جست‌وجوی استاد و پژوهشگر

Tina Pericaمشاهده پروفایل

استادیار

• Biochemistry
• Systems Biology
• Protein Biochemistry
• +۵ مورد دیگر

Prof. Tina Perica is an Assistant Professor (tenure-track) in the Department of Biochemistry at the University of Zurich, joining in July 2021. Her research focuses on understanding how biochemical properties of proteins encode systems-level functions in signal transduction and gene regulation. Her lab integrates protein biochemistry, biophysics, functional genomics, and computational biology to study cellular regulation mechanisms. Education: B.Sc. in Biology, University of Zagreb (2008) Ph.D. in Biochemistry, University of Cambridge (2013) Postdoctoral training at University of California, San Francisco (2013–2021) Research Interests: Systems Biochemistry Allosteric Regulation in GTPases and Kinases Genotype-to-Phenotype Mapping Functional Genomics and Computational Biology Her team explores how molecular mechanisms of proteins interact within cellular networks to regulate complex processes, aiming to predict therapeutic effects and disease mutations. Lab Activities: Develops experimental and computational tools to map functional interactions in signaling pathways Focuses on targeted perturbations of proteins to study systems-level effects Emphasizes interdisciplinary approaches blending biochemistry with systems biology

• Bioinformatics
• Structural Biology
• Computational Biology
• +۳ مورد دیگر

Martin Steinegger is a researcher affiliated with Johns Hopkins School of Medicine and previously held roles at institutions such as the Max Planck Institute for Biophysical Chemistry and Technical University of Munich. His research focuses on bioinformatics, protein structure prediction, and computational methods for analyzing large genomic datasets. He has contributed to tools like MMseqs2, ColabFold, and Foldseek, advancing fields like metagenomics and structural biology. His work emphasizes scalable algorithms and open-source software development. Education includes a Master of Computer Science from Ludwig-Maximilians-Universität München (2013-2014) and a Ph.D. from Technical University of Munich (2014-2018). He has also held visiting scholar positions at Seoul National University, Centre for Genomic Regulation, and University of California, San Francisco. Key research interests revolve around protein structure prediction, metagenomic analysis, and developing machine learning frameworks for biological data. His publications highlight innovations in protein language models, structural phylogenetics, and database management systems. Notable contributions include the AlphaFold Protein Structure Database, MMseqs2 sequence search tool, and ColabFold for accessible protein folding predictions. His work bridges computational methods with biological discovery, addressing challenges in structural biology and genomic data interpretation.

Lenka Zdeborováمشاهده پروفایل

دانشیار

• Statistical Physics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Lenka Zdeborová is an Associate Professor at EPFL, jointly affiliated with the School of Basic Sciences and School of Computer and Communication Sciences. She leads the Laboratory of Statistical Physics of Computational Systems, where her research bridges statistical physics, machine learning, and computational biology. Education: PhD in Physics, Université Paris-Cité (2012) MSc in Fundamental Physics, École Normale Supérieure (2009) BSc in Physics, École Normale Supérieure de Lyon (2007) Her work focuses on phase transitions in learning algorithms, high-dimensional statistics, and neural network theory. Current projects investigate fundamental limits of machine learning, dynamics of graph neural networks, and applications to biological systems. Recent publications explore attention mechanisms in transformers, neural network depth advantages, and Bayes-optimal learning. Methodological innovations include cavity methods for hypergraphs and analysis of high-dimensional inference problems. Supervises doctoral students researching statistical physics approaches to machine learning and optimization. Teaches graduate courses in data science and machine learning for physicists.

