Michael Böhlen heads the Database Technology Group at the University of Zurich's Department of Informatics. His research encompasses time-varying information systems, data warehousing architectures, similarity search algorithms, and analytical data processing techniques. His work has technological focus areas including data-centric system construction, query processing optimization, data modeling frameworks, and distributed database architectures. He maintains active collaborations with healthcare and industrial partners through projects like MEDAN (Medical Data Warehousing and Analysis).
Thomas Reber is an Associate Professor of Psychology at the Distance Learning University Switzerland (FernUni Schweiz), leading the Bachelor and Master DE programs in the Faculty of Psychology. His research focuses on neural correlates of memory, perception, and consciousness, particularly in the human medial temporal lobe. He employs techniques like intracranial EEG, fMRI, and single-cell recordings. At FernUni Schweiz, he integrates cognitive neuroscience insights into educational technology to enhance learning processes. Education & Career: PhD in Psychology, University of Bern (2012) Research Fellow at the University of Bonn's Department of Epileptology (2013–2018) Joined FernUni Schweiz in 2018; promoted to Associate Professor in 2022 Research Projects: Workload and Learning Behavior in Distance Learning Online Learning Environment Design Second Language Acquisition Principles Cognitive Impact of Smartphone Presence Teaching: Courses include Biological Psychology, Introduction to Psychology, and Methodological Training in Neuroscience. Grants & Labs: Active in applied research on educational technology, collaborating on projects like EDUDL+ and the School of Tomorrow initiative.
Nikoleta Anicic is a Scientific Collaborator in Vector Ecology at the Department of Environment Constructions and Design (DACD) at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI), where she focuses on monitoring and researching invasive mosquito species and their ecological impacts. Education: PhD in Evolutionary Ecology, University of Namur (Belgium), 2017 Master of Science in Biology with specialization in environmental microbiology and ecology, University of Zurich, 2017 Bachelor of Science in Biology, University of Neuchâtel, 2014 Anicic's research centers on vector ecology with particular expertise in mosquito monitoring and control. Her work combines molecular biology, entomology (specializing in mosquitoes), data management, ArcGIS, and programming in R to address public health challenges posed by invasive species. She has developed specialized skills in high-resolution optical identification of mosquito eggs and spatio-temporal modeling of invasive species dynamics. Her research has direct applications for arboviral disease surveillance and prevention, particularly for diseases like Dengue, Chikungunya, and Zika transmitted by invasive Aedes species. Analysis of her publication record reveals a strong interdisciplinary approach spanning entomology, environmental microbiology, and computational ecology. Her research trajectory shows progression from fundamental ecological studies on subterranean biodiversity and freshwater zooplankton to increasingly applied work on invasive mosquito surveillance. Recent publications demonstrate sophisticated integration of machine learning techniques with traditional ecological monitoring to predict seasonal mosquito population dynamics across multiple European countries. Nikoleta Anicic leads and participates in numerous research projects focused on invasive mosquito surveillance across Swiss cantons (including Zurich, Uri, Glarus, Schwyz, and the Romandy region) and Liechtenstein. These projects involve close collaboration with cantonal environmental offices and national authorities, with SUPSI serving as the national coordination center for invasive species monitoring designated by the Swiss Federal Office for the Environment. Her work directly informs Swiss public health strategies for controlling invasive mosquito species including Ae. albopictus, Ae. japonicus, and Ae. koreicus. At SUPSI, Anicic is a core member of the Vector Ecology Sector (SECOVETT) within the Institute of Microbiology. She contributes significantly to the Swiss mosquito network, providing scientific expertise for developing monitoring protocols, analyzing field samples, and interpreting spatial patterns of mosquito invasion. Her recent presentation at the 26th International Conference on Subterranean Biology demonstrates her continued engagement with broader ecological questions beyond her primary mosquito research focus.
Philippe Cudré-Mauroux is a Full Professor in the Department of Computer Science at the University of Fribourg, affiliated with the Faculty of Science and Medicine. His research spans data management, big data systems, knowledge graphs, semantic web, and human-AI collaboration. His primary research interests include Data Management , Big Data Systems , Knowledge Graphs , Time Series Analytics , Database Systems , Human-AI Collaboration , and Machine Learning for Data Cleaning . His work integrates theoretical database research with practical applications in smart cities, social media, and healthcare analytics. The recent publications reflect a strong trend toward knowledge graph embeddings , large language models for data quality , time series benchmarking , and human-in-the-loop systems . His research combines symbolic and neural methods, emphasizing schema awareness, explainability, and real-world deployment. He actively supervises numerous PhD and Master’s students and collaborates widely across institutions. His group contributes to open-source tools and benchmarking frameworks for database and AI systems.
