Prof. Dr. Siegfried Handschuh is a Full Professor for Data Science and Natural Language Processing at the Institut für Informatik (ICS-HSG), University of St. Gallen. His research focuses on advanced NLP techniques, financial text analysis, and AI-driven solutions in cybersecurity and education. He leads projects like CS-AWARE-NEXT, enhancing cybersecurity awareness in public institutions. Prof. Handschuh has authored over 100 publications, with recent work emphasizing transformer optimization, generative AI applications, and educational tools for argumentative writing. His team collaborates with companies like Rheasoft and Peracton on AI-powered financial analytics and cybersecurity systems. Education: Doctorate in Computer Science, specialized in knowledge representation and semantic web technologies. Research Interests: Data science, machine learning, financial NLP, cybersecurity, and AI in education. His work bridges academia and industry, addressing real-world challenges in finance, cybersecurity, and educational technology through innovative AI frameworks.
Haozhe Zhang is a Postdoctoral Researcher in the Data Systems and Theory (DaST) group at the Department of Informatics, University of Zurich, supervised by Prof. Dan Olteanu. His career bridges theoretical research and practical implementation in database systems. Education: DPhil in Computer Science, University of Oxford (2023) MSc in Computer Science, University of Oxford (2017) BSc in Computer Science, University of Nottingham (2016) Research Focus: Haozhe's work centers on database theory, emphasizing incremental view maintenance and cardinality estimation . His research explores efficient algorithms for dynamic relational data, theoretical foundations of conjunctive queries, and robust cardinality estimation techniques like LpBound. Publications & Trends: His contributions include theoretical analyses of conjunctive queries under updates, practical systems like F-IVM for analytics over evolving data, and worst-case optimal algorithms for triangle counting. Recent work at SIGMOD 2025 and ICDT/AMW workshops highlights advancements in dynamic query evaluation and cardinality estimation guarantees. Scientific Recognition: Best Paper Award, SIGMOD 2025 Best Paper Award, ICDT 2019 Teaching Contributions: Instructor, Foundations of Data Sciences (UZH, Fall 2024) Teaching Assistant for Foundations of Data Sciences (UZH, Fall 2020–Fall 2023), Efficient Algorithms (UZH, Spring 2021–Spring 2025), and Modern Data Analytics (UZH, Fall 2023).
Andreas Sonderegger is a Lecturer at the Department of Psychology , University of Fribourg , and group leader of the Human Factors Lab . His research focuses on Human-Computer Interaction , Usability Engineering , and User Experience (UX) , with particular emphasis on: Web accessibility for diverse user groups Neurofeedback systems for cognitive enhancement Human factors in automated driving Cross-cultural usability testing Physiological computing for user state assessment His recent publications analyze: Neurofeedback applications for tinnitus treatment Human-AI decision-making comparisons Physiological indicators in automated driving Usability of no-code robotics programming He has contributed to journals such as: Computers in Human Behavior Scientific Reports Ergonomics International Journal of Human Computer Studies
Mark Sawley is a Senior Scientist at École Polytechnique Fédérale de Lausanne (EPFL) with dual appointments in the Institute of Mechanical Engineering (IGM) for administration and the School of Management (SGM) for teaching. With over 30 years of experience in academic, government laboratory, and private institutions across Switzerland and Australia, he holds a PhD in Physics and has founded/co-founded two high-tech startup companies. His administrative roles include coordinating ACCES for Computational Engineering promotion and serving as Coordinator for Academic Affairs at the STI Faculty. Dr. Sawley's research spans computational fluid dynamics, discrete element method simulations, and high-performance computing applications. His work bridges multiple disciplines with applications in materials science (concrete simulation), bioengineering (blood flow modeling), marine engineering (America's Cup yacht design), avalanche dynamics, and industrial particulate processing. His recent publications (2018-2021) demonstrate continued activity in applying numerical methods to solve complex engineering problems. His scientific contributions include 37 peer-reviewed journal papers, 58 conference proceedings, and 21 articles for general audiences. His work uniquely connects technical research with science communication through projects like TIMBRE!! and the DEM Gallery, emphasizing the relationship between science and art. 37 scientific papers in international peer-reviewed journals 58 papers in international conference proceedings 21 science communication articles for general audiences Professor Sawley has advised PhD students including Serge Wüthrich (1992) and Olivier Byrde (1997). His Computational Granular Dynamics lab (http://cgd.epfl.ch) provides a platform for interdisciplinary research connecting physics, engineering, and computational science. His administrative leadership in academic affairs and computational engineering promotion demonstrates his commitment to advancing both research and education at EPFL.
