Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.
Peter Bühlmann is a Professor at ETH Zürich within the Seminar für Statistik , focusing on high-dimensional statistics, causal inference, and machine learning. His work bridges theoretical advancements with practical software implementations in R packages like pcalg , mboost , and glmmlasso , impacting fields such as genomics, proteomics, and intensive care analytics. Key Contributions : Causal structure learning, stability selection, anchor regression, and deconfounding. Software : Developed widely used R packages for statistical modeling and causal inference. Teaching : Courses on high-dimensional statistics at ETH Zürich and international institutions. Research Trends : Recent articles emphasize causal robustness, domain adaptation, and applications in medicine. His work addresses challenges in heterogeneous data, missing values, and covariate shifts using methods like spectral deconfounding and residual prediction tests. Scientific Recognition : Co-author of a paper designated as a New Hot Paper (Meinshausen and Bühlmann, 2006) by Essential Science Indicators, indicating significant impact in high-dimensional multiple testing.
April Yi Wang is a tenure-track Assistant Professor in the Department of Computer Science at ETH Zürich, where she directs the Programming, Education, and Computer-Human Interaction Lab (PEACH Lab). She is a core faculty member at the Institute for Intelligent Interactive Systems and associated with the ETH AI Center. Wang is also an active member of ETH HCI and Swiss CHI communities, contributing significantly to human-computer interaction and educational technology research. Dr. Wang's educational background includes: Ph.D. in Information Science from University of Michigan (2023), advised by Steve Oney and Christopher Brooks M.Sc. in Computer Science from Simon Fraser University (2018), advised by Parmit Chilana B.Eng in Computer Science from Zhejiang University (2016) Dr. Wang's research focuses on human-centered approaches to programming and data science. Her work reimagines programming as a form of literature that communicates with both machines and people, exploring creative representations like text, shapes, animations, and everyday objects. She investigates how to make programming more natural and intuitive through literate programming environments, with applications in professional and educational contexts. Her research spans human-computer interaction, educational technology, and AI-assisted programming tools. Analysis of Dr. Wang's recent publications reveals a strong focus on AI-enhanced educational tools, particularly for programming and data literacy. Her work increasingly integrates large language models to scaffold learning while maintaining user agency. There's a clear trajectory toward developing situated learning approaches that connect abstract concepts to real-world contexts through augmented reality and tangible interfaces. Her research bridges HCI, education, and AI to create more accessible and engaging technical learning experiences. Dr. Wang has received numerous prestigious awards including: 2023 Gary M. Olson Award and Honourable Mention Award at ACM CHI 2022 Rising Stars in EECS and Heidelberg Laureate Forum Young Researcher 2020 Best Short Paper Award at IEEE VL/HCC and Honourable Mention at ACM CHI 2019 Best Paper Award at ACM CSCW Dr. Wang actively mentors students through thesis projects at ETH Zürich, supervising numerous bachelor's and master's students on topics ranging from AI-assisted programming to data literacy tools. Her lab, PEACH Lab, has secured funding including the recent innovedum funding for the Coducate project. She serves on program committees for major conferences including CHI and UIST, and regularly reviews for top HCI and education journals. The PEACH Lab, directed by Dr. Wang, focuses on creating expressive, intelligent, and human-centered systems that make technical topics more accessible. The lab explores textual, visual, and embodied representations for programming, with emphasis on enhancing communication, collaboration, and learning. Current research directions include balancing automation with user agency, supporting diverse learning needs, and developing tools for interdisciplinary technical communication.
