Prof. Dr. Kuangyu Shi is a faculty member at the Bern Center for Precision Medicine (BCPM), affiliated with the University of Bern and the Inselspital (Bern University Hospital). His work is centered on advancing precision medicine through innovative imaging and data-driven methodologies. His research interests lie at the intersection of nuclear medicine and computational analytics, focusing on: Precision Medicine Medical Imaging and Radiomics Quantitative Image Analysis for Oncology Molecular and Functional Imaging Computational Biomarker Development Personalized Cancer Therapy Monitoring The trends in his research, as implied by his center affiliation, emphasize the integration of high-dimensional imaging data with clinical outcomes to enable individualized treatment strategies. His work likely involves multimodal imaging, machine learning in radiology, and translational applications in cancer care. Scientific awards and honors have not been mentioned in the available text. There is no public information provided regarding student advising, research grants, or leadership in specific labs or teams within the text. However, his role at BCPM suggests collaboration within interdisciplinary research groups focused on data-driven healthcare innovation.
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
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.
Mathias Müller is a postdoctoral researcher at the University of Zurich, where he works on sign language translation and natural language processing (NLP) with a focus on scientific integrity and accessibility. He collaborates with Sarah Ebling , Annette Rios , and others as part of the EU-funded project EASIER . His research interests span Sign language processing Scientific reproducibility Robustness of NMT systems Teaching neural machine translation (NMT) Markup technologies (XML, XSLT, etc.) Phonetics and acoustics His recent publications highlight trends in sign language translation, including multimodal approaches, gloss-based translation, and Signwriting integration. These works emphasize evaluation protocols and addressing bias in NLP systems. Scientific awards include Outstanding Paper Award at ACL 2023 Best Paper Award at AfricaNLP 2023 Best Poster Award at Swisstext 2023 He advises students like Shester and collaborates with teams on NMT system development. His work is supported by a 4-year Bridge Postdoc grant funded by the University of Zurich's Digital Society Initiative (DSI).
Lucas Pelkmans is a Full Professor at the Department of Molecular Life Sciences at the University of Zurich. He leads the Pelkmans Lab, a multi-disciplinary research group focused on understanding how biological scales are crossed from single molecules to tissues. His lab develops quantitative and scalable experimental approaches combined with computational and statistical methods to study fundamental biological questions. His research interests center on quantitative cell biology, systems biology, and the study of membrane-less organelles through phase separation. Pelkmans' work explores how cells process information across different scales, with particular focus on single-cell heterogeneity, cellular decision-making, and the principles governing biological organization. His lab actively encourages researchers to develop both wet-lab and dry-lab skills to tackle complex biological problems. The Pelkmans Lab has produced numerous high-impact publications, particularly in the areas of single-cell analysis, membrane-less organelles, quantitative imaging, and systems biology. Recent work demonstrates strong trends toward multimodal data integration, advanced computational approaches for analyzing cellular states, and understanding phase separation phenomena across biological scales. The lab's publications frequently appear in top-tier journals including Nature, Science, and Cell. Scientific Awards: ERC Advanced Grant (2020) EMBO member (2015) ERC Consolidator (2015) Ernst Hadorn Foundation-endowed Chair (2010) European Young Investigator Award (2005) ETH Medal (2003) Pelkmans actively mentors PhD students and postdoctoral fellows, with his lab comprising researchers from diverse backgrounds including physics, chemistry, computer science, and biology. His teaching includes courses on quantitative and molecular systems biology, and systems dynamics in cell and developmental biology. The lab has received significant grant support, including multiple ERC grants, enabling their innovative research on cellular organization and scale-crossing phenomena. The Pelkmans Lab maintains a highly collaborative environment that emphasizes both individual brilliance and team support. They have developed innovative technologies such as iterative indirect immunofluorescence imaging (4i) for multiplexed protein mapping, which has applications in precision medicine. The lab's work bridges fundamental biological questions with technological innovation, creating new approaches for studying cellular organization across multiple scales.
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.
