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Federica LanzaView profile

Lecturer

• Seismology
• Geophysics
• Volcanology
• +3 more

Federica Lanza is a Lecturer at the Department of Earth and Planetary Sciences at ETH Zurich, affiliated with the Schweiz. Erdbebendienst (SED), the Swiss Seismological Service. Her work focuses on seismology, geophysics, and geothermal systems, with expertise in induced seismicity, fault dynamics, and advanced monitoring technologies like Distributed Acoustic Sensing (DAS). She teaches courses such as Seismic Waves II in the Autumn Semester 2025. Her research integrates field experiments, computational modeling, and machine learning to address challenges in seismic hazard assessment, geothermal energy development, and tectonic processes. Key areas include forecasting induced earthquakes at geothermal sites, analyzing fault interactions in fold-and-thrust belts, and developing innovative sensor systems for subsurface monitoring. Dr. Lanza collaborates on large-scale projects like the Utah FORGE initiative, advancing techniques for real-time seismic monitoring and fracture network characterization. Her contributions bridge fundamental geophysical research with practical applications in energy systems and risk mitigation.

• Machine Learning
• Biomedical Informatics
• Medical Genomics
• +3 more

Prof. Gunnar Rätsch is a Full Professor in the Department of Computer Science at ETH Zürich and Deputy Head of the Institute for Machine Learning. His research focuses on developing machine learning methods for biomedical applications, including genomics, medical imaging, and clinical decision support systems. He specializes in integrating multi-omics data, spatial transcriptomics, and time-series analysis to address challenges in precision medicine and critical care. His work emphasizes ethical AI frameworks, algorithmic fairness, and robust clinical prediction models. Key research areas include: Deep learning for medical imaging and histopathology Single-cell analysis and tumor profiling Reinforcement learning for treatment optimization in ICUs Multimodal data integration for clinical applications Recent work highlights advancements in: Standardizing single-cell cytometry readouts for clinical use Developing foundation models for critical care time-series analysis Creating interpretable survival models for ICU patients His contributions span foundational machine learning theory and applied healthcare technologies, with a focus on translational research to improve clinical outcomes. Current projects include the Tumor Profiler Study for multi-omic tumor analysis and ethical frameworks for clinical AI systems.

Andreas VitalisView profile

Researcher

• Molecular Biophysics
• Protein Aggregation
• Molecular Simulations
• +4 more

Andreas Vitalis is a Senior Scientist in the Department of Biochemistry at the University of Zurich, where he leads the development of molecular simulation software (CAMPARI) and research platforms. He holds a Ph.D. in Molecular Biophysics from Washington University (St. Louis) and conducted postdoctoral research at UC San Diego and Zurich. His expertise spans protein aggregation mechanisms, computational biophysics, and high-performance computing. Education: Ph.D. in Molecular Biophysics, Washington University in St. Louis (2003-2009) Research Scholar, University of California San Diego (2001-2002) Diploma in Biochemistry, Ruhr-Universität Bochum (1998-2001) Research Interests: Protein aggregation (Alzheimer's, Huntington's diseases) Molecular simulation methods (enhanced sampling, FAIR data) Drug discovery platforms and computational tools Neuroscience applications of biophysical modeling His work bridges computational methods with experimental biology, emphasizing scalable solutions for complex systems. Key contributions include CAMPARI software and FAIR-compliant data management frameworks.

