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Rui YaoView profile

Lecturer

• Large-scale equilibrium modeling in multi-modal transport systems
• Individual mobility choice modeling
• Demand management strategies
• +3 more

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 FaveroView profile

Lecturer

• Building Sustainability
• Thermal Comfort
• Occupant Behavior Modeling
• +3 more

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.

Mahsa ShoaranView profile

Assistant Professor

• Neural interfaces
• Neuroprosthetics
• Analog and mixed-signal integrated circuits
• +5 more

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.

Marcel HoneggerView profile

Lecturer

• Robotics
• Mechatronics
• Control Systems
• +4 more

Prof. Dr. Marcel Honegger is a Lecturer in Robotics and Mechatronics at the Institute of Mechatronic Systems within the School of Engineering at Zurich University of Applied Sciences (ZHAW). He co-leads the Institute and focuses on the development of robot systems, drive controls, and control/regulation software for robotics and mechatronics. Ph.D., ETH Zurich (1996-1999) Dipl. Mech. Eng., ETH Zurich (1990-1996) His research spans robotics, mechatronics, and control systems, with recent work on predictive maintenance using autoencoder-based anomaly detection for delta robots and RGBD segmentation for agricultural automation. Earlier projects involved adaptive controllers for parallel manipulators and Java frameworks for robot control systems. Honegger has led projects such as the full automatic sprinkler robot (FASR) and the Advanced Rail Track Information System . He previously worked as a Senior R&D Engineer at CSEM Alpnach (2001-2009) and Project Manager at maxon motor ag (2009-2014). His publications reflect expertise in industrial automation, sensor integration, and mechanical design. He is involved in teaching and R&D collaborations, with an ORCID ID: 0009-0004-8178-136X.

Michael FeluxView profile

Professor

• Aviation Communication, Navigation and Surveillance (CNS)
• Global Navigation Satellite Systems (GNSS)
• Ground-Based Augmentation System (GBAS)
• +5 more

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.

• Data Science
• Big Data
• Natural Language Query Processing
• +4 more

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.

Daniele SilvestroView profile

Researcher

• Evolutionary Biology
• Phylogenetics
• Macroevolution
• +5 more

Daniele Silvestro is a researcher at ETH Zürich's Department of Biosystems Science and Engineering, working within the Computational Evolution group based in Basel, Switzerland. His research spans evolutionary biology, computational methods, and biodiversity science, with a focus on developing and applying novel analytical approaches to understand macroevolutionary patterns. Dr. Silvestro's research interests center on evolutionary biology and computational approaches to understanding biodiversity patterns through time. His work bridges micro- and macroevolutionary scales, with particular emphasis on phylogenetic methods, speciation processes, and the integration of fossil data with molecular phylogenies. He applies machine learning and artificial intelligence techniques to analyze large-scale biodiversity datasets, addressing questions about species diversification, extinction dynamics, and ecological interactions across deep time. His recent publications demonstrate a strong trend toward computational innovation in evolutionary biology, with increasing integration of artificial intelligence methods to tackle complex questions in biodiversity science. His work spans multiple biological systems, from plant-soil interactions to mammalian evolution, reflecting an interdisciplinary approach that combines theoretical modeling with empirical data analysis. Dr. Silvestro collaborates extensively with researchers across institutions and disciplines, contributing to major initiatives such as the 2030 Declaration on Scientific Plant and Fungal Collecting. His research has significant implications for biodiversity conservation, particularly in understanding how species and ecosystems respond to environmental change. His work on computational methods, including software development like DeepDiveR, demonstrates a commitment to creating practical tools for the broader scientific community. His research group at ETH Zürich appears to focus on developing and applying cutting-edge computational approaches to evolutionary questions, emphasizing the importance of integrating multiple data sources and analytical frameworks.

Florian WeigertView profile

Professor

• Empirical Asset Valuation
• Hedge Funds
• Mutual Funds
• +5 more

Florian Weigert is a Full Professor of Financial Risk Management at the University of Neuchâtel, Switzerland, where he has held his position since February 2020. He also serves as the Director of the Master of Science in Finance program. Prior to this, he was an Assistant Professor of Finance at the University of St. Gallen from 2014 to 2020. Weigert has held visiting scholar positions at prestigious institutions including New York University, Georgetown University, University of Texas at Austin, and Georgia State University. Weigert's research focuses on empirical asset valuation, hedge funds, mutual funds, financial technology, risk management, and behavioral finance. His work employs sophisticated quantitative methods to analyze financial markets, with particular emphasis on crash risk, option pricing, and the application of machine learning in finance. His research has been published in top-tier finance journals including the Journal of Finance, Journal of Financial Economics, and Review of Financial Studies. Currently, Professor Weigert leads two major research projects: an FNS project on "Measuring, Understanding, and Predicting Mutual Fund Performance Worldwide" (2022-2027) and an Innosuisse Project on "Fund Manager Selection with Machine Learning" (2022-2026). His recent publications demonstrate a growing interest in the intersection of financial technology, machine learning, and traditional finance topics, with several forthcoming papers in 2025 addressing cryptocurrency returns, weather risk, and advanced modeling techniques for equity options. Best Paper Awards Excellence Awards Weigert actively contributes to the academic community as the managing editor of the scientific journal Financial Markets and Portfolio Management since July 2024. He is also a research member of several prominent organizations including the Centre of Financial Research Cologne, the Swiss Society for Financial Market Research, and the Association of University Professors of Business Administration.

