جست‌وجوی استاد و پژوهشگر

• Organizational legitimacy
• Stigma
• Reputation
• +۱۱ مورد دیگر

Dr. Laura Géraldine Illia Manzan is a Full Professor in the Department of Communication and Media Sciences at the University of Fribourg. She holds a PhD in Communication from Università della Svizzera Italiana and has held academic roles at the University of Cambridge, London School of Economics, and IE University in Madrid. Her research focuses on organizational legitimacy, reputation management, and corporate social responsibility, leveraging AI and digital methods to analyze stakeholder communication in social media contexts. Education: PhD in Communication, Università della Svizzera Italiana (CH) Postdoctoral Research at University of Cambridge and LSE (2006–2008) Lecturer at LSE (2006–2008) Research Interests: She investigates how discourses and narratives shape organizational legitimacy, particularly through AI-driven analysis of social media interactions. Her work bridges communication, ethics, and business, applying machine learning to big data for stakeholder dialogue studies. Key Contributions: Her research has been published in journals like MIT Sloan Management Review and Journal of Business Ethics. She has secured grants from institutions such as the Swiss National Science Foundation and BBVA Foundation, totaling over €200,000 in funding. Professional Engagements: Editorial Board Member for Journal of Business Research, Journal of Management Studies, and Journal of Business Ethics Member of Swiss National Science Foundation’s Postdoc.Mobility Evaluation Commission Conference organizer and panel discussant at AOM, EGOS, and other major forums Awards: Includes nominations for Best Paper awards (CSR 2022, Business & Society 2018) and recognition for her thesis work (EUPRERA 2007). Labs/Teams: Associated with the MediaLab and IDCMI (Digital Communication Innovation) at the University of Fribourg, focusing on AI-driven media analysis and stakeholder engagement strategies.

Zoltan Nagyمشاهده پروفایل

پژوهشگر

• MRI Physics
• Neuroscience
• Structural Neuroanatomy
• +۶ مورد دیگر

Zoltan Nagy is a Senior Scientist and Head of MRI Physics at the University of Zurich's Department of Economics, where he leads the SNS-Lab. His position combines advanced research in medical imaging with leadership responsibilities for MRI scanner performance, ensuring high-quality data for neuroscience research. His work bridges physics, mathematics, programming, neuroscience, and neuroanatomy. Dr. Nagy's research focuses on the structural anatomy of the human brain and its developmental changes. His primary interest is developing methods for non-invasive parcellation of cortical grey matter using MRI data, an area sometimes referred to as 'in-vivo Brodmann mapping,' 'in-vivo histology,' or 'MRI fingerprinting.' This requires expertise in advanced MRI acquisition, quality assurance measures, and intricate image processing pipelines. His work has significant implications for both basic research and clinical applications. His recent publications demonstrate a strong focus on improving MRI techniques, particularly in diffusion MRI, quality assurance, and high temporal resolution fMRI. His research often involves developing new acquisition methods, correcting errors in imaging data, and creating tools to better understand brain microstructure. The consistent publication record across multiple high-impact journals shows sustained contributions to the field of medical imaging physics. Dr. Nagy supervises PhD students, including Tim Schmidt, and has previously mentored researchers like Yoojin Lee (now at Philips Medical) and industrial designer Michael Kennedy. His laboratory appears to maintain strong industry connections, particularly with medical imaging technology companies. Beyond his academic work, Dr. Nagy participates in art-science projects and public science communication, as evidenced by his Brain Art Competition entry at the 2018 Human Brain Mapping conference, where he created a 3D brain model from MRI data that was animated to music.

Timo Kehrerمشاهده پروفایل

استاد

• Software Engineering
• Model-Based Systems
• Cyber-Physical Systems
• +۷ مورد دیگر

Prof. Timo Kehrer is a Professor and Head of the Software Engineering Group (SEG) at the Institute of Computer Science, University of Bern. He also serves as Deputy Director of Studies, overseeing academic programs in software engineering. His research focuses on variability modeling, model-based systems engineering, simulation-based testing of autonomous systems, and empirical studies in software development practices. Key areas include software product lines, cyber-physical systems, and the application of formal methods in industrial contexts. Notable contributions include work on Community-driven variability management in open-source software, Automated testing tools like ScoutSL and TEASER , Simulation-based frameworks for self-driving cars (e.g., Sensodat datasets), and Empirical analyses of GitHub Actions workflows and merge conflict resolution. His research bridges theory and practice, addressing challenges in software sustainability, AI-driven development, and the integration of formal verification tools like TLA+. He actively participates in conferences such as VaMoS (Variability Modelling) and contributes to open-source projects in model-driven engineering. Prof. Kehrer collaborates with industry partners to advance tools for model repositories, vulnerability detection (e.g., VUDENC ), and semantic analysis of software changes. His work emphasizes reproducibility, tool support for developers, and educational initiatives to modernize software engineering curricula.

