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Michal FriedmanView profile

Assistant Professor

• Systems
• Concurrent Computing
• Programming Languages
• +3 more

Michal Friedman is an Assistant Professor at the Department of Computer Science, ETH Zurich, specializing in systems, concurrent computing, programming languages, and sustainable computing. He leads research on designing system fundamentals across software and hardware to enhance performance and efficiency in next-generation computing platforms. Prior to ETH, he completed a postdoc at the same institution and earned his Ph.D. from the Technion under Prof. Erez Petrank, focusing on concurrent data structures for non-volatile memories. Education: Ph.D. in Computer Science (Technion, advised by Erez Petrank), BSc Summa Cum Laude (Technion). Awards include the Eric and Wendy Schmidt Postdoctoral Award (2022), Blavatnik Prize (2021), and Azrieli Fellowship (2018–2021). His work spans persistent memory systems, concurrent algorithms, and energy-efficient computing. Key contributions include PCcheck (ML checkpointing), Dirigent (serverless orchestration), and foundational research on non-volatile memory correctness conditions. He has authored over 15 papers in top conferences like ASPLOS, SOSP, and VLDB, and serves on program committees for systems and programming languages venues.

Marco RagliantiView profile

Research Fellow

• Software Engineering
• Software Development Lifecycle
• Software Documentation
• +3 more

Marco Raglianti is a Postdoctoral Fellow and Research Assistant in the Reverse Engineering, Visualization, and Evolution Analysis Lab (REVEAL) at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on software engineering, documentation landscapes, developer communities, and visualization tools. He holds a PhD in Informatics from USI (2025) and M.Sc./B.Sc. degrees in Computer Science from the University of Pisa (Cum Laude). Key contributions include tools like DwarvenMail for documentation analysis, DiscOrDance for Discord community visualization, and Vizor for interactive graph exploration. He co-supervised multiple thesis projects and taught courses in software engineering at USI. His work bridges empirical software engineering with practical tool development, emphasizing developer-centric solutions. Raglianti's publications address topics like UML evolution, VR-based refactoring, and microservices data access patterns. He actively reviews for journals like ACM Transactions on Software Engineering and conferences such as ICSE and ESEC/FSE. His lab's focus on reifying software documentation reflects a commitment to improving developer workflows through systematic analysis and visualization.

Andrzej StasiakView profile

Associate Professor

• DNA structure
• Chromosomal organization
• Topological domains (TADs)
• +3 more

Andrzej Stasiak is an Associate Professor at the University of Lausanne (UNIL), affiliated with the Center for Integrative Genomics (CIG) within the Faculty of Biology and Medicine. He holds a PhD from the Polish Academy of Sciences (1981) and has held postdoctoral positions at ETH Zurich and the Laboratory of Ultrastructural Analysis at UNIL. His research focuses on understanding the physical and biological mechanisms underlying genome organization, including DNA topology, chromatin structure, and protein knotting. He is a SIB Group Leader (2015) and has contributed to groundbreaking studies on topological domains (TADs), DNA supercoiling, and knotting mechanisms in proteins and DNA. Education: PhD in Biochemistry, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw (1981) Postdoctoral Fellow, Institute for Cell Biology, ETH Zurich (1981–1989) Research Interests: Mechanisms of TAD formation via transcription-induced supercoiling Effects of DNA crowding on supercoiling and transcription Biological mechanisms of DNA unknotting and decatenation Role of knotting in protein structure and function Publications: Over 100 peer-reviewed articles, including studies on DNA topology, protein knotting, and computational models of chromatin organization. Recent work focuses on theta-curves in proteins, topological constraints in DNA replication, and knot classification. Awards: Swiss Institute of Bioinformatics (SIB) Group Leader status (2015) Lab & Collaborations: Leads a group at CIG with expertise in computational biology, including members such as Fabrizio Benedetti and Julien Dorier. Collaborates on projects like KnotProt, a database of knotted proteins.

