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Dennis KommView profile

Associate Professor

• Algorithm Design and Analysis
• Computer Science Education
• Theoretical Computer Science
• +3 more

Prof. Dennis Komm is an Associate Professor at ETH Zurich's Department of Computer Science, leading the group for Algorithms and Didactics. He chairs the Center for Computer Science Education (ABZ) and serves on committees such as the Swiss Maturity Board (Schweizerische Maturitätskommission) and the STEM Commission of the Swiss Academies. Previously, he held roles at RWTH Aachen University (Master's, 2008), ETH Zurich (PhD, 2012), University of Zurich (external lecturer, 2014–2020), and PH Graubünden (including department head and professor of 'Fachdidaktik Informatik'). Education: He completed a Master's in Computer Science at RWTH Aachen (2008), a PhD at ETH Zurich (2012), and studies in Information Technology at Queensland University of Technology (2006). His academic journey includes visiting roles at King's College, Stanford, and Comenius University. He has taught extensively across institutions, emphasizing Python and LOGO-based approaches for beginners. Research focuses on algorithm design, approximation algorithms, reoptimization, and advice complexity in theoretical CS. His work in education explores computational thinking, programming pedagogy (especially for K–12), and interdisciplinary approaches (e.g., robotics in math). Recent trends in his articles highlight advancements in online algorithms, optimization under dynamic conditions, and initiatives to integrate CS into Swiss school curricula sustainably. He actively promotes CS education through platforms like WebTigerPython and collaborates on projects such as CyberQuest and MINTerlink. His outreach includes organizing conferences (e.g., STIU 2025) and workshops on programming and cybersecurity for teachers and students. Despite no listed scientific awards, his contributions to education and theoretical CS are recognized through editorial roles in journals like Informatics in Education and contributions to the TigerJython Group. Grant-related advising includes co-supervising doctoral theses on robotics, USOs, and programming didactics. He advocates for equitable educational opportunities via the Passerelle exam and the Swiss Beaver Competition. His team's work spans teacher training, didactic certifications, and bridging university-school collaborations through initiatives like MINTerlink. Labs and teams: Head of ABZ (ETH's CS education center), collaborator with the Computational Robotics Lab, and part of the TigerJython Group. He also co-organizes the Colloquium on Mathematics, Computer Science, and Education with ETH's Mathematics Department.

Michael GastparView profile

Professor

• Information Theory
• Signal Processing
• Communications
• +3 more

Michael Gastpar is a full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, where he leads the Laboratory for Information in Networked Systems (LINX). He previously held faculty positions at the University of California, Berkeley (2003-2011, earning tenure in 2008) and Delft University of Technology. His research spans information theory, signal processing, communications, and systems neuroscience. His research interests focus on network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. Recent work demonstrates a strong shift toward exploring the theoretical foundations of modern machine learning, particularly investigating transformer architectures from an information-theoretic perspective. His research group at EPFL explores how information theory principles can provide fundamental limits and novel approaches for contemporary machine learning problems. His recent publications reveal a clear trend toward bridging classical information theory with modern machine learning. The 15 most recent papers show increasing focus on theoretical analysis of transformers, rate-distortion frameworks for language models, universal prediction methods, and applications of information measures to machine learning theory. This represents a strategic evolution from his earlier work on sensor networks and physical-layer network coding toward foundational questions in artificial intelligence. Scientific Awards: IEEE Fellow 2013 Communications Society & Information Theory Society Joint Paper Award Information Theory Society Distinguished Lecturer (2009-2011) ERC Starting Grant (2010) Okawa Foundation Research Grant (2008) NSF CAREER award (2004) 2002 EPFL Best Thesis Award Professor Gastpar has advised over 20 PhD students who have gone on to successful careers in both academia and industry. His research has been generously supported by major grants including an ERC Starting Grant "ComCom" (2011-2016) and ongoing support from the Swiss National Science Foundation. He has served in significant editorial roles, including as Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory (2008-11) and as Technical Program Committee Co-Chair for the IEEE International Symposium on Information Theory in 2010 and 2021. He leads the Laboratory for Information in Networked Systems (LINX) at EPFL, which brings together researchers working at the intersection of information theory, machine learning, and networked systems. The lab maintains strong connections with both theoretical research communities and practical applications in communications and neuroscience.