Elisa Ricci is a Full Professor at the Department of Information Engineering and Computer Science (DISI) at the University of Trento and serves as Head of the Research Unit Deep Visual Learning at Fondazione Bruno Kessler. She coordinates the Doctoral Program in Information Engineering and Computer Science at the University of Trento and holds prestigious fellowships from ELLIS and IAPR. Her research focuses on advancing computer vision and deep learning systems capable of operating in open-world environments. Key interests include domain adaptation, continual learning, and self-supervised learning for visual and multi-modal data processing. Her work addresses critical challenges in enabling machines to adapt to new domains without forgetting prior knowledge, with applications spanning robotics perception, medical imaging, and privacy-preserving AI systems. Recent publications reveal a dominant trend toward leveraging vision-language models for open-vocabulary tasks, training-free adaptation methods, and federated learning architectures. Significant research thrusts include machine unlearning for privacy, robustness against bias in visual classifiers, and novel class discovery using foundation models—particularly evident in 2025 publications addressing medical imaging, 3D segmentation, and collaborative generative systems. Her major recognitions include: ELLIS Fellow IAPR Fellow As Doctoral Program Coordinator at the University of Trento, she oversees PhD training while leading the Deep Visual Learning unit at Fondazione Bruno Kessler. Her research group secures competitive grants in AI-driven perception systems, though specific funding sources aren't detailed in the source material. The Deep Visual Learning Research Unit specializes in open-world computer vision challenges, developing frameworks for domain adaptation, continual learning, and multi-modal perception. Current projects integrate generative models with robotics applications while addressing privacy concerns in vision-language systems through unlearning techniques.
Gustavo Alonso is Full Professor at the Department of Computer Science (D-INFK) of ETH Zurich and Head of the Institute for Computing Platforms . He has been at ETH since 1995, first as a post-doc, then as Assistant Professor from April 1998, and promoted to Full Professor in October 2001. Within the Systems Group he leads the Information and Communication Systems Research Group . Education: 1989 – Telecommunications Engineering (undergraduate), Madrid Technical University (UPM-ETSIT), Spain 1992 – M.S. Computer Science, University of California, Santa Barbara (UCSB) 1994 – Ph.D. Computer Science, University of California, Santa Barbara (UCSB) Research Interests: His work spans databases, distributed systems, cloud-computing architecture, FPGAs, hardware acceleration for data science, parallel and reconfigurable computing . The group investigates how modern heterogeneous hardware—from GPUs to SmartNICs—can be integrated into data-processing systems to achieve orders-of-magnitude performance gains, energy savings, and new functionality such as in-network computation and serverless acceleration. Scientific Awards & Honors: Fellow of the ACM (Association for Computing Machinery) Fellow of the IEEE (Institute of Electrical and Electronics Engineers) Distinguished Alumnus, Department of Computer Science, UC Santa Barbara Four Test-of-Time / Most Influential Paper Awards across databases, programming languages, cloud computing, and software engineering Labs & Projects: He directs the Information and Communication Systems Research Group within the Systems Group ( systems.ethz.ch ). The lab develops open-source platforms such as Coyote v2 for FPGA abstractions, Shuhai for HBM benchmarking, and MicroRec for micro-second recommendation serving, while collaborating with industry on SmartNICs, serverless analytics, and cloud-scale data analytics.
Prof. Dr. Timothy Roscoe is a Full Professor in the Department of Computer Science at ETH Zurich. His research focuses on operating systems, networking, and distributed systems. He was inducted as an ACM Fellow in 2014 for contributions to these fields. Previously, he worked at Intel Research Berkeley, UC Berkeley (Adjunct Professor), and Sprint Labs. His work includes foundational contributions to systems like PlanetLab, P2 Declarative Networking, and the Nemesis OS. Roscoe holds a PhD from the University of Cambridge and has led projects in cloud computing, wide-area network measurement, and secure systems design. His current research emphasizes hardware-software co-design, formal verification of systems, and scalable memory architectures. Education: PhD in Computer Science, University of Cambridge (1995) Key Roles: Principal Researcher at Intel Berkeley Lab, Visiting Researcher at National ICT Australia Research Highlights: Contributions to distributed systems (PlanetLab), declarative networking (P2), and secure microkernels (seL4 integration). Current projects address challenges in disaggregated memory systems, coherent interconnects, and trustworthy embedded systems. Awards: ACM Fellow (2014), numerous grants and industry collaborations. Labs/Teams: Systems Group at ETH Zurich, focusing on OS design, hardware architectures, and formal methods.