Matteo Dal Peraroمشاهده پروفایل

دانشیار

• Computational Biophysics
• Molecular Modeling
• Structural Biology
• +۵ مورد دیگر

Matteo Dal Peraro is an Associate Professor at École polytechnique fédérale de Lausanne (EPFL) in the School of Life Sciences, where he leads the Laboratory for Biomolecular Modeling (LBM) within the Interfaculty Institute of Bioengineering (IBI). He also holds significant administrative roles as Head of IBI-SV Administration and Co-Director of IBI-STI Administration, demonstrating his leadership across both the School of Life Sciences and School of Engineering. His research bridges computational approaches with experimental validation to understand complex biological systems at multiple scales. His educational background includes a B.S. and M.S. in Physics from the University of Padua (2000), followed by a Ph.D. in Biophysics from the International School for Advanced Studies (SISSA) in Trieste (2004). He then completed postdoctoral training at the University of Pennsylvania under Professor M. L. Klein before joining EPFL as a Tenure Track Assistant Professor in late 2007. Dal Peraro's research focuses on computational biophysics and multiscale modeling of biological systems, with particular emphasis on membrane-protein interactions, nanopore sensing technologies, and structural biology. His work spans fundamental molecular mechanisms to applied educational technologies, demonstrating a commitment to both scientific discovery and knowledge dissemination. He has made significant contributions to understanding protein-membrane interactions, antibiotic resistance mechanisms, mitochondrial disorders, and viral pathogenesis through advanced computational approaches. His publication record shows a strong trend toward integrating augmented and virtual reality technologies with molecular modeling, exemplified by his development of the moleculARweb platform for chemistry and structural biology education. His research spans computational methods development, structural characterization of biomolecules, membrane biophysics, and applications to medically relevant problems including antibiotic resistance and neurodegenerative disorders. This interdisciplinary approach connects fundamental biophysical principles with practical applications in medicine and education. Dal Peraro has mentored numerous doctoral students through EPFL's PhD programs, particularly in Computational and Quantitative Biology. His leadership extends to serving on PhD program committees and directing research groups focused on computational molecular biology. He has established collaborations across multiple disciplines, facilitating integrative approaches to complex biological problems. He leads the Laboratory for Biomolecular Modeling (LBM), which develops and applies computational methods to study biological systems at multiple scales. The lab bridges molecular simulations with experimental validation, creating a synergistic approach to understanding complex biological phenomena. Dal Peraro's team has made significant contributions to membrane biophysics, protein folding, and the development of educational technologies that make structural biology accessible through augmented reality platforms.

Tamar Kohnمشاهده پروفایل

استاد

• Environmental Virology
• Waterborne Pathogens
• Airborne Pathogens
• +۵ مورد دیگر

Dr. Tamar Kohn is a Full Professor at EPFL, leading the Laboratory of Environmental Virology (LEV) within the Institute of Environmental Engineering (IIE), School of Architecture, Civil and Environmental Engineering (ENAC). She holds additional roles as Director of the IIE-GE unit and serves on the ENAC School Direction. Her expertise focuses on environmental virology, waterborne pathogens, and disinfection mechanisms. Education: 2000–2004: PhD in Environmental Engineering, Johns Hopkins University 1999: Diploma in Environmental Sciences, ETH Zurich 2004–2006: Postdoc at UC Berkeley Research Interests: Her work investigates viral behavior outside hosts, particularly in water and air. Key areas include viral inactivation mechanisms, environmental stability of pathogens, and applications in wastewater surveillance. She explores how factors like pH, salinity, organic compounds, and microbial interactions influence virus survival and transmission. Key Contributions: Recent studies highlight aerosol pH's role in influenza stability, bacterial promotion of viral inactivation in lakes, and mpox monitoring via wastewater. Her lab also models virus transport in Lake Geneva under climate change scenarios. Advising & Grants: Supervised over 20 PhD students, including Chaojie Li (2023), Aline Schaub (2024), and Margot Olive (2022). Her research leverages advanced techniques like mass spectrometry and metagenomics for pathogen detection. Labs/Teams: Leads the LEV lab, collaborating on projects combining virology, environmental chemistry, and engineering to combat waterborne and airborne diseases.

Christian Panseمشاهده پروفایل

پژوهشگر

• Computational Mass Spectrometry
• Proteomics
• Bioinformatics
• +۳ مورد دیگر

Dr. Christian Panse is the Unit Head of Computational Mass Spectrometry at the Functional Genomics Center Zurich , ETH Zurich. His work spans bioinformatics, data processing, and visualization in proteomics, with a focus on method development and software engineering. Research Interests: Christian Panse specializes in proteomics and computational mass spectrometry , developing tools like prolfqua and rawrr for quantitative proteomics analysis. His research addresses standardization in proteomics core facilities and cross-resource data comparison. Recent work includes harmonizing quality controls across proteomics laboratories Creating user-friendly R packages for differential expression analysis Advancing fragmentation techniques for post-translational modification studies Publications Trends: His articles emphasize proteomics data reliability , software tools , and method validation in mass spectrometry. Collaborations with institutions like the Core for Life alliance highlight his role in community-driven standardization efforts.

