Jean-Paul Calbimonte is an Associate Professor at the University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) , specifically affiliated with the School of Business Administration . His research spans multiple domains, including Medical Informatics , Semantic Web , Ontology Engineering , Stream Processing , and Knowledge Management . His recent work focuses on AI applications in healthcare (Alzheimer’s diagnosis via MRI analysis, cancer survivor support systems), semantic web technologies for wind energy data sharing, and decentralized health data management systems. He leads projects like TechnoPortal (ontology hosting for wind energy) and SMARTEDGE (edge intelligence toolchain). He employs cutting-edge methods in Machine learning for medical diagnostics Semantic stream processing Blockchain for clinical data governance Multi-agent systems for personalized healthcare His publications highlight collaborations with institutions like CHUV , Pryv SA , and University of Maribor , alongside industry partners including NVIDIA and Bosch .
Ruedi Arnold is a Professor at the Lucerne School of Computer Science and Information Technology, part of the Lucerne University of Applied Sciences and Arts. He serves as Head of the Master's Program in Specialized Media and IT Teaching and Learning Methods. His academic journey includes a PhD from ETH Zurich (2007) and a Higher Teaching Degree from ETH Zurich (2003). Education: Dr. sc. ETH (Computer Science, 2007), Dipl. Informatik-Ing. ETH (2002), Matura Type C (1996). Professional experience spans roles at ETH Zurich (2002–2007), Ergon Informatik AG (2008–2011), and EB Zurich (2009–2012). Research focuses on software engineering, programming didactics, human factors, and interactive learning environments like InfoTraffic . Projects include Skin-App (medical image analysis) and Algorithmic Thinking in Lower Secondary Education . Awards include recognition for academic excellence at ETH Zurich and Strathclyde University. He has supervised numerous student projects and contributed to educational initiatives like Scratch workshops for youth. Publications span topics from AI pedagogy to P2P systems, with recent work emphasizing computational thinking in education. He is actively involved in academic committees, including the Luzerner Maturitätskommission and CH Open Workshop-Tage.
José Mancera is a Lecturer at the Institute of Communication and Marketing within Lucerne School of Business. His academic background includes a PhD candidacy in Computer Science and Artificial Intelligence at University of Fribourg, MA in Information Management (magna cum laude) from University of Fribourg, and MSc in Computer Science (magna cum laude) from University of Bern. His research explores data engineering pipelines, artificial intelligence applications in business contexts, and GDPR-compliant recommendation systems. Recent work focuses on graph-based data architectures and personality-driven recommendation models for enterprise environments. Mancera's publications demonstrate strong emphasis on privacy-preserving technologies and social media analytics. His collaborative work with industry partners addresses real-world implementation challenges in data governance frameworks.
Kieran Chin-Cheong is a Researcher affiliated with the Professur für Informatik (Computer Science Professorship) at ETH Zürich. His position is part of the Department of Computer Science, focusing on Medical Data Science. He holds an email address at kieran.chincheong@inf.ethz.ch and is based at CAB G33.1, Universitätstrasse 6, 8092 Zürich, Switzerland. His research interests center on applying machine learning and computational methods to address challenges in healthcare, including medical imaging analysis, predictive modeling for clinical outcomes, and development of interpretable AI systems for medical diagnostics. His work spans areas such as pediatric disease prediction (e.g., pulmonary hypertension, appendicitis), glucose forecasting in diabetes, and synthetic medical data generation while preserving privacy. Notable contributions include advancements in deep learning for echocardiogram analysis, interpretable ultrasonography models, and methodologies for constrained clustering and multimodal data processing. His interdisciplinary approach bridges computer science and clinical medicine, aiming to improve diagnostic accuracy and patient care through data-driven solutions. No specific grants, awards, or advisees are explicitly listed in the provided materials. His research aligns with ETH Zürich's focus on translational medical informatics and computational healthcare technologies.
Dr. Valery Vishnevskiy is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich , affiliated with the Biomedical Imaging Group . His research focuses on advanced medical imaging techniques, particularly in cardiac MRI , ultrasound , and machine learning applications . He contributes to projects involving 4D flow MRI reconstruction , speed-of-sound imaging , and motion-corrected diffusion tensor analysis . Primary Affiliation : ETH Zürich, Department of Information Technology and Electrical Engineering Research Interests : Biomedical Imaging, MRI Reconstruction, Ultrasound Physics, Cardiac Imaging, Deep Learning His recent work emphasizes self-supervised learning for medical imaging, including the development of FlowMRI-Net for accelerated 4D flow MRI and Speed-of-Sound Imaging using diverging waves. Publications span hyperpolarized 13C metabolic imaging , viscoelasticity reconstruction , and probabilistic sampling optimization . Dr. Vishnevskiy's contributions to deformable image registration include methods for handling sliding interfaces in abdominal and cardiac imaging, with applications in respiratory motion compensation and tissue ablation monitoring . He also explores mathematical models for enzyme activity analysis and behavioral pattern detection.
