جست‌وجوی استاد و پژوهشگر

• Marketing
• Consumer Behavior
• Brand Loyalty
• +۶ مورد دیگر

Marcel Paulssen is a Full Professor of Marketing and Director of the Institute of Management at the University of Geneva's Geneva School of Economics and Management. He previously served as Assistant Professor at Humboldt University Berlin and held visiting roles at the University of Michigan. His academic journey includes doctoral research under Prof. Rick Bagozzi and professional experience in strategic marketing at Daimler AG's Society and Technology Research Group. Education: Ph.D. in Business Administration, Technical University of Berlin Prior professional roles included four years as a strategic marketing manager at Daimler AG, and a Japanese-German Center scholarship at Matsushita in Tokyo. Research Interests: Focuses on the intersection of psychology and marketing, particularly consumer behavior dynamics. Current topics include: Brand loyalty and choice mechanisms Fair trade consumption patterns Transport mode selection behavior Product defect perceptions and recovery strategies Boundary spanners in business relationships Norms in customer-company interactions Awards: Best Paper Award at IMP-Conference (2007) Runner-up for German Marketing Association's Best Dissertation Credit Suisse Award for Best Teaching Shortlisted for Reimagine Education Award Collaborations & Grants: Secured funding from Credit Suisse, EADS, Daimler, Volkswagen, and German Federal institutions. Active in cross-company research partnerships. Entrepreneurial Contributions: Co-founded FactWorks (research analytics) and SofaTutor (edtech), demonstrating industry-academia integration.

• Operations Research
• Optimization
• Integer Programming
• +۴ مورد دیگر

Dr. Peter Fusek is a researcher at the Zurich University of Applied Sciences (ZHAW) School of Engineering, affiliated with the Institute of Data Analysis and Process Design. He specializes in operations research and optimization, focusing on integer programming applications for staff scheduling and business efficiency improvements. Affiliation: Zurich University of Applied Sciences School: School of Engineering Department: Institute of Data Analysis and Process Design Email: peter.fusek@zhaw.ch Research Focus: Integer programming, staff scheduling, energy optimization, business process automation His recent publications explore advanced scheduling techniques for airports and energy systems, demonstrating practical implementations of optimization algorithms. Projects include airport ground staff scheduling (2023), operational staff planning (2021), and automated staff scheduling (2020).

• Machine Translation
• Natural Language Processing
• Computational Linguistics
• +۷ مورد دیگر

Dr. Steffen Eger is a professor at the Technical University of Darmstadt, focusing on Natural Language Processing, Machine Translation, and Computational Linguistics. His work spans both theoretical and applied aspects of NLP, including adversarial robustness, evaluation metrics, and creative text generation. Key Contributions: • Pioneering research in LLM-based evaluation metrics • Development of efficient translation quality frameworks • Analysis of social solidarity in historical discourse • Exploration of poetic creativity through neural models His recent publications demonstrate expertise in: - Human-Aware Translation Evaluation - Summarization of Historical Texts - Adversarial Defense Mechanisms - Explainable AI for Generation Metrics While no explicit awards or student supervision details are provided, his collaborative work appears across major NLP venues like ACL, EMNLP, and COLING. His research frequently intersects with human-centric NLP tasks, emphasizing reproducibility and robustness in system design.

Stephan Brunnerمشاهده پروفایل

مدرس ارشد

• Plasma Physics
• Magnetic Confinement Fusion
• Gyrokinetic Simulations
• +۷ مورد دیگر

