جست‌وجوی استاد و پژوهشگر

Rui Yaoمشاهده پروفایل

مدرس

• Large-scale equilibrium modeling in multi-modal transport systems
• Individual mobility choice modeling
• Demand management strategies
• +۳ مورد دیگر

Rui Yao is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Engineering (ENAC) and the Department of Civil Engineering. Additionally, he works as a Scientist in the Laboratory for Human-Oriented Mobility Eco-system (HOMES) within EPFL. His research focuses on large-scale equilibrium modeling in multi-modal transport systems, individual mobility choice modeling, and demand management strategies. Education: B.Sc. in Civil Engineering, Technion – Israel Institute of Technology Direct-track Ph.D. in Transportation Engineering, Technion – Israel Institute of Technology His research spans both theoretical and applied domains, including stochastic traffic equilibrium , multi-passenger ridesharing systems , perturbed utility models , and deep learning for choice analysis . He has contributed to advancements in data-driven route choice modeling , integrated equilibrium models for electrified logistics , and stable matching frameworks for mobility platforms . Rui Yao is affiliated with the Human-Oriented Mobility Eco-system (HOMES) lab at EPFL, which focuses on innovative transportation solutions. His work bridges theoretical modeling with real-world applications in smart mobility and transportation policy.

Fabian Torresمشاهده پروفایل

مدرس

• Transportation Systems
• Operations Research
• Statistical Modeling
• +۳ مورد دیگر

Fabian Torres is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Engineering ( ENAC ). He works in the Transportation and Mobility Laboratory (TRANSP-OR) and SGC-ENS (Civil Engineering Teaching). His research spans transportation systems, operations research, and algorithm design. Research Focus : Modeling driver behavior and decision support in transportation Statistical modeling of transportation systems Algorithm development for pricing and routing optimization Scientific Contributions : Recent work includes continuous pricing algorithms, crowdshipping logistics, stochastic vehicle routing, and elevator dispatching optimization. His publications emphasize integrating advanced discrete choice models with operational challenges in transportation.

Matteo Faveroمشاهده پروفایل

مدرس

• Building Sustainability
• Thermal Comfort
• Occupant Behavior Modeling
• +۳ مورد دیگر

Matteo Favero is a Lecturer and Scientist at the Swiss Federal Institute of Technology Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC). He conducts research at the ETHOS Lab, focusing on data-driven interventions to enhance environmental and social sustainability in the built environment. His work integrates computational tools with human-centric approaches to optimize building performance and occupant well-being. Education: PhD in Civil and Environmental Engineering, Norwegian University of Science and Technology (NTNU), 2022 MSc in Building Engineering, Politecnico di Milano, 2015 BSc in Building Engineering, Politecnico di Milano, 2012 Research Interests: Favero's research explores occupant behavior modeling, statistical analysis of thermal comfort, and human-building interactions. He emphasizes multi-domain studies to bridge gaps between energy efficiency, indoor environmental quality, and user satisfaction. His work supports the development of adaptive building controls and sustainable design guidelines through empirical data and interdisciplinary methodologies. Publication Trends: Recent articles (2020–2024) prioritize occupant-centric building operations, statistical rigor in comfort studies, and standardized documentation for behavior models. Themes include predictive algorithms for thermal preferences, validation of human-in-the-loop methods, and critical reviews of multi-domain research practices, reflecting a consistent focus on enhancing data reliability and practical applicability in sustainable building science. Awards: 2022 Best Paper Award: A guideline to document occupant behavior models for advanced building controls (Building and Environment) 2022 Best Paper Award: Quality criteria for multi-domain studies in the indoor environment (Building and Environment) Labs and Teams: He is a core member of the ETHOS Lab at EPFL, an interdisciplinary group advancing sustainability through computational and engineering solutions for human-oriented built environments.

