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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).

Sabine SüsstrunkView profile

Professor

• Computational Imaging
• Computational Photography
• Color Image Processing
• +4 more

Sabine Süsstrunk is a Full Professor and Director of the Images and Visual Representation Laboratory (IVRL) at EPFL's School of Computer and Communication Sciences. She holds a BS in Scientific Photography from ETH Zürich, MS from Rochester Institute of Technology, and PhD from University of East Anglia. Her career includes positions at Hewlett-Packard Labs and Corbis Corporation. Her research explores computational imaging, computational photography, color processing, computer vision, and image quality. Key interests include near-infrared applications, multispectral imaging, and computational aesthetics. Her work bridges hardware and software solutions for imaging challenges. Publications demonstrate consistent focus on advancing generative models (diffusion models, neural cellular automata), 3D reconstruction (NeRF variants), and media integrity (DeepFake detection). Recent trends show increased emphasis on 3D vision, robustness in generative AI, and video analysis. Awards & Honors: IS&T/SPIE Electronic Imaging Scientist of the Year (2013) Raymond C. Bowman Teaching Award (2018) EPFL AGEPoly IC Polysphere Award (2020) 8 Best Paper/Demo Awards Fellowships: IEEE, IS&T, ELLIS, AIIA She leads the IVRL lab and advises PhD candidates while serving as President of the Swiss Science Council. Research is supported through competitive grants and industry collaborations.

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.

Oskari Ville PakariView profile

Lecturer

• Nuclear Engineering
• Reactor Physics
• Radiation Detection
• +3 more

Oskari Ville Pakari is a Lecturer at the School of Basic Sciences, École polytechnique fédérale de Lausanne (EPFL), affiliated with both the Institute of Physics (IPHYS) and the Swiss Plasma Center (SPH-ENS). He contributes to teaching and research, particularly in reactor physics and radiation detection. His research focuses on nuclear reactor diagnostics , gamma noise analysis , and neutron spectroscopy . He actively develops mixed reality visualization tools for radiation detection data and participates in the European CORTEX project for reactor monitoring. Selected publications highlight his work in gamma-ray imaging , neutron noise simulations , and detector system validation using advanced statistical methods like bootstrapping and Welch's technique. Teaching activities include courses on Radiation biology, protection, and applications Radiation and reactor experiments He advises PhD student Saliba Michel and collaborates with international institutions such as CEA, KIT, and LRS (Laboratory of Reactor Physics and Systems Behaviour) at EPFL.

• Cartography
• Geomatic Engineering
• Historical Map Analysis
• +4 more

Dr. Yizi Chen is a Researcher affiliated with the Professorship for Cartography at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. Their work focuses on advancing cartographic techniques through AI-driven methods, historical map analysis, and geospatial technologies. Key contributions include automated map vectorization, semantic segmentation of historical maps, and integrating multimodal data for robotic systems. They have published extensively in top-tier journals and conferences, addressing challenges in deep learning applications for geomatic engineering. Education details are not explicitly provided in the text. Research interests include semantic segmentation, generative AI for cartography, and steganography in image translation. Notable publications span topics from eye-tracking segmentation to urban land use mapping, reflecting a strong interdisciplinary approach. Dr. Chen collaborates on projects involving historical map digitization and benchmarking datasets for computer vision tasks. No awards or grants are mentioned. Their work contributes to advancing geomatic engineering through innovative solutions in digital mapping and spatial data analysis.

Volker DellwoView profile

Associate Professor

• Phonetics
• Speech Recognition
• Computational Linguistics
• +4 more

Prof. Dr. Volker Dellwo is an Associate Professor of Phonetics and head of the Department of Computational Linguistics. His research focuses on phonetics, speech recognition, computational linguistics, and dialectology, with applications in forensic analysis, voice biometrics, and multimodal emotion recognition. Academic Rank: Associate Professor Department: Computational Linguistics His work explores phonetic convergence , speaker discrimination, and the role of prosodic features in voice recognition. Recent studies analyze whispered speech processing, cross-dialect accommodation, and neural mechanisms of speaker identity encoding. Key article trends include self-supervised learning for speech recognition , multimodal emotion detection , and forensic voice analysis . Subfields span acoustic variability, temporal envelope dynamics, and voice quality metrics. Publications emphasize computational phonetics , cross-linguistic studies , and neural network applications in speaker identification. Research also addresses challenges in forensic audio analysis and synthetic speech dataset generation.