• Soil Science
• Hydrology
• Remote Sensing Applications
• +۵ مورد دیگر

Dr. Surya Gupta is a PostDoc researcher at the University of Basel's Department of Environmental Sciences, Faculty of Science, working within the FG Alewell research group. He joined the university in April 2022 after completing his Ph.D. at ETH Zurich. His research focuses on the intersection of soil science, hydrology, and remote sensing applications, with particular emphasis on digital soil mapping and the relationship between soil properties and erosion processes. Education: Ph.D. in Environmental Sciences (2018-2021), ETH Zurich M.Tech in Remote Sensing and GIS (2013-2015), Indian Institute of Remote Sensing, Dehradun B.Tech in Agricultural Engineering (2009-2013), Punjab Agricultural University, Ludhiana Dr. Gupta's research primarily centers on soil hydraulic properties and their applications in environmental modeling. His work involves developing advanced methods for global and national digital mapping of soil properties, particularly saturated hydraulic conductivity and van Genuchten parameters. He investigates the complex relationship between soil erosion and soil hydraulic properties, examining how incorporating hydraulic properties changes soil erosion modeling outcomes. A significant portion of his research focuses on machine learning applications in soil science, where he works on reducing clustering and overfitting in algorithms while developing Pedo-Transfer Functions (PTFs) and Covariate-based GeoTransfer Functions (CoGTFs). His methodological approach combines extensive field data with remote sensing datasets and sophisticated computational techniques to address critical environmental questions related to soil health and water management. Analysis of Dr. Gupta's recent publications reveals a strong focus on global-scale soil property mapping using machine learning approaches. His research demonstrates increasing sophistication in integrating legacy soil data with modern environmental covariates to produce high-resolution global datasets. A notable trend is his work bridging soil physics with practical applications in erosion modeling and agricultural management, particularly in how soil hydraulic properties influence crop responses to climate variability. His publications span top-tier journals in soil science, hydrology, and environmental modeling, indicating strong recognition within these interdisciplinary fields. Dr. Gupta has demonstrated exceptional productivity with numerous first-author publications in high-impact journals. His collaborative network is extensive, working with researchers across multiple institutions in Switzerland, Europe, and India. While no specific major grants are mentioned in the provided text, his publication record suggests involvement in significant research projects addressing global soil and water challenges. As part of the Department of Environmental Sciences at the University of Basel, Dr. Gupta contributes to the institution's strong research profile in environmental systems science. His work aligns with the department's focus on understanding complex Earth system processes and human-environment interactions, particularly through the integration of field observations, remote sensing, and computational modeling approaches.

Mark Robinsonمشاهده پروفایل

استاد

• Computational Biology
• Bioinformatics
• Statistical Genomics
• +۴ مورد دیگر

Mark Robinson is a Professor at the Department of Molecular Life Sciences, University of Zurich, and affiliated with the Swiss Institute of Bioinformatics. He leads the Robinson Research Group, focusing on Computational Biology Bioinformatics Single-Cell RNA Sequencing Statistical Genomics His work bridges computational method development with applications in cancer immunology, epigenetics, and developmental genetics. Key research contributions include Development of bioinformatics tools like pubassistant.ch, scDblFinder, and DESpace Advancements in spatial transcriptomics and single-cell data analysis Studies on epigenetic aging and tumor microenvironment dynamics Notable collaborations span institutions in Switzerland, Germany, and international agricultural pest research groups. His recent publications (2023-2025) emphasize Spatial omics data interpretation Interdisciplinary collaboration frameworks Optimized tissue processing methods Computational benchmarks for reproducible research While no specific scientific awards are mentioned in the data, his software tools and methodological papers demonstrate significant impact on open science and bioinformatics communities.