Matthias Grossglauserمشاهده پروفایل

استاد

• Machine Learning
• Stochastic Networks
• Discrete Choice Models
• +۴ مورد دیگر

Matthias Grossglauser is a Full Professor at the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he co-directs the Information and Network Dynamics lab. He serves on the Federal Communications Commission (ComCom), Switzerland's telecommunications regulatory authority, and previously directed EPFL's Doctoral School in Computer and Communication Sciences (2016-2019). His career includes positions at Nokia Research Center (Internet Laboratory lead), AT&T Research, and EPFL (Assistant Professor). Education Ph.D. in Computer Science from Sorbonne Universités M.Sc. in Electrical Engineering from Georgia Institute of Technology Engineering degree in Communication Systems from EPFL Research Focus Grossglauser's research integrates machine learning, stochastic networks, and discrete choice models to address challenges in artificial intelligence, network science, computational social sciences, and recommender systems. His work emphasizes both theoretical foundations and practical applications, including political forecasting, climate communication, and network dynamics. Publication Trends Recent articles demonstrate strong focus on causal inference, optimal learning algorithms, and social network analysis. Dominant themes include reinforcement learning optimization, graph-based modeling, and NLP applications in political science. Methodological innovations in matrix factorization, Bayesian modeling, and stochastic processes recur throughout. Awards & Honors Fellow of IEEE and ELLIS Cor Baayen Award (1998) CoNEXT/SIGCOMM Rising Star Award (2006) Best Paper Awards: ACM COSN (2014), IEEE INFOCOM (2001) Nokia Mobile Data Challenge Winner (2012) Academic Leadership Has advised 16+ PhD students to completion and currently supervises 4 doctoral candidates. Secured research funding for projects including dynamic recommender systems, network alignment algorithms, and computational social science tools (e.g., Predikon.ch vote prediction platform). Leads the Information and Network Dynamics lab, focusing on AI-driven network analysis.

Mahsa Shoaranمشاهده پروفایل

استادیار

• Neural interfaces
• Neuroprosthetics
• Analog and mixed-signal integrated circuits
• +۵ مورد دیگر

Mahsa Shoaran is a Tenure-Track Assistant Professor at EPFL (École Polytechnique Fédérale de Lausanne) jointly appointed at the Center for Neuroprosthetics and the Institute of Electrical Engineering . She is also the founding director of the Integrated Neurotechnologies Laboratory (INL) and contributes to several doctoral programs and teaching missions across EPFL’s School of Engineering (STI). Education: PhD in Electrical Engineering, EPFL (2015) M.Sc. & B.Sc., Sharif University of Technology Postdoctoral Fellow, California Institute of Technology (Caltech) (2015-2017) Former Assistant Professor, School of Electrical and Computer Engineering, Cornell University (2017-2019) Research Focus: Dr. Shoaran’s work sits at the convergence of integrated circuit design , machine learning , and neuroscience . Her group develops ultra-low-power, miniaturized system-on-chips (SoCs) capable of real-time neural recording , pathology detection , and closed-loop therapeutic intervention such as adaptive neurostimulation. Machine-learning algorithms running on-chip enable precise symptom detection in neurological and psychiatric disorders, while advanced circuit techniques guarantee energy efficiency suitable for long-term implantable or wearable neural interfaces. Scientific Awards & Grants: ERC Starting Grant 2021 Google Faculty Research Award in Machine Learning 2019 Swiss NSF Postdoctoral Fellowships NSF Award for Young Professionals – Smart & Connected Health MIT EECS Rising Star 2015 Doctoral Advising & Service: She currently supervises 14 PhD students in the Integrated Neurotechnologies Laboratory. Additionally, she serves on the PhD program committees for Electrical Engineering (EDEE), Microsystems & Microelectronics (EDMI), and contributes to the Neuro-X and SEL teaching programs. She is a Technical Program Committee member for IEEE CICC and serves on the Student Research Preview committee for ISSCC. Laboratory & Collaborative Teams: The Integrated Neurotechnologies Laboratory (INL) at EPFL Campus Biotech in Geneva hosts her interdisciplinary team of circuit designers, machine-learning researchers, and neuroscientists. The lab collaborates closely with clinicians to translate innovations into real-world neuroprosthetic and diagnostic devices.