• Artificial Intelligence
• Deep Learning
• Recurrent Neural Networks
• +12 more

Juergen Schmidhuber is Associate Professor at the Faculty of Informatics of Università della Svizzera italiana and a leading researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI). He is also Chief Scientist at NNAISENSE, a company dedicated to building practical general-purpose AI. His work has profoundly influenced modern artificial intelligence, particularly through the development of Long Short-Term Memory (LSTM) networks in 1991, now deployed across billions of devices for speech recognition, machine translation, and virtual assistants. His research interests span Artificial Intelligence, Deep Learning, Recurrent Neural Networks, Universal AI, Meta-Learning, Algorithmic Information Theory, Artificial Curiosity, Robotics , and Low-Complexity Art . He has pioneered mathematically rigorous frameworks for self-improving AI systems and formal theories of creativity and beauty. His work bridges theoretical foundations with real-world applications in computer vision, natural language processing, and autonomous robotics. The recent articles reflect a consistent trajectory of innovation, combining deep theoretical insights with scalable machine learning architectures. His publications emphasize sequence modeling, universal learning, intrinsic motivation, and computational creativity , demonstrating both foundational contributions and industrial impact. From LSTM to Goedel machines, his work consistently targets the long-term goal of self-improving general AI. Scientific Awards: Numerous awards in AI and machine learning (specific names not listed) Schmidhuber leads a research group at IDSIA, where he mentors students and researchers in advancing the frontiers of AI. His lab has secured significant recognition and industrial collaboration, though specific grants are not detailed. He promotes the 'New AI'—general, sound, and relevant to physics—and continues to explore the convergence of intelligence, computation, and the universe. Labs and Teams: Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) NNAISENSE (as Chief Scientist)

Mathias PayerView profile

Associate Professor

• Software Security
• System Security
• Binary Exploitation
• +3 more

Mathias Payer is an Associate Professor at EPFL's School of Computer and Communication Sciences (IC), leading the HexHive Laboratory . His work focuses on software security, particularly addressing memory corruption and type violations through binary analysis and compiler-based techniques. He contributes to research in secure system design, fault isolation, and fuzzing methodologies. Current PhD students : Di Bartolomeo Luca, Feng Zhiyao, Hofhammer Florian, Lyu Tao, Mao Philipp Yuxiang, Zhang Chibin, Zheng Han Past EPFL PhD students: Badoux Nicolas Daniel, Bhattacharyya Atri, Hazimeh Ahmad His research explores software security in areas like: Protecting applications from vulnerabilities Binary exploitation and mitigation Compiler-driven security hardening Strong sanitization and privilege separation Memory corruption detection The HexHive group develops tools and frameworks for: Automated fuzz driver generation Gradual compartmentalization State inference for feedback optimization Secure cell architectures Recent publications highlight advancements in: Fuzzing hybrid approaches (e.g., DUMPLING, MendelFuzz) Memory safety validation (QMSan, Pacmem) Compiler-assisted defenses (Gradient, Type++)

• Formal Methods
• Software Engineering
• Software Verification and Validation
• +7 more