• Algorithmic Graph Theory
• Computational Complexity
• Structural Graph Theory
• +۳ مورد دیگر

Narmina Baghirova is a Researcher at the Department of Informatics within the Interfaculty Informatics Department at the University of Fribourg. She holds a bachelor’s degree in Mathematics from the University of Primorska (Slovenia), a master’s in Mathematical Sciences from the same institution, and has completed exchange semesters at Universidad del País Vasco (Spain) and Eötvös Loránd University (Hungary). She is currently pursuing a PhD under Prof. Bernard Ries and Dr. David Schindl. Her research focuses on Algorithmic Graph Theory, Computational Complexity, and Combinatorial Optimization. Key areas include k-community structure detection in graphs, proportionally dense subgraphs (PDS), and parameterized complexity analysis. Recent work explores efficiently solvable cases for these problems and their applications in social network modeling. Her publications (2022–2025) span theoretical computer science, discrete mathematics, and bioinformatics, addressing graph partitioning, phylogenetic algorithms, and structural graph properties. She has contributed to both journal articles and conference proceedings. Baghirova collaborates with international researchers and is affiliated with the Department of Informatics at the University of Fribourg, where she contributes to research on computational complexity and graph-theoretic problems.

• Food Chemistry
• Flavor Science
• Sensory Analysis
• +۳ مورد دیگر

Imre Blank serves as a Lecturer in the Department of Chemistry and Chemical Engineering at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences. His research focuses on food chemistry and flavor science, particularly investigating taste-active compounds, aroma perception mechanisms, and sensory evaluation of food products. His recent work explores umami taste perception through EEG-based brain rhythm analysis Computational modeling of taste peptides Multi-omics characterization of odorants in plant-based and traditional foods Mechanistic studies of saltiness enhancement by volatiles His publications emphasize analytical chemistry techniques like LC–MS and GC-IMS for flavor profiling. Current research trends examine the molecular dynamics of odorant release, biosensor development for taste detection, and sustainable flavor production inspired by natural processes. He also investigates the impact of processing conditions on coffee aroma formation and bitterness reduction.

Michael Baudisمشاهده پروفایل

استاد

• Bioinformatics
• Tumorgenomics
• Genomics
• +۴ مورد دیگر

Michael Baudis is a Professor of Bioinformatics and Tumorgenomics at the Institute of Molecular Biology , Faculty of Science, University of Zurich. He leads the development of the Progenetix database, a global reference for cancer genomic copy number alterations, and contributes to international standards through his membership in the Global Alliance for Genomics and Health (GA4GH) . Research focuses on genomic data representation , cancer subtype classification , and data sharing protocols Key projects include Beacon networks , Phenopackets , and GA4GH standards Email: michael.baudis@uzh.ch His recent work explores short tandem repeat variations , attention-based deep learning for CNAs , and heterogeneity in cancer classifications . Methodological contributions include segment_liftover , CNARA , and pgxRpi for genomic data calibration and analysis.

Monika Maurhofer Bringolfمشاهده پروفایل

استاد

• Plant Pathology
• Microbial Ecology
• Biological Control
• +۵ مورد دیگر

Monika Maurhofer is a Professor at the Department of Environmental Systems Science, ETH Zurich , specializing in multitrophic interactions within the rhizosphere. Her research bridges plant pathology, microbial ecology, and biological control of plant diseases and insect pests. 1988: Dipl. sc. nat., ETH Zurich 1993: Dr. sc. nat., ETH Zurich Research focuses on biocontrol pseudomonads and their molecular interactions with plants, fungi, and insects. Key projects include: Understanding Pseudomonas protegens mechanisms in rhizosphere colonization Comparative genomics of entomopathogenic bacteria Developing disease-suppressive compost diagnostics Investigating microbial consortia for pest control 2024-2025 publications highlight her work on entomopathogenic pseudomonads and nematode-bacteria symbiosis , with applications in sustainable agriculture. She received the Golden Owl Award for teaching excellence in 2014 and 2017. Current collaborations include Syngenta Crop Protection and interdisciplinary teams at University of Zurich and University of Basel.