Sandro PedrazziniView profile

Adjunct Professor

• Software Engineering
• Programming Languages
• Natural Language Processing
• +5 more

Sandro Pedrazzini is an Adjunct Professor in Software Engineering and the Head of the Bachelor's Program in Computer Engineering at the Department of Innovative Technologies (DTI) of SUPSI. He holds an M.Sc. in Computer Science from ETH Zurich and a Ph.D. in Natural Sciences from the University of Basel. His research focuses on Software Engineering methodologies, Programming Languages, Natural Language Processing, and Information Retrieval. He has extensive experience in both academia and industry, co-founding companies like Canoo AG and Karakun AG. His teaching responsibilities include courses on computer graphics, programming paradigms, and software engineering practices. Research Interests: Software Engineering: Agile methodologies, continuous integration, and project-based learning Natural Language Processing: Term extraction, morphological analysis, and e-learning tools Web Technologies: 3D interfaces, enterprise systems, and middleware architectures Publications reflect a blend of theoretical and applied research, with contributions to enterprise search systems, semantic analysis, and educational technologies. His work emphasizes practical applications of academic research in real-world contexts. Professional Experience: Co-founder of Canoo AG (2000) and Karakun AG (2018) Head of Computer Science Bachelor Program since 2012 Supervisor for multiple academic projects and courses Labs/Teams: Active in SUPSI’s DTI department, leading projects like 3D MMA (3D interface design) and TicinoTurismo II (web-based tourism platform).

Christoph KochView profile

Professor

• Database Theory
• Query Optimization
• Data Management Systems
• +4 more

Christoph Koch is a Full Professor of Computer Science at École Polytechnique Fédérale de Lausanne (EPFL), leading the Data Analysis Theory and Applications Laboratory (DATA). His academic journey includes previous roles as Associate Professor at Cornell University (2007-2010) and Saarland University (2005-2007), with academic foundations from TU Vienna (PhD 2001, Habilitation 2004) and research at CERN. Education: PhD in Artificial Intelligence (TU Vienna & CERN, 2001), Habilitation (TU Vienna, 2004) Appointments: EPFL (2010-present), Cornell University (2007-2010), Saarland University (2005-2007), TU Vienna (2003-2005) His research focuses on database theory and systems , with recent work spanning query optimization , transaction processing , and parallel data management . His publications address topics like robustness under SQL isolation levels , quantifier elimination for query optimization , and Datalog extensions . Articles trend toward high-dimensional data cubes , transactional concurrency , and embedded DSLs for performance-critical systems . Scientific accolades include: Best Paper Awards: PODS (2002) ICALP (2005) SIGMOD (2011) VLDB (2014) GPCE (2017) ERC Consolidator Grant (2011) Google Research Award (2009) ACM SIGLOG Alonzo Church Award (2021) He has advised numerous PhD students including Immanuel Trummer (now at Cornell), Milos Nikolic (Edinburgh), and Lionel Parreaux (HKUST). His lab contributes to query engine architectures , parallel processing systems , and quantum computing applications in database optimization.

• Computational Chemistry
• Molecular Machine Learning
• Catalyst Design
• +2 more

Clémence Corminboeuf is a Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Laboratory of Computational Molecular Design (LCMD). Her research focuses on computational chemistry, machine learning for molecular design, and catalyst optimization using physics-inspired models. Email: clemence.corminboeuf@epfl.ch Scopus Author ID: 6603320500 Researcher ID: I-5973-2018 Current research involves: Developing integer linear programming algorithms for molecular machine learning training set selection Generative inverse design pipelines for singlet fission materials discovery Automated volcano plot analysis tools (SPOCK) for catalyst design Enantiospecific synthesis methods for planar chiral organometallic complexes Neural network potential frameworks for solute-solvent reaction dynamics Recent methodological advances include: Cost-informed Bayesian optimization (CIBO) for efficient chemical experimentation 3DReact geometric deep learning architecture for reaction property prediction MARC software for automated transition state ensemble analysis