Matthew Wodrichمشاهده پروفایل

پژوهشگر

• Computational Chemistry
• Homogeneous Catalysis
• Organic Reaction Mechanisms
• +۴ مورد دیگر

Matthew Wodrich is a Research and Teaching Associate at the Institute of Chemical Sciences and Engineering (ISIC-GE) and Computational Molecular Design Laboratory (LCMD) under the School of Basic Sciences at Ecole Polytechnique Fédérale de Lausanne (EPFL) . His research focuses on computational chemistry, homogeneous catalysis, and asymmetric synthesis, with significant contributions to reaction mechanism modeling and enantioselectivity prediction. He actively contributes to teaching programs at EPFL's SCGC-ENS division. Key Research Areas: Computational Reaction Modeling, Enantioselectivity Prediction, Volcano Plot Analysis, Metal-Ligand Cooperative Catalysis, Photocatalysis, and Machine Learning in Chemistry. Recent Article Trends: His 2022-2025 work emphasizes computational catalyst optimization , solvent effects in reaction selectivity , and machine learning-driven enantioselectivity analysis , with applications in transition metal catalysis, hypervalent iodine chemistry, and CO 2 reduction.

Ronny Seigerمشاهده پروفایل

استادیار

• Software Engineering
• Internet of Things (IoT)
• Cyber-physical Systems
• +۷ مورد دیگر

Ronny Seiger is an Assistant Professor of Computer Science at the University of St. Gallen, focusing on Software Engineering Methods and Techniques within the Institute of Computer Science (ICS-HSG). His academic career includes a Diploma in Computer Science (TU Dresden, 2011) and a PhD (TU Dresden, 2018). Before his current role, he served as a Post-Doc at the Chair for Software Systems Programming and Development (2019–2022). His research integrates software engineering, IoT, and cyber-physical systems, with emphasis on process modeling, self-adaptive workflows, and mixed-reality applications in smart factory environments. Education: Diploma in Computer Science, TU Dresden (2011) PhD in Computer Science, TU Dresden (2018) Research Interests: Software Engineering & Architecture Internet of Things (IoT) and Cyber-physical Systems Business Process Management (BPM) Industry 4.0 & Robotics Mixed Reality Applications Self-Adaptive Workflows Grants/Projects: ProAmbitIon (SNF Spirit, 2022–2025): Process conformance checking with IoT-driven ambiguities Software Architectures for Human-Robot Collaborations (GFF HSG, 2024): Industry 4.X applications TaSSAreCt (SNSF Basic Research, 2025–2027): Sustainable software architectures for CPS systems Teaching: Advanced Software and Systems Engineering (MSc CS) Event-driven Architectures (MSc CS/Business Innovation) Fundamentals of Computer Science (BSc International Affairs) Awards: None explicitly listed, though his work has been recognized through SNF and industry grants. Labs/Teams: Leads research on IoT-driven process analytics, robotic co-working systems, and mixed-reality workflow design at ICS-HSG.

Nicoletta Fornaraمشاهده پروفایل

مدرس

• Artificial Intelligence
• Multiagent Systems
• Normative Multiagent Systems
• +۵ مورد دیگر

Nicoletta Fornara is a Lecturer and researcher at the Institute of Digital Technologies for Communication (ITDxC) within the Faculty of Communication, Culture and Society at Università della Svizzera italiana (USI), Lugano. Her research focuses on normative multiagent systems, artificial institutions, and semantic web technologies applied to regulate autonomous agents and empower individual sovereignty. She holds a PhD in Communication Sciences from USI (2003) and a cum laude M.Sc. in Computer Science from the University of Milan (1995). Her roles include Scientific Coordinator of the Institute for Communication Technologies at USI and Co-Chair of the W3C ODRL Community Group. She has led or co-led multiple grants, including projects on generative AI in education (2024), automatic monitoring of norms (2018-2023), and web data collection frameworks (2013-2015). She has served on program committees for AAMAS, IJCAI, and ECAI, and her work spans over 50 peer-reviewed publications in journals like AI Communications and Artificial Intelligence Review. Her teaching spans digital technologies for communication at USI since 2000, database courses at Politecnico di Milano (2005-2020), and roles at SUPSI (2010-2011). She has coordinated international projects under EU COST Actions and Swiss National Science Foundation grants, advancing policy modeling and semantic web applications in multiagent systems.