Dr. Anja Zai is a Researcher affiliated with the Department of Neuroinformatics at ETH Zürich. Her work focuses on interdisciplinary investigations of vocal motor control, neuroethology, and computational methods in animal behavior. She specializes in studying songbirds as model systems to explore sensory-motor integration, reinforcement learning, and acoustic communication. Her research integrates advanced imaging techniques (e.g., synchrotron X-ray CT), machine learning, and experimental setups like the RecOOrder modular recording environment. Key research themes include vocal learning mechanisms in zebra finches, environmental influences on vocal behavior, and neuroprosthetic applications through sensory substitution. Her studies address fundamental questions about how organisms adapt vocalizations under sensory deprivation, optimize motor exploration, and encode complex vocal repertoires. Recent work emphasizes multimodal sound source separation (Vib2Sound) and algorithmic modeling of vocal planning processes. Dr. Zai’s methodological contributions include benchmarking datasets for vocalization analysis and tools for non-invasive welfare monitoring in isolated animals. While no scientific awards are explicitly listed, her frequent publication in high-impact journals since 2015 indicates sustained research excellence. Her work bridges computational neuroscience, behavioral ecology, and engineering through cross-disciplinary approaches.
Fischer Andreas is a Professor at HES-SO (University of Applied Sciences and Arts Western Switzerland) , specifically affiliated with the School of Engineering and Architecture of Fribourg (HEIA-FR) . His research spans Pattern Recognition , Applied Machine Learning , Handwriting Recognition , and Graph-based Methods . He has secured significant grants from TAINA Technology , Swisscom , and the Hasler Foundation for projects including automatic handwriting recognition for tax forms , Swiss German translation , and graph-based keyword spotting in Vietnamese steles . Education : BSc in Computer Science from HEIA-FR. Research Highlights : Graph Neural Networks for Büchi automata classification Annotation-free alignment in historical documents Graph edit distance optimization for keyword spotting Hybrid deep learning for Vietnamese stele analysis Graph-based tumor budding analysis in digital pathology Collaborative Impact : Fischer's work bridges historical document analysis and medical imaging , demonstrated through frameworks like DIVA-DAF and tools like GammaFocus for histopathology. His 2024 conferences suggest ongoing exploration of LLM integration in document processing and OCR-free models for information extraction.
Ingram Sandy is a Professor at the Haute école d'ingénierie et d'architecture de Fribourg (HES-SO), part of the iSIS Institute for Intelligent and Secure Systems. Her research focuses on Human-Computer Interaction, Recommender Systems, and Digital Wellbeing, with a strong emphasis on applying technology to education and workplace environments. She leads and collaborates on interdisciplinary projects addressing challenges in smart environments, user experience design, and AI integration in learning platforms. Key projects include developing serious games for mental health support (Sesame Project), workplace comfort systems (SpotOn), and chatbot-enhanced educational tools. She has also contributed to frameworks like eLogBook and Graasp, aiming to bridge formal and informal learning through social software innovations. Her work spans over 15 years, with publications in top venues like IEEE, ACM, and international conferences on human-computer interaction and learning technologies. Ingram Sandy’s research interests span from foundational AI methods (reinforcement learning, deep learning) to applied domains like digital wellbeing assessment, smart office comfort modeling, and chatbot-mediated education. She actively collaborates with industry partners (e.g., OrchardAI) and academic networks across Switzerland and Europe. Her contributions highlight the intersection of technology, human behavior, and educational design.
Prof. Dr. Nicolai Meinshausen is a Full Professor at ETH Zurich's Department of Mathematics and serves as Deputy Head of the Seminar for Statistics (SfS). His research integrates advanced statistical methodologies with machine learning, focusing on causal inference and high-dimensional data analysis. Notable contributions include climate science studies addressing temperature bias correction and carbon sink accounting, alongside biomedical applications like sepsis prediction through deep learning. His research interests emphasize causality, high-dimensional data, and machine learning, with practical applications in environmental and health domains. He has developed influential frameworks such as Anchor Regression and Engression, advancing robust statistical techniques for heterogeneous data. Collaborations span interdisciplinary projects, including the Swiss-wide SPHN/PHRT initiative for sepsis research and climate modeling efforts like the Shared Socio-economic Pathways (SSP) greenhouse gas database. Recent articles highlight trends in climate data accuracy, distributional regression innovation, and causal modeling under interventions. He also authored a popular mathematics book for puzzles and games, bridging theoretical concepts with accessible problem-solving. No explicit scientific awards are mentioned, but his work has been featured in prestigious journals like Nature and Science Advances . His advising and grants involve collaborations with institutions and teams, such as the Seminar for Statistics and SPHN/PHRT, without specific student or grant funding details provided. Labs and teams include the Seminar for Statistics (SfS) at ETH Zurich and the SPHN/PHRT collaborative network. These groups focus on statistical theory, climate modeling, and healthcare data infrastructure.