Olaf Hoeneckeمشاهده پروفایل

پژوهشگر

• Digital Signal Processing
• Power Systems
• Renewable Energy
• +۳ مورد دیگر

Olaf Hoenecke is a Researcher at Zurich University of Applied Sciences (ZHAW) within the School of Engineering, specializing in Digital Signal Processing. Based in Winterthur at Technikumstrasse 71, he actively contributes to research projects in sensor fusion, renewable energy systems, and power grid simulation methodologies. His research spans Digital Signal Processing, Power Systems, Renewable Energy integration, Sensor Fusion techniques, Fuel Cell heating systems, and advanced Simulation Modeling. Core expertise includes developing measurement-based simulation frameworks for power system transients and stability analysis, with emphasis on computational efficiency and real-world applicability. Publication analysis reveals consistent focus on power systems simulation, particularly transient analysis and measurement-driven modeling. His work addresses critical challenges in power grid dynamics, renewable energy integration, and computational methods for electromagnetic transient programs. Scientific Awards: No awards or fellowships were documented in the source material. Regarding academic advising, no graduate students or doctoral candidates are listed. His grant portfolio includes completed projects: 3D Time-of-Flight in Sensor Fusion, RENERG2 (Renewable energies in future energy supply), Optimized fuel cell heating systems, and SoE Project MPIG – all indicating collaborative industry-academia partnerships. Hoenecke operates within ZHAW's Research Focus group for Digital Signal Processing, which serves as his primary research unit for developing sensor fusion algorithms and power system simulation tools.

• Data Science
• Big Data
• Natural Language Query Processing
• +۴ مورد دیگر

Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.

Tanja Stadlerمشاهده پروفایل

استاد

• Computational Evolution
• Phylodynamics
• Macroevolution
• +۵ مورد دیگر

Tanja Stadler is a Full Professor at the Department of Biosystems Science and Engineering , ETH Zürich, and has served as Vice-Chair of the department since February 2024. She is also the president of the Swiss Science Advisory Panel COVID-19 and a member of the German National Academy of Sciences Leopoldina and EMBO. Education: MSc in Applied Mathematics, Technical University of Munich (2006) PhD in Applied Mathematics, Technical University of Munich (2008) Research Focus: Tanja's work integrates computational evolution and phylodynamics to address questions in macroevolution , epidemiology , and immunology . Her lab develops statistical tools to extract evolutionary and population dynamics from genomic data, with applications ranging from biodiversity studies to infectious disease outbreaks like COVID-19 and Ebola. Awards & Honors: ERC Starting Grant (2013) ERC Consolidator Grant (2020) SMBE Mid-Career Excellence Award (2021) Carus Medal (2021) Cloëtta Jubilee Prize (2023) Grants & Leadership: She has led major consortia producing genomic data and has advised the Swiss government through the Swiss National COVID-19 Science Task Force. Her lab continues to push boundaries in Bayesian phylogenetics and real-time pathogen surveillance .

Marcel Pfahrerمشاهده پروفایل

استاد

• Information Systems
• Decision-oriented Information Systems in Public Administration
• Multimedia Information Systems
• +۶ مورد دیگر

Professor Marcel Pfahrer is a faculty member at the Bern University of Applied Sciences in the Engineering and Computer Science department. He serves as the leader of the Data Science and Engineering (DSE) research group within the Institute for Data Applications and Security (IDAS) and is responsible for the e-Government research theme at IDAS. His institutional affiliations include: Bern University of Applied Sciences, Engineering and Computer Science (since 1992, full-time since 2006) Institute for Data Applications and Security (IDAS) Urs Sauter + Marcel Pfahrer AG (1995-2006) University of Bern, Institute for Business Informatics (1995-2003) Professor Pfahrer's research focuses on Information Systems, particularly decision-oriented Information Systems in public administration, Multimedia Information Systems with a focus on Geographic Information Systems (GIS), Web-based and distributed Information Systems, Data Engineering, and Digital Business Systems. His work bridges theoretical computer science with practical applications in government and business contexts, emphasizing the development of robust, user-centered information solutions that address real-world challenges. With extensive experience supervising over 30 Bachelor's and Master's students on diverse projects, Professor Pfahrer demonstrates a commitment to practical, applied research with tangible outcomes. His supervision approach appears to emphasize real-world problem solving across multiple domains including public administration, healthcare, and business applications. Professor Pfahrer has received recognition for his academic achievements: Diploma with distinction and diploma prize in Business Informatics (1988) With over 30 years of professional experience spanning academia, industry, and community service, Professor Pfahrer brings a wealth of practical knowledge to his teaching and research. His career demonstrates a consistent focus on developing information systems that effectively address practical challenges in public administration and business contexts. He leads the Data Science and Engineering research group, which develops advanced data solutions for applications including e-government systems, sustainability evaluation, and information management in public administration, with numerous successfully completed research projects funded by Innosuisse and other organizations.

