learning. Collaborations include developing specialized algorithms for automated error detection and improving decision-making in simulated sports. Labs/Teams: Pattern Recognition Group (PRG) at the University of Bern.

Jonathan S. Skinnerمشاهده پروفایل

استاد

• Health Economics
• Economics of Aging
• Health Policy
• +۷ مورد دیگر

Jonathan S. Skinner is a Professor at Dartmouth College, Department of Economics, and a professor at the Dartmouth Institute for Health Policy and Clinical Practice within the Geisel School of Medicine. He serves as the Aging Program Director at the National Bureau of Economic Research (NBER) and has contributed extensively to understanding healthcare productivity, technology adoption, and policy implications for aging populations. Key research areas: Health Economics, Economics of Aging, Public Policy Current projects focus on Medicare/Medicaid spending, geographic healthcare disparities, and pandemic impacts on vulnerable populations. Publications (2023–2010) reveal trends in healthcare fraud networks , racial segregation in hospital admissions , and end-of-life expenditures . His work often integrates Medicare claims data and network analysis to assess systemic inefficiencies. Recent scientific collaborations include partnerships with institutions such as the NBER and JAMA Network Open. He has analyzed regional mortality disparities and technology diffusion in clinical settings, with implications for public health strategy.

Julian Tachellaمشاهده پروفایل

پژوهشگر

• Signal Processing
• Machine Learning
• Computational Imaging
• +۵ مورد دیگر

Dr. Julian Tachella is a CNRS Research Scientist at the Sisyph Laboratory of École Normale Supérieure de Lyon, with co-founder/CSO roles at Blur Labs. His career spans signal processing, machine learning, and computational imaging, focusing on inverse problems and self-supervised learning. Affiliation: CNRS (French National Centre for Scientific Research), Sisyph Laboratory, École Normale Supérieure de Lyon Co-founder & CSO: Blur Labs (AI/Imaging startup) Research Interests: At the intersection of signal processing and deep learning , his work addresses imaging inverse problems through self-supervised methodologies (e.g., UNSURE, Generalized R2R) that eliminate ground-truth requirements. Key contributions include equivariant imaging frameworks for stability, spline sketches for photon-counting lidar compression, and uncertainty quantification techniques with equivariant bootstrapping. Recent Trends: 2025 publications emphasize lightweight architectures for multi-domain reconstruction (CT, super-resolution) and noise-agnostic SURE methods. 2024 works focus on audio declipping , compressed lidar , and nonlinear algorithm unrolling with applications in autonomous vehicles and medical imaging. Scientific Awards: Best Student Paper Award at ICASSP’22 Collaborations & Leadership: He leads the DeepInverse open-source project and develops algorithms for real-time 3D lidar reconstruction. His team includes researchers from University of Edinburgh and Grenoble INP, with applications in automotive lidar and underwater imaging.

Anna-Lena Horlemannمشاهده پروفایل

دانشیار

• Algebraic Coding Theory
• Post-Quantum Cryptography
• Network Coding Theory
• +۳ مورد دیگر

Anna-Lena Horlemann is an Associate Professor for Foundations of Computation at the School of Computer Science, University of St. Gallen. Her research focuses on algebraic coding theory, post-quantum cryptography, and network coding. She leads the Coding Theory and Cryptography research group, actively contributing to NIST's post-quantum standardization efforts. Her work addresses quantum computing threats to current cryptosystems and develops error-correcting codes for reliable data transmission and storage. Education: PhD in Mathematics from the University of Zurich (2013), supervised by Prof. Joachim Rosenthal. Diploma in Mathematics from Ruhr-Universität Bochum. Research interests span coding theory (e.g., rank-metric codes, subspace codes), cryptography (code-based systems, privacy protocols), and neuroinformatics (connectome analysis). Recent publications emphasize Lee metric applications, MDS code classification, and cryptanalysis of Gabidulin-based systems. She mentors doctoral researchers such as Nadja Willenborg and Violetta Weger, focusing on topics like code densities and quantum-resistant algorithms. Her team's work bridges theoretical foundations and practical implementations, with contributions to error-correction resilience and cryptographic security in post-quantum scenarios. No formal awards listed, but actively participates in international conferences (IEEE, CBCrypto) and journal reviews.

• Applied Analysis
• Numerical Analysis
• Data Science
• +۶ مورد دیگر

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.

Franck Iutzelerمشاهده پروفایل

استاد

• Numerical optimization
• Statistical learning
• Stochastic programming
• +۵ مورد دیگر

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.