Thomas La Grange serves as a Research and Teaching Associate at École Polytechnique Fédérale de Lausanne (EPFL) across multiple units including the Laboratory for Ultrafast Microscopy and Electron Scattering (LUMES) within the Institute of Physics, the Doctoral Program in Physics (EDPY), and the Physics Section (SPH). He additionally holds a Scientific Staff Member position on the School Council of the School of Basic Sciences. His academic credentials include a Bachelor's and Master's in Materials Science from Michigan State University, culminating in a Ph.D. in Applied Physics from EPFL. La Grange's research pioneers ultrafast electron microscopy techniques to capture non-equilibrium material dynamics at nanosecond timescales. His work focuses on dislocation-mediated phase transitions, skyrmionics, and defect dynamics in rapidly deforming materials, with significant contributions to instrumentation like time-resolved q-EELS and cryo-LTEM. Current investigations explore uranium oxide reduction mechanisms, magnetic phase transitions in FeRh, and photonic modulation of electron beams. Recent publications reveal dominant themes in ultrafast imaging of magnetic materials and radiation-sensitive systems, with strong emphasis on electron spectroscopy applications for uranium oxides and magnetite. His group actively develops novel methodologies for momentum-resolved visualization of quantum phenomena in materials like graphite. His experimental innovations have earned significant recognition: Two R&D100 Awards for DTEM instrumentation Nano50 Award Microscopy Today Innovation Award La Grange supervises PhD candidates Andrieux Antoine Nicolas and Cattaneo Paolo while teaching core courses including "Physics of materials" and "Electron Matter Interactions in Transmission Electron Microscopy". His research is supported by institutional collaborations across EPFL's physics infrastructure. As a key member of Fabrizio Carbone's Laboratory for Ultrafast Microscopy and Electron Scattering (LUMES), he leads instrumentation development for capturing material dynamics at unprecedented temporal resolutions, with ongoing projects focused on PINEM techniques and skyrmion manipulation.
Prof. Dr. Jasmina Bogojeska is a Professor for Artificial Intelligence and Machine Learning at ZHAW School of Engineering, where she leads the Explainable Artificial Intelligence Group since March 2024. She previously held roles as Senior Principal Data Scientist at Roche (2022-2024) and Research Staff Member at IBM Research Zurich (2013-2022), with postdoctoral work at Max Planck Institute for Informatics (2011-2013). Her affiliations include ZHAW Datalab and Digital Health Lab. PhD in Computer Science (2011), Saarland University/Max Planck Institute MSc in Computer Science (2007), Saarland University BSc in Computer Science (2004), University Ss. Cyril and Methodius Her research bridges Explainable AI with applications in Healthcare , IT Infrastructure Management , and Critical Care Time-Series Analysis . Key focus areas include domain-specific foundation models , conversational data systems , and low-resource NLP solutions . Recent work explores multi-modal medical AI for chest X-ray interpretation and large-scale clinical time-series datasets . Notable contributions include GIT-CXR for automated radiology reports and domain-specific protein language models in immunology. Her publications span IEEE, BMC, and Nature journals, emphasizing practical AI deployment in healthcare and IT operations. Scientific Recognition: Edelman Prize Finalist (2020) IBM Research Accomplishment Award (2019) IBM Corporate Award (2017) Best Paper Award at CNSM (2013) She actively develops reliable AI systems for server incident reduction (PASIR) and child injury monitoring, while advancing automated medical analytics through projects like Antibiotika-Resistenz Tracker. Her teaching covers Machine Learning , Data Mining , and Safer AI at bachelor and master levels.