• Climate Policy
• Energy Economics
• Environmental Economics
• +3 more

Florian Landis is a Researcher at the Center for Energy and Environment within the School of Management and Law at Zurich University of Applied Sciences (ZHAW). His work focuses on the distributional impacts of energy and climate policy , particularly examining how carbon pricing mechanisms affect different socioeconomic groups in Switzerland. Previously, he held postdoctoral research positions at ETH Zurich (2015-2021) and ZEW Mannheim (2013-2015), and worked as a Short Term Consultant at the World Bank Group (2012). His educational background includes a Doctor of Science (2009-2012) and Master of Science in Physics (2001-2007), both from ETH Zurich. This interdisciplinary foundation enables his unique approach to energy economics problems. Landis specializes in computable general equilibrium modeling applied to climate policy analysis, with particular expertise in carbon pricing, decarbonization pathways, and policy design for energy transitions. His research frequently addresses the tension between policy efficiency and equity considerations, examining how different carbon tax designs impact welfare distribution across households. His publication portfolio shows a clear evolution from computational neuroscience (early work) toward energy economics and climate policy modeling. Recent work focuses on Swiss decarbonization scenarios, with consistent themes of carbon pricing efficiency, distributional impacts, and integrated energy-economic modeling approaches across multiple publications since 2017. Current research projects include: Renewable Fuels and Chemicals for Switzerland (Team member), Integrating very high shares of decentralized renewable energy into the Swiss energy system: Electricity market design and policy (Co-project leader), and DEcarbonisation of Cities and Regions with Renewable GAses (Team member).

• Statistics
• Econometrics
• Risk Management
• +5 more

Ruben Seiberlich is a Lecturer at the Zurich University of Applied Sciences (ZHAW), School of Management and Law, Department of Banking, Finance, and Insurance. He holds a PhD in Econometrics from the University of Konstanz (2013) and has taught courses such as Advanced Quantitative Methods, Risk Management, and Finanzinstrumente & Portfoliotheorie. His research focuses on statistics, econometrics, and risk management, with notable work on real estate bubble risk, gender discrimination in housing, and partisan bias in inflation expectations. Education: Diplom Volkswirt (2009), Dr. rer. pol. Ökonometrie (2013) Current roles: Study Program Leader for MSc Banking and Finance, Model Performance and Backtesting Leader at Credit Suisse AG Research Interests span empirical economics, risk analysis, and applied econometric methods. His recent articles analyze real estate and gold market bubbles, gender bias in rental housing, and political bias in economic expectations. Key methodologies include semi-parametric decomposition, propensity score matching, and shrinkage estimation. Scientific Networks: Member of the German Statistical Society’s committee for Empirical Economics and Applied Econometrics; ORCID ID 0000-0002-5258-5499.

• Deep Learning
• Statistics
• Data Science
• +4 more

Matthias Nyfeler is a Lecturer for Physics and Statistics at the Institute of Computational Life Sciences, Zurich University of Applied Sciences (ZHAW), since 2018. He previously served as a Lecturer at Jönköping University (2016-2018) and as a High School Teacher in Physics and Mathematics (2010-2016). His roles include Programme Director for the MSc specialisation in Applied Computational Life Sciences, Head of the Research Group Advance Signal Analytics, and Head of ICLS statistical consulting. Education: PhD in Theoretical Physics (University of Bern, 2005-2010), Master of Science in Physics (University of Bern, 2006-2009), Teaching Diploma for Physics and Mathematics (PHBern, 2010-2011), Postgraduate Studies in Secondary Education (Jönköping University, 2016-2017). His research focuses on Deep Learning and Statistical Signal Processing for applications like Drone Signal Classification and Bioacoustics . He also contributed to Quantum Antiferromagnetism and Cluster Algorithms earlier in his career. His recent publications highlight Robust CNN-based Drone Detection in low SNR environments and Multiscale Deep Learning for RF signal analysis. He led projects such as ChirpNet for AI biodiversity monitoring and TinyML Grasshopper Classifier. Matthias engages in Statistical Consulting and Mathematical Modeling , with a focus on Physical Computing and Radio Signal Processing . His work spans both academic research and applied technology development, including datasets for drone signal classification.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +7 more