Stephan Brunner is a Maître d'enseignement et de recherche (Senior Lecturer) at École polytechnique fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences (SB), the Center for Plasma Physics Research – Theory (SPC-TH), and education units EDPY-ENS and SPH-ENS. He is based at the PPB 312 building on EPFL's Lausanne campus and is actively involved in teaching and research in plasma physics and fusion energy. His research interests focus on plasma physics , particularly gyrokinetic simulations of turbulence in magnetic confinement fusion devices . He investigates microinstabilities such as ion temperature gradient (ITG) modes and trapped electron modes (TEM), zonal flows, wave-particle interactions, and non-adiabatic electron dynamics. His work bridges theory, simulation, and experiment, often using synthetic diagnostics to validate models against data from tokamaks like TCV and JT-60SA. The most recent articles highlight a strong trend in advanced computational modeling and diagnostic validation . His publications emphasize the development and verification of gyrokinetic codes (e.g., ORB5, PICLS), the analysis of turbulence in realistic plasma geometries, and the integration of experimental measurements with simulation outputs. Key themes include flux-tube and global simulations, collisional effects, magnetic shear, and the role of zonal flows in transport regulation. Stephan Brunner has advised numerous PhD students, including Emmanuel Lanti, Xavier Lapillonne, and Julien Dominski, and has contributed to major collaborative projects in fusion research. He has not received any explicitly mentioned scientific awards in the provided text. He leads or contributes to research on plasma turbulence, computational methods, and fusion energy science, working within teams at EPFL’s CRPP and collaborating with international fusion facilities. His work is foundational to improving predictive capabilities for next-generation fusion reactors.

David Atienza Alonsoمشاهده پروفایل

استاد

• Embedded Systems Design
• Internet of Things (IoT)
• Edge Computing
• +۱۳ مورد دیگر

David Atienza Alonso is a Full Professor in the Department of Electrical and Electronics Engineering at the School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He leads the Embedded Systems Laboratory (ESL) and serves as Associate Vice President for Centers and Platforms, overseeing major research infrastructure. His work spans embedded systems, IoT, edge AI, and sustainable computing. His research interests focus on system-level design for high-performance and low-power computing systems. Key areas include thermal-aware design of multi-processor systems-on-chip (MPSoC), energy-efficient embedded machine learning, wireless body sensor networks, and electronic design automation (EDA). His lab develops novel methodologies for hardware-software co-design, memory optimization, and edge computing architectures. The analysis of his recent publications reveals a strong trajectory in intelligent, energy-efficient computing systems. His work integrates machine learning with traditional EDA techniques for data center optimization, applies ultra-low power heterogeneous architectures to healthcare wearables, and advances thermal modeling for 3D ICs. Themes of sustainability, real-time processing, and edge intelligence are consistent across his research. Dr. Atienza has received numerous accolades, including: ERC Consolidator Grant (2016) DAC Under-40 Innovators Award (2018) IEEE TCCPS Mid-Career Award (2018) ACM SIGDA Outstanding New Faculty Award (2012) ICCAD 10-Year Most Influential Paper Award (2020) Best paper awards at top-tier conferences He has advised over 40 PhD students, many of whom have gone on to successful academic and industry careers. His research has been supported by major grants, including the ERC grant, and he has co-authored over 450 publications and 14 licensed patents. He plays a significant leadership role in the academic community, having served as Editor-in-Chief of IEEE Transactions on CAD, President of IEEE CEDA (2018–2019), and currently as Chair of the European Design Automation Association (EDAA). He is a Fellow of both IEEE and ACM. His laboratory, the Embedded Systems Laboratory (ESL), is a leading center for research in embedded and cyber-physical systems, fostering interdisciplinary collaboration and innovation in sustainable computing technologies.