Carlo Alberto Furia is an Associate Professor and Vice Dean at the Faculty of Informatics, Università della Svizzera italiana (USI). He is affiliated with the Software Institute, where he leads the ATOM research group. His academic journey includes prior roles as an Associate Professor at Chalmers University of Technology and a Senior Researcher at ETH Zurich’s Chair of Software Engineering. PhD in Computer Science, Politecnico di Milano Master of Science in Computer Science, University of Illinois at Chicago Laurea in Computer Science and Engineering, Politecnico di Milano His research centers on formal methods for software engineering, aiming to enhance software correctness, reliability, and quality through rigorous techniques. Key areas include automated program verification, contract-based development, loop invariant inference, and empirical evaluation using Bayesian data analysis. He emphasizes practical applicability and automation in formal methods. His recent publications reflect a strong focus on program analysis at the bytecode level, multilingual software analysis, automated repair of Android security issues, and empirical methodologies. These works span topics such as JVM substitutability, exception behavior in Java bytecode, and information flow security, demonstrating a consistent thread in improving software robustness through formal and automated techniques. He is actively involved in the software engineering research community as an Associate Editor of the Empirical Software Engineering (EMSE) journal and as a Program Committee member for major conferences including FASE, FM, ASE, ICSE, and CauSE. Carlo Furia has advised multiple research projects and supervised student theses. He has led and contributed to funded research initiatives, particularly in program analysis and verification. His group has developed tools such as AutoProof and other software artifacts available through the ATOM software page. He regularly teaches courses such as Software Analysis, Programming Fundamentals, and Software Design & Modeling. He leads the ATOM research group, which focuses on advancing automated techniques for software testing, analysis, and verification. The group develops practical tools and conducts empirical studies to validate research outcomes.

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +7 more

Barbara Plank is a full professor and chair for AI and Computational Linguistics at Ludwig Maximilian University of Munich (LMU), where she heads the Munich AI and NLP (MaiNLP) lab and co-directs the Center for Information and Language Processing (CIS). She additionally serves as a visiting full professor at the IT University of Copenhagen, maintaining active dual institutional affiliations in computational linguistics and NLP research. Her research focuses on human-centric natural language processing challenges, particularly learning under sample selection bias (domain adaptation, transfer learning) and annotation bias, learning with limited data through continual/semi-supervised/weakly-supervised methods, multimodal learning at language-vision-speech interfaces, and fortuitous supervision for variety-space aware language understanding. She pioneers methodologies addressing human label variation as a critical factor in model robustness rather than mere noise. Recent publications (2024-2025) reveal dominant trends in modeling human label variation across NLP tasks, especially natural language inference and entity recognition, alongside dialectal language processing and LLM evaluation frameworks. Her work systematically investigates how human disagreement in annotations can be leveraged to build more robust, adaptable systems rather than treated as errors. Scientific recognition includes: ERC Consolidator Grant for the DIALECT project advancing natural language understanding for non-standard languages and dialects ACL 2024 Area Chair Award for the paper 'VariErr NLI: Separating Annotation Error from Human Label Variation' Leading the MaiNLP lab at CIS (LMU), she directs research integrated with MCML (Munich Center for Machine Learning), Munich Intelligent Robotics, ELLIS Unit Munich, UniDive, and COST action. Current projects include ERC-funded DIALECT and KLIMA-MEMES, focusing on human-facing NLP solutions for real-world language diversity challenges. She actively shapes the field through ACL leadership as VP-Elect and numerous keynotes emphasizing human-centric approaches. The MaiNLP lab at Akademiestr. 7, 80799 Munich, drives innovation in computational linguistics through interdisciplinary collaboration, maintaining strong ties with European research networks while developing practical applications for language variation and robust NLP systems. The lab's work directly informs her teaching in LMU's Computational Linguistics programs, bridging research and education in cutting-edge NLP methodologies.