Rafael Medina Morillas is a researcher at the Embedded Systems Laboratory (ESL) at Ecole Polytechnique Fédérale de Lausanne (EPFL), where he focuses on computer architecture and hardware acceleration for edge AI systems. His research addresses the memory wall problem through innovative architectural designs that improve energy efficiency and performance in data-intensive applications. His primary research interests include: Compute-near-Memory architectures Hardware acceleration for machine learning Edge AI systems Chiplet architectures and interconnects Wireless communication for computing systems Medina Morillas' publication record demonstrates significant advancements in memory systems and hardware acceleration. His work on SideDRAM shows up to 83% EDAP reduction compared to state-of-the-art designs, while his research on wireless communication achieves up to 2.64x speedup for deep neural networks. His recent publications focus on structured pruning techniques for transformers, co-design frameworks for edge AI, and thermal management solutions for heterogeneous systems. His research is supported by collaborations with IMEC, Université de Bordeaux, and HEIG-VD, as well as funding from EC H2020 projects and the ACCESS-AI Chip Center. These partnerships enable comprehensive exploration of architectural innovations across different technology domains. As evidenced by his doctoral thesis 'System-aware Architectural Co-design to Tackle the Memory Wall,' Medina Morillas takes a cross-layer approach to system design, integrating hardware and software optimizations to address fundamental bottlenecks in modern computing systems. His work demonstrates how system-aware architectural design can achieve improvements in runtime, energy consumption, and thermal behavior for data-intensive applications.
Current research associate and doctoral student at ETH Zurich's Chair of Production and Operations Management (POM), focusing on industrial engineering and augmented reality applications in manufacturing. Holds M.Sc. with distinction in Industrial Engineering and Management from Karlsruhe Institute of Technology (KIT), with research conducted at University of Cambridge's Institute of Manufacturing. Educational background: B.Sc. & M.Sc. Industrial Engineering and Management, KIT, Germany Master's thesis research at University of Cambridge, UK Research integrates machine learning and augmented reality into manufacturing processes, particularly for quality inspection and guided assembly systems. Publications examine data application frameworks in VUCA environments and evaluate AR technology effectiveness in industrial settings. Recent publications highlight: Contextual AR evaluation methodologies Smart manufacturing data integration User-centered quality inspection systems Scientific achievements: Published in IEEE Transactions on Visualization and Computer Graphics Presented at IEEE ISMAR-Adjunct Contributed to Springer LNCS series publications Also develops public transport navigation apps integrating 50+ networks across Germany, Austria, and Switzerland, while maintaining active engagement in sports and outdoor activities.
Robert Jakob is a postdoctoral researcher at the Chair of Information Management at ETH Zurich and Co-Director of the Agentic Systems Lab. He completed his PhD in Applied Machine Learning at the Centre for Digital Health Interventions at ETH Zurich, where his doctoral research focused on user churn prediction and prevention in digital health interventions using machine learning methods. His academic journey includes international research experiences at Harvard University's John A. Paulson School of Engineering and Applied Sciences with Prof. Susan Murphy's Statistical Reinforcement Learning Lab and at the SEC Future Health Technologies Lab at the National University of Singapore. His collaborative work extends to major public health institutions including the Federal Office of Public Health of the Swiss Confederation (BAG), the Federal Food Safety and Veterinary Office (BLV), and the Swiss Research Institute for Public Health and Addiction (ISGF), as well as private sector partners like Pathmate Technologies AG and WayBetter Inc. Robert's research interests center on digital health interventions, with a particular focus on understanding factors that influence user adherence, developing machine learning models to predict user churn, and evaluating strategies to prevent nonadherence. His work spans multiple health domains including nutrition, weight management, mental health, and chronic disease management, bridging the fields of computer science, behavioral science, and public health. His publication record demonstrates a strong emphasis on systematic reviews of factors influencing mHealth app adherence, development of predictive models for user churn, and evaluation of behavior change techniques in digital health interventions. His research has been published in high-impact journals including Journal of Medical Internet Research, Annals of Behavioral Medicine, and Computers in Human Behavior Reports, with multiple publications in 2022-2024 showing consistent research productivity. ETH Doc.Mobility Fellowship (Harvard University) 2023 KITE Award Nominee - Digital Health Project 2022 BayStartUp Award - Jury price for 'Bavaria's best founders' 2017 EXIST Business Startup Grant - German Government Stipend 2017 Volkswagen Foundation Grant for 'aspiring young academics' 2013 Robert's educational background combines technical and business perspectives, with a PhD in Applied Machine Learning from ETH Zurich, a master's degree in Technology and Management from Technical University of Munich, and a bachelor's degree in Industrial Engineering from Karlsruhe Institute of Technology. His pre-academic career included founding a mobile games startup and professional experience at major corporations including Accenture, Fortiss, Infineon, and Volkswagen, providing him with practical industry insights that inform his academic research. As Co-Director of the Agentic Systems Lab, Robert contributes to cutting-edge research at the intersection of artificial intelligence, human-computer interaction, and digital health, with a mission to develop more adaptive, effective, and engaging health interventions through data-driven approaches that can address the global challenge of noncommunicable diseases.
