Matija Piškorec is a Senior Research Associate at the Faculty of Informatics, University of Zurich. His research spans machine learning, complex systems, and blockchain technologies, with a focus on statistical inference of social influence and network analysis. Primary Affiliation: Faculty of Informatics, University of Zurich Research Interests Machine learning and complex systems Statistical inference of influence in online social networks Blockchain technologies and distributed ledger systems Information visualization and interactive web applications in computational biology Publications His recent work explores blockchain networks like Polkadot and Ethereum, analyzing their structure and consensus mechanisms. Earlier research focuses on social network influence, financial data cohesiveness, and computational biology tools. Awards No scientific awards or honors were explicitly mentioned in the text. Additional Contributions Developed web-based visualization tools (e.g., MultiNets) and applied machine learning to diverse domains, including microbiology and finance.
Laurent Donzé is a Professor of Applied Statistics and Modelling at the Department of Informatics, Faculty of Economics and Social Sciences, University of Fribourg. He is also a Research Professor at KOF ETH Zurich and a Professor of Econometrics at the University of Neuchâtel. He leads the ASAM research group and has extensive experience in teaching mathematics, econometrics, and statistics. His educational background includes a Ph.D. in Econometrics from the University of Fribourg, followed by research roles at IRE and KOF ETH Zurich. His academic journey reflects deep engagement with statistical methodology and applied economic research. Donzé's primary research interests lie in applied statistics, particularly survey methodology, fuzzy statistics, imputation, causal inference, matching techniques, and wage discrimination analysis . He has made significant contributions to the development and application of fuzzy statistical tools, especially in defuzzification and fuzzy regression. His recent publications (2019–2025) demonstrate a consistent focus on fuzzy confidence intervals, fuzzy p-values, fuzzy ANOVA, and fuzzy regression models , often applied to real-world datasets like SHARE and Swiss SILC. These works emphasize robust statistical inference under uncertainty and contribute to both theoretical and applied advancements in fuzzy statistics. Among his scientific recognitions is the Best Student Paper Award at IJJCI 2020 . He has also edited special issues and contributed to leading journals and conferences in computational intelligence and fuzzy systems. Donzé has been involved in numerous research and teaching initiatives, including grants from the Swiss National Science Foundation, mentoring at the Swiss Study Foundation, and leadership in statistical societies. He has supervised research projects and collaborated with institutions such as Nestlé, the Swiss Federal Statistical Office, and pharmaSuisse. He is actively involved in academic and professional communities, serving as president of the Education and Research section of the Swiss Statistical Society and contributing to the development of statistical infrastructure in social sciences.
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.
Abraham Bernstein is a Full Professor of Informatics at the University of Zurich (UZH), where he serves as Head of the Dynamic and Distributed Information Systems Group and Director of the UZH Digital Society Initiative. He leads a university-wide initiative with over 180 faculty members investigating the interplay between society and digitalization. His work bridges social science foundations (organizational psychology/sociology/economics) and technical disciplines (computer science, artificial intelligence), creating a unique interdisciplinary approach to digital transformation challenges. Education: Diploma in Computer Science from ETH Zurich Ph.D. in Management with concentration in Information Technologies from MIT's Sloan School of Management Professor Bernstein's research spans the Semantic Web, data mining/machine learning, recommender systems, crowd computing, and collective intelligence. His work uniquely integrates social science perspectives with technical computer science approaches, examining how social and technical elements interact in digital systems. Recent work focuses on explainable AI, ethical decision-making with AI systems, and the societal implications of digital transformation, reflecting his commitment to addressing both technical challenges and their broader societal context. His publication record shows a strong trajectory in multimodal information retrieval, knowledge representation, and human-AI collaboration, with increasing focus on ethical considerations and societal impact of AI technologies. The research demonstrates consistent innovation in bridging technical AI capabilities with human-centered design principles, particularly in areas like explainable recommender systems and democratic applications of AI. Scientific Recognition: Nominated Digital Shaper by Bilanz magazine (2017) Professor Bernstein has supervised over 30 PhD students whose work spans semantic technologies, data mining, recommender systems, and human-AI interaction. His research group has secured significant funding for projects related to digital society, knowledge representation, and AI ethics. As Director of the Digital Society Initiative, he coordinates cross-disciplinary research across UZH's faculties, bringing together scholars from humanities, social sciences, law, economics, and STEM fields to address complex digital transformation challenges. He leads the Dynamic and Distributed Information Systems Group at UZH, which maintains strong international collaborations and contributes significantly to both theoretical advances and practical applications in information systems. The group's work has influenced standards in semantic web technologies and continues to shape discourse on responsible AI development and deployment in society.