Zhendong SuView profile

Professor

• Programming Languages
• Compilers
• Software Engineering
• +3 more

Zhendong Su is a full professor in the Department of Computer Science at ETH Zurich since August 2018. Previously, he held a full professorship at UC Davis from 2003 until June 2019. He earned his Ph.D. in Computer Science from UC Berkeley and dual Bachelor’s degrees in Computer Science and Mathematics from UT Austin in 1995. Affiliations: ETH Zurich: Full Professor (since 2018) UC Davis: Full Professor and Chancellor’s Fellow (2003–2019) IEEE Fellow, ACM Fellow, and Member of Academia Europaea His research focuses on programming languages, compilers, software engineering, computer security, and education technologies . Key contributions include compiler validation (e.g., Project Yin-Yang for SMT solvers and DBMS testing), testing tools like SQLancer, and educational innovations such as the Algot visual programming language. Recent work emphasizes secure AI (e.g., CipherSteal for TEE-shielded models) and compiler reliability (e.g., Artemis/Apollo for JIT validation). He has pioneered techniques like metamorphic testing and equivalence modulo inputs (EMI) for compiler validation, uncovering thousands of bugs in GCC/LLVM and SMT solvers. Awards: ICSE MIP Award (2022), ACM SIGSOFT Impact Paper (2018), NSF CAREER Award, and multiple industrial awards. His students have won IEEE TCSE Rising Star and SIGSOFT Impact Paper awards, securing roles at top universities and companies like Google and NVIDIA. Service: Steering committee member of ISSTA and ESEC/FSE, ACM Distinguished Speaker, and Associate Editor for ACM TOSEM. Program chaired ISSTA 2012 and co-chaired FSE 2016. Labs/Teams: Leads research groups on compiler validation, secure AI, and education technologies. Projects include Yin-Yang (SMT testing), SQLancer (DBMS fuzzing), and Algot (visual programming for education).

Peter MüllerView profile

Professor

• Formal Methods
• Program Verification
• Programming Languages
• +4 more

Prof. Peter Müller is a Full Professor at the Department of Computer Science at ETH Zurich since 2008. Previously, he held positions as Assistant Professor at ETH Zurich (2003-2008), Researcher at Microsoft Research Redmond (2007-2008), and IT project manager at Deutsche Bank. He earned his Diploma in Computer Science from Technical University of Munich (1996) and his Dr. rer. nat. from University of Hagen (2001) with a dissertation on modular verification of object-oriented programs. His research focuses on enabling correct software development through programming languages, verification methods, and tools. Key areas include formal verification for Rust and Go programs (Prusti and Gobra projects), separation logic, security protocols, and distributed systems verification. Müller's work emphasizes practical verification techniques for real-world systems, including secure router implementations and smart contract verification. Recent research trends highlight advancements in hyperproperties, modular reasoning for iterators and closures in Rust, and formal validation of verification tools. His methodologies bridge theoretical foundations with industrial applications, addressing challenges in concurrency, memory safety, and security assurance. Notable contributions include the SCION internet architecture, the Prusti verifier for Rust, and formal verification frameworks for distributed systems. His work often integrates rigorous mathematical foundations with scalable software engineering practices.

Sebastijan DumancicView profile

Assistant Professor

• Program Synthesis
• Probabilistic Programming
• Neuro-Symbolic AI
• +2 more

Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.

Lorenzo BaraldiView profile

Associate Professor

• Deep Learning
• Vision-and-Language integration
• Multimodal Retrieval
• +5 more

Lorenzo Baraldi is an Associate Professor at the University of Modena and Reggio Emilia, where he leads research in deep learning, vision-language integration, and multimodal AI systems. He serves as an ELLIS Scholar and Coordinator of the Modena ELLIS Unit, and has held the position of deputy director at the Interdepartmental Center on Digital Humanities since 2021. Previously, he worked at Facebook AI Research laboratory in Paris in 2017, developing video-matching algorithms for content moderation. His research spans multiple areas including Vision-and-Language integration, Multimodal Retrieval, Image and Video Captioning, Visual-Semantic alignment, Large-Scale model development, High Performance Computing, and Embodied AI. With over 120 publications in international journals and conferences, his work demonstrates consistent contributions to advancing multimodal AI capabilities. He has served as an Associate Editor for Computer Vision and Image Understanding and Pattern Recognition, and as Area Chair for major conferences including ICCV, WACV 2026, and ACM Multimedia 2025. His recent publication record shows significant impact in the field, with multiple papers accepted to top-tier conferences in 2024-2025 including CVPR, ICCV, BMVC, ICLR, ECCV, and NeurIPS. Notably, his paper "Hyperbolic Safety-Aware Vision-Language Models" was selected as a highlight paper at CVPR 2025. His research often involves collaboration with Rita Cucchiara and other researchers at his institution. ELLIS Scholar and Coordinator of the Modena ELLIS Unit Associate Editor for Computer Vision and Image Understanding Area Chair for ICCV and major multimedia conferences Highlight paper at CVPR 2025 Professor Baraldi teaches courses in Computer Vision and Cognitive Systems, Scalable AI, and Computer Architecture for the Artificial Intelligence Engineering and Computer Engineering programs. His teaching spans both undergraduate and graduate levels, with a focus on providing students with both theoretical foundations and practical implementation skills. He has developed educational materials including Deep Learning tutorials for classroom instruction.

