جست‌وجوی استاد و پژوهشگر

Alexander Albersمشاهده پروفایل

پژوهشگر

• Industrial Engineering
• Manufacturing
• Machine Learning
• +۴ مورد دیگر

Current research associate and doctoral student at ETH Zurich's Chair of Production and Operations Management (POM), focusing on industrial engineering and augmented reality applications in manufacturing. Holds M.Sc. with distinction in Industrial Engineering and Management from Karlsruhe Institute of Technology (KIT), with research conducted at University of Cambridge's Institute of Manufacturing. Educational background: B.Sc. & M.Sc. Industrial Engineering and Management, KIT, Germany Master's thesis research at University of Cambridge, UK Research integrates machine learning and augmented reality into manufacturing processes, particularly for quality inspection and guided assembly systems. Publications examine data application frameworks in VUCA environments and evaluate AR technology effectiveness in industrial settings. Recent publications highlight: Contextual AR evaluation methodologies Smart manufacturing data integration User-centered quality inspection systems Scientific achievements: Published in IEEE Transactions on Visualization and Computer Graphics Presented at IEEE ISMAR-Adjunct Contributed to Springer LNCS series publications Also develops public transport navigation apps integrating 50+ networks across Germany, Austria, and Switzerland, while maintaining active engagement in sports and outdoor activities.

Zahid Ahmad Buttمشاهده پروفایل

استادیار

• Infectious Disease Epidemiology
• Syndemic Framework
• Machine Learning
• +۴ مورد دیگر

Dr. Zahid Ahmad Butt is an Assistant Professor at the School of Public Health and Health Systems , University of Waterloo, where he combines clinical expertise as a physician with advanced epidemiological research. His work employs a syndemic framework to investigate interconnected epidemics of infectious diseases, social disparities, comorbidities, and substance use through epidemiological, statistical, spatial, and machine learning methods. Key research areas: Digital Health, Global Health, Spatial Epidemiology Editorial roles: Associate Editor for BMC Public Health, Section Editor-in-Chief at IJERPH Recent publications focus on AI applications in outbreak detection, vaccine hesitancy dynamics, emergency department optimization, and harm reduction policy analysis

Jean-Louis Reymondمشاهده پروفایل

استاد

• Chemical Space
• Cheminformatics
• Peptide Chemistry
• +۵ مورد دیگر

Professor Jean-Louis Reymond is a distinguished academic at the University of Bern, currently serving as Dean of the Faculty of Science since 2024. He leads the Reymond Research Group within the Department of Chemistry, Biochemistry and Pharmaceutical Sciences. Previously, he served as Director of the Department of Chemistry and Biochemistry from 2015-2017. His educational background includes diploma studies in Chemistry and Biochemistry at ETH Zürich (1981-1985), a Ph.D. in Natural Products Synthesis at the University of Lausanne (1986-1989), and post-doctoral research on Catalytic Antibodies at Scripps Research Institute (1990-1991). He joined the University of Bern as a Professor in 1997 after serving as Assistant Professor at Scripps Research Institute (1992-1997). Reymond's research centers on The Chemical Space Project, which explores the vast universe of possible chemical compounds with potential medical applications. His work spans three interconnected areas: Peptide Chemistry (design of bioactive peptides including dendrimers and polycyclic structures), Cheminformatics (development of the GDB chemical universe database), and Medicinal Chemistry (computer-aided design of small molecule modulators). His research has significant implications for addressing antimicrobial resistance through novel peptide-based therapeutics. Analysis of his recent publications reveals a strong focus on navigating and exploiting chemical space for drug discovery, particularly in antimicrobial applications. His work combines computational methods with synthetic chemistry to identify novel scaffolds and optimize bioactive compounds. The 2024-2025 publications demonstrate continued innovation in peptide design, chemical space visualization, and applications in drug delivery systems. ERC Advanced Grant (2020) for project SPACE4AMPS (€2.5 million over 5 years) Professor Reymond actively mentors numerous graduate students and leads a vibrant research group that collaborates across disciplines. His research is supported by significant grants including the ERC Advanced Grant and participation in the NCCR TransCure initiative. The Reymond Research Group maintains strong connections with other research units at the University of Bern and international collaborators. The research group operates state-of-the-art facilities for peptide synthesis, cheminformatics analysis, and biological testing. They have developed specialized tools including the GDB chemical space database (www.gdb.unibe.ch) and collaborate with the NCCR TransCure consortium on transporter and ion channel research. Their work on antimicrobial peptides positions them at the forefront of addressing the global antimicrobial resistance crisis.