Iulian CiorascuView profile

Lecturer

• Knowledge representation
• Ontologies
• Data modeling
• +1 more

Iulian Ciorascu serves as a Lecturer at the Institute of Information Management within the Faculty of Economics at the University of Neuchâtel, Switzerland. His academic base is located at Rue A.-L. Breguet 2, 2000 Neuchâtel, with office room R. 139, and he maintains active teaching and research responsibilities in the institution. His research specializes in knowledge representation, ontologies, data modeling, and data architecture—core disciplines essential for semantic data systems and information infrastructure design. Complementing this, his teaching portfolio includes Data Management, Machine Learning, and Statistical Learning, demonstrating a cohesive integration of theoretical and applied data science methodologies in his academic practice. As an integral member of the Institute of Information Management's research team, he contributes to collaborative projects advancing data management frameworks and architectural solutions, directly supporting the institute's mission in developing next-generation information systems and analytical approaches.

• Social Media
• Sharing Economy
• Artificial Intelligence
• +4 more

Matthes Fleck serves as Professor and Director of the Institute for Communication and Marketing (IKM) at Lucerne University of Applied Sciences and Arts, School of Business, leading research initiatives since 2010 with expertise spanning digital communication, social media strategy, and AI-driven marketing analytics. His leadership drives industry-relevant projects examining Switzerland's sharing economy landscape and intelligent transportation systems. Academic credentials include a Doctorate in Business Administration (Dr. oec.) from University of St. Gallen (2011) and Master of Arts in Journalism and Business Administration from Freie Universität Berlin (2006), establishing an interdisciplinary foundation for his research. This dual expertise enables rigorous analysis of corporate communication through both business strategy and media theory lenses. Research focuses on digital transformation's impact on business communication, with three interconnected pillars: social media dynamics in corporate contexts (examining CSR blogging and stakeholder engagement), sharing economy strategic frameworks (particularly through projects like Sharecity), and artificial intelligence applications for marketing analytics. Methodologically, Fleck pioneers natural language processing techniques to analyze economic news coverage, corporate culture statements, and chatbot efficacy—evidenced by his 2022 agenda-setting study of pandemic-related economic reporting and 2021 assessment of AI mental health solutions for elderly populations. Recent publication trends (2020-2022) reveal intensified focus on pandemic-era digital adaptations, with 60% of recent work applying computational linguistics to crisis communication and health tech. Cross-cutting themes include ethical considerations in AI deployment, data-driven branding for SMEs, and social media's evolving role in stakeholder capitalism—demonstrating consistent alignment between technical methodology and strategic business application. No scientific awards or major honors were documented in source materials. Research leadership manifests through continuous project funding including "Mobile als Innovator in Marketing" (2017), "B2B Social Media" (2014), and current AI/mobility initiatives, though specific grant amounts remain undisclosed. As IKM Director, Fleck manages a multidisciplinary team conducting industry-partnered research in mobile marketing analytics and social media impact measurement, positioning the institute as Switzerland's hub for applied communication science in digital business transformation.

Anastasios KouvelasView profile

Lecturer

• Traffic flow theory
• System and control theory
• Optimization
• +10 more

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.

• Work and Health
• Health Diagnostics
• Data Science
• +4 more

Dr. Patric Wyss is a Research Associate at the School of Applied Psychology, University of Applied Sciences Northwestern Switzerland (FHNW) and concurrently serves as a Research Associate at the Institute for Social and Preventive Medicine (ISPM), University of Bern. His interdisciplinary work bridges psychology, data science, and healthcare, focusing on innovative approaches to health diagnostics and workplace mental health. Dr. Wyss completed his academic training at the University of Bern, Switzerland with a Doctorate in Biomedical Sciences (2019-2022), Master's degree in Statistics and Data Science (2017-2020), Master's degree in Psychology (2014-2017), and Bachelor's degree in Psychology with a Minor in Mathematics (2010-2014). His research interests center on work and health, predicting health variables with complex algorithms, and data-driven personalized health diagnostics. Dr. Wyss has made significant contributions to dementia diagnosis through data-driven approaches, cognitive assessment tools, and digital interventions for mental health. His work often integrates machine learning with clinical psychology to develop predictive models for health outcomes. Analysis of his recent publications reveals a strong focus on neurodegenerative diseases, particularly dementia and cognitive decline in aging populations. His research increasingly incorporates digital health technologies, virtual reality, and algorithmic approaches to develop personalized diagnostic tools. The publications demonstrate a progression from fundamental cognitive neuroscience to applied health diagnostics with practical workplace applications. Dr. Wyss has been actively involved in several significant projects including Digital Balance, Healthy@Work, and work-related stress and mental disorders in the workplace. His current position at the Institute for Mental and Organizational Health at FHNW focuses on how digitalization, social upheavals, and new forms of work present both opportunities and challenges for mental and organizational health. His professional activities include research collaboration across multiple institutions, development of digital assessment tools, and investigation of HR analytics applications for health monitoring. Dr. Wyss has also contributed to the development of serious games for cognitive assessment and rehabilitation, particularly for older adults and patients with neurological conditions.