Harald C. GallView profile

Professor

• Software Engineering
• Software Evolution
• Software Architecture
• +3 more

Harald C. Gall is a Professor of Software Engineering and Dean of the Faculty of Business, Economics, and Informatics at the University of Zurich (UZH). He leads the Software Evolution and Architecture Lab, focusing on software evolution analysis, mining software repositories, and cloud-based software engineering. His research emphasizes improving software development productivity through data-driven insights. He has held visiting positions at Microsoft Research and the University of Washington. Education: PhD (Dr. techn.) and Master's (Dipl.-Ing.) in Informatics from TU Vienna Research Interests: Software evolution, mining software archives, cloud-based tools, developer productivity, and empirical software engineering. Notable contributions include the Evolizer , ChangeDistiller , and SOFAS systems. Key Contributions: Established the Mining Software Repositories (MSR) research area, program chair for ICSE 2011 and ESEC/FSE 2005, associate editor of leading journals like Empirical Software Engineering and IEEE Software. Awards: Most Influential Paper Award, Test of Time Award, and multiple Best Paper Awards. Recognized for contributions to SE research methodologies and tool development. Professional Activities: ACM SIGSOFT awards chair, board member of Informatics Europe, and executive committee member of CHOOSE (Swiss SIG for OO Systems). Labs/Teams: Director of the Software Evolution and Architecture Lab at UZH, leading projects like SURF-MobileAppsData (SNSF-funded) and DevCloud (Hasler Foundation).

• Formal Methods
• Software Engineering
• Program Verification
• +7 more

Carlo A. Furia is an Associate Professor at the Software Institute within the Faculty of Informatics at Università della Svizzera italiana (USI). He leads the ATOM research group and is actively involved in advancing formal methods in software engineering. His work bridges theoretical rigor with practical applicability, particularly in verification, automated repair, and empirical analysis of software systems. PhD in Computer Science, Politecnico di Milano Master of Science in Computer Science, University of Illinois at Chicago Laurea in Computer Science and Engineering, Politecnico di Milano His research focuses on making formal methods practical through automation, combining diverse techniques, and conducting thorough empirical evaluations. He is particularly interested in using Bayesian data analysis to assess software engineering data. His work spans program verification (e.g., AutoProof), contract inference, API usability, and multilingual program analysis. His recent publications highlight trends in automated program repair, JVM bytecode analysis, Android security, and empirical methodologies. These works reflect a consistent emphasis on correctness, reliability, and empirical validation in software development. Scientific service includes: Associate Editor, Empirical Software Engineering (EMSE) journal Program Committee member, FM 2026, FormaliSE 2026, ASE 2025, iFM 2025 He has advised students and leads the ATOM group, which develops tools for software analysis. He teaches courses such as Software Analysis, Programming Fundamentals, and Software Design & Modeling. Current research directions include improving empirical evaluation rigor and enhancing verification at lower code levels like bytecode.