• Human-Machine Interaction
• Usability Testing
• User Experience (UX)
• +5 more

Jürgen Sauer is a Full Professor at the University of Fribourg , affiliated with the Department of Psychology under the Faculty of Letters and Human Sciences . With over 120 publications, his research focuses on Human-Machine Interaction , Usability Testing , User Experience (UX) , and Automation Design , particularly in high-stakes environments like X-ray baggage screening and spaceflight simulations . Email: juergen.sauer@unifr.ch Phone: +41 26 300 7622 Address: RM 01 bu. C-1.117, Rue PA de Faucigny 2, 1700 Fribourg Orcid: 0000-0003-2105-1694 His research projects, funded by the Swiss National Science Foundation (FNS), include: Improving work design for airport security officers (2019-2024): Developed pictorial scales for measuring psychological constructs in security environments. Social stress and support in hybrid teams (2018-2023): Investigated machine-induced social stressors and mitigation through social support. Automation in visual inspection tasks (2014-2018): Examined adaptable automation for baggage screening and system reliability effects. Usability testing effectiveness (2012-2016): Analyzed cultural background impacts and non-usability product features influencing test outcomes. Key contributions include the Luggage Inspection Simulation (LIS) environment for modeling work environments and the development of pictorial usability scales for multilingual applicability. His work bridges ergonomics , human factors , and applied psychology , with notable collaborations with researchers like Adrian Schwaninger and Andreas Sonderegger . His recent publications (2025-2014) analyze: Human-machine performance under false alarms and miscues Social stressor dynamics in hybrid teams Usability scale animation effects Phubbing behavior in professional contexts Accessible website design for non-disabled users

• Digital Trust
• Cybersecurity
• Blockchain
• +12 more

Dr. Felix Härer is a Lecturer and researcher at the University of Applied Sciences FHNW, School of Business, Basel, Switzerland, and also teaches externally at the University of Fribourg. He is affiliated with the Digital Trust Competence Center, where he conducts research and teaching in IT Security, Cybersecurity, Digital Trust, Blockchain, AI, Cloud Computing, and Systems Modeling. His research interests span a broad and interdisciplinary range, including: Digital Trust and Cybersecurity Blockchain and Decentralized Systems AI and Knowledge-based Systems (including LLMs and RAG) Software and Systems Modeling (BPMN, ArchiMate) Data Science and ETL-based Analytics Zero Trust and Secure Architectures His recent publications (2020–2023) demonstrate a strong focus on blockchain interoperability, model-driven engineering, decentralized applications, and the integration of AI with conceptual modeling. He explores scalable architectures, cross-chain query languages, and secure attestation mechanisms, often combining modeling approaches with emerging technologies. His work bridges academic rigor with practical implementation in distributed and cloud environments. Scientific awards include: Best Paper Award at IEEE PKIA 2023 He actively supervises bachelor’s and master’s theses and student projects in digital trust and related domains. He has served on PhD committees externally and is a reviewer and program committee member for journals and conferences such as IEEE Transactions, WWW, CAiSE, and EMISAJ. His professional experience includes industry work at Siemens Healthineers in software engineering. He is a member of the IEEE Blockchain Group (Switzerland) and contributes to UN/CEFACT standards for e-commerce and supply chain data. He is involved in organizing workshops and conferences, including B4ISE 2025, B4TDS 2023–2024, and DESRIST 2023, and has delivered keynotes on Computational Trust and Blockchain Interoperability.

Tova MiloView profile

Professor

• Databases
• Big Data Management
• Machine Learning for Data Management
• +7 more

Professor Tova Milo is the Chair for Information Management at the School of Computer Science, Tel Aviv University, leading the prominent Databases Lab (DB Group). Her research spans databases, big data management, crowd-based data sourcing, and business process querying, with significant contributions to data integration and semi-structured data systems. Her research interests focus on innovative approaches to data management challenges through machine learning integration, crowd computing, and business process analysis. Key projects include Business Process Querying (BPQ) for analyzing BPEL specifications, MoDaS for crowd-based data management, and PROX for data provenance summarization. Her work bridges theoretical foundations with practical applications in fraud detection, recommendation systems, and data cleaning. Analysis of her recent publications reveals strong trends in human-in-the-loop data management systems, with growing emphasis on crowd integration for data cleaning and knowledge acquisition. Her research increasingly combines traditional database theory with machine learning techniques for scalable big data processing, while maintaining focus on business process modeling and provenance tracking. ACM PODS Alberto O. Mendelzon Test-of-Time Award (2010) ERC Advanced Investigators grant MoDaS (2011) The Weizmann Prize for Exact Sciences (2017) VLDB Women in Database Research award (2017) IEEE TCDE Impact award (2022) ISF Breakthrough Research Grant (2022) Doctorate Honoris Causa, University of Zurich (2023) ACM Fellow Member of Academia Europaea Professor Milo has advised over 30 graduate students including PhD candidates like Yael Amsterdamer and Ohad Greenshpan, and numerous MSc students working on projects including MoDaS, BPQ, and EDOS. Her research has been supported by major grants including the ERC Advanced Investigators grant and ISF Breakthrough Research Grant. She actively collaborates with industry partners including IBM and Microsoft, particularly in business process management standards. She directs Tel Aviv University's Databases Lab, which maintains the DB Group with multiple research streams including business process querying (BPQ), crowd-based data management (MoDaS), and self-adaptive data dissemination (EDOS/COLT). The lab operates from the Schreiber Building (M-20) and maintains strong international collaborations, particularly with European institutions through the ERC-funded MoDaS project.

