Statistical Methods for Network Data Professors and Supervisors in Switzerland

Lucas Pelkmansمشاهده پروفایل

استاد

University of ZurichSwitzerland

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.

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Simon Aeschbacherمشاهده پروفایل

پژوهشگر ارشد

University of ZurichSwitzerland

Simon Aeschbacher is an Independent Research Fellow at the Department of Evolutionary Biology and Environmental Studies, University of Zurich. His work bridges mathematical theory, computational methods, and genomic data to address fundamental questions in evolutionary biology. His educational background includes: M.Sc. in Zoology (2007) and undergraduate studies (2001-2003) at University of Zurich Ph.D. in Evolutionary Biology (2008-2011) at University of Edinburgh/IST Austria under Nick Barton Postdoctoral positions at University of Vienna (2011-2013), UC Davis (2014-2016), and University of Bern (2017) Aeschbacher's research centers on population genomics, specializing in the interplay between gene flow, natural selection, and recombination. His work combines mathematical modeling with genomic analyses to investigate local adaptation, speciation, and human evolutionary history. Key contributions include developing methods for demographic inference and quantifying selection against gene flow. His publication record shows a clear trajectory from theoretical foundations (early work on linkage effects) to applied genomic methodologies (recent development of gIMble for barrier detection). Current research emphasizes human evolution, plant speciation, and hybridization dynamics across diverse taxa. Scientific recognition includes: Swiss NSF Advanced Postdoc.Mobility Fellowship While not explicitly mentioned in the text, his role as Independent Research Fellow implies grant leadership and potential mentoring responsibilities. His work with multiple international collaborators suggests active participation in research networks across Europe and North America.

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Anna Graffمشاهده پروفایل

پژوهشگر ارشد

University of ZurichSwitzerland

Dr. Anna Graff is a Postdoctoral Researcher at the Distributional Linguistics Lab in the Department of Comparative Language Science at the University of Zurich, Switzerland. With an interdisciplinary background spanning biological anthropology and linguistics, her research focuses on the relationship between structural linguistic diversity patterns and molecular genetic diversity distributions across global populations. Dr. Graff completed her PhD in Linguistics at the University of Zurich (2020-2024), following specialized training in Human Evolutionary Studies at the University of Cambridge (2019-2020) and undergraduate studies in Biology and General Linguistics at the University of Zurich (2016-2019). Her academic path includes research assistant positions at the University of Zurich's Paleontology Institute and Mathematics Institute, demonstrating her broad interdisciplinary foundation. Her research interests prominently feature linguistic typology , evolutionary linguistics , and molecular anthropology , with specialized focus on linguistic diversity hotspots and their emergence through contact and isolation processes. Dr. Graff employs innovative methodological approaches that bridge linguistic analysis with population genetics, using genetic admixture as a proxy for historical population contact to study linguistic borrowing patterns globally. Dr. Graff's publication record demonstrates significant contributions to interdisciplinary research, with recent publications in high-impact journals including Science Advances and Scientific Data. Her work spans linguistic database development (ATLAs database), methodological innovation (Densify R package), cultural evolution of musical instruments, and paleoparasitology, reflecting her commitment to cross-disciplinary scholarship. Her scientific contributions include: Groundbreaking research on genetic-linguistic correlations in language contact scenarios Development of the Areal Typology of Languages of the Americas (ATLAs) database Creation of computational tools for linguistic data processing Studies on cultural evolution across diverse domains Innovative approaches to interdisciplinary data integration As a postdoctoral researcher, Dr. Graff continues to advance interdisciplinary methodologies that connect linguistic patterns with genetic and anthropological data, contributing to our understanding of human cultural and biological evolution through innovative research approaches that transcend traditional disciplinary boundaries.