Beat Suterمشاهده پروفایل

مدرس ارشد

• Game Design
• Game Studies
• Digital Humanities
• +۶ مورد دیگر

Dr. Beat Suter is a Senior Lecturer for Game Design at the Zurich University of the Arts (ZHdK) , where he has co-developed educational programs and founded the GameLab ZHdK. He holds a Dr. Phil. from the University of Zurich and has previously taught at the Merz Academy Stuttgart (2006–2014). His interdisciplinary work bridges game design, digital humanities, and electronic literature, with a focus on narrative mechanics, game preservation, and Swiss game culture. Education: Dr. Phil. (2000) – University of Zurich Lic. Phil. I (1987) – University of Zurich PALC Certificate – University of California San Diego Studies in German Literature, Linguistics, and Art History – University of Zurich and University of California His research explores intersections between game studies , digital archiving , and interactive narration , with recent publications analyzing Swiss youth culture in games, historical game packaging, and archiving digital interactive media. He co-authored the Narrative Mechanics (2021) and Games and Rules (2018) anthologies, emphasizing rule-based design and storytelling frameworks. From 2023, his 15 most recent articles (2023–2025) trace trends in game history , Swiss game design , and digital preservation , often published via the Confoederatio Ludens blog. These works highlight his focus on documenting marginalized game practices and cultural contexts. As founder of the art group AND-OR and co-director of the GameZ & RuleZ conference, Suter actively shapes interdisciplinary dialogues. He reviews for national research funds in Austria, Germany, and Switzerland and has curated exhibitions like RuleZ for the Magic Circle and Forgotten Game Mechanics , blending academic rigor with creative practice.

• Mathematical Physics
• Differential Geometry
• Topology
• +۴ مورد دیگر

Prof. Alberto S. Cattaneo is a faculty member at the University of Zurich, affiliated with the Department of Mathematics. He holds the rank of Professor and has been actively involved in teaching and research since at least 1998. His research interests span mathematical physics, differential geometry, topology, and interdisciplinary areas like computational biology, genomics, and digital forensics. He has developed courses on topics such as field theory, quantum mechanics, and differential manifolds, reflecting his expertise in theoretical and applied mathematics. Prof. Cattaneo has contributed to numerous publications, including works on distributed genomic analysis, sensor pattern noise (PNU) in forensics, and algorithm optimization for big data frameworks like Hadoop and Spark. His work bridges pure mathematics with applications in bioinformatics and cybersecurity. Though no awards are explicitly mentioned, his extensive publication record and teaching roles highlight his academic standing. He maintains an active presence through courses and research collaborations, with no indication of part-time roles or retirement.

• MRI
• Brain Imaging (EEG, MRI)
• Neuroscience
• +۴ مورد دیگر

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.

• Geometric Deep Learning
• Topological Deep Learning
• Topological Data Analysis
• +۷ مورد دیگر

Bastian Alexander Grossenbacher is a Full Professor of Machine Learning at the University of Fribourg, where he leads the AIDOS Lab within the Department of Informatics, Faculty of Science and Medicine. He holds secondary appointments at the Institute of AI for Health and the Helmholtz Pioneer Campus of Helmholtz Munich. He is also a TUM Junior Fellow and a member of ELLIS and AI-LIFE. He co-directs the Applied Algebraic Topology Research Network (AATRN) and is involved in the Topology, Algebra, and Geometry in Data Science (TAG DS) initiative. His research lies at the intersection of geometry, topology, and machine learning, with a focus on Geometric Deep Learning, Topological Deep Learning, and Topological Data Analysis. He develops novel machine learning methods that use topological and geometric priors to improve model robustness and interpretability, particularly in biomedical applications. His work spans theoretical foundations and practical implementations in areas such as single-cell analysis, medical imaging, and network science. The 15 most recent publications (2023–2025) reveal a strong trend toward integrating algebraic and differential topology into deep learning architectures. Key themes include the use of Euler Characteristic and magnitude transforms, curvature-based analysis of graphs and data, simplicial and manifold-based neural representations, and the application of topological methods to biomedical data. His articles appear in top-tier venues such as Nature Communications, ICML, NeurIPS, ICLR, and IEEE conferences, reflecting both theoretical depth and high-impact applications. ERC Starting Grant Bastian Rieck is actively involved in mentoring and academic leadership. He supervises PhD students and postdoctoral researchers in the AIDOS Lab and is open to new collaborations, particularly with candidates from interdisciplinary backgrounds. He has secured significant research funding, including the ERC Starting Grant, and leads multiple collaborative research initiatives. He emphasizes open science, sharing code, data, and educational materials publicly. He leads the AIDOS Lab, which focuses on advancing machine learning through topological and geometric principles. He is also a co-director of the Applied Algebraic Topology Research Network (AATRN), which hosts a large online seminar series. Additionally, he maintains DONUT, a curated database of non-theoretical applications of topology, and is active in the ELLIS and AI-LIFE networks, fostering international collaboration in AI and health.