Anna Carratala Ripollesمشاهده پروفایل

مدرس

• Environmental Microbiology
• Aquatic Ecology
• Virology
• +۷ مورد دیگر

Anna Carratala Ripolles is a Research Scientist at the Laboratory of Environmental Virology (LEV) and Lecturer at the Swiss Federal Institute of Technology in Lausanne (EPFL) . She holds affiliations across multiple departments including the Environmental Engineering Institute (IIE) , ENAC-SSIE (School of Engineering), and EDCE (Environmental and Climate Change Education). Ph.D. in Environmental Microbiology (University of Barcelona, 2013) Master in Advanced Microbiology (University of Barcelona, 2008) Her research focuses on: Ecology of aquatic microorganisms under environmental change Cyanobacteria monitoring and cyanotoxin mechanisms Virus-bacteria-protist interactions in freshwater Biotechnological applications of lake bacteria Carbon/nitrogen metabolism in aquatic systems Scientific diving for environmental research Recent publications analyze: Climate-driven cyanotoxin production (2025) Microbial interactions in alpine lakes (2024-2023) Disinfection resistance mechanisms in enteric viruses (2020-2017) Photo-Fenton process optimization for wastewater virology (2016-2015) Scientific accolades include: 2014 Extraordinary PhD Award (University of Barcelona) 2019 International Geneva Award (Swiss Network of International Studies) 2023 Prix Source Innovation (EPFL) She combines genomic analysis and experimental work to study microbial adaptation in cold environments, with expertise spanning viral persistence , waterborne pathogen control , and environmental biotechnology .

Alain Borelمشاهده پروفایل

مدرس

• Research Data Management
• Molecular Modeling
• Electron Paramagnetic Resonance Spectroscopy
• +۳ مورد دیگر

Alain Borel is a Lecturer and Research Data Management Specialist at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Scientific Information Service (SISB-SOAR) and EDCH-ENS teaching unit. His work bridges academic research and information science, with a focus on research data management, computational chemistry, and MRI contrast agent development. Role : Lecturer in data management for chemical sciences Affiliation : EPFL Library's SOAR (Scientific Information Service) Expertise : Molecular modeling, EPR spectroscopy, programming (Python, JavaScript, Fortran), and FAIR data principles His research interests span Scientific Data Management (including dataset organization, naming conventions, and repository systems), Molecular Modeling of transition metal complexes using classical MD and DFT methods, and MRI Contrast Agent Development through analysis of gadolinium complexes' magnetic properties. He has contributed to tools like MarcXimiL for library record deduplication and ChemCalc for chemical infrastructure. Key article trends show continuous contributions to data management frameworks (2025-2019) alongside inorganic chemistry research (2009-2002) focused on gadolinium-based MRI agents, paramagnetic relaxation, and zero-field splitting phenomena. Alain Borel's technical and pedagogical work is evident in publications covering: Data Management : Decision trees, Zenodo communities, README templates Chemical Research : Gadolinium complexes, relaxivity, DFT calculations Library Science : UDC automation, bibliographic record comparison He teaches courses on Research Data Management in Chemistry and Information Literacy for Chemists , emphasizing practical data organization and scientific communication.

Dorina Thanouمشاهده پروفایل

پژوهشگر

• Data Science
• Machine Learning
• Graph Signal Processing
• +۳ مورد دیگر

Dorina Thanou is a senior researcher and lecturer at École Polytechnique Fédérale de Lausanne (EPFL), leading strategic research initiatives on AI for Health and Science under the EPFL AI Center. Her work spans graph signal processing , machine learning , and AI for biomedical applications . She holds affiliations with multiple EPFL units, including the Signal Processing Laboratory 4 (STI IEM LTS4), the Chair of Mathematical Data Science (SB MATH MDS1), and the AI Center (VPA-AVP-CP AICENTER). Education: Diploma in Electrical and Computer Engineering (2008), University of Patras, Greece M.Sc. in Communication Systems (2010), EPFL Ph.D. in Electrical Engineering (2016), EPFL Her research focuses on interpretable and robust machine learning models for biomedicine , leveraging graph-based signal processing to analyze complex data in healthcare. Her publication trends emphasize graph representation learning , dynamic 3D data compression , and biomedical applications , particularly in cardiology and neuropathology. Scientific Awards: ELLIS Scholar in Geometric Deep Learning (2021) Best Student Paper Award, IEEE ICASSP (2015) Top 10% Paper Award, IEEE ICIP (2015) Best Paper Award, Picture Coding Symposium (2016) IEEE Senior Member (2023) She mentors PhD students Monteiro Lança Madeira Manuel and Ögüt Sevda and has led interdisciplinary collaborations through the Swiss Data Science Center (2016-2020) and Center for Intelligent Systems (2020-2024). Her labs include the Signal Processing Laboratory 4 , Mathematical Data Science Lab , and the AI Center .