Dr. Mirela Beloiu Schwenke is a Lecturer at ETH Zurich's Department of Environmental Systems Science, specializing in forest ecology, remote sensing, and climate change impacts. Her research focuses on understanding forest resilience to climate-driven disturbances like droughts and developing advanced monitoring techniques using remote sensing and machine learning. She contributes to projects such as the SNSF COST Action for tree species identification and the UPSCALE initiative for forest vitality monitoring. Education & Research Interests PhD in Environmental Sciences (implied from professional context) Focus areas: Tree mortality dynamics, drought tolerance mechanisms, and integrating ground-based and remote sensing data Key Projects Leading development of deep learning models for tree species identification in mixed forests Contributing to global forest monitoring frameworks through Sentinel satellite integration Investigating drought impacts on sapling recovery and forest succession patterns Awards & Recognition Recipient of the 2023 Young Scientist Award for her presentation on tree mortality mechanisms at the EARSel Symposium. Teaching Teaches courses on forest ecosystems and practical field methodologies for environmental science students. Grants & Collaborations Swiss National Science Foundation (SNSF) funded projects International collaborations with institutions like the Africa CDC and ICOS Switzerland Labs & Teams Part of ETH Zurich's forest ecology and remote sensing research groups, actively contributing to open-access databases like 'deadtrees.earth'.
Dr. Mikko Tiusanen is a Researcher at ETH Zürich's Department of Plant Ecology, focusing on plant communities and ecosystem responses to global changes. His work integrates ecological field studies with advanced data technologies, such as computer vision algorithms for tracking alpine meadow phenology via deployed cameras. He investigates how environmental conditions shape species distributions and interactions, particularly in Arctic and alpine regions. Current research explores phenological shifts, plant-fungal symbiosis dynamics, and pollination network stability under climatic changes. Research interests include understanding environmental drivers of species performance, phenological timing effects on interspecific interactions, and predictive modeling of community changes based on traits and distributions. Methodological innovations include high-resolution ecological data acquisition through automated imaging and DNA metabarcoding for foraging analysis. Recent articles highlight work on plant-pollinator disruption, Arctic microbial networks, and agroecological disease management. While no scientific awards are listed, his contributions to ecological methodology and Arctic-Alpine systems are notable. No advising or grant details are provided here, though his projects likely involve interdisciplinary collaborations within ETH Zürich's ecological research groups.
Jürg Schwarz is a Lecturer and Project Head at the Lucerne School of Business (Lucerne University of Applied Sciences and Arts), affiliated with the Institute of Financial Services Zug (IFZ) CC Corporate Finance. He holds a Dr. sc. techn. (Physicist) from ETH Zurich, where he completed his dissertation in Energy Analysis. His professional career spans roles as a researcher, consultant, and educator, with expertise in empirical methods, statistics, and data science. He teaches at both Lucerne University and the University of Zurich, specializing in research methods and project management. Education: PhD (Dr. sc. techn.) in Physics, ETH Zurich Studies in Physics and Statistics, ETH Zurich Apprenticeship as Machine Draughtsman/Designer Research Focus: Empirical methods, statistical analysis, project management in academic and corporate contexts, gambling studies, healthcare analytics, sustainable tourism, and AI applications in education. His work emphasizes interdisciplinary approaches, integrating quantitative methods with practical problem-solving. Key Projects: Automated Data Quality Assessment in Online Surveys (Machine Learning) Prediction Model for Early Detection of Gambling Risks Comfort Analysis in Railway Travel Light and Communication in Healthcare: Dynamic Lighting Systems Awards: 2024: Publication Prize for Hybrid Teaching and Learning Research 2014: Publication Prize for Sustainable Tourism Communication Study 2012: Publication Prize for Community-Based Geriatric Care Research Consulting & Training: Founder of schwarz & partners GmbH, providing statistical consulting, research design, and AI training. Leads the University of Zurich's Methods Consulting Center (Centre for Empirical Methods), offering workshops on prompting engineering and research tools. Labs/Teams: Collaborates with academic institutions and corporations on projects involving data analysis, project management, and AI-driven solutions in education and research.
Prof. Ralf Jung is an Assistant Professor at ETH Zürich's Department of Computer Science, leading the Programming Language Foundations Lab under the Institute for Programming Languages and Systems. His work focuses on formal verification of programming languages, particularly Rust and Iris. Previously, he earned his PhD at Saarland University and MPI-SWS, advised by Derek Dreyer, followed by a postdoc at MIT CSAIL's PDOS group. Research Interests: Formal foundations of Rust, including tools like Miri for detecting undefined behavior and MiniRust for precise specification. Iris logical framework for modular verification of programming languages at scale. Concurrent and distributed systems verification using separation logic. Advising & Labs: He leads the Programming Language Foundations Lab and is hiring postdocs. His work integrates theoretical rigor with practical tooling for real-world language verification challenges. Labs/Teams: Programming Language Foundations Lab at ETH Zürich, collaborating with the Rust language team and global research community.