• Geometric Deep Learning
• Topological Deep Learning
• Topological Data Analysis
• +۷ مورد دیگر

Bastian Alexander Grossenbacher is a Full Professor of Machine Learning at the University of Fribourg, where he leads the AIDOS Lab within the Department of Informatics, Faculty of Science and Medicine. He holds secondary appointments at the Institute of AI for Health and the Helmholtz Pioneer Campus of Helmholtz Munich. He is also a TUM Junior Fellow and a member of ELLIS and AI-LIFE. He co-directs the Applied Algebraic Topology Research Network (AATRN) and is involved in the Topology, Algebra, and Geometry in Data Science (TAG DS) initiative. His research lies at the intersection of geometry, topology, and machine learning, with a focus on Geometric Deep Learning, Topological Deep Learning, and Topological Data Analysis. He develops novel machine learning methods that use topological and geometric priors to improve model robustness and interpretability, particularly in biomedical applications. His work spans theoretical foundations and practical implementations in areas such as single-cell analysis, medical imaging, and network science. The 15 most recent publications (2023–2025) reveal a strong trend toward integrating algebraic and differential topology into deep learning architectures. Key themes include the use of Euler Characteristic and magnitude transforms, curvature-based analysis of graphs and data, simplicial and manifold-based neural representations, and the application of topological methods to biomedical data. His articles appear in top-tier venues such as Nature Communications, ICML, NeurIPS, ICLR, and IEEE conferences, reflecting both theoretical depth and high-impact applications. ERC Starting Grant Bastian Rieck is actively involved in mentoring and academic leadership. He supervises PhD students and postdoctoral researchers in the AIDOS Lab and is open to new collaborations, particularly with candidates from interdisciplinary backgrounds. He has secured significant research funding, including the ERC Starting Grant, and leads multiple collaborative research initiatives. He emphasizes open science, sharing code, data, and educational materials publicly. He leads the AIDOS Lab, which focuses on advancing machine learning through topological and geometric principles. He is also a co-director of the Applied Algebraic Topology Research Network (AATRN), which hosts a large online seminar series. Additionally, he maintains DONUT, a curated database of non-theoretical applications of topology, and is active in the ELLIS and AI-LIFE networks, fostering international collaboration in AI and health.

• Molecular Plant Biology
• Phytopathology
• Genome Evolution
• +۵ مورد دیگر

Prof. Thomas Wicker is a Professor at the University of Zurich's Department of Plant and Microbial Biology, affiliated with the Mathematisch-naturwissenschaftliche Fakultät (Faculty of Mathematics and Natural Sciences). His research focuses on molecular plant biology, phytopathology, and genome evolution with an emphasis on transposable elements (TEs) and their role in genomic dynamics. Wicker leads the Molecular Plant Biology/Phytopathology group, developing computational tools for genome analysis and contributing to projects like the TREP database for transposable elements. His work integrates comparative genomics and bioinformatics to study plant-pathogen interactions, particularly in wheat and its fungal pathogens like powdery mildew. He also explores metagenomics of lichens and contributes to crop improvement through genomic studies. Education details are not explicitly stated in the provided texts, but his academic career includes positions as a Privatdozent (2011) and Titularprofessor (2021). His research has led to over 100 publications, including landmark studies on pathogen genome evolution and wheat resistance mechanisms. Collaborations include projects on einkorn wheat genomics and international efforts to map disease resistance genes. Wicker's lab develops publicly available bioinformatics software for genomic analysis, underscoring his commitment to open science. His work bridges fundamental research and applied agriculture, addressing challenges in crop resilience and genetic diversity. Wicker's scientific contributions include identifying mechanisms of TE-driven genome evolution, analyzing effector genes in pathogens, and tracing the global spread of plant diseases linked to historical human activity. His team's studies on wheat powdery mildew revealed hybridization events influencing pathogen adaptation, while metagenomics work on lichens explores symbiotic complexity. Teaching responsibilities include courses on plant biology and research internships, reflecting his engagement in training the next generation of scientists.