Fernando Benites de Azevedo e Souza is a researcher at Zurich University of Applied Sciences (ZHAW) specializing in Natural Language Processing (NLP) and text analytics. He collaborates across disciplines including cultural management, speech recognition, and low-resource language technologies. Key research areas: NLP applications in arts management, speech-to-text systems, and German dialect identification Active in SwissNLP community and VarDial conference series Focus on practical implementations: e-assessment integrity, transliteration resources, and resource-constrained deep learning His work demonstrates technical expertise in: Comparative analysis of ASR systems (CEASR corpus) Transliteration pattern detection (TRANSLIT resource) Dialect identification challenges Combining lexical and sentiment analysis Scientific achievements include: Ranked first in ALTA 2018 patent classification competition Multiple top-three finishes at VarDial evaluations First place in GermEval 2020 low-resource speech recognition task

Jan Pieter AbrahamsView profile

Professor

• Structural Biology
• Electron Microscopy
• Protein Crystallography
• +5 more

Jan Pieter Abrahams is a Professor and Group Leader of the Nanodiffraction group at the Laboratory for Multiscale Bioimaging, Paul Scherrer Institute (PSI) in Switzerland. His work focuses on developing electron diffraction technologies for atomic-resolution imaging of frozen hydrated biological samples, leveraging PSI's detector expertise to advance structural biology beyond conventional microscopy limitations. Current applications target mitochondrial stress mechanisms in neurodegeneration and aging, with active collaborations across international institutions. Research Interests: Abrahams pioneers electron diffraction and cryo-EM methodologies, emphasizing computational-phasing innovations and machine learning integration. His group specializes in: Overcoming dynamical scattering for atomic-level cellular visualization Hybrid pixel detector applications (JUNGFRAU, EIGER) in electron microscopy Deep learning frameworks for diffraction data processing (e.g., DiffraGAN) Structural analysis of protein nanocrystals in disease contexts Mitochondrial protease mechanisms in aging Bacterial cell division and sporulation structures Publication Trends: Recent work (2020–2024) reveals heavy emphasis on AI-driven structural biology, including generative networks for diffraction phasing and lossless data compression. Instrumentation advancements (e.g., Boersch phase shifters) and disease-focused studies (Alzheimer’s amyloid-beta, malaria heme processing) dominate, showcasing interdisciplinary convergence of physics, computation, and biomedicine. Scientific Awards: No specific awards or fellowships documented in source text Advising and Collaborations: Abrahams has mentored PhD students including Thakkar, Pooja; Rheinberger, Jan; Schärer, Martin; and Wennmacher, Julian. His team collaborates with PSI's detector group on sensor development (GaAs/CdTe) and international labs for structural studies. Funding likely stems from PSI instrumentation projects and disease-focused research initiatives. Labs and Teams: He directs the Nanodiffraction group under PSI's Center for Life Sciences, comprising scientists (van Genderen, Latychevskaia) and postdocs (Blum). The lab integrates cryo-EM, electron diffraction, and computational modeling to visualize cellular processes at nanometer scales, with strong ties to detector engineering and pharmaceutical structural analysis.

• Human-Machine Interaction
• Human-Robot Interaction
• Multimodal Interaction
• +7 more

Nabil Ouerhani is a Professor in Computer Science at Haute Ecole Arc // HES-SO, leading the 'Interaction Technologies' research group focused on Human-Machine Interaction (HMI), Human-Robot Interaction (HRI), and Industry 4.0 applications. His work integrates IoT, cyber-physical systems, and multi-agent systems to develop innovative industrial solutions. Education: BSc HES-SO in Computer Science - Haute Ecole Arc - Ingénierie Key Research Areas: Collaborative robotics and AI-driven automation Thermal error prediction in machine tools IoT-enabled energy efficiency solutions Virtual/Augmented Reality for industrial safety Recent Projects: BonsAPPs (EU H2020): AI-as-a-Service platform for Deep Edge applications Decolleteur 4.0 (Innosuisse): AI-based Swiss-Type lathe programming system TherMoMac : Hybrid thermal error prediction for machine tools Grants & Funding: BonsAPPs: CHF 5,750,000 (EU H2020) Decolleteur 4.0: CHF 1,217,809 (Innosuisse) ECOMAC25: CHF 439,062 (Innosuisse) Research Collaborations: Industry partners: Tornos SA, NVISO SA, ST Microelectronics Academic partners: University of Bologna, ETH Zurich, SUPSI