Christian IseliView profile

Lecturer

• Bioinformatics
• Computational Biology
• Genomics
• +7 more

Christian Iseli is a Lecturer and Head of Unit at the Bioinformatics Competence Center (BICC) within the School of Life Sciences at École polytechnique fédérale de Lausanne (EPFL). He is also a Scientific Staff Member of the School Council SV and contributes to teaching in the SSV - Teaching program. His work bridges bioinformatics, genomics, and immunology, with a strong emphasis on computational methods for biological data analysis. His research interests include bioinformatics, computational biology, genomics, transcriptomics, immunoinformatics, epigenetics, and algorithm development for next-generation sequencing. He has made significant contributions to understanding gene regulation, immune responses in cancer, and viral diversity, often through collaborative, interdisciplinary studies. His recent publications reveal a strong trend in applying computational tools to complex biological systems, including cancer immunology, genome architecture, and microbial evolution. He has co-developed resources such as SPICA for immune cell analysis and tools for viral diversity exploration, highlighting his role in building bioinformatics infrastructure. Honors and awards: None listed. Iseli actively mentors and teaches, offering courses in RNA-sequencing bioinformatics and omics methods in biomedical research. He has been involved in numerous collaborative research projects, often as a key bioinformatics expert. He is affiliated with the Bioinformatics Competence Center and supports EPFL’s research through technical and educational leadership. He leads a research unit focused on bioinformatics services and development, contributing to both academic research and educational initiatives at EPFL.

Karl AbererView profile

Professor

• Distributed Information Management
• Peer-to-Peer Systems
• Semantic Interoperability
• +6 more

Karl Aberer is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Computer and Communication Sciences (IC) and leading the Distributed Information Systems Laboratory (LSIR) . He also serves as Director of the Swiss National Research Center for Mobile Information and Communication Systems (NCCR-MICS) since 2005. Education : PhD in Mathematics (1991, ETH Zurich); Postdoctoral Fellow at ICSI, UC Berkeley (1991-1992). Research Interests : Focus on distributed information management , decentralized architectures , trust management , and participatory sensing . Key themes include self-organization in peer-to-peer systems, semantic interoperability, and fairness in resource sharing. Publications : Recent works explore truthful data collection , privacy in participatory sensing , and trust-aware P2P systems , with applications to environmental monitoring and semantic web services. Leadership : Member of EPFL's School of Computer and Communication Sciences Direction, Director of the Swiss Science and Technology Council (2003-2011), and participant in PhD program committees. Labs & Collaborations : LSIR Laboratory (EPFL); NCCR-MICS (Swiss national center).

• Materials Science
• Electron Microscopy
• Ultrafast Microscopy
• +3 more

Thomas La Grange serves as a Research and Teaching Associate at École Polytechnique Fédérale de Lausanne (EPFL) across multiple units including the Laboratory for Ultrafast Microscopy and Electron Scattering (LUMES) within the Institute of Physics, the Doctoral Program in Physics (EDPY), and the Physics Section (SPH). He additionally holds a Scientific Staff Member position on the School Council of the School of Basic Sciences. His academic credentials include a Bachelor's and Master's in Materials Science from Michigan State University, culminating in a Ph.D. in Applied Physics from EPFL. La Grange's research pioneers ultrafast electron microscopy techniques to capture non-equilibrium material dynamics at nanosecond timescales. His work focuses on dislocation-mediated phase transitions, skyrmionics, and defect dynamics in rapidly deforming materials, with significant contributions to instrumentation like time-resolved q-EELS and cryo-LTEM. Current investigations explore uranium oxide reduction mechanisms, magnetic phase transitions in FeRh, and photonic modulation of electron beams. Recent publications reveal dominant themes in ultrafast imaging of magnetic materials and radiation-sensitive systems, with strong emphasis on electron spectroscopy applications for uranium oxides and magnetite. His group actively develops novel methodologies for momentum-resolved visualization of quantum phenomena in materials like graphite. His experimental innovations have earned significant recognition: Two R&D100 Awards for DTEM instrumentation Nano50 Award Microscopy Today Innovation Award La Grange supervises PhD candidates Andrieux Antoine Nicolas and Cattaneo Paolo while teaching core courses including "Physics of materials" and "Electron Matter Interactions in Transmission Electron Microscopy". His research is supported by institutional collaborations across EPFL's physics infrastructure. As a key member of Fabrizio Carbone's Laboratory for Ultrafast Microscopy and Electron Scattering (LUMES), he leads instrumentation development for capturing material dynamics at unprecedented temporal resolutions, with ongoing projects focused on PINEM techniques and skyrmion manipulation.