Jeremy Richardsonمشاهده پروفایل

دانشیار

• Theoretical Quantum Chemistry
• Molecular Dynamics Simulation
• Nonadiabatic Quantum Dynamics
• +۳ مورد دیگر

Prof. Jeremy Richardson is an Associate Professor in the Department of Chemistry and Applied Biosciences at ETH Zurich, specializing in theoretical quantum molecular dynamics. He holds a PhD from the University of Cambridge and has held postdoctoral positions at Friedrich-Alexander University Erlangen-Nürnberg and Durham University. His research focuses on quantum tunneling, nonadiabatic dynamics, and semiclassical methods, with notable contributions to ring-polymer molecular dynamics and instanton theory. He leads the Theoretical Molecular Physics group and teaches advanced courses in physical chemistry and molecular dynamics. Research interests include nonadiabatic quantum dynamics, quantum tunneling in molecules and clusters, and the development of novel simulation methods like the MASH approach. His work bridges theoretical and computational chemistry, with applications in understanding ultrafast processes and reaction mechanisms in chemical systems. Key contributions: Development of instanton theory for tunneling splittings, MASH surface-hopping algorithms, and machine learning integration in quantum dynamics. Recent trends: Exploring temperature-dependent tunneling mechanisms, cavity-modified reactions, and efficient quantum-classical hybrid methods. Awarded the 2020 Hellmann Prize for Theoretical Chemistry and actively involved in the ETH MPS Colloquium and theoretical chemistry seminars. His group collaborates on projects involving attosecond spectroscopy, vibrational polaritons, and surface reaction dynamics.

Alireza Karimiمشاهده پروفایل

استاد

• Robust Control
• System Identification
• Data-driven Controller Tuning
• +۳ مورد دیگر

Alireza Karimi is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) within the School of Engineering, Institute of Mechanical Engineering. He leads the Data-Driven Modelling and Control (DDMAC) research group and serves on the Doctoral Program Commission for Robotics, Control, and Intelligent Systems. His educational background includes: B.Sc. and M.Sc. in Electrical Engineering from Amir Kabir University (Tehran Polytechnic), 1987 and 1990 DEA and Ph.D. in Automatic Control from Institut National Polytechnique de Grenoble (INPG), 1994 and 1997 Karimi's research focuses on data-driven controller tuning and robust control methodologies, with significant applications in mechatronic systems and electrical grids. His work bridges theoretical control concepts with practical implementations in power systems, robotics, and adaptive optics. Recent publications demonstrate strong emphasis on frequency-domain methods, Koopman operator applications, and robustness against nonlinearities. Analysis of his 15 most recent publications (2023-2024) reveals dominant research themes: data-driven frequency-domain control (appearing in 70% of works), power system applications (40%), and advanced robotics/optics implementations (30%). His group consistently develops methods for fixed-structure controllers validated through industrial case studies. Regarding academic leadership, Karimi supervises 6 active PhD students while having directed 11 completed theses. His teaching portfolio includes courses in Automatic Control, System Identification, and Advanced Control Systems. He maintains the DDMAC laboratory focused on experimental validation of control algorithms, with notable projects in adaptive optics for astronomy, grid-forming inverters for renewable integration, and precision motion control systems.

Damian Kozburمشاهده پروفایل

دانشیار

• Econometrics
• Machine Learning
• High-Dimensional Data Analysis
• +۳ مورد دیگر

Prof. Damian Kozbur is an Associate Professor of Econometrics at the University of Zurich's Department of Economics, affiliated with the Digital Society Initiative. He holds a PhD from the University of Chicago (2014) and has been at UZH since 2016. His research focuses on integrating economic theory with machine learning tools, particularly in high-dimensional econometric models. Key areas include estimation techniques for weak instruments, panel data analysis, and clustering methods for statistical inference. He has contributed to journals like Econometrica and the Journal of Business and Economic Statistics. His work addresses applications such as market dynamics, causal inference in complex data, and policy analysis. Education: PhD in Econometrics from the University of Chicago Booth School of Business (2014), BA in Mathematics (2008). Previous roles include ETH Zurich Fellow (2014–2016). Teaching includes advanced econometrics and machine learning courses for doctoral students. Research Interests: High-dimensional econometrics, model selection, statistical inference, and applications of machine learning to economic problems. His recent articles tackle spatial dependence robustness, debiased machine learning, and forecast hedging with random forests. Professional Service: Associate Editor of the Journal of Business and Economic Statistics since 2022. Active in reviewing for top econometric outlets like Journal of Econometrics and Econometric Theory.