• Computer Vision
• Machine Learning
• Robotics
• +۷ مورد دیگر

Alexandre Alahi is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Visual Intelligence for Transportation (VITA) laboratory. He is affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC), the Institute of Infrastructure (IIC), and also contributes to diversity initiatives at ENAC. His research focuses on integrating computer vision, machine learning, and robotics to develop socially-aware AI for transportation and autonomous systems. University: École Polytechnique Fédérale de Lausanne (EPFL) School: School of Architecture, Civil and Environmental Engineering Department: Institute of Infrastructure, IIC Research Lab: Visual Intelligence for Transportation (VITA) Alexandre's research interests center on computer vision, machine learning, robotics, and AI safety, particularly in human trajectory prediction, depth estimation, and socially-aware autonomous navigation. He investigates how AI can understand and predict human behavior in complex environments to improve safety in mobility systems. His work bridges theoretical advances with real-world applications in autonomous driving, urban planning, and healthcare. His recent publications span a wide array of topics including omnidirectional stereo matching, trajectory forecasting, cross-view localization, AI security, and depth estimation. These works demonstrate a strong trend toward building generalizable, robust, and socially-compliant AI systems, with increasing focus on uncertainty quantification, safety certification, and real-world deployment. The integration of multimodal data and the development of foundation models are recurring themes. Alexandre has received numerous scientific accolades, including: Top 100 Most Influential Scholar in Computer Vision (2022–2023) Editor’s Choice Award, Image and Vision Computing (2021) Honorable Mention, ICCV Workshop (2019) CVPR Open Source Award (2012) ICDSC Challenge Prize (2009) Top 20 Swiss Venture Leaders (2010) He has advised numerous PhD students whose theses cover diverse topics such as human motion prediction, person re-identification, trajectory forecasting, and AI security. His lab has secured significant recognition and funding, enabling impactful research with real-world applications. Alexandre has also co-founded startups like Visiosafe, demonstrating strong industry engagement and technology transfer. The VITA lab fosters interdisciplinary collaboration, working across computer vision, robotics, transportation engineering, and human-centered AI. The team develops datasets, benchmarks, and open-source tools to advance the field and promote reproducibility.

Daniel Kuhnمشاهده پروفایل

استاد

• Data-driven Optimization
• Stochastic Programming
• Robust Optimization
• +۳ مورد دیگر

Daniel Kuhn is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), where he holds the Chair of Risk Analytics and Optimization in the College of Management of Technology. His research focuses on developing computational methods for data-driven decision-making under uncertainty, with applications in engineered systems, machine learning, and finance. Previously, he held positions at Imperial College London and Stanford University. Education includes: PhD in Economics, University of St. Gallen MSc in Theoretical Physics, ETH Zurich Research interests span data-driven optimization , stochastic programming , and robust decision-making frameworks . His work develops computationally tractable methods for uncertainty quantification in complex systems, bridging operations research with statistical learning. Current investigations focus on distributionally robust optimization using Wasserstein metrics and applications in energy markets and fair machine learning. Publication analysis reveals three primary trends: 1) Fundamental advances in distributionally robust optimization theory, 2) Machine learning applications with uncertainty guarantees, and 3) Energy system optimization under regulatory constraints. His methodological work consistently emphasizes computational tractability and practical applicability. As lab director of the Risk Analytics and Optimization group, he leads research on: Stochastic control systems Data-driven decision frameworks Robust machine learning Current PhD students include researchers working on federated learning fairness, optimal power flow, and reinforcement learning theory. Past graduates have made significant contributions to Wasserstein distributionally robust optimization and vehicle-to-grid frequency regulation.