Natalia UmanskyView profile

Research Fellow

• Computational Social Science
• Political Communication
• International Relations
• +4 more

Natalia Umansky serves as a Postdoctoral Research Fellow in Political Science at the University of Zurich's Faculty of Arts and Social Sciences, affiliated with the Institute of Political Science and the Digital Democracy Lab within the Digital Society Initiative (DSI). Her institutional profile highlights interdisciplinary engagement across DSI's Cybersecurity, Democracy, and Digital Humanities communities. Academic Background: PhD in Politics and International Relations (defence pending) from University College Dublin Iseult Honohan Scholar at University College Dublin's Connected_Politics Lab Her research pioneers Computational Social Science methodologies to analyze social media ecosystems, specializing in securitization theory applications to digital political communication. Current projects examine agenda-setting constellations in financial policy, hate speech detection frameworks, and TikTok's impact on youth political engagement. Methodologically, she integrates network analysis, natural language processing, and comparative case studies to investigate how security narratives propagate across digital platforms and influence democratic processes. Publication trends reveal a strategic evolution from foundational work on terrorism-migration linkages (2016) toward cutting-edge examinations of AI moderation systems (2024) and generative AI's role in security discourse (2025). Her scholarship consistently bridges political theory, data science, and platform governance with emphasis on empirical validation of computational models. Scientific Recognition: Iseult Honohan Scholar (University College Dublin) As a core member of Zurich's Digital Democracy Lab, Umansky contributes to high-impact collaborative projects analyzing digital threats to electoral integrity and developing transparency standards for algorithmic governance. Her work positions her at the forefront of computational political science, with emerging leadership in establishing methodological best practices for social media analysis in security contexts.

• Security of Deep Learning Systems
• Side-Channel Attacks
• Privacy-Preserving Machine Learning
• +3 more

Dr. Yuanyuan Yuan is a Researcher at the Department of Computer Science, ETH Zurich, Switzerland, based at CNB H 104.1, Universitätstrasse 6, 8092 Zurich. Her work bridges computer security and machine learning with a focus on practical vulnerabilities in deployed AI systems. Her research centers on exposing and mitigating security flaws in deep learning deployments, particularly targeting trusted execution environments (TEEs) and on-device inference systems. Key contributions include pioneering side-channel attacks against TEE-shielded neural networks (CipherSteal, HyperTheft), bit-flip attack surfaces in DNN executables (BitShield), and novel testing methodologies for neural network robustness. She investigates cache/timing side channels, ciphertext analysis, privacy leakage in partitioned ML, and concept-based explainability. Analysis of her 2023-2025 publications reveals a cohesive focus on offensive security research for AI infrastructures, with consistent contributions to top venues in security and machine learning. Her work demonstrates expertise in low-level system interactions (memory, cryptography) applied to ML security, spanning attack vectors, defensive mechanisms, and validation frameworks. The research trajectory shows increasing sophistication in exploiting hardware-software interfaces while developing practical hardening techniques for real-world deployments.

Patrick JermannView profile

Lecturer

• Computer Science
• Educational Technology
• Human-Computer Interaction
• +3 more

Patrick Jermann is a Lecturer at EPFL’s School of Computer and Communication Sciences, affiliated with the Centre for Digital Education (CEDE) , SIN-ENS , and SSC-ENS departments. As Executive Director of CEDE since 2013, he leads Swiss MOOC Service (SMS), NOTO, and Campus Analytics initiatives. His research focuses on Computer Supported Collaborative Learning (CSCL) , Learning Analytics , and MOOCs , analyzing student interactions, gaze patterns, and attrition behaviors through clickstream data. Recent work explores pedagogical design, statistical methods, and software development for educational tools. His publications (2001–2015) reveal trends in eye-tracking for MOOCs, interaction analysis, and collaborative learning technologies. Notable contributions include frameworks for gaze-based feedback and metrics for perceived video difficulty. He has mentored PhD students such as Sharma Kshitij and Nüssli Marc-Antoine , and secured grants via the DRIL fund . As a CDS Member, he contributes to institutional digital education strategies.