• Multiscale/multiphysics modeling
• Biophysics
• Cellular and molecular biomechanics
• +۶ مورد دیگر

Igor V. Pivkin is a full professor at the Institute of Computing within the Faculty of Informatics at the University of Lugano (USI). He holds a B.Sc. and M.Sc. in Mathematics from Novosibirsk State University, followed by an M.Sc. in Computer Science and a Ph.D. in Applied Mathematics from Brown University. Before joining USI, he was a Postdoctoral Associate at MIT's Department of Materials Science and Engineering. His research focuses on multiscale/multiphysics modeling , numerical methods, and large-scale simulations of biological and physical systems. Key areas include biophysics, cellular/molecular biomechanics, stochastic modeling, and coarse-grained molecular simulations. He leverages high-performance computing and particle-based methods to address complex biological phenomena. His work spans diverse applications, including cancer cell dynamics, bioleaching bacterial biofilms, and erythrocyte mechanics in human spleen circulation. He has pioneered computational tools such as the Bayesian recursive global optimizer (BaRGO) and the in-silico lab-on-a-chip framework, enabling petascale simulations of microfluidic systems at cellular resolution. Pivkin collaborates extensively with institutions like the SIB Swiss Institute of Bioinformatics and has contributed to advancing methodologies for multi-model scientific simulations. His research integrates experimental data with computational models to bridge gaps between microscopic and macroscopic biological processes.

Bruno Correiaمشاهده پروفایل

دانشیار

• Protein engineering
• Computational design
• Immunoengineering
• +۷ مورد دیگر

Bruno Correia is an Associate Professor at the Laboratory of Protein Design & Immunoengineering (LPDI) within the Institute of Bioengineering at École Polytechnique Fédérale de Lausanne (EPFL) , Switzerland. His work bridges computational protein design with experimental validation, focusing on immunoengineering applications for vaccines and cancer immunotherapy. PhD in Computational Biology (Universidade Nova de Lisboa, 2010) BSc in Chemistry (Universidade de Coimbra, 2004) Correia's research spans protein engineering , synthetic biology , and computational design , with key contributions in: Developing MaSIF for protein surface interaction analysis Designing chemically-controlled protein switches for CAR T-cell therapy Creating de novo functional proteins for vaccine development His publications highlight vaccinology , structural bioinformatics , and machine learning applications in protein science. Awards include the Protein Society Young Investigator Award (2021) and ERC Starting Grant (2016) . Scientific contributions: Protein surface fingerprinting for interaction prediction Rational immunogen design for HIV and hepatitis C Deep learning frameworks for protein structure generation

Shahin Tavakoliمشاهده پروفایل

مدرس ارشد

• Statistics
• Functional Data Analysis
• Neuro-imaging
• +۴ مورد دیگر

Shahin Tavakoli is a Senior Lecturer in the Research Institute for Statistics and Information Science at the Geneva School of Economics and Management (University of Geneva). He holds a PhD in Mathematical Statistics from EPFL and has held positions as a University Research Fellow at the University of Cambridge and a tenure-track Assistant Professor at the University of Warwick. His research focuses on functional data analysis with applications in neuroimaging, phonetics, biophysics, econometrics, and genomics. Education: BSc/MSc in Mathematics (EPFL), PhD in Mathematical Statistics (EPFL). Key roles include Associate Editor for the Journal of Statistical Planning and Inference and proposer for a JRSS B discussion paper. Teaching includes Applied Bayesian Statistics, Multivariate Analysis, and Mathematics courses at the University of Geneva. Research interests emphasize statistical methodologies for complex data structures, including high-dimensional functional time series and spatial modeling of linguistic data. Notable recent publications address phonetic analysis, brain imaging, and econometric factor models. Collaborations span institutions like the University of Cambridge, University of Warwick, and LMU Munich. Advising includes PhD students Marco Palma and Beatrice Matteo, with contributions to projects such as functional regression clustering and normative brain mapping. His work bridges theoretical statistics with applied domains, reflecting interdisciplinary impact across natural and social sciences.