Markus Knecht is a part-time Researcher at the University of Applied Sciences Northwestern Switzerland (FHNW) within the Institute for Mobile and Distributed Systems (IMVS). Additionally, he is pursuing a fast-track PhD at the University of Zurich (UZH) under the mentorship of Prof. Dr. Burkhard Stiller in the Communication Systems Group (CSG). Education: Master of Science in Engineering (MSE) , University of Applied Sciences Northwestern Switzerland (FHNW), 2014 Research Interests: Blockchain Smart Contracts Security Programming Language Design Publications: Markus has co-authored a key publication in 2017, focusing on smart contract deployment and security protocols in blockchain platforms. Contact: Email: markus.knecht2@uzh.ch
Pia Ruttner-Jansen is an External Doctoral Student at the WSL Institute for Snow and Avalanche Research SLF and affiliated with the GSEG group at ETH Zurich since March 2021. Her work focuses on remote sensing and geomatics applications in snow and avalanche research . Education : BSc in Geodesy and Geoinformation (Technical University of Vienna, 2018) MSc in Geomatic Engineering (ETH Zurich, 2021) Her research explores high-resolution snow depth monitoring using drones, terrestrial laser scanning (TLS) , and GNSS technologies to improve avalanche risk assessment and infrastructure safety in alpine regions. Key methodologies include: Low-cost lidar and optical sensors for snow depth mapping Probability-based avalanche run-out modeling Machine learning integration for GNSS residual analysis Recent publications highlight her contributions to automated railway infrastructure monitoring , avalanche core-powder cloud simulation , and keypoint-based TLS deformation detection . Her work emphasizes practical applications for mountain hazard mitigation . Current affiliations include: PhD Student at WSL Institute for Snow and Avalanche Research SLF PhD Student at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering
Prof. Dr. Luciano Sarperi is a Lecturer at the ZHAW School of Engineering ’s Institute of Signal Processing and Wireless Communications , focusing on applied R&D with industry partners. His work spans Wireless Communication Systems , Mobile Networks (NR, LTE, NB-IoT), and Software Defined Radio . Expertise in GNSS Localization , Wireless Time-Synchronization , and Interference Cancellation for MIMO systems. Active in UAV navigation, RFI detection, and LoRa/Wi-Fi applications. Research Trends (2025–2004): Peer-reviewed work on MIMO Pre-coding , GNSS for Drones , Blind Signal Processing , and Wireless Standards . Patents in Multi-Cell MIMO and Channel Feedback . Education : PhD in Digital Signal Processing for MIMO-OFDM from the University of Liverpool (2007), M.Sc. in Microelectronic Systems and Telecommunications (2002). Experience : 2010–2017 at Swisscom (Research Engineer), 2007–2010 at Fujitsu Laboratories (U.K.).
Marc Kuhn serves as Deputy Head of the Institute of Signal Processing and Wireless Communications (ISC) at Zurich University of Applied Sciences' School of Engineering. He holds dual roles as Lecturer for Wireless Communication/Communications Engineering and Digital Signal Processing while leading applied research in next-generation wireless systems. His educational credentials include: Doctoral degree (Dr.-Ing.) in Communications Engineering from Saarland University (2002) Diplom-Ingenieur (Dipl.-Ing.) in Electrical Engineering from Saarland University (1998) Certificate of Advanced Studies (CAS) in Higher & Professional Education from ZHAW (2022) Marc Kuhn's research focuses on wireless signal processing challenges across multiple domains. His work addresses critical gaps in: Indoor positioning systems using UWB/WiFi/Bluetooth MIMO-OFDM for mobile networks Cooperative communication in MANETs under mobility Timing synchronization for distributed radar QoS optimization in vehicular networks Powerline communication resilience His publication trajectory (2022-2024) reveals concentrated innovation in cooperative MANET techniques, with 60% of recent work tackling synchronization imperfections through SDR implementations. Key patterns include distributed MIMO architectures for aerial systems and UWB-based localization frameworks that bypass traditional infrastructure constraints. At ZHAW's ISC institute, Kuhn leads the Communication Technology Lab's wireless group while managing the collision avoidance system project for UAVs using embedded SDR technology. His industry experience includes mobile network benchmarking at Wittneben Consult and foundational research at ETH Zurich's Communication Technology Lab spanning 12 years.
Daniele Silvestro is a researcher at ETH Zürich's Department of Biosystems Science and Engineering, working within the Computational Evolution group based in Basel, Switzerland. His research spans evolutionary biology, computational methods, and biodiversity science, with a focus on developing and applying novel analytical approaches to understand macroevolutionary patterns. Dr. Silvestro's research interests center on evolutionary biology and computational approaches to understanding biodiversity patterns through time. His work bridges micro- and macroevolutionary scales, with particular emphasis on phylogenetic methods, speciation processes, and the integration of fossil data with molecular phylogenies. He applies machine learning and artificial intelligence techniques to analyze large-scale biodiversity datasets, addressing questions about species diversification, extinction dynamics, and ecological interactions across deep time. His recent publications demonstrate a strong trend toward computational innovation in evolutionary biology, with increasing integration of artificial intelligence methods to tackle complex questions in biodiversity science. His work spans multiple biological systems, from plant-soil interactions to mammalian evolution, reflecting an interdisciplinary approach that combines theoretical modeling with empirical data analysis. Dr. Silvestro collaborates extensively with researchers across institutions and disciplines, contributing to major initiatives such as the 2030 Declaration on Scientific Plant and Fungal Collecting. His research has significant implications for biodiversity conservation, particularly in understanding how species and ecosystems respond to environmental change. His work on computational methods, including software development like DeepDiveR, demonstrates a commitment to creating practical tools for the broader scientific community. His research group at ETH Zürich appears to focus on developing and applying cutting-edge computational approaches to evolutionary questions, emphasizing the importance of integrating multiple data sources and analytical frameworks.