• Human Factors in Privacy and Security
• Usable Security and Privacy
• Security/Privacy Awareness and Behavior Change
• +6 more

Prof. Nico Ebert is a Professor of Business Information Systems and Head of the Information Systems Human Factors & Risks Group at the ZHAW School of Management and Law. His work focuses on human factors in privacy/security, usable security, and organizational cybersecurity practices. He leads research projects such as the Cyber Resilience Network (Canton of Zurich) and studies TikTok privacy behavior among Swiss youth. Ebert has authored over 50 peer-reviewed articles, with recent emphasis on cybersecurity decision-making, privacy-aware design, and organizational data collaboration frameworks. Education background not explicitly stated in provided texts, but his academic career spans over 20 years with a focus on business information systems and cybersecurity. Active in professional networks including ACM's Swiss CHI Chapter and the Digital Society Initiative (DSI). Research integrates behavioral science principles with technical cybersecurity solutions. Key research areas include: human-centered cybersecurity strategies, privacy-by-design frameworks, and evaluating security technologies like trusted execution environments. His work bridges organizational practices with user behavior studies, often employing mixed-methods approaches. Grants and collaborations include leadership in multiple EU-funded projects and industry partnerships. Advises on cybersecurity policies for regulated industries and publishes regularly in top venues like Communications of the ACM and Computers & Security.

Suryanarayana SankagiriView profile

Researcher

• Discrete Choice Models
• Recommendation Systems
• Blockchain Security
• +4 more

Suryanarayana Sankagiri is a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, affiliated with the Information and Network Dynamics (INDY1) group under Professor Matthias Grossglauser. Previously, he earned his Ph.D. in Electrical & Computer Engineering (2018-2022) from the University of Illinois at Urbana-Champaign , where he was supervised by Bruce Hajek and participated in the Coordinated Science Lab . He also holds an M.S. in Electrical & Computer Engineering from the University of Illinois (2016-2018) and a B.Tech. in Electrical Engineering from the Indian Institute of Technology Bombay (2012-2016). Education : Ph.D., Electrical & Computer Engineering, University of Illinois (2018-2022) M.S., Electrical & Computer Engineering, University of Illinois (2016-2018) B.Tech., Electrical Engineering, IIT Bombay (2012-2016) Suryanarayana's research focuses on discrete choice models and their application to recommendation systems , with a particular emphasis on learning from choice data and developing novel models for human decision-making. His broader interests include blockchain security under adverse network conditions, network dynamics , probabilistic modeling , and algorithm design . Recent work explores nonconvex matrix factorization and contextual dueling bandits for recommendation systems. His publications span theoretical and applied domains, including high-impact venues like ICML , Stochastic Systems , and IEEE Transactions on Networking . Themes include blockchain efficiency , hidden community detection in preferential attachment graphs, and temporal analysis of Indian classical music. Current projects involve refining recommendation systems through sparse comparison data and designing protocols for resilient blockchain networks. Scientific Awards : zkCapital Paper of the Week (2021) Rambus Fellowship (2021) Mavis Future Faculty Fellowship (2019) List of Teachers Ranked as Excellent (2019) Nomination for IIT Bombay Undergraduate Colloquium (2016) Best Poster Award, IIT Bombay Undergraduate Research Symposium (2013) Suryanarayana has advised no students listed in the provided materials. His work has been supported by fellowships such as the Mavis Future Faculty Fellowship and Rambus Fellowship . He contributes to the INDY1 group at EPFL, which investigates information and network dynamics through interdisciplinary approaches combining probability , network theory , and algorithmic design .

• Autonomous Driving
• Computer Vision
• Robotics
• +3 more

Markus Enzweiler serves as Professor of Computer Science and Autonomous Systems at Esslingen University of Applied Sciences within the Department of Computer Science and Engineering. He concurrently holds the leadership position of Director at the Institute for Intelligent Systems, where he oversees research initiatives focused on intelligent systems development for real-world autonomous applications. His research program centers on computer vision for autonomous systems , with specialized expertise in visual-inertial SLAM, collective perception, and neural rendering techniques. Key investigation areas include environmental robustness across agricultural and urban settings, real-time processing constraints for embedded systems, sensor fusion methodologies (particularly camera-radar integration), and the application of generative models for perception enhancement. His work consistently addresses practical implementation challenges such as computational efficiency and sensor calibration in unstructured environments. Analysis of his 2023-2025 publications reveals three dominant research trajectories: (1) Advancement of lightweight perception systems through stixel-based representations and neural rendering; (2) Development of infrastructure-supported collective perception frameworks with datasets like CoopScenes and OPNV; and (3) Rigorous benchmarking of SLAM components in domain-specific contexts including agricultural robotics and multi-season navigation. His recent systematic review on LLM-based vulnerability detection also demonstrates expanding interest in software security for autonomous systems. As Director of the Institute for Intelligent Systems, Prof. Enzweiler leads a research ecosystem focused on translating theoretical advances into practical autonomous vehicle technologies. His team develops specialized datasets (Rover, OPNV) and software stacks for smart city environments, emphasizing the integration of novel perception approaches with vehicle dynamics modeling and real-time operational constraints.