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Tomislav Medićمشاهده پروفایل

مدرس

ETH ZurichSwitzerland

Dr. Tomislav Medić is a Lecturer and PostDoc at the Department of Civil, Environmental and Geomatic Engineering, ETH Zurich. His research focuses on advanced geospatial technologies, particularly terrestrial laser scanning (TLS) applications in deformation monitoring, sensor calibration, and precision agriculture. MSc in Geodesy and Geoinformation, University of Zagreb, Croatia PhD in Geodesy, Bonn University (IGG), Germany Dr. Medić’s research spans geomatics, remote sensing, and sensor engineering. Key areas include TLS radiometric calibration, point cloud processing for 3D displacement analysis, and multispectral LiDAR applications in agriculture. He contributes to improving geodetic measurement accuracy through innovative calibration strategies. His recent publications emphasize TLS integration with RGB data for geomonitoring (2025), hyperspectral scanning for fruit quality assessment (2024), and calibration field design for panoramic scanners (2023). Articles demonstrate expertise in error modeling, multi-sensor fusion, and environmental monitoring applications. At ETH Zurich’s Geosensors and Engineering Geodesy (GSEG) group, he leads the Alpine Measurement Lab collaboration project. He also participates in PhenoRob, a cluster of excellence in robotics and phenotyping for sustainable crop production at Bonn University.

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Matthias Grossglauserمشاهده پروفایل

استاد

Swiss Federal Institute of Technology in LausanneSwitzerland

Matthias Grossglauser is a Full Professor at the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he co-directs the Information and Network Dynamics lab. He serves on the Federal Communications Commission (ComCom), Switzerland's telecommunications regulatory authority, and previously directed EPFL's Doctoral School in Computer and Communication Sciences (2016-2019). His career includes positions at Nokia Research Center (Internet Laboratory lead), AT&T Research, and EPFL (Assistant Professor). Education Ph.D. in Computer Science from Sorbonne Universités M.Sc. in Electrical Engineering from Georgia Institute of Technology Engineering degree in Communication Systems from EPFL Research Focus Grossglauser's research integrates machine learning, stochastic networks, and discrete choice models to address challenges in artificial intelligence, network science, computational social sciences, and recommender systems. His work emphasizes both theoretical foundations and practical applications, including political forecasting, climate communication, and network dynamics. Publication Trends Recent articles demonstrate strong focus on causal inference, optimal learning algorithms, and social network analysis. Dominant themes include reinforcement learning optimization, graph-based modeling, and NLP applications in political science. Methodological innovations in matrix factorization, Bayesian modeling, and stochastic processes recur throughout. Awards & Honors Fellow of IEEE and ELLIS Cor Baayen Award (1998) CoNEXT/SIGCOMM Rising Star Award (2006) Best Paper Awards: ACM COSN (2014), IEEE INFOCOM (2001) Nokia Mobile Data Challenge Winner (2012) Academic Leadership Has advised 16+ PhD students to completion and currently supervises 4 doctoral candidates. Secured research funding for projects including dynamic recommender systems, network alignment algorithms, and computational social science tools (e.g., Predikon.ch vote prediction platform). Leads the Information and Network Dynamics lab, focusing on AI-driven network analysis.

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Roland Tormeyمشاهده پروفایل

پژوهشگر

Swiss Federal Institute of Technology in LausanneSwitzerland

Roland Tormey is a Senior Scientist and Head of Service at the Teaching Support Centre (CAPE) within the School of Humanities and Social Sciences (CDH-SHS) at EPFL. His work focuses on engineering education, with emphasis on diversity, equity, and the role of emotion in learning. He has co-authored Facilitating Experiential Learning in Higher Education (2021) and co-edited The Routledge International Handbook of Engineering Ethics Education (2025). Education Doctorate in Sociology, Trinity College Dublin (1999) Postgraduate Certificate in Environmental & Development Education, London South Bank University (2000) B.Sc. in Mathematics & Statistics, The Open University (2024) B.Soc.Sc. in Sociology & Social Administration, University College Dublin (1992) Roland's research spans emotional dynamics in engineering education , ethics pedagogy , and experiential learning . He explores how emotions influence team projects, ethical reasoning, and curriculum design. His work integrates sociology and educational science to address equity in STEM. Recent Publications analyze emotional labor in student teams, compassion in ethics cases, and learning analytics tools for Jupyter Notebooks. He has contributed to frameworks for ethical digital tool design and interdisciplinary sustainability education. Teaching Leadership includes courses like Ethics for Life Sciences Engineers and Science & Engineering Teaching & Learning . He manages EPFL's Teaching Support Centre, which develops pedagogical strategies for STEM educators.