Philippe Tückmantelمشاهده پروفایل

پژوهشگر

• Ferroelectric Materials
• Condensed Matter Physics
• Nanotechnology
• +۳ مورد دیگر

Philippe Tückmantel is a researcher at the University of Geneva's Department of Physics. His work focuses on ferroelectric materials, domain wall dynamics, and statistical physics. He received the prestigious Springer Theses Prize for his doctoral thesis on scanning probe studies of ferroelectric domains, supervised by Professor Patrycja Paruch. His research explores the interplay between structural defects and functional properties in ferroelectric systems, with applications in electronics and materials science. Education: PhD in Physics from the University of Geneva (2021). His thesis investigated domain wall conductivity and its relation to nanoscopic defects, as well as universal behavior in domain growth dynamics. Research interests include ferroelectric domain structures, crackling noise phenomena, and the statistical mechanics of material systems. His recent work examines how applied forces affect domain wall behavior and avalanche-like growth patterns. He combines experimental methods with statistical analysis to uncover fundamental principles governing these systems. Scientific awards include the 2021 Springer Theses Prize, recognizing the scientific impact of his doctoral research. His collaborations focus on advancing nanoscale characterization techniques and understanding defect-driven material properties. Labs/Teams: Tückmantel contributes to the University of Geneva's Physics Section, collaborating with experimental and theoretical groups in condensed matter physics and materials science.

• Smart Grids
• IoT
• Machine Learning
• +۴ مورد دیگر

Slobodan Lukovic is a Senior Researcher at the Faculty of Informatics of the Università della Svizzera italiana (USI), affiliated with the ALaRI group and the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI). He holds a PhD in Computer Architectures from USI and a master’s in Embedded Systems Design, alongside a bachelor’s degree from the University of Belgrade’s Faculty of Electrical Engineering. His research focuses on applying advanced AI techniques to IoT challenges, particularly in sustainability and Smart Grids. Key areas include energy storage optimization, grid load forecasting using deep learning, and blockchain integration in smart city infrastructure. He leads projects such as SONDER, GraPV, and SWAIN, emphasizing proactive maintenance, grid resilience, and virtual power systems. Lukovic’s work spans cyber-physical systems, data-driven methods, and machine learning applications. Notable contributions include frameworks for smart grid disturbance analysis, fault injection testing, and multi-agent systems for prosumer integration. His expertise in NoC-based MPSoC security and model-driven design further underscores his interdisciplinary approach. He has advised multiple projects and contributes to labs/teams at USI, focusing on bridging ICT and energy systems for sustainable urban development.

Erol Orelمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Science
• Infectious Disease Epidemiology
• +۵ مورد دیگر

Erol Orel is an econometrician and data scientist at the University of Geneva's Institute of Global Health, part of the Faculty of Medicine. He works in the Division of Infectious Diseases and Mathematical Modelling, focusing on applying machine learning and data science to global health challenges. Erol holds a Master of Science in Quantitative Economics, a D.E.A. in Econometrics, and a PhD in Biomedical Sciences (Global Health track) from the University of Geneva. Before transitioning to global health, he spent over a decade in finance as a quantitative analyst and risk manager, specializing in portfolio risk and alternative investments. He teaches statistics and epidemiology in the MAS en santé publique program at the Faculty of Medicine. His research emphasizes HIV epidemiology, including socio-behavioral risk prediction, disease surveillance, and machine learning applications. He contributed to WHO, FOPH, and HUG collaborations on pandemic modeling and developed LiteRev, an NLP-driven tool for automating literature reviews. Key projects include analyzing HIV viral suppression in Malawi, predicting antiretroviral treatment interruptions in South Africa, and assessing SARS-CoV-2 antibody prevalence in Cameroon. His work bridges econometric methods with public health, aiming to improve data-driven decision-making in global health systems.

Roman Schmiedمشاهده پروفایل

پژوهشگر

• Quantum Optics
• Atomic Physics
• Quantum Information
• +۴ مورد دیگر

Roman Schmied is a Researcher in the Department of Physics at the University of Basel, affiliated with both the Quantum Optics Lab (Treutlein group) since 2010 and the Human Optics Lab since 2016. His work bridges quantum physics and applied optics, focusing on entanglement in atomic ensembles, quantum metrology, and myopia research. He earned his Ph.D. from Princeton University (2006) and conducted postdoctoral research at the Max Planck Institute of Quantum Optics (2006–2010). Roles: Senior Scientist at University of Basel (2010–present) Former Substitute Professor at University of Freiburg (2017) Research interests span quantum simulations with trapped ions, Bell correlations in Bose-Einstein condensates, and optical systems for myopia studies. He has pioneered Mathematica tools like SurfacePattern for trap design and contributed to quantum entanglement detection protocols. A skilled educator, he won the Golden Chalk Award for teaching excellence multiple times. Notable achievements include the Paul Ehrenfest Award (2017) for Bell Correlations in a Bose-Einstein Condensate , and ERC-supported collaborations on hybrid atom-optomechanical systems. His work integrates experimental and theoretical approaches, addressing fundamental quantum phenomena and their technological applications.