Behzad Bozorgtabarمشاهده پروفایل

مدرس

• Computer Vision
• Medical Image Analysis
• Machine Learning
• +۶ مورد دیگر

Behzad Bozorgtabar is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) and a Senior Scientist at the Lausanne University Hospital (CHUV) . He leads the Medical Imaging Group at EPFL's Signal Processing Laboratory (LTS5) , with affiliations to the European Lab for Learning & Intelligent Systems (ELLIS) and EPFL’s ELLIS Unit . His research focuses on self-supervised learning , test-time adaptation , and multimodal foundation models , aiming to create robust AI systems that adapt to changing environments. His work addresses domain shift , anomaly detection , and ethical AI deployment in medical imaging and computational pathology. His recent publications highlight trends in test-time adaptation (e.g., ReservoirTTA, OptTTA), self-supervised learning (CrIBo, CrOC), and medical imaging applications (histopathology, chest X-rays, colorectal cancer detection). Scientific Awards: IEEE TMI Distinguished Reviewer Award First Runner-up - AIMIA Workshop, ECCV Elected Member - European Lab for Learning & Intelligent Systems (ELLIS) IBM Research Division Image Award IBM Patent Award Imagine Cup, Australian Finals, Microsoft Advising & Funding: He has mentored PhD students (Tomar, Vray, Stegmüller) and secured grants from the Personalized Health and Related Technologies (PHRT) , Swiss Cancer League , and Discovery Translation Fund (DTF 2.0) . His service includes Area Chair roles for CVPR 2025, WACV 2026, and AAAI 2026.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Amir Zamir is a Tenure-Track Assistant Professor of Computer Science at the Swiss Federal Institute of Technology (EPFL) and leads the Visual Intelligence and Learning Lab (VILAB). Previously affiliated with UC Berkeley, Stanford, and UCF, his research spans computer vision, machine learning, and AI, focusing on embodied intelligence and multimodal learning. Current Appointments: EPFL (Tenure-Track Assistant Professor) Industry Experience: Chief Scientist at Aurora Solar (2015-2022), Chief Scientist at Duranta His research emphasizes advancing vision systems beyond narrow approaches toward general multi-modal/multi-task models that operate in real-world environments. Key projects include 4M (Multimodal Foundation Models), Taskonomy (Task Transfer), Gibson (Sim-to-Real), and Omnidata (Steerable Datasets). Current work explores flexible-length tokenization (FlexTok), visual personalization (ViPer), and robust cross-domain learning . Recent publications demonstrate expertise in autoregressive image generation , vision-language modeling , and embodied robotics . Awards include the ECCV Young Researcher Award , NVIDIA Pioneering Research Award , and multiple CVPR/SIGGRAPH best paper recognitions . He advises PhD students in vision systems, AI, and robotics, and teaches courses on Visual Intelligence, AI Product Management, and Autonomous Robotics.