Jasmin Wandelمشاهده پروفایل

استاد

• Biomechanics
• Medical Engineering
• Computational Modeling
• +۵ مورد دیگر

Prof. Dr. Jasmin Wandel serves as a Professor at Bern University of Applied Sciences within the School of Engineering and Computer Science. Her academic profile combines teaching responsibilities in engineering disciplines with active research in biomechanics and medical engineering applications. Her educational background includes a 2013 PhD dissertation on statistical methods for extreme value analysis in Gaussian processes, establishing her foundation in quantitative research methodologies. This statistical expertise informs her interdisciplinary approach to engineering problems. Professor Wandel's research program centers on biomechanical analysis of bone structures and medical device optimization . Key focus areas include: Computational modeling of fracture mechanics using finite element analysis Thermal management in surgical robotics and bone drilling procedures Biomechanical characterization of bone-implant interfaces Statistical methods for medical engineering data analysis Her work bridges theoretical engineering principles with clinical applications, particularly in orthopedics. Analysis of her publication trends (2013-2019) reveals consistent output in high-impact biomechanics journals, with increasing focus on clinically applicable research. The majority of her work involves collaborative projects with University of Bern researchers, demonstrating strong institutional partnerships in the Swiss medical engineering ecosystem. While specific awards aren't documented in available sources, her publication record in journals like Bone and Medical Engineering & Physics indicates peer recognition of her contributions to biomechanical research. Professor Wandel maintains active supervision of master's students at BFH, with research projects typically involving computational modeling and experimental validation. Her collaborations with University of Bern and industry partners like SVGW provide students with access to clinical datasets and real-world engineering challenges. Current research appears funded through institutional partnerships and applied research grants typical of Swiss Universities of Applied Sciences. Based at the Burgdorf campus, Professor Wandel contributes to BFH's strategic focus on Humane Digital Transformation through her work in medical robotics and computational biomechanics, integrating engineering solutions with human-centered healthcare applications.

Anastasios Kouvelasمشاهده پروفایل

مدرس

• Traffic flow theory
• System and control theory
• Optimization
• +۱۰ مورد دیگر

Dr. Anastasios Kouvelas is a Lecturer at ETH Zurich, where he serves as head of the Road Traffic Engineering research group at the Institute of Transport Planning and Systems (IVT), Department of Civil, Environmental and Geomatic Engineering. He has held this position since August 2018, succeeding Dr. Monica Menendez who moved to New York University in Abu Dhabi. Prior to joining ETH Zurich, he was a research associate at the Urban Transport Systems Laboratory (LUTS) at EPFL (2014-2018) and a postdoctoral fellow at Partners for Advanced Transportation Technology (PATH) at the University of California, Berkeley (2012-2014). Dr. Kouvelas' research focuses on modeling, simulation, optimization and traffic flow control. His work aims to develop real-time solutions based on control theory and operations research methods. The Road Traffic Engineering group develops algorithmic solutions that are components of intelligent transportation systems used in traffic control centers. Recent technological advances in autonomous vehicles have expanded their research topics as the industry seeks efficient operational solutions for autonomous mobility. They are particularly interested in extending their work to the design of advanced management strategies for urban networks that utilize connected vehicles to improve traffic operations and develop network-wide control strategies that minimize environmental impacts. His recent publications (2023-2025) demonstrate strong focus on traffic prediction using deep learning techniques, bike lane allocation impacts on urban networks, transit network resilience against disruptions, vehicle trajectory extraction from aerial recordings, and traffic control for mixed traffic systems with connected and autonomous vehicles. His work bridges theoretical developments in control theory with practical traffic engineering challenges. Scientific Awards No specific scientific awards were mentioned in the provided information. Advising and Grants Dr. Kouvelas supervises PhD and Master's students in traffic engineering and intelligent transportation systems. His research is supported by various grants including a grant from the Hong Kong Research Grant Council (Grant No. GRF 11216323) for research on traffic speed prediction. Laboratories and Teams Dr. Kouvelas leads the multidisciplinary Road Traffic Engineering research group at IVT, which consists of researchers with backgrounds in civil engineering, electrical engineering, mechanical engineering, computer science, control, and operations research. The group's work spans multiple areas including traffic flow theory, traffic operations, connected and automated vehicles, and intelligent transportation systems.