Zayene Oussama is a Researcher at the HES-SO University of Applied Sciences and Arts Western Switzerland, affiliated with the Fribourg School of Engineering and Architecture and the Institute of Complex Systems (iCoSys). His work focuses on advancing video text detection, recognition, and OCR systems with a specialization in Arabic script challenges. He has contributed to the development of the AcTiV dataset and associated tools, widely used in international competitions like ICPR and ICDAR. Completed the 'Video Protector Smart AID' project (2021), integrating vision-based surveillance systems Collaborated with Morphean SA on intelligent video analysis PhD thesis (2018) developed novel Arabic text detection/recognition methods using deep learning Research interests include: Arabic text processing in videos Computer vision applications Machine learning for OCR Dataset standardization Recent work (2025) explores truck classification via YOLOv5 and evaluates Vision-Language Models for critical tasks.
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.
Laurence Mylène Brandenberger is a Postdoctoral Researcher at the Department of Political Science, University of Zurich, specializing in legislative behavior and parliamentary networks. Their research examines the intersection of social relations and political decision-making, with a focus on systemic spill-over effects in policy networks and enhancing democratic transparency through projects like the DemocraSci Knowledge Graph. Current focus on temporal reciprocity in congressional collaborations Developing computational tools (embed2discover, Data2neo) for policy network analysis Recipient of SNF and SDSC grants for political performance and advocacy studies Teaching course: The Politics of Influence: Interest Groups, Lobbying, and Political Advocacy (2025) Their work bridges network science and political decision-making, utilizing large-scale datasets spanning decades and multiple policy sectors. Laurence applies causal network analysis to unravel dependencies in political systems and advocates for citizen-oriented platforms to reverse electoral decline and enhance civic engagement. Prior research includes quantifying triadic closure in political networks, studying cross-continental policy networks, and analyzing conflict event data through relational event models. Their methodological contributions in dictionary-based content analysis and multi-edge network modeling have advanced empirical approaches in political science. SNF Grant (2024): Measuring Political Performance of Swiss MPs SDSC Grant (2025): Studying Political Advocacy As part of the Institute of Political Science at the Faculty of Philosophy, Laurence actively contributes to computational infrastructure development and open science initiatives aimed at improving policy transparency and reducing polarization in Western democracies.
Meredith Christine Schuman is an Assistant Professor at the University of Zurich (UZH), with dual affiliations in the Department of Geography and the Department of Chemistry. She previously held positions as a group leader at the Max Planck Institute for Chemical Ecology and a Junior Group Leader at the German Center for Integrated Biodiversity Research (iDiv). Her research program integrates molecular ecology, chemical signaling, and remote sensing to investigate genetic and functional diversity in plant systems. Research Interests: Her work spans molecular ecology, chemical ecology, and ecological genomics, with a focus on how spatial and temporal variation in plant traits influences ecological outcomes. She investigates plant volatiles, defense mechanisms, and the role of genetic diversity in species interactions. Her lab leverages advanced techniques such as imaging spectroscopy, metabolomics, and robotic sampling to study plant responses to herbivory and environmental stress. The most recent publications highlight a strong interdisciplinary trend, combining remote sensing , plant genomics , and chemical ecology . Key themes include the use of leaf spectroscopy to detect drought responses, the role of the circadian clock in plant defense, and the integration of metabolomics with biodiversity monitoring. Her research increasingly bridges field ecology with data science, particularly in trait imputation and satellite-based biodiversity mapping. Scientific Awards: Max Planck Society Otto Hahn Medal Fulbright Scholarship Advising and Grants: She actively mentors graduate students, as evidenced by her supervision of multiple master’s theses and doctoral dissertations. She leads scientific working groups such as the International Team "Genes from Space" and has secured research funding through her affiliations with MPICE, iDiv, and UZH. Her collaborative network is extensive, involving researchers across Europe and North America in large-scale ecological and genomic initiatives. Labs and Teams: Her research group combines molecular biology, chemical analysis, and remote sensing technologies. She collaborates closely with the Schaepman lab at UZH and participates in interdisciplinary teams focused on biodiversity monitoring, plant–insect interactions, and global change ecology.
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.
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