Professor Martin Schrimpf is a Tenure Track Assistant Professor at EPFL, holding dual appointments in the School of Life Sciences (SV) and the School of Computer and Communication Sciences (IC). His research bridges computational neuroscience, deep learning, and cognitive science to model human natural intelligence in vision and language. He leads the NeuroAI Lab, focusing on aligning artificial neural networks with brain mechanisms and human behavior. Education: PhD in Brain and Cognitive Sciences from MIT (2017–2022), MSc in Software Engineering from TUM/LMU/UNA (2014–2017), and BSc in Information Systems from TUM (2011–2014). His work has been recognized with awards including the Neuro-Irv Open Science Prize, McGovern Fellowship, and Takeda AI+Health Fellowship. He co-founded Integreat, a social impact startup recognized with Google.org’s Impact Challenge and TUM’s Social Impact Award. Research interests include neuroAI, brain-like models, and clinical translation (e.g., visual prosthetics). He has published in top venues like Neuron, Nature Human Behavior, NeurIPS, and ICLR. Current projects involve developing topographic language models (TopoLM) and investigating causal language network interactions using LLMs. Teaching: Courses include Neuroscience Foundations for Engineers and Brain-like Computation and Intelligence . Supervised over 26 students, including PhD candidates Badr Alkhamissi, Ben Lönnqvist, and Yingtian Tang. Active in grants from SNSF, NeuroX, and EPFL’s AI Center. Labs/Teams: NeuroAI Lab at EPFL Neuro-X Institute. Future directions include advancing brain-inspired models for clinical applications and expanding interdisciplinary collaborations between neuroscience and AI.
Babak Falsafi is a Full Professor at the School of Computer and Communication Sciences (IC) at EPFL, leading the Parallel Systems Architecture Laboratory (PARSA). He is a renowned expert in computer architecture, datacenter systems, and cloud-native server design. His research focuses on post-Moore era computing, emphasizing heterogeneous architectures, energy efficiency, and scalable IT infrastructure. Falsafi is the founder of EcoCloud, an EC-sponsored industrial-academic consortium investigating sustainable information technology. He holds ACM and IEEE fellowships, a Sloan Research Fellowship, and has contributed to major projects like Optimus Prime (data transformation acceleration), AstriFlash (flash-based online service systems), and Midgard (virtual memory re-design). His work spans hardware-software co-design, memory systems, and security. Falsafi advises numerous PhD students and collaborates with industry partners such as Google and Cavium. Key achievements include pioneering scalable multiprocessor architectures, snoop filters in IBM BlueGene, and spatial memory streaming in ARM cores. His lab develops open-source tools like QFlex for server simulation. He frequently presents at top conferences (HPCA, ISCA, MICRO) and chairs workshops on post-Moore infrastructure. Teaching roles include leading courses in computer architecture and parallel systems across multiple EPFL departments (SIN, EDIC, SSC, SMA). His work addresses datacenter challenges like the 'data tax' and mitigating latency through specialized accelerators.
Alexander Mathis is an Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the Brain Mind Institute (School of Life Sciences). His research bridges computational neuroscience and machine learning to decode sensorimotor behaviors and develop AI tools for behavioral analysis. Pure Mathematics MSc, Ludwig Maximilians University Munich PhD in Computational Neuroscience, Ludwig Maximilians University Munich His work focuses on understanding how the brain generates behavior through computational models and algorithms. Key contributions include the DeepLabCut toolbox, hBehaveMAE , and other frameworks for pose estimation, action segmentation, and brain-inspired AI. His group also explores proprioception, motor control, and neural coding theories. Recent publications include unsupervised hierarchical behavior modeling via masked autoencoders (ECCV 2024), synthetic basketball benchmarks (Shot7M2), and extensions of BABEL into hBABEL. These works highlight his interest in temporal hierarchies in behavior and scalable AI solutions for neuroscience. Scientific Honors Robert Bing Prize (2024) Eric Kandel Young Neuroscientists Prize (2023) Frontiers of Science Award (2023) Marie Skłodowska-Curie Postdoctoral Fellowship Studienstiftung des deutschen Volkes scholarship He advises PhD candidates in neuroscience and life sciences, leads courses on brain-like computation and software engineering for life sciences , and collaborates across AI4Science initiatives. His group actively participates in competitions, such as NeurIPS' MyoChallenge, where brain-inspired reinforcement learning algorithms have won awards.