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.

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.

Michael Feluxمشاهده پروفایل

استاد

• Aviation Communication, Navigation and Surveillance (CNS)
• Global Navigation Satellite Systems (GNSS)
• Ground-Based Augmentation System (GBAS)
• +۵ مورد دیگر

Prof. Dr. Michael Felux is full Professor and team leader of the Aviation Infrastructure group at the ZHAW School of Engineering , Zurich University of Applied Sciences. He also co-founded and co-owns the Estonian consultancy Navaid OÜ , providing GNSS/CNS expertise while ensuring non-conflict with his academic role. Education Dr.-Ing. in Mechanical Engineering, TU München (2012 – 2018) Dipl.-Tech. Math. in Mathematics, TU München (2003 – 2009) CAS Hochschuldidaktik (Higher-Education Didactics), PHZH (2021) Research Focus Michael Felux’s research centres on safe, secure and efficient aviation communication, navigation and surveillance (CNS) . He investigates GNSS-based augmentation systems (GBAS, SBAS) for precision approach and landing, develops real-time interference detection & localization techniques to counteract jamming and spoofing, and explores high-integrity navigation solutions for unmanned aerial vehicles (UAVs). Additional interests include environmental optimisation of flight procedures and multi-constellation, multi-frequency signal processing . Across more than 50 peer-reviewed publications since 2015, his work consistently targets the intersection of technical robustness and operational feasibility . Recent papers map GNSS disruption events across European airspace, quantify fuel-burn reductions enabled by GBAS-guided continuous-descent approaches, and introduce cost-efficient machine-learning frameworks for real-time localisation of malicious radio-frequency interference. Scientific Awards & Recognition (no specific awards listed in supplied material) Research Funding & Projects Spoofer Localization – Swiss project leader, ongoing EGNSS DFMC for GBAS based operations – EU project leader, ongoing Making I-CNS A Reality – integrated CNS technology, project leader, ongoing High Integrity Satellite Navigation for UAV using Galileo HAS – project leader, ongoing LINA – Shared large-scale infrastructure for safe testing of autonomous systems, team member, ongoing Collision avoidance system for manned & unmanned aircraft via SDR – completed Emission Reduction using Satellite Navigation for Approach Guidance – completed Laboratory & Team As head of the Aviation Infrastructure team at ZHAW, Prof. Felux directs a multidisciplinary group developing next-generation CNS technologies. The team operates dedicated GNSS/GBAS testbeds, flight-trial aircraft, and spectrum-monitoring networks to validate concepts from simulation through to real-world deployment.

Sven Grossriederمشاهده پروفایل

مدرس

• Business Management
• Agricultural Economics
• Cost Accounting and Controlling
• +۵ مورد دیگر

Dr. Sven Grossrieder serves as a Lecturer and Head of the Agronomy Study Program at the Bern University of Applied Sciences, specifically within the School of Agricultural, Forest and Food Sciences HAFL, Department of Agronomy in Zollikofen, Switzerland. His academic role combines teaching responsibilities with research leadership and program administration. Dr. Grossrieder's research interests span business management within agricultural contexts, with particular expertise in cost accounting and controlling, strategic business planning, applied statistics including Monte Carlo simulations and machine learning. His work bridges economic theory with practical agricultural applications, focusing on how business planning systems and simulation techniques can optimize agricultural operations and decision-making. His recent publications (2021-2022) reveal a strong focus on organizational behavior concepts like individual ambidexterity and management controls, alongside practical applications of algorithms in transportation planning. These works demonstrate his interdisciplinary approach connecting management theory with practical business applications. As Head of the Agronomy Study Program, Dr. Grossrieder oversees curriculum development, faculty coordination, and program quality assurance. He teaches business economics courses in the BSc Agronomy program and supervises student research projects. His industry experience at SBB Cargo AG and Simon-Kucher informs his practical teaching approach. Dr. Grossrieder is actively involved in multiple research projects including the Bern Organic Initiative 2025, ReLait (antibiotic reduction in dairy farming), and climate risk adaptation initiatives. His multilingual capabilities (German, English, French, and basic Italian) support international collaboration within the agricultural research community.

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.