• Blockchain
• Web Mining
• Information Retrieval
• +7 more

Dr. Arnaud Gaudinat is an Associate Professor at the Geneva School of Business Administration (HEG Genève), part of the University of Applied Sciences and Arts of Western Switzerland (HES-SO). He is affiliated with the Economy and Services faculty and primarily works within the Department of Information Science. His educational background includes expertise in information science, with teaching responsibilities spanning multiple programs: MSc HES-SO in Information Science - teaching Data Curation and Information Architecture BSc HES-SO in Management Information Systems - teaching On-chain Analysis BSc HES-SO in Information Science - teaching Web Analytics, Survey Techniques, and Advanced Drupal Content Management BSc HES-SO in Business Economics - teaching Data Analytics and Data 4.0 Dr. Gaudinat's research focuses on the intersection of information science, web technologies, and data analytics. His work spans Web Mining, Information Retrieval, Blockchain applications, and Web Analytics. He has made significant contributions to biomedical text mining, developing systems for dataset retrieval and exploring applications of blockchain in information science. His recent work includes stablecoin taxonomy and using AI for information monitoring. Dr. Gaudinat approaches information science with a practical perspective, often developing tools and methods that address real-world information challenges in both academic and industry contexts. His research output shows a consistent focus on practical applications of information retrieval and text mining techniques, with a growing interest in blockchain technologies and their implications for information management. His most recent publications demonstrate expertise in cross-lingual analysis, stablecoin classification, and AI-assisted information monitoring. Dr. Gaudinat has received research funding from multiple sources including Innosuisse, swissuniversities-P5, and HES-SO. His projects often involve collaboration with both academic and industry partners, reflecting his commitment to bridging theoretical research with practical applications. His current research projects include: Precise Intelligence : Big Data Analytics for comprehensive Global Trade Flow Intelligence INCIPIT : Infrastructure Nationale d'un Complément d'Identifiants Pérennes, Interopérables et Traçables GOES : Global Online Expertise Search, a Web mining project for finding relevant people for specific surveys Dr. Gaudinat's work demonstrates a strong commitment to advancing information science through both theoretical contributions and practical applications that address contemporary challenges in data management and analysis.

• Robotic Manipulation
• Control Theory
• Differential Geometry
• +5 more

Fares J. Abu-Dakka is an Assistant Professor in the Mechanical Engineering Program at New York University Abu Dhabi, where he joined in September 2024. Previously, he served as a Lecturer and Researcher in the Faculty of Engineering - Electronic and Informatic Department at Mondragon Unibertsitatea (2023-2024) and as a Senior Scientist and Leader for the Robot Learning Group at the Munich Institute of Robotics and Machine Intelligence, Technische Universität München (2022-2023). His research focuses on advancing robotic manipulation through the integration of control theory, differential geometry, and machine learning, with particular emphasis on enhancing robotic systems' performance, adaptability, and safety. Dr. Abu-Dakka explores how advanced mathematical frameworks and intelligent algorithms can empower robots to interact more effectively with complex environments, with applications spanning industrial automation, prosthetics, and human-robot collaboration. His recent publications demonstrate expertise in geometry-aware motion planning, variable impedance control, safe execution of robotic skills, and human-robot skill benchmarking. He has organized multiple workshops at major robotics conferences including IROS and ECC, focusing on formal methods in robotics systems, certified control synthesis, and variable impedance learning and control. IEEE Senior Member (2024) Member of the ELLIS Society e.V (2024) Dr. Abu-Dakka actively contributes to the robotics community through workshop organization and collaborative research, with recent work examining AI-driven intent detection, neural interfaces, sensory feedback systems for prosthetics, and certified control synthesis with rigorous safety guarantees. His research bridges theoretical control methods with practical robotic applications, emphasizing mathematical rigor while addressing real-world implementation challenges.

