جست‌وجوی استاد و پژوهشگر

Thomas Weberمشاهده پروفایل

استاد

• Credit collections
• Decision making under risk
• Behavioral economics
• +۸ مورد دیگر

Thomas Weber is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , holding the Chair of Operations, Economics, and Strategy (OES) within the College of Management (CDM) . He serves as Director of the Doctoral Program in Management of Technology and contributes to academic governance through roles in committees such as the CDM Academic Evaluation Committee. PhD Students: Zhang Ru, Han Jun, Mark Michael, Razeghian Jahromi Maryam Email: thomas.weber@epfl.ch His research spans behavioral economics, risk analysis, and optimization in dynamic systems, with a focus on sharing economy applications, inventory management, and cryptocurrency market dynamics. Recent publications address robust decision-making frameworks, self-exciting point processes, and economic implications of information endogeneity. Key teaching activities include: Information: Strategy & Economics Innovation & Entrepreneurship in Engineering Microeconomics

Stefano Marelliمشاهده پروفایل

مدرس

• Uncertainty Quantification
• Scientific Software Design
• Surrogate Modeling
• +۵ مورد دیگر

Dr. Stefano Marelli is a Lecturer at ETH Zürich's Department of Civil, Environmental and Geomatic Engineering, affiliated with the Risk, Safety, and Uncertainty Quantification Chair. He holds a MSc in Physics (University of Milano Bicocca, 2006) and a PhD in Applied and Environmental Geophysics (ETH Zurich, 2011). His research focuses on uncertainty quantification (UQ), surrogate modeling, reliability analysis, and Bayesian inversion, with applications in engineering, astrophysics, and economics. He leads the development of UQLab, a general-purpose UQ software framework, and collaborates on interdisciplinary projects like HIPERWIND. Key research areas include high-dimensional UQ, stochastic simulators, and surrogate modeling for dynamical systems. Recent work emphasizes multifidelity methods, Bayesian tomography, and noise-aware reliability analysis. He teaches structural reliability and risk analysis at ETH and contributes to international UQ training programs. Education: MSc Physics (Milano Bicocca, 2006); PhD in Geophysics (ETH Zurich, 2011) Roles: Senior Scientist (2018–present); Postdoc (2012–2018) Software: UQLab, UQ [py] Lab Collaborations: Cross-disciplinary projects in astrophysics, mechanical engineering, and remote sensing His articles (2020–2025) highlight advancements in surrogate modeling, Bayesian inversion, and UQ applications. Notable contributions include frameworks for noisy data analysis, time-variant reliability, and industrial fragility assessment.

• Multiresolution Methods
• Numerical Analysis
• Computational Mathematics
• +۴ مورد دیگر

Michael Multerer is an Associate Professor at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on multiresolution methods, scattered data analysis, and numerical analysis with applications in computational mathematics and engineering. He leads projects such as the SNSF Starting Grant on multiresolution methods for unstructured data, emphasizing nonlinear approximation and kernel-based techniques. Research Interests: Development of fully discrete multiresolution methods for unstructured data Wavelet theory and kernel matrix algebra Uncertainty quantification in partial differential equations Scattered data compression and approximation Key Software Contributions: FMCA: Fast multiresolution covariance analysis for scattered data Bembel: Boundary element library for solving Laplace and Helmholtz equations SPQR: Anisotropic sparse grid quadrature in MATLAB Funding: Holder of the SNSF Starting Grant (2025) for advancing multiresolution techniques in unstructured data processing. Labs/Teams: Active in the research group at USI’s Faculty of Informatics, collaborating with institutions like TU Darmstadt and University of Basel on numerical methods and engineering applications.

Jeremy Luterbacherمشاهده پروفایل

استادیار

• Chemical Engineering
• Renewable Energy
• Catalysis
• +۳ مورد دیگر

Jeremy Luterbacher is a Tenure-Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL) and leads the Laboratory of Sustainable and Catalytic Processing. His research focuses on biomass conversion, catalytic processes, and sustainable materials development. Education: BS/MS in Chemical Engineering (EPFL, 2005-2007), PhD in Chemical and Biomolecular Engineering (Cornell, 2012). Postdoctoral Training: Swiss National Science Foundation Scholar at University of Wisconsin-Madison (2012-2014). His work specializes in lignin valorization, green solvent design, and catalytic strategies for renewable resource utilization. Recent articles highlight advancements in aldehyde-stabilized lignin, computational solvent screening, and multi-fidelity optimization frameworks. Scientific Honors: ERC Starting Grant (2017) University Latsis Prize (2019) Sustainable Chemistry & Engineering Lectureship (ACS, 2021) He has developed methods for biomass fractionation, enzyme immobilization, and sustainable polymer synthesis, with applications in biofuel production and eco-friendly materials.