• Embedded Systems
• Virtualization of Systems
• Data Processing
• +4 more

Glück Florent is an Associate Professor at the Geneva School of Landscape, Engineering and Architecture (HES-SO), affiliated with the Technical and IT school's Computer Science and Communication Systems department. He specializes in embedded systems, system virtualization, and interdisciplinary projects at the intersection of engineering and healthcare. Affiliations: HES-SO Geneve, hepia inIT, and collaborations with medical and industrial partners. Education: Not explicitly listed, but implied through roles and projects involving systems engineering and computer science. Research Interests: His work spans embedded systems design, real-time data processing, and applied machine learning. Key focuses include secure hardware-software co-design (e.g., FPGA-based security), medical device development (e.g., neonatal monitoring systems), and IoT infrastructure for smart buildings and recycling. Project Trends: Florent leads projects combining engineering with societal impact, such as automated recycling systems (LusTra), secure medical diagnostics (BrainCheckX), and educational virtualization platforms (Nexus VDI). Recent work emphasizes AI-driven solutions for healthcare (e.g., cochlear implant support) and decentralized energy management. Grants and Funding: Multiple projects funded by HES-SO Rectorat, CTI, and industry partners, totaling over CHF 400,000 since 2014. Labs/Teams: Active in distributed embedded systems research, leading teams on projects like DPESI (distributed storage) and HERVA (random number validation platforms).

• Computer Science
• Data Science
• Artificial Intelligence
• +3 more

Martin Simon is a Scientific Assistant at HES-SO Valais-Wallis, part of the School of Management and Economy and Services department. He specializes in developing data-driven platforms for real estate pricing prediction, hiking safety, and citizen engagement. His work integrates neural networks, geographic information systems, and collaborative frameworks to address societal challenges in tourism, energy, and healthcare. Key projects include: Frepann: A neural network-based real estate price prediction platform. HikeSafe: A web platform assessing hiking trail difficulty and recommending routes based on user profiles. Citoyenne Valais: A digital tool for citizen participation in public policy. His research spans energy data integration (e.g., REDI project), e-government process optimization (SamProC), and smart grid systems (i-BATs). He collaborates with institutions like the CREM and private firms like PostAuto Schweiz AG. Martin’s technical expertise includes Python, PHP, Docker, and RESTful services. His work emphasizes practical solutions for public administration, environmental sustainability, and safety in outdoor activities.

• Machine Learning
• Data Mining
• Artificial Intelligence
• +7 more

Prof. Alexandros Kalousis holds the position of Ordinarius HES Professor at the Geneva School of Economics and Management (HES-SO). His primary affiliation is within the Department of Management Information Systems under the School of Economics and Services. His research focuses on machine learning, data mining, and their applications in biomedical systems, cybersecurity, and generative modeling. Key projects include SimGait (SNSF-funded), which develops neuromechanical models for pathological gait analysis using machine learning, and RAWFIE (EU-funded), creating a mixed network testbed for autonomous vehicle experimentation. He has also led projects on time series forecasting, olfactory modeling for perfume creation, and metric/kernel learning optimization. Notable contributions span graph generative models like DGAE and GLAD, cybersecurity implications of LLMs, and reproducible indoor positioning systems. His work integrates theory-driven models with deep learning, emphasizing interpretability and extrapolation capabilities. Research collaborations include EPFL's Biorobotics Lab, Geneva University Hospitals, and industry partners like Firmenich. Total funding exceeds CHF 3M across projects like SimGait (CHF 2.1M) and RAWFIE (CHF 623K).