Lena Jaeger is an Associate Professor of Digital Linguistics at the University of Zurich, where she leads research at the intersection of linguistics, computational cognitive science, and machine learning. She joined the Chair of Computational Linguistics at UZH in July 2020 after establishing a Machine Learning Junior Research Group at the University of Potsdam, funded by the German Federal Ministry of Education and Research. Her educational background spans multiple disciplines: she earned an MA in Chinese Language and Culture (Sinology) from the University of Freiburg im Breisgau, Tongji University Shanghai, Beijing Language and Culture University, and Université Paris 7 Denis-Diderot; followed by an MSc in Experimental and Clinical Linguistics at the University of Potsdam; and completed her doctorate in cognitive science at the same institution. Notably, she also earned a bachelor's degree in computer science during or after her doctoral studies. Professor Jaeger's research focuses on investigating cognitive mechanisms underlying human language processing using experimental psycholinguistics, computational modeling, and machine learning methods. Her current work develops machine learning techniques for analyzing eye-tracking data to understand cognitive processes reflected in eye movement behavior. This interdisciplinary approach combines insights from linguistics, cognitive science, and artificial intelligence to create models that bridge human and machine language understanding. Her recent publications reveal a strong trend toward developing eye-tracking methodologies, creating multilingual corpora, and applying machine learning to understand reading behavior and language processing. Her work spans from fundamental research on cognitive mechanisms to practical applications in educational technology, medical diagnostics, and AI development. Best student late breaking work award for Reporting Eye-Tracking Data Quality: Towards a New Standard Best short paper award for Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models Professor Jaeger actively supervises multiple PhD students across computational linguistics, machine learning, and phonetics disciplines. Her research group collaborates extensively on large-scale projects like the MultiplEYE initiative, which establishes standards for multilingual eye-tracking data collection. She has secured significant research funding, including a Machine Learning Junior Research Group grant from the German Federal Ministry of Education and Research before moving to UZH. Her laboratory work centers on eye-tracking methodologies, developing tools like pymovements for eye movement data processing, and creating comprehensive corpora such as MECO (Multilingual Eye-Movement Corpus), MultiplEYE, and CoLAGaze. These resources support cross-linguistic research on reading behavior and language processing across diverse populations.
Denis Ribeaud serves as Senior Research Associate and Co-Project Director of the Zurich Project on the Social Development from Childhood to Adulthood (z-proso) at the University of Zurich's Faculty of Arts and Social Sciences. Since 2006, he has directed the Zurich Youth Surveys (ZYS), which track youth violence trends in Zurich canton using representative student samples. The z-proso project, ongoing since 2003 with over 1,300 participants, examines longitudinal development of violence and problem behaviors through repeated surveys. His educational background includes: Sociology and Social Psychology studies at the University of Zurich PhD in Criminology from the University of Lausanne, where he worked at the Criminological Institute for several years Ribeaud's research focuses on criminology and developmental psychology, specifically investigating how peer victimization, parenting behaviors, socio-emotional skills, and harsh environments influence violence perpetration, mental health, and substance use across adolescence. His work integrates biological measures (e.g., hair cortisol analysis), ecological momentary assessment, and advanced statistical modeling to map causal pathways in youth development. Key specialties include longitudinal cohort design, transdiagnostic mental health analysis, and validation of behavioral instruments. Recent publications reveal heavy emphasis on multi-method approaches combining biological data with psychological assessments. He explores gene expression changes from victimization, hormonal correlates of aggression, and machine learning applications in EMA data, while consistently addressing Zurich-specific youth cohorts. His articles highlight Switzerland-focused violence research, substance use patterns in immigrant vs. native adolescents, and pandemic impacts on young adult coping mechanisms. No scientific awards were documented in the provided materials. Ribeaud's project leadership implies grant management and student supervision, though specific advisees or funding details are absent. His z-proso role involves coordinating cross-disciplinary teams across 13+ years of data collection, suggesting substantial advisory responsibilities in research design and execution. He directs the z-proso project and Zurich Youth Surveys (ZYS), major longitudinal initiatives based at the University of Zurich. These projects maintain archives of data collections from 2004-2022 and employ interdisciplinary teams for analyzing youth violence, mental health, and social decision-making through combined survey and biomarker approaches.