Prof. Mackenzie Weygandt Mathis is Tenure-Track Assistant Professor and Bertarelli Foundation Chair of Integrative Neuroscience at EPFL’s Brain Mind Institute . Leading the Mathis Lab since 2020, she unites machine learning and systems neuroscience to decipher how brains learn and control movement. Education : PhD in Neuroscience, Harvard University (2017, advisor Naoshige Uchida) Post-doctoral training, University of Tübingen with Matthias Bethge (2017) Rowland Fellow, Harvard University (2017-2020) Research Focus : the lab develops open-source AI tools ( DeepLabCut , CEBRA , AmadeusGPT ) and combines them with large-scale neural recordings in behaving mice to uncover the neural basis of adaptive motor control. Core themes include sensorimotor learning, proprioception, and brain-inspired algorithms for robotics. Publications Trend : recent work spans robust machine-learning methods (ICLR, AISTATS 2025), foundational models for pose estimation ( SuperAnimal , 2024), and integrative studies linking neural population dynamics to muscle-level control ( Nature 2023, Cell 2024). Scientific Awards : Swiss Science Prize Latsis 2024 Robert Bing Prize 2024 Eric Kandel Young Neuroscientist Prize 2023 FENS EJN Young Investigator Prize 2022 Vallee Scholar, ELLIS Scholar, NSF Graduate Fellow Advising & Funding : she mentors 4 current PhD students and several postdocs, supported by SNSF Starting Grant (1.5 M CHF), CZI, Novartis, Radala Foundation, and Kavli Foundation grants. Labs & Teams : the Mathis Lab is located at Campus Biotech , Geneva, and actively collaborates with the Alexander Mathis group, Allen Institute, and international consortiums on open-source neuroscience tools.
Chabbi Houda is a Full Professor at the Fribourg School of Engineering and Architecture, University of Applied Sciences and Arts Western Switzerland. She leads research at the iCoSys Institute for Complex Systems, focusing on AI-driven solutions for industrial and societal challenges. Her work bridges computer vision, deep learning, and practical applications from medical imaging to construction materials. Her research explores: Advanced computer vision for industrial inspection and monitoring Deep learning architectures for anomaly detection and predictive modeling Multimodal data fusion in surveillance systems AI applications in material science and healthcare She leads significant projects including: HIGH-ROAD (2025-2026): Hierarchical AI for road asset inventory using orthoimagery VideoCognition (2023-2025): Explainable AI for surveillance systems with Morphean SA SoNIT (2025-2028): Sonography nerve tracking with FHNW Concrete AI (2022-2025): Machine learning for recycled concrete optimization Her lab at iCoSys Institute collaborates with academic partners including University of Applied Sciences Northwestern Switzerland and industry leaders in medical tech, surveillance, and construction.
Dr. Anastasios Kouvelas is a Lecturer at ETH Zurich, where he serves as head of the Road Traffic Engineering research group at the Institute of Transport Planning and Systems (IVT), Department of Civil, Environmental and Geomatic Engineering. He has held this position since August 2018, succeeding Dr. Monica Menendez who moved to New York University in Abu Dhabi. Prior to joining ETH Zurich, he was a research associate at the Urban Transport Systems Laboratory (LUTS) at EPFL (2014-2018) and a postdoctoral fellow at Partners for Advanced Transportation Technology (PATH) at the University of California, Berkeley (2012-2014). Dr. Kouvelas' research focuses on modeling, simulation, optimization and traffic flow control. His work aims to develop real-time solutions based on control theory and operations research methods. The Road Traffic Engineering group develops algorithmic solutions that are components of intelligent transportation systems used in traffic control centers. Recent technological advances in autonomous vehicles have expanded their research topics as the industry seeks efficient operational solutions for autonomous mobility. They are particularly interested in extending their work to the design of advanced management strategies for urban networks that utilize connected vehicles to improve traffic operations and develop network-wide control strategies that minimize environmental impacts. His recent publications (2023-2025) demonstrate strong focus on traffic prediction using deep learning techniques, bike lane allocation impacts on urban networks, transit network resilience against disruptions, vehicle trajectory extraction from aerial recordings, and traffic control for mixed traffic systems with connected and autonomous vehicles. His work bridges theoretical developments in control theory with practical traffic engineering challenges. Scientific Awards No specific scientific awards were mentioned in the provided information. Advising and Grants Dr. Kouvelas supervises PhD and Master's students in traffic engineering and intelligent transportation systems. His research is supported by various grants including a grant from the Hong Kong Research Grant Council (Grant No. GRF 11216323) for research on traffic speed prediction. Laboratories and Teams Dr. Kouvelas leads the multidisciplinary Road Traffic Engineering research group at IVT, which consists of researchers with backgrounds in civil engineering, electrical engineering, mechanical engineering, computer science, control, and operations research. The group's work spans multiple areas including traffic flow theory, traffic operations, connected and automated vehicles, and intelligent transportation systems.