Kaveh Razavi is an Assistant Professor at ETH Zurich in the Department of Information Technology and Electrical Engineering, supervising the Computer Security Group. He is also affiliated with the Department of Computer Science. Research focuses on commodity hardware and operating systems security Teaching: Computer Engineering (BSc 2022–2025), Hardware Security (MSc 2016–2025), Advanced Operating Systems (MSc 2016–2019) Research Interests: Razavi's work bridges computer systems and security, emphasizing microarchitectural vulnerabilities, hardware exploitation, and security verification. His group develops tools for analyzing hardware-software interactions and mitigating memory attacks like Rowhammer. Scientific Awards: Distinguished/Best Paper Awards at IEEE S&P, USENIX Security, MICRO Five Pwnies Awards for innovative research Dutch Veni Grant, ERC Starting Grant Jochen Liedtke Young Researcher Award Golden Owl Teaching Award (2022) ETH Medal, Intel Bounty Reward, BlackHat USA Recognition
Jakob Merane is a postdoctoral researcher at ETH Zurich's Professorship of Law, Economics and Business, and a visiting researcher at the Max Planck Institute and Harvard Law School. He holds a Ph.D. from ETH Zurich and law degrees from the University of Basel, with additional studies in applied statistics. His research focuses on AI-driven legal enforcement, legal technology, and compliance-by-design approaches, leveraging computational and empirical methods. Merane's work includes developing tools like SwiLTra-Bench for multilingual legal translation and LEXam for benchmarking AI legal reasoning. He has contributed to GDPR compliance studies, automation of legal enforcement processes, and quantitative analyses of Swiss legal datasets. Awards include the 2024 Young Scholar Prize from the German Law and Economics Association. Teaching responsibilities include ETH's Law & Tech course since 2020, emphasizing AI regulation and interdisciplinary collaboration between law and computer science. Merane's research also addresses AI liability in medical contexts, digital advertising regulations, and ethical AI integration in legal systems. His work bridges legal theory with practical applications, aiming to enhance justice accessibility through technology.
Hoda Heidari is the K&L Gates Career Development Assistant Professor in Ethics and Computational Technologies at Carnegie Mellon University (CMU), with joint appointments in the Machine Learning Department and the Institute for Software, Systems, and Society. She is affiliated with the Human-Computer Interaction Institute and the Heinz College of Information Systems and Public Policy, and co-leads the university-wide Responsible AI Initiative and K&L Gates Initiative for Ethics and Computational Technologies. Education: PhD in Computer and Information Science (University of Pennsylvania), MSc in Statistics (Wharton School) Her research focuses on the Ethical, Societal, and Policy Implications of AI , particularly fairness and accountability in high-stakes domains. Her work includes evaluating risks/benefits of general-purpose AI, human-AI decision-making systems, and AI governance frameworks. She has received multiple awards, including best paper honors at AIES, FAccT, and SAT-ML. Her research is supported by the NSF Program on Fairness in AI, PwC, CyLab, Meta, and J. P. Morgan. Recent Publications examine generative AI safety, fairness measurement, AI incident documentation, and ethical governance. Her teaching includes courses on Responsible AI, ML Ethics, and Societal Decision-Making, with a focus on preparing students to critically analyze AI's societal impact. Scientific Awards: Best Paper (AIES 2024, FAccT 2021, SAT-ML 2023), Exemplary Track Award (EC 2021) Grants: NSF, PwC, CyLab, Meta, J. P. Morgan She advises doctoral students and postdocs across CMU departments and collaborates with interdisciplinary teams. Her service includes organizing AI safety workshops and advising on NIST guidelines for AI red-teaming.
Giacomo Indiveri is a dual Professor at the Faculty of Science of the University of Zurich and the Department of Information Technology and Electrical Engineering of ETH Zurich . He serves as the Director of the Institute of Neuroinformatics at both institutions. Indiveri holds an M.Sc. in Electrical Engineering (1992) from the University of Genoa and a Ph.D. in Computer Science (2004) from the same university. Primary Affiliation: University of Zurich (Faculty of Science, Institute of Neuroinformatics) Secondary Affiliation: ETH Zurich (Department of Information Technology and Electrical Engineering) Indiveri's research bridges neuroscience , computer science , and machine learning to develop neuromorphic cognitive systems . His work focuses on spike-based learning , recurrent neural networks , and analog/digital circuit design for real-time sensory-motor systems . He integrates emerging memory technologies into fault-tolerant event-based architectures, enabling brain-inspired computing paradigms in applications like robotics and medical monitoring. His recent publications emphasize neuromorphic hardware for epileptic seizure detection , spiking neural networks in robotic painting , and scalable processors with on-chip learning . These works explore biologically plausible neurons , delay lines , and memory arrays for temporal processing, with applications in healthcare , edge computing , and adaptive control . Scientific Awards & Recognitions: 2021 IEEE Biomedical Circuits and Systems Best Paper Award Senior Member of IEEE Society ERC Fellow with three European Research Council grants Indiveri's group at the Institute of Neuroinformatics develops event-based systems for real-world validation of brain-inspired computing. His work includes multi-core processors , feedback optimizers , and dynamic routing architectures , supported by grants for advancing neuromorphic technologies .