• Machine Learning
• Computational Chemistry
• Artificial Intelligence
• +3 more

Dr. Idil Ismail is an Assistant Professor at the Chair of Computer Science and Chemistry within the Institute of Molecular Physical Sciences (IMPS) at ETH Zurich. She specializes in computational chemistry and machine learning applications for chemical reaction discovery. Her research focuses on integrating graph-driven methods with artificial intelligence to predict reaction mechanisms and accelerate chemical reaction discovery. This includes developing algorithms for traversing dense reaction networks and automating the generation of multi-step reaction pathways. Her recent publications highlight the use of graph-based search strategies and machine learning in kinetic simulations and reaction mechanism prediction. These contributions reflect her commitment to advancing computational methodologies in molecular physical sciences. Work: +41 44 633 23 38 Secretariat: +41 44 632 43 01 Email: idil.ismail@phys.chem.ethz.ch ORCID: 0000-0003-0922-2227

• Applied Mathematics
• Algorithms
• Optimization
• +4 more

Dr. Richard Bödi is a Researcher at the ZHAW School of Engineering's Departement Applied Mathematics, Physics, Systems and Operations. He specializes in applied mathematics, algorithms, optimization, and computer science with a focus on databases and geometry. His work spans NoSQL systems, inventory optimization, and geometric structures like projective planes and collineation groups. Dr. Bödi has contributed to over 30 peer-reviewed publications since 1989, including foundational research on smooth stable planes and ZurichNoSQL query processing. He collaborated on a NoSQL Data Warehouse project for optimizing air transportation logistics and authored articles on decision-making scoring systems. His research interests include algorithmic efficiency in databases, symmetry exploitation in optimization, and geometric modeling. Recent work emphasizes data-driven business analytics and forecast accuracy in supply chains. Notable contributions include collaborative efforts with IBM on inventory budgeting and medical imaging algorithms for brachytherapy. Dr. Bödi’s research demonstrates a blend of theoretical mathematics and applied computational methods. His publications span disciplines from differential geometry to logistics optimization, reflecting a commitment to bridging abstract mathematical concepts with real-world applications.

• Machine Learning
• Database Systems
• Bioinformatics
• +3 more

Dr. Pavel Sulimov is a Researcher at the ZHAW School of Engineering, affiliated with the Intelligent Information Systems research group at ZHAW Zurich University of Applied Sciences. His work focuses on advancing machine learning applications in database systems, quantum computing, bioinformatics, and network analysis. He is a team member in projects such as DataGEMS (Horizon Europe) and GraphQueryML (SNSF/DFG), contributing to query optimization and quantum-inspired technologies. His research interests span machine learning-driven query optimizers, quantum algorithms for database cardinality estimation, proteomics data analysis using neural networks, and network embedding for co-authorship link prediction. His recent work explores the behavior of learned query optimizers and bias in mass spectrometry-based peptide identification. Pavel’s publications reflect interdisciplinary strengths, bridging database systems with quantum computing and applying machine learning to both bioinformatics and collaborative network analysis. His contributions emphasize practical applications in data management and computational biology.

• Species Distribution Modeling
• Hydrological Modeling
• Ecosystem Services Assessment
• +6 more

Prof. Anthony Lehmann is a leading researcher at the University of Geneva , affiliated with the EnviroSPACE group and the Department of Environmental and Water Sciences . His career spans over three decades, pioneering innovations like the GRASP software for Species Distribution Modeling (SDM) and coordinating large-scale projects such as enviroGRIDS , ACQWA , and ValPar.CH . He serves as Associate Editor for Environmental Sciences and Policy and co-led a MOOC on Ecosystem Services.