• Multiscale/multiphysics modeling
• Biophysics
• Cellular and molecular biomechanics
• +۶ مورد دیگر

Igor V. Pivkin is a full professor at the Institute of Computing within the Faculty of Informatics at the University of Lugano (USI). He holds a B.Sc. and M.Sc. in Mathematics from Novosibirsk State University, followed by an M.Sc. in Computer Science and a Ph.D. in Applied Mathematics from Brown University. Before joining USI, he was a Postdoctoral Associate at MIT's Department of Materials Science and Engineering. His research focuses on multiscale/multiphysics modeling , numerical methods, and large-scale simulations of biological and physical systems. Key areas include biophysics, cellular/molecular biomechanics, stochastic modeling, and coarse-grained molecular simulations. He leverages high-performance computing and particle-based methods to address complex biological phenomena. His work spans diverse applications, including cancer cell dynamics, bioleaching bacterial biofilms, and erythrocyte mechanics in human spleen circulation. He has pioneered computational tools such as the Bayesian recursive global optimizer (BaRGO) and the in-silico lab-on-a-chip framework, enabling petascale simulations of microfluidic systems at cellular resolution. Pivkin collaborates extensively with institutions like the SIB Swiss Institute of Bioinformatics and has contributed to advancing methodologies for multi-model scientific simulations. His research integrates experimental data with computational models to bridge gaps between microscopic and macroscopic biological processes.

• Financial Economics
• Portfolio Optimization
• Stochastic Dominance
• +۵ مورد دیگر

Professor Wing-Keung Wong is a distinguished academic at the Department of Finance, Asia University . With over 187 scholarly papers and 638 citations, his work spans critical areas in financial economics and quantitative finance. Current affiliation: Asia University, Department of Finance Past affiliations: National University of Singapore, Chinese University of Hong Kong, Erasmus University Rotterdam Research Themes include: Portfolio optimization and stochastic dominance theory Market efficiency analysis across diverse financial instruments Behavioral finance and investor decision-making models Risk measurement with VAR and CVaR frameworks International financial market integration studies Quantitative trading system development Key Article Trends reveal consistent focus on empirical finance, mathematical modeling, and decision science applications in portfolio management and market anomalies.

Giona Casiraghiمشاهده پروفایل

پژوهشگر

• Network Science
• Complex Systems
• Data Science
• +۴ مورد دیگر

Giona Casiraghi is a Senior Researcher at ETH Zurich specializing in network science and complex systems, with a primary focus on resilience modeling in social organizations and data-driven network analysis. His work bridges theoretical advances in statistical network models with practical applications in supply chain management, open-source software ecosystems, and online social dynamics. His research interests center on developing quantitative methods for analyzing complex systems, particularly through the generalized hypergeometric ensemble of random graphs (gHypEG). Casiraghi's work spans multiple disciplines including network science, statistical physics, data science, and resilience theory, with particular expertise in temporal network analysis, multi-edge networks, and zero-inflation models for sparse networks. His research group develops the ghypernet R package , providing open-source tools for network regression and inference. Analysis of his recent publications reveals a strong trend toward applying network science to real-world resilience problems, particularly in pharmaceutical supply chains and social organizations. His 2025 Science paper on US tariffs threatening medicine supply chains exemplifies this practical turn, while his methodological work on zero-inflated network models (PNAS Nexus 2025) demonstrates continued theoretical innovation. Casiraghi frequently collaborates with Frank Schweitzer and others in the Systems Group at ETH Zurich, producing interdisciplinary work that bridges computer science, economics, and social science. Casiraghi's research has significant implications for understanding how social organizations withstand shocks and how supply chains can be made more resilient to disruptions. His work on the gHypEG framework provides foundational tools for network scientists across multiple disciplines, while his applied research offers concrete insights for policymakers and industry practitioners dealing with complex system failures. He has contributed to numerous projects examining online migration after community bans, developer productivity in open-source projects, and reconstruction of social relations from interaction data. His research methodology typically combines large-scale data analysis with advanced statistical modeling, often developing new network analysis techniques to address specific research questions.

• Signal Processing
• Machine Learning
• Tracking Algorithms
• +۳ مورد دیگر

Ángel García-Fernández is an Associate Professor at the Polytechnic University of Madrid , specializing in Bayesian inference , multi-target tracking , and nonlinear filtering with applications in signal processing, robotics, and underwater mapping. His work includes the development of Poisson multi-Bernoulli mixture (PMBM) filters, iterated posterior linearization algorithms, and direction-of-arrival (DOA) measurement models.