Bernice Simone ElgerView profile

Professor

• Biomedical Ethics
• Medical Ethics
• Clinical Ethics
• +5 more

Professor Bernice Simone Elger serves as Head of the Institute for Biomedical Ethics (IBMB) at the University of Basel, holding a dual affiliation with both the Faculty of Science and Faculty of Medicine. Established in 2011, the IBMB operates as Switzerland's unique ethics institute bridging scientific and medical disciplines under her leadership. Her research expertise spans biomedical ethics with particular focus on ethical challenges in emerging medical technologies, organ donation protocols, palliative care frameworks, and international research ethics. The institute's methodology combines empirical fieldwork with normative theoretical analysis, examining stakeholder perspectives across diverse healthcare contexts including developing countries and prison populations. Recent publications demonstrate her commitment to improving medical communication, particularly through plain-language translation of complex research for patient understanding. The IBMB under her direction has published extensively on topics ranging from smart home health technologies to ethical considerations in AI-driven medical decision-making. The institute maintains active research programs including the "Aging in Prison" project and hosts major international events such as the 2025 Experimental Philosophical Bioethics (BioXPhi) Summit. With over a dozen PhD students and multiple post-doctoral researchers, the IBMB receives consistent funding from major Swiss research bodies including SNF and SAMW. Professor Elger oversees comprehensive teaching initiatives including the "Contemporary Issues in Bioethics" lecture series and specialized ethics courses for both undergraduate students and medical professionals, emphasizing practical application of ethical frameworks in clinical settings.

Hugo SubtilView profile

Researcher

• Political Economy
• Political Science
• Computational Social Science
• +3 more

Hugo Subtil is a postdoctoral researcher at the Department of Political Science, Faculty of Arts and Social Sciences, University of Zurich, affiliated with the Chair of Comparative Politics in Political Institutions and European Politics. He is an active member of the University's Digital Society Initiative (DSI), particularly engaged with the DSI Community AI & Law and multiple other interdisciplinary communities including Cybersecurity, Digital Humanities, and Ethics. His academic background includes: Economics studies at École Polytechnique (France) PhD from École des Mines and École Polytechnique (France) Subtil's research program integrates Political Economy, Political Science, and Computational Social Science to investigate European political dynamics through quantitative text analysis. He specializes in extracting political ideologies from large-scale text corpora using machine learning algorithms and social media data, with dual research foci: (1) how institutional design shapes social norms and behaviors, and (2) longitudinal evolution of political ideologies. His methodological approach bridges traditional political science with cutting-edge computational techniques for analyzing legislative discourse and public communication. Analysis of his 12 publications (2020-2025) reveals a clear trajectory from historical economic analysis toward contemporary computational political science. Early work (2020-2021) examined Marxian economics and pandemic-era urban migration patterns, while recent research (2023-2025) increasingly focuses on European Parliament discourse, populism, and emotional rhetoric using advanced text mining techniques. This evolution demonstrates growing specialization in computational analysis of political communication within European institutions. No scientific awards or major honors are documented in the available information. While no formal advisees or grant funding details are specified, Subtil's collaborative work within the DSI framework suggests engagement with interdisciplinary research teams addressing digital society challenges. His position within the Chair of Comparative Politics indicates involvement in departmental research initiatives related to European governance. Subtil operates at the nexus of multiple research ecosystems: the Department of Political Science's European Politics research group, the university-wide Digital Society Initiative, and specialized DSI communities exploring AI-law intersections. His work contributes to the DSI's mission of addressing societal challenges through digitalization research, particularly in computational analysis of political discourse and institutional behavior.

Jinhan KimView profile

Researcher

• Software Engineering
• Mutation Testing
• Fault Localization
• +4 more

Jinhan Kim is a Postdoctoral Researcher at the Università della Svizzera italiana (USI) in the Faculty of Informatics, working in the TAU lab under Prof. Paolo Tonella. He earned his Ph.D. from KAIST under Prof. Shin Yoo, focusing on software engineering research in mutation testing, fault localization, and deep learning system testing. His work bridges traditional software engineering techniques with AI-driven methodologies, emphasizing AI4SE and SE4AI paradigms. Education: Ph.D. in Software Engineering, KAIST, 2023 Research Interests: Mutation Testing Deep Learning System Testing Autonomous Systems Testing Adversarial Attack Detection Empirical Software Engineering Service and Leadership: Organized SBFT 2026 and DeepTest 2026 (co-located with ICSE 2026) Program Committee Member for ASE, ISSTA, Mutation, and DeMeSSAI Board of Distinguished Reviewers for TOSEM (2024–2025) Labs and Teams: Active contributor to the TAU Lab at USI, focusing on advanced software testing and AI integration.