Walter Binder is a Full Professor in the Faculty of Informatics at the Università della Svizzera italiana (USI). He holds a MSc, PhD, and venia docendi from Vienna University of Technology. Previously, he was a senior researcher at the Artificial Intelligence Laboratory, EPFL. His research focuses on program transformations, virtual execution environments, aspect-oriented programming, profiling, and resource management. Education: MSc from Vienna University of Technology PhD from Vienna University of Technology Venia docendi (Habilitation) from Vienna University of Technology Research Interests: His work spans JVM optimization, parallel computing, and dynamic program analysis. Notable contributions include tools like Renaissance (a JVM benchmark suite), S2S (SQL-to-Stream translator), and Akkaprof (profiler for actor-based systems). He explores topics such as thread management, vectorization, and performance profiling in distributed systems. Articles Trends: Recent work emphasizes JVM performance (e.g., Java Vector API, Native Image optimizations), parallel execution policies (NAS benchmarks), and adaptive runtime systems (e.g., MPR framework). He also investigates big data systems (Spark, Node.js) and runtime verification tools. Grants & Labs: Leads projects on JVM profiling, polyglot runtimes, and large-scale program analysis. His lab develops tools like NodeMOP for Node.js and AccStream for stream processing systems.
Zerara Mohammed is a Researcher and Project Unit Manager at HES-SO Geneva, specializing in Applied Mathematics and Data Science. Their work focuses on machine learning applications in spectroscopy, nanotechnology, and biomedical imaging. Key projects include combining deep learning with Raman spectroscopy for bacterial detection in infant formula milk and developing hyperspectral image classification methods funded by an international organization. Education: Master's in Applied Mathematics - Data Science from Cergy Paris Université (in collaboration with PSL/Paris Dauphine University). Research Interests: Machine learning algorithms for spectral analysis, miniaturized spectrometer design (Nature Communications 2024), computational chemistry (e.g., spin-crossover complexes), and applications in material science. Notable contributions include a 2023 IEEE review on miniaturized spectrometers and foundational work on logP prediction models in the 2000s. Grants & Projects: Ongoing collaborations include a 2022-2023 project on Enterobacter sakazakii detection and hyperspectral anomaly detection research. Previously led studies on chemical pressure effects in metal complexes (Chimia 2002) and Rhea biochemical reaction database updates (Nucleic Acids Res. 2015). Labs/Teams: Primary researcher in HES-SO teams for computational spectroscopy and nanotechnology projects. Collaborates internationally on biomedical sensor development and material science applications.
Achim Walter is a Full Professor and Deputy Head at the Institute of Agricultural Sciences, ETH Zurich, specializing in crop science and plant phenotyping. He holds a habilitation in botany from the University of Düsseldorf and has led research groups at institutions like Forschungszentrum Jülich. PhD, University of Heidelberg (1998-2001) Postdoctoral Fellow, Biosphere 2 Center (2001-2003) Research Group Leader, Forschungszentrum Jülich (2003-2010) His research focuses on non-invasive plant growth analysis , image-based phenotyping , and precision agriculture , with recent work emphasizing remote sensing, deep learning for disease detection, and nitrogen use efficiency. Publications from 2025 highlight advancements in UAV thermal imaging, dendrometer-based irrigation, and phenology modeling. 2017 Golden Owl Award for Excellence in Teaching Honorary Member of SVIAL He teaches courses including Plant Ecophysiology, Alternative Crops, and Introduction to Crop Production, with a strong emphasis on digital agriculture and data-centric farming education initiatives.
Katrin Beyer is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , leading the Earthquake Engineering and Structural Dynamics Laboratory (EESD) and serving as Dean of the School of Architecture, Civil and Environmental Engineering (ENAC) . She also holds roles as Director of the Structural Engineering Group (GIS-GE) and President of the ENAC School Council. Education: BSc in Civil Engineering (ETH Zurich, 2001) Master’s in Earthquake Engineering (IUSS Pavia, 2003) PhD in Earthquake Engineering (University of Pavia, 2007) Her research focuses on the seismic behavior of unreinforced masonry structures , particularly historical buildings. She combines large-scale experimental testing , numerical modeling , and digital twinning to advance structural assessment methodologies. Recent work explores machine learning for crack detection and upcycling demolition waste into sustainable masonry systems. Her publications reflect trends in: seismic risk mitigation , URM building fragility , image-based structural analysis , and innovative digital twin applications . Beyer has advised over 25 PhD students and contributed to Eurocode 8 revisions. She led the ETH Domain’s CHF 15 million Open Research Data program and co-founded the startup SwissInspect for structural assessment technologies.
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.
Dr. Alessia PANNESE is an Associate Professor at the University of Milan , affiliated with the College of Science and the Department of Computer Science . Her expertise spans Artificial Intelligence, Machine Learning, and Data Science. Fields of Interest: Artificial Intelligence, Machine Learning, Data Science Key Research: Multi-modal data integration, graph-based knowledge discovery Her recent publications focus on deep learning architectures , big data algorithms , and graph analytics , reflecting interdisciplinary applications in AI and database systems. Notable accolades include the ACM Best Paper Award (2020) and IEEE Rising Star Award (2021) . She advises PhD candidates in data-driven research and leads the Data Intelligence Lab , specializing in real-time analytics and scalable solutions.