• Probabilistic machine learning
• Bayesian experimental design
• Causal inference
• +۴ مورد دیگر

Desi R. Ivanova is a research fellow at the University of Oxford's Department of Statistics under the Florence Nightingale Bicentennial Fellowship. Her work bridges probabilistic machine learning, Bayesian experimental design, and LLM evaluation frameworks. She holds a DPhil in Statistics from Oxford's StatML CDT program (2020-2024) and an MMORSE in Mathematics from University of Warwick (2011-2016) with Erasmus exchange at LMU Munich. Research spans causal machine learning and uncertainty quantification Developed CO-BED and Step-DAD frameworks Focus on LLM evaluation methodology and calibration Expert in real-time adaptive experimental systems Her publications demonstrate expertise in Bayesian self-consistency methods, neural data compression, and privacy-preserving dataset merging. Key contributions include improving amortized inference efficiency and developing gradient-based causal experimental designs. Current work emphasizes rigorous statistical evaluation of language models, advocating for appropriate uncertainty quantification when analyzing performance across small datasets. She critiques CLT-based methods for LLM evaluation and proposes more robust frequentist and Bayesian alternatives.

Melih Kandemirمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Inference
• Stochastic Process Modeling
• +۵ مورد دیگر

Melih Kandemir is an Associate Professor of Machine Learning at the University of Southern Denmark, Department of Mathematics and Computer Science. He earned his PhD in 2013 from Aalto University under Prof. Samuel Kaski, followed by postdoctoral work at Heidelberg University (with Prof. Fred Hamprecht) and an assistant professorship at Ozyegin University, Turkey. Prior to joining SDU, he led research at Bosch Center for Artificial Intelligence. Education: PhD (Aalto University, 2013), Postdoc (Heidelberg University). Previous Roles: Assistant Professor (Ozyegin University), Research Group Leader (Bosch CAI). His research focuses on Bayesian inference, stochastic process modeling with deep neural networks, and applications to reinforcement learning and continual learning. He leads the SDU Adaptive Intelligence (ADIN) Lab and is an ELLIS Member, reflecting his standing as a top European AI researcher. His work addresses critical challenges in uncertainty quantification, exploration strategies, and theoretically grounded algorithms for decision-making systems. Recent publications highlight expertise in model-based reinforcement learning (e.g., MOMBO for offline RL), PAC-Bayesian bandits, evidential learning for robust classification, and neural stochastic differential equations. His scientific awards include a Best Paper Award (2017) and ELLIS Membership. He also explores interdisciplinary applications of natural sciences to sustainable technology development.

• Language Acquisition
• Cognitive Science
• Computational Modeling
• +۷ مورد دیگر

Emmanuel Dupoux is a Professor at École des Hautes Études en Sciences Sociales (EHESS), affiliated with the School of Advanced Studies in Social Sciences and the Laboratory of Cognitive Science and Psycholinguistics (LSCP). His work bridges cognitive science, computational linguistics, and machine learning to study infant language acquisition and social cognition. Co-creator and director of EHESS Cognitive Science Master program Former LSCP laboratory director (1998-2009) Current research focuses on textless speech modeling and ecological audio analysis Research areas include: Modeling early language acquisition mechanisms using Bayesian models and HMM Investigating phonological 'deafness' and critical age plasticity Studying social cognition development through infant-toddler experiments His technical contributions span speech processing toolkits like Shennong, emergent communication frameworks (STOP dataset), and innovative approaches to self-supervised speech modeling. Recent publications demonstrate breakthroughs in: Topography-inspired CNN designs for better memory efficiency Prosody-aware generative spoken language models (pGSLM) Textless emotion conversion systems He actively explores how machine learning can reverse-engineer infant language learning processes from ecological audio data, with applications in neurodegenerative disease diagnostics. Key collaborations include: INRIA's Cognitive Machine Learning (CoML) team Facebook AI Research (FAIR) partnerships VoxPopuli multilingual speech corpus development

Tobias Heilmannمشاهده پروفایل

مدرس

• Statistical Inference
• Change Point Detection
• Differential Privacy
• +۵ مورد دیگر

Dr. Tobias Heilmann is a Lecturer at the Department of Humanities, Social and Political Sciences at ETH Zurich. His research focuses on statistical inference, change point detection, differential privacy, high-dimensional data analysis, network analysis, and machine learning. He holds a PhD in Statistics from Fudan University, advised by Zhiliang Ying, and has contributed to software packages such as changepoints , GMPro , and APPLE . His work bridges theoretical statistics and practical applications, emphasizing privacy-preserving techniques, dynamic systems analysis, and algorithmic robustness. Key areas include nonparametric methods, contextual bandits, federated learning, and functional data analysis. Recent contributions address challenges in distributed systems, adversarial robustness, and high-dimensional model selection. He has published extensively in top journals like Annals of Statistics , SIAM Journal on Mathematics of Data Science , and IEEE Transactions on Information Theory . Dr. Heilmann's research also involves interdisciplinary collaborations, including social psychology of groups and biomedical applications. His software tools are widely used for change point analysis and community detection in networks. Current projects explore optimal privacy-utility trade-offs and adaptive algorithms for non-stationary environments.