• Machine Learning
• Smart Buildings
• Energy Efficiency
• +3 more

Frédéric Montet is a Researcher and Doctoral Student at the Fribourg School of Engineering and Architecture (HES-SO), part of the iCoSys Institute for Complex Systems. His primary role involves leading and contributing to projects focused on smart building technologies, energy efficiency, and machine learning applications. He is the Principal Applicant of the ongoing FACILITY 4.0 project (2019-2021), which develops data-driven solutions for building management and facility optimization using AI and IoT technologies. Research interests include: Machine Learning for energy systems, predictive modeling in smart buildings, radon gas monitoring infrastructure, and integration of large language models into control systems. His work bridges theoretical data science with practical applications in renewable energy management and industrial automation. Key contributions include the BBData 2.0 platform for smart building data integration, predictive domestic hot water temperature modeling in district heating systems, and benchmarking zero-shot time series forecasting models. His projects often involve collaboration with industry partners to co-create scalable ICT solutions. Current focus areas: Optimizing PV installations at grid level, predictive maintenance systems, and ethical challenges in LLM-based control systems for shared appliances.

Konrad TiefenbacherView profile

Assistant Professor

• Climate Science
• Remote Sensing
• Artificial Intelligence
• +5 more

Konrad Tiefenbacher is a dual tenure-track Assistant Professor at the University of Basel and ETH Zürich, holding positions in the Department of Chemistry (Basel) and the Department of Biosystems Science and Engineering (ETH Zürich). He specializes in applying artificial intelligence and remote sensing to study glaciology, climate science, and environmental systems. His research bridges molecular synthesis (from his PhD in natural product chemistry) with cutting-edge AI-driven Earth observation. Education: Chemical studies at the Technical University of Vienna and University of Texas at Austin, followed by a PhD under Prof. Mulzer (University of Vienna) focusing on total synthesis of bioactive natural products. Postdoctoral research in molecular recognition at The Scripps Research Institute (Prof. Rebek). Became an independent researcher at TU Munich in 2012 before his current dual appointment since 2016. Research interests include deep learning for glacier calving front detection, retrogressive thaw slump mapping, GNSS reflectometry, and self-supervised environmental monitoring. His work integrates multi-sensor data and transformer networks for climate foundation models, with projects like AI-CORE addressing Arctic and Antarctic cryosphere challenges. Publications emphasize AI applications in polar regions, such as calving front dynamics in Greenland/Svalbard, permafrost disturbances, and methane detection. His methods combine semantic segmentation (e.g., PixelDINO) and transformer architectures (DDM-Former) for high-resolution Earth surface analysis. Labs/Teams: Leads the Synthesis of Functional Modules group, collaborating on AI-driven environmental monitoring tools and large-scale datasets like DARTS and IceLines.

Markus MeiererView profile

Assistant Professor

• Marketing
• Network Science
• Computational Linguistics
• +7 more

Markus Meierer is an Assistant Professor at the Institute of Management, University of Geneva, and serves as an External Lecturer at the Chair of Marketing for Social Impact, Department of Business Administration, University of Zurich. His interdisciplinary work integrates marketing, network science, and data science to study large-scale consumer networks and behavior. His research focuses on understanding how consumers' social ties influence behavior, using advanced methodological tools such as multilevel modeling to account for contextual effects. His expertise spans machine learning, marketing analytics, and computational linguistics, applied to real-world challenges in telecommunications, insurance, banking, and retail sectors. Markus holds a PhD and has an extensive educational background, including studies at the Kellogg School of Management, University of Zurich, and University of Trier. He is fluent in German, English, and French, and brings both academic rigor and industry experience to his roles. He is actively involved in leveraging data science to build customer-centric organizations, leading research initiatives on social networks and consumer behavior at the University of Zurich. Assistant Professor, Institute of Management, University of Geneva External Lecturer, Chair of Marketing for Social Impact, University of Zurich His professional experience includes roles as a Data Scientist and Research Group Leader at UZH, where he collaborates with major industries to apply data-driven strategies. No scientific awards are listed in the provided text. He advises on data strategy and market development, with a strong focus on translating academic research into practical business applications. His teaching includes Machine Learning at UZH. He leads the research group 'Social Networks & Consumer Behavior' at the University of Zurich, fostering interdisciplinary collaboration between marketing and data science.