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Robert Feldmannمشاهده پروفایل

استاد

University of ZurichSwitzerland

Robert Feldmann is a Professor of Astrophysics at the University of Zurich's Department of Astrophysics within the Faculty of Science. His research combines computational astrophysics with data science to investigate galaxy formation and evolution across cosmic history. As a key contributor to the Feedback in Realistic Environments (FIRE) project and leader of MassiveFIRE, he develops sophisticated cosmological simulations to understand how galaxies form stars, grow, and evolve within the cosmic web. Feldmann's research interests focus on understanding how galaxies and their properties evolve over cosmic time, particularly examining the processes that determine galaxy sizes, regulate star formation rates, and shape morphology. His work bridges observational astronomy with theoretical modeling, leveraging the exponential growth in computing power and data science techniques to tackle complex astrophysical problems. He investigates the role of stellar feedback, cosmological starvation, and dark matter halo properties in shaping galaxy evolution, with particular emphasis on massive galaxies during the cosmic noon period (redshifts z~1.5-3). His publication record shows consistent output in leading astrophysics journals, with recent work focusing on applying machine learning to cosmological simulations (EMBER framework), studying submillimeter-bright galaxies, and analyzing quiescent galaxy formation. Feldmann has developed significant open-source tools including LEO-Py for statistical analysis of astronomical data and ZEBRA for photometric redshift determination, demonstrating his dual expertise in astrophysics and computational methods. Feldmann actively collaborates with researchers worldwide, including Phil Hopkins, Rachel Bezanson, and Eliot Quataert, and participates in major international projects. He regularly presents at conferences, including organizing the 2024 Ascona conference on 'Observing and Simulating Galaxy Evolution in the Era of JWST.' His work has been featured in media outlets like Sueddeutsche Zeitung, highlighting the public impact of his research on galaxy morphology. As an educator, Feldmann mentors Master's students from both University of Zurich and ETH Zurich, offering research projects in astrophysics and data science. His computational approach to galaxy evolution provides students with valuable experience in high-performance computing and data analysis techniques applicable across scientific disciplines.

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Daniele Silvestroمشاهده پروفایل

پژوهشگر

ETH ZurichSwitzerland

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.

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Jasmin Wandelمشاهده پروفایل

استاد

Bern University of Applied SciencesSwitzerland

Prof. Dr. Jasmin Wandel serves as a Professor at Bern University of Applied Sciences within the School of Engineering and Computer Science. Her academic profile combines teaching responsibilities in engineering disciplines with active research in biomechanics and medical engineering applications. Her educational background includes a 2013 PhD dissertation on statistical methods for extreme value analysis in Gaussian processes, establishing her foundation in quantitative research methodologies. This statistical expertise informs her interdisciplinary approach to engineering problems. Professor Wandel's research program centers on biomechanical analysis of bone structures and medical device optimization . Key focus areas include: Computational modeling of fracture mechanics using finite element analysis Thermal management in surgical robotics and bone drilling procedures Biomechanical characterization of bone-implant interfaces Statistical methods for medical engineering data analysis Her work bridges theoretical engineering principles with clinical applications, particularly in orthopedics. Analysis of her publication trends (2013-2019) reveals consistent output in high-impact biomechanics journals, with increasing focus on clinically applicable research. The majority of her work involves collaborative projects with University of Bern researchers, demonstrating strong institutional partnerships in the Swiss medical engineering ecosystem. While specific awards aren't documented in available sources, her publication record in journals like Bone and Medical Engineering & Physics indicates peer recognition of her contributions to biomechanical research. Professor Wandel maintains active supervision of master's students at BFH, with research projects typically involving computational modeling and experimental validation. Her collaborations with University of Bern and industry partners like SVGW provide students with access to clinical datasets and real-world engineering challenges. Current research appears funded through institutional partnerships and applied research grants typical of Swiss Universities of Applied Sciences. Based at the Burgdorf campus, Professor Wandel contributes to BFH's strategic focus on Humane Digital Transformation through her work in medical robotics and computational biomechanics, integrating engineering solutions with human-centered healthcare applications.

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