• Human-Computer Interaction
• Social Neuroscience
• Information Theory
• +۵ مورد دیگر

Juan Carlos Farah serves as both a Scientific Collaborator and Lecturer at the Fribourg School of Engineering and Architecture, part of the University of Applied Sciences and Arts of Western Switzerland (HES-SO). His academic appointments indicate an active role in both research and teaching within the institution's engineering and technology programs. Dr. Farah holds a PhD in Robotics, Control, and Intelligent Systems from the École Polytechnique Fédérale de Lausanne (EPFL), a Master of Science in Computing from Imperial College London, and a Bachelor of Arts in Economics from Harvard University. This interdisciplinary background spans computer science, engineering, and economics, providing a strong foundation for his current research endeavors. His research focuses on the intersection of human-computer interaction, social neuroscience, and information theory, with particular emphasis on how artificial intelligence with anthropomorphic traits affects human behavior and learning. Farah has made significant contributions to educational technology, especially in the development and evaluation of conversational agents for learning environments. His work examines how chatbots and large language models can be effectively integrated into educational contexts to support students while maintaining pedagogical integrity. An analysis of his recent publications reveals a strong trajectory in human-AI interaction within educational settings, with increasing focus on large language models since their emergence. His research spans multiple dimensions of educational technology including code review systems, computational thinking development, and the psychological impacts of technology on learners. The interdisciplinary nature of his work connects computer science, cognitive science, and educational theory. While no specific scientific awards are mentioned in the provided documentation, Dr. Farah's research has been published in reputable journals and presented at significant international conferences in educational technology and human-computer interaction. His research methodology combines both qualitative and quantitative approaches, often conducting controlled experiments with student populations to evaluate the effectiveness of educational technologies. Farah frequently collaborates with colleagues from multiple institutions, suggesting strong interdisciplinary research networks. His work appears to focus on practical applications of technology in real educational settings rather than purely theoretical investigations. Dr. Farah's research program appears centered around developing frameworks and tools for educational technology, with particular attention to how conversational agents can be designed to support specific learning objectives. His TRACE model for educational chatbot design and work on code review notebooks represent structured approaches to integrating AI technologies into pedagogical practices while addressing the unique challenges of educational contexts.

Helmut Bölcskeiمشاهده پروفایل

استاد

• Applied Mathematics
• Machine Learning Theory
• Mathematical Signal Processing
• +۳ مورد دیگر

Prof. Dr. Helmut Bölcskei is a Full Professor of Mathematical Information Science at ETH Zurich's Department of Information Technology and Electrical Engineering. He holds a joint affiliation with the Department of Mathematics. His academic journey includes a Dipl.-Ing. and Dr. techn. from Vienna University of Technology, followed by postdoctoral research at Stanford University and industry roles at Iospan Wireless and Celestrius AG. He has been at ETH Zurich since 2002, contributing to applied mathematics, machine learning theory, signal processing, and statistics. Education : 1994: Dipl.-Ing., Vienna University of Technology 1997: Dr. techn., Vienna University of Technology Industry Experience : Co-founder of Iospan Wireless (acquired by Intel) and Celestrius AG His research focuses on applied mathematics , machine learning theory , and data science , with emphasis on neural network approximation, metric entropy, and signal processing. Recent work explores theoretical limits of deep learning and nonlinear system identification. His publications highlight advancements in quantization, compression, and system complexity analysis. Prof. Bölcskei has received numerous accolades, including IEEE Fellow status, the 2010 Vodafone Innovations Award, and the ETH 'Golden Owl' Teaching Award. He served as Editor-in-Chief of the IEEE Transactions on Information Theory (2010–2013) and has held editorial roles in multiple journals. His leadership includes roles on the Board of Governors of the IEEE Information Theory Society and as a delegate for faculty appointments at ETH Zurich. Labs/Teams : Mathematical Information Science Group at ETH Zurich's Department of Information Technology and Electrical Engineering

Giuseppe Santoroمشاهده پروفایل

استاد

• Dynamics of quantum systems out-of-equilibrium
• Quantum dissipation
• Nanoscale friction
• +۵ مورد دیگر

Giuseppe Santoro is a Full Professor at the International School for Advanced Studies (SISSA) , affiliated with the Condensed Matter Theory sector. His research focuses on understanding the dynamics of quantum systems out-of-equilibrium , including thermalization, integrability, and periodically driven systems, as well as nanoscale dissipation in both quantum and classical contexts. Dynamics of closed and open quantum systems Nano-friction and lubrication Quantum annealing and optimal control Topological quantum phenomena His recent publications (2023-2025) highlight expertise in quantum simulation , topological effects , optimal control , and dissipative dynamics , with applications to nanoscale systems and frustrated models. Key themes include quantum annealing , Floquet theory , and many-body localization , reflecting interdisciplinary work at the intersection of quantum physics, materials science, and computational methods.