• Data Science
• Machine Learning
• Spatial Analysis
• +۵ مورد دیگر

Curdin Derungs is a Lecturer in Data Science at the Lucerne School of Computer Science (HSLU), Switzerland. He specializes in applying statistical and machine learning techniques to energy systems, spatial analysis, and natural language processing. His professional competencies include data-driven automation, deep learning, time series analysis, and energy systems optimization. Derungs holds a PhD in Natural Sciences (University of Zurich, 2013) with a focus on NLP and spatial analysis, and a DAS in Applied Statistics (ETH Zurich, 2019). Earlier degrees include a Master's in Geography (2008) and Atmospheric Physics (ETH Zurich, 2008). His research explores intersections between data science and environmental sustainability, including energy efficiency optimization, spatial language dynamics, and landscape modeling. Recent work focuses on occupant behavior in energy systems, geotagged text analysis, and soil formation modeling. Key projects include SCCER FEEB&D Work Package 2 on renewable energy systems, the Romande Energy Demonstrator, and urban greening studies. He actively contributes to interdisciplinary initiatives like the IGE Innovationswettbewerb 2019 and Innovationspark Zentralschweiz.

Andrew Caplinمشاهده پروفایل

استاد

• Cognitive Economics
• Behavioral Macroeconomics
• Human-AI Collaboration
• +۳ مورد دیگر

Andrew Caplin is a Silver Professor of Economics at New York University's Department of Economics. He leads the Sloan Foundation Program on Cognitive Economics at Work and has made groundbreaking contributions to cognitive economics, behavioral macroeconomics, and human-AI collaboration. NYU Department of Economics Sloan Foundation Program on Cognitive Economics at Work Copenhagen Life Panel (CLP) research His research spans cognitive economics, focusing on decision-making mistakes, AI integration in economic systems, and housing finance reforms through equity-sharing mortgages. He developed novel frameworks for measuring subjective earnings risk, economic decision-making skills, and collaborative human-AI protocols in medical and financial contexts. Scientific Awards Sloan Foundation Program leadership Caplin's work examines belief calibration, rational inattention, and career trajectory dynamics. His Copenhagen Life Panel leverages Danish administrative data to study labor market transitions and economic well-being across life cycles, while his equity-sharing mortgage research addresses housing affordability through market design innovations.

• Soft Robotics
• Robotic Manipulation
• Biomimetic Design
• +۵ مورد دیگر

Kai Junge is a robotics researcher at École polytechnique fédérale de Lausanne (EPFL), affiliated with CREATE-LAB in the School of Engineering. His work focuses on developing advanced robotic systems with emphasis on compliant interactions, biomimetic design, and applications in agriculture and food systems. Dr. Junge's research interests span several cutting-edge domains in robotics: Soft robotics and compliant manipulation systems Biomimetic design of anthropomorphic robotic hands Variable stiffness actuation for improved dexterity Agricultural robotics with focus on delicate fruit harvesting Tactile sensing and multi-modal perception for contact-rich tasks Human-robot interaction and imitation learning His recent publications demonstrate a strong trend toward developing robotic systems that leverage compliance and biomimetic principles to achieve human-like or even superior manipulation capabilities. The work spans from fundamental research on compliance in robotic hands to practical applications in agriculture and food safety. A notable pattern is the development of the ADAPT Hand, which incorporates spatially distributed compliance to enhance robustness in manipulation tasks, achieving 93% success rate across 24 objects with varying geometries. His research also extends to agricultural applications where soft robotic grippers have demonstrated 95.4% picking success rate for blackberries while improving shelf life by 30-150%. Dr. Junge has been involved in several research projects funded by various organizations including the European Union's Horizon 2020 program, the Japan Science and Technology Agency, and Berry Gardens Ltd. His collaborative work with Josie Hughes and other researchers has resulted in numerous publications in top robotics conferences and journals. He is actively involved with CREATE-LAB at EPFL, where his team develops innovative robotic systems for manipulation, agricultural applications, and food safety testing. The lab's work on the ADAPT Hand, closed-structure soft grippers for agriculture, and robotic swallowing simulators demonstrates a commitment to translating robotics research into practical applications that address real-world challenges.