Ljubisa Miskovicمشاهده پروفایل

پژوهشگر ارشد

• Systems Biology
• Machine Learning
• Dynamical Systems
• +۴ مورد دیگر

Ljubisa Miskovic is a Senior Scientist at École Polytechnique Fédérale de Lausanne (EPFL), holding multiple positions including at the Laboratory of Computational Systems Biotechnology (LCSB) within the School of Basic Sciences (SB) and Institute of Chemical Sciences and Engineering (ISIC), as well as at the Swiss Center for Scientific Computing (SCGC) and the School Council SB. He earned his Ph.D. degree in Automatic Control from EPFL in 2006 under the co-supervision of Dominique Bonvin and Alireza Karimi. Following his doctoral studies, he pursued postdoctoral research at the Centre for Systems Engineering and Applied Mechanics at Universite Catholique de Louvain with Michel Gevers before returning to EPFL to work with Vassily Hatzimanikatis. Dr. Miskovic's research spans systems biology, metabolic engineering, synthetic biology, data-driven control design, system identification, stochastic processes, and estimation theory. His work focuses on developing computational approaches for metabolic network analysis, kinetic modeling of genome-scale metabolic networks, and applying machine learning techniques to metabolic engineering problems. His recent publications demonstrate increasing integration of generative AI methods with traditional metabolic modeling approaches. His publication trends over the last five years reveal a clear progression from traditional metabolic modeling to sophisticated machine learning-enhanced approaches, with particular emphasis on genome-scale kinetic modeling, strain optimization for biochemical production, and uncertainty quantification in biological systems. Much of his recent work centers on applications in S. cerevisiae and P. putida for producing valuable biochemicals including p-coumaric acid and muconic acid. Dr. Miskovic has advised several doctoral students including Toumpe Ilias, Andreozzi Stefano, Choudhury Subham, and Tokic Milenko. He teaches courses in numerical methods and chemical process control at EPFL, demonstrating his commitment to both research and education in systems biotechnology. As a member of the Laboratory of Computational Systems Biotechnology led by Professor Vassily Hatzimanikatis, Dr. Miskovic contributes to a multidisciplinary team that combines expertise in computational biology, systems engineering, and metabolic modeling to advance the field of systems biotechnology.

Dongyang Fanمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۵ مورد دیگر

Dongyang Fan is a Researcher at the Swiss Federal Institute of Technology Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences and the Institute of Computer Science. She works in the Machine Learning and Optimization Laboratory (MLO) under EPFL's Doctoral Program in Computer and Communication Sciences, conducting cutting-edge research in artificial intelligence and machine learning. Her research spans Machine Learning, Deep Learning, Natural Language Processing, Optimization, Federated Learning, Mixture of Experts architectures, On-device Learning, and Ethics in AI. She focuses on developing efficient, ethical, and personalized AI systems with practical applications in edge computing and collaborative environments, addressing challenges in model training, data valuation, and real-world deployment constraints. Analysis of her recent publications reveals a concentrated effort on large language models (LLMs), with significant contributions to on-device collaborative learning frameworks, Mixture of Experts optimization, and ethical implications of web data usage. Her work bridges theoretical advancements in model architecture with practical implementations for resource-constrained devices, emphasizing privacy-preserving techniques and fairness-aware design principles. She is an integral member of EPFL's Machine Learning and Optimization Laboratory, contributing to research teams focused on next-generation AI algorithms and their optimization for real-world applications in distributed systems and edge computing environments.

Suzanne Rivardمشاهده پروفایل

استاد

• Strategic alignment of IT
• IT outsourcing
• Software projects risk management
• +۱ مورد دیگر

Dr. Suzanne Rivard is a Professor at HEC Montréal's Department of Information Technologies, where she holds the Chair in Strategic Management of Information Technology. She earned her MBA from HEC Montréal and PhD in Information Systems from Western Ontario. Her research focuses on strategic IT alignment, outsourcing, and risk management in technology projects. Research interests include: Strategic integration of IT with business objectives Emerging organizational structures enabled by technology Risk mitigation in software development lifecycle Governance models for IT outsourcing partnerships Her recent publications demonstrate strong emphasis on IT outsourcing frameworks, organizational capability development, and theoretical foundations of information systems research, frequently published in premier journals like MIS Quarterly and Journal of Information Technology. Major honors include: Fellowship in the Royal Society of Canada Association for Information Systems LEO Award Honorary Doctorate from University of Lausanne Fellowship in the Association for Information Systems Endowed Chair in Strategic IT Management Recently supervised graduate researchers: Guillaume Bergeron (thesis), Florence Lefrançois, Alexandre Riviere, and Charline Colliaux (projects). Actively contributes to the Research Group on Information Systems (GReSI).