Dr. John Francis Clinton is the Director of Seismic Networks and Head of the Earthquake Monitoring Section at the Swiss Seismological Service (SED), ETH Zurich. He leads the Marsquake Service for NASA's InSight mission and oversees Switzerland's broadband and strong-motion seismic networks. His expertise spans earthquake early warning systems, seismic instrumentation, and glacial seismology. Clinton is also a Co-Investigator on the Mars InSight mission and involved in international projects like EPOS and RAMSIS. Education PhD in Civil Engineering (Minor in Geophysics), California Institute of Technology (2004) MSc in Civil Engineering, California Institute of Technology (1998) BEng in Civil Engineering, University College Dublin (1997) Research Interests Dr. Clinton focuses on real-time seismology, seismic instrument design, structural health monitoring, and glacial seismology. His work bridges engineering applications with geophysical data analysis, particularly in earthquake early warning systems and induced seismicity studies. He collaborates internationally on projects such as the Valais Near Fault Observatory and Nicaragua’s Earthquake Early Warning development. Publications Overview His 15 most recent papers (2011–2015) highlight advancements in seismic network optimization, Marsquake detection algorithms, and glacial icequake mechanisms. Key themes include improving early warning accuracy, understanding subglacial dynamics, and validating high-rate GPS for structural monitoring. Awards & Memberships Member, Swiss Academy of Sciences (since 2008) Member, IRIS Quality Assurance Advisory Committee (since 2013) Chair, European Integrated Data Archives (EIDA) (2013–2015) Labs & Teams Clinton directs the SED’s Earthquake Monitoring team and collaborates with ETH Zurich’s Geophysics Masters Program. His group manages Switzerland’s seismic networks and leads the Marsquake Service, which analyzes InSight lander data for Martian seismic events.
Josie Hughes is a Tenure Track Assistant Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) within the School of Engineering's Institute of Mechanical Engineering. She leads the Computer Robot Design and Fabrication Laboratory (CREATE Lab) and serves on the doctoral program committee for Robotics, Control and Intelligent Systems. Her roles include teaching courses in product development, engineering design, and data-driven manufacturing methods while maintaining active research and student supervision. Her research centers on soft robotics with emphasis on adaptive design, fabrication techniques, and real-world applications. Key areas include agricultural robotics (exemplified by the GraspBerry raspberry harvester), biomimetic materials like self-healing e-skins, and developmental robotics (BabyBot project). She pioneers approaches integrating morphological computation, variable stiffness mechanisms, and machine learning for robotic control, while championing open-source principles and diversity in robotics through accessible educational initiatives like balloon robot kits. Analysis of her recent publications reveals dominant trends in soft robotics adaptability, particularly in variable-stiffness structures, sensor integration, and task-specific optimization. Her work bridges fundamental material science with practical applications in agriculture, food science, and human-robot interaction, frequently employing computational methods like Bayesian optimization and neural networks for design and control. Scientific awards: No specific awards were mentioned in the source materials. Dr. Hughes actively supervises 16 doctoral students across diverse projects including soft grippers for agriculture, biomimetic locomotion, and robotic manipulation systems. Her advising portfolio shows strong alignment with her research themes, with current students working on topics like modular soft arms, agricultural automation, and developmental robotics. While grant details weren't specified, her high publication volume indicates robust research funding. The CREATE Lab under her leadership focuses on holistic co-design of hardware and software for soft robotic systems, with notable projects including the GraspBerry agricultural harvester and BabyBot developmental platform. The lab emphasizes open-source development and educational outreach, maintaining strong industry and academic collaborations while pushing boundaries in reconfigurable robotics and human-centered applications.