Dr. Srikanth Madikeri is a Lecturer and Senior Researcher at the University of Zurich's Department of Computational Linguistics. He holds a Ph.D. in Computer Science from IIT Madras (2013) and a Bachelor of Engineering from Anna University (2008). Before joining UZH, he spent 11 years at Idiap Research Institute as a Postdoctoral Researcher and Research Associate. His research focuses on low-resource speech technologies, including Automatic Speech Recognition (ASR), Speaker Diarization, Language Recognition, and Spoken Dialog Systems. He specializes in adapting models for challenging domains like air traffic control and criminal investigations. Recent publications demonstrate strong trends in multimodal systems, domain adaptation for ASR, and efficient data selection techniques. His work frequently integrates speech processing with NLP tasks and criminal network analysis. Awards: Best Paper Award in Signal Processing Track (NCC 2011) International Create Challenge 2017 Winner He teaches Speech Technology and Machine Learning for Computational Linguistics at UZH. Professional activities include serving as Area Chair for Interspeech (2021-2022) and developing open-source toolkits like pkwrap for LF-MMI training.
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 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.
Andrea Cavalli is a Group Leader in Computational Structural Biology at the Institute for Research in Biomedicine (IRB), which is affiliated with Università della Svizzera italiana (USI) in Bellinzona, Switzerland. He joined IRB as an Associate Member in December 2012 and was appointed as Group Leader in June 2016. His research group focuses on integrating computational approaches with experimental methodologies to understand molecular structures and their functional implications in biological and pathological processes. Dr. Cavalli earned his degree in theoretical physics at the ETH in Zurich in 1995 and completed his Ph.D. in mathematics in 2001. Following his doctoral studies, he worked with Amedeo Caflisch at the University of Zurich before joining the research groups of Christopher Dobson and Michele Vendruscolo at the University of Cambridge in 2004. During his time at Cambridge, he was supported by an Advanced Researcher Fellowship from the Swiss National Science Foundation. His early work focused on developing theoretical and computational methods for protein structure determination from sparse experimental data, which led to the development of the CHESHIRE method for accurate protein structure determination using NMR chemical shifts. Dr. Cavalli's research integrates cutting-edge computational approaches, including molecular dynamics simulations and machine learning, with interdisciplinary experimental methodologies. His work bridges the gap between molecular structure and functional implications, with applications in drug discovery and therapeutic development. The research spans multiple areas including protein folding, protein-protein interactions, computational drug design, and structural analysis of biological macromolecules. His group has made significant contributions to understanding protein dynamics, developing computational methods for structure determination, and applying these approaches to biomedical problems ranging from cancer research to infectious diseases. Analysis of Dr. Cavalli's recent publications reveals a strong focus on computational approaches to biomedical problems, with particular emphasis on protein structure-function relationships, peptide and small molecule design, and applications in cancer research, immunology, and infectious diseases. His work often combines computational modeling with experimental validation, demonstrating a multidisciplinary approach to solving complex biological problems. The research spans structural biology, computational chemistry, and translational medicine, with increasing applications in drug discovery and therapeutic development. Advanced Researcher Fellowship from the Swiss National Science Foundation Development of the CHESHIRE method for protein structure determination As Group Leader at IRB, Dr. Cavalli oversees a research team that combines expertise in computational biology, structural biology, and drug design. His laboratory utilizes advanced computational techniques including molecular dynamics simulations, machine learning algorithms, and structure-based drug design approaches. The group collaborates extensively with experimental laboratories both within IRB and at other institutions to validate computational predictions and translate findings into biomedical applications. Current research directions include developing computational methods for protein structure determination, designing peptide inhibitors for therapeutic applications, and applying computational approaches to understand disease mechanisms and identify potential therapeutic targets.
Prof. Effy Vayena is a Professor of Bioethics at the Swiss Institute of Technology (ETH Zurich) and Principal Investigator of the Health Ethics and Policy Lab. She serves as a Visiting Professor at Harvard Medical School’s Center for Bioethics and Faculty Associate at the Berkman Klein Center for Internet and Society. Her work bridges medicine, data science, and ethics, focusing on societal challenges in digital health and genomic technologies. Prof. Vayena earned a PhD in Medical History from the University of Minnesota, specializing in health policy. She previously coordinated the PhD program in Biomedical Ethics and Law at the University of Zurich and has held leadership roles in ethics education and research governance. Her research spans AI ethics , health data governance , genomic technologies , and digital health policy . Recent work examines fairness in clinical machine learning, decentralized trials, and the ethical implications of large language models. Key scientific achievements include a Swiss National Science Foundation professorship and a Berkman Klein Center Fellowship. She has contributed to WHO guidance on AI governance in healthcare and led studies on health data privacy, biobanking, and digital epidemiology. Labs/Teams: Founded the Health Ethics and Policy Lab at ETH Zurich.
