Shweta Shinde is an Assistant Professor at ETH Zürich's Department of Computer Science. She leads the Secure & Trustworthy Systems (SECTRS) group and is affiliated with the Institute of Information Security and the ZISC Center. Her research focuses on foundational and practical aspects of trusted computing, system security, and program analysis to protect devices ranging from smartphones to cloud servers. Key areas include AMD SEV-SNP, Arm CCA, Intel SGX, and hardware-software co-design for security. Dr. Shinde has made significant contributions to confidential computing, including frameworks like OpenCCA and tools for analyzing hardware vulnerabilities. Her work has been recognized with Best Paper Awards and Distinguished Paper Awards at top venues. She advises a group of PhD students, including Mark Kuhne, Supraja Sridhara, and Benedict Schlüter. Her service includes program committee roles at IEEE Security & Privacy, USENIX Security, and as Track Co-Chair for AsiaCCS 2026.
Nate Foster is a Professor of Computer Science at Cornell University and currently serves as the Associate Dean for Research in the Ann S. Bowers College of Computing and Information Science. He is also a Visiting Researcher at Jane Street and served as a Visiting Professor at École Polytechnique Fédérale de Lausanne during the 2023-24 academic year. His research uses ideas from programming languages to solve problems in networking, databases, and security. BA in Computer Science, Williams College (2001) MPhil in History and Philosophy of Science, University of Cambridge (2008, all work completed in 2003) PhD in Computer and Information Science, University of Pennsylvania (2009) Foster's research focuses on developing languages and tools that make it easy for programmers to build secure and reliable systems. His current work centers on the design and implementation of languages and tools for programmable networks, particularly using the P4 language. His past work includes bidirectional languages (also known as 'lenses'), database query languages, data provenance, type systems, mechanized proof, and formal semantics. His research group at Cornell has made significant contributions to network verification, software-defined networking, and formal foundations for programmable data planes. Analysis of Foster's recent publications reveals a strong focus on network verification and programming language foundations for networking. His work consistently applies formal methods to practical networking problems, with a particular emphasis on the NetKAT and P4 languages. Over the past five years, his research has evolved toward more complex network verification techniques, including infinite state verification, active learning of network models, and dependently-typed approaches to network programming. His work bridges theoretical computer science with practical networking systems, making formal methods accessible to network engineers. ACM Fellow (2025) ACM SIGPLAN Robin Milner Award (2023) ACM SIGCOMM Rising Star Award (2018) NSF CAREER Award (2013) Alfred P. Sloan Fellowship (2012) Multiple distinguished paper awards across top conferences including POPL, PLDI, and SIGCOMM Foster has advised numerous PhD and Master's students who have gone on to prominent positions in both industry and academia, with many continuing work in programming languages and networking. He has led multiple significant research grants including an NSF CAREER Award and has been involved in the P4 Language Consortium, serving as Chair of the P4 Language Governing Board. His work has been supported by various organizations including NSF, DARPA, and industry partners like Intel and Jane Street. Foster is also active in the programming languages research community, serving on numerous program committees and as Vice Chair of DARPA's Information Science and Technology (ISAT) study group. Foster leads a vibrant research group at Cornell focused on programming languages for networks, with collaborators from academia and industry. His group has developed several influential tools and frameworks including NetKAT, Petr4, and KATch. They maintain strong connections with the P4 community and work closely with industry partners to ensure their research has practical impact on real-world networking systems.
Nasir Memon is a Professor of Computer Science and Engineering at the New York University Tandon School of Engineering and concurrently serves as the Dean of Engineering at NYU Shanghai. He has been a faculty member at NYU Tandon since September 1998. Memon is a co-founder of NYU's Center for Cyber Security (CCS) and NYU Abu Dhabi, and the founder of key initiatives such as the OSIRIS Lab, CyberSecurity Awareness Week (CSAW), the NYU Tandon Bridge program, and the Cyber Fellows program. His work focuses on advancing cybersecurity education and addressing systemic biases in AI-driven systems. Education: Ph.D., Computer Science, University of Nebraska Master of Science, Mathematics, Birla Institute of Technology and Science (BITS), Pilani Bachelor of Engineering, Chemical Engineering, BITS, Pilani Research Interests: Media Forensics and Authentication Biometric Security and Privacy Data Compression and Privacy-Preserving Techniques Network Security and Incident Response AI Ethics and Fairness in Machine Learning Cybersecurity Education and Workforce Development Awards and Honors: IEEE Fellow (2010) SPIE Fellow (2014) Jacobs Excellence in Education Award (2002) NSF CAREER Award (1997) Advising and Grants: Advises Ph.D. students like Anubhav Jain and Govind Mittal, and mentors Master’s students such as Rishit Dholakia. Recipient of NSF grants and funding from NYU Abu Dhabi and Indiana University Bloomington for projects like computational tools for fact-checking and AI-driven bias mitigation. Labs and Teams: Directs the OSIRIS Lab, a leading research group in cybersecurity and AI. Leads initiatives at the NYU Center for Cybersecurity (CCS) and collaborates with NYU Abu Dhabi’s Center for Cyber Security.
Ashley Montanaro is Professor of Quantum Computation in the School of Mathematics at the University of Bristol, and co-founder of the quantum software startup Phasecraft. He is a member of the Quantum Information Theory research group at Bristol. His research focuses on the theory of quantum computing, with particular interest in quantum algorithms, computational complexity, quantum query and communication complexity, and classical algorithms. His work spans both theoretical foundations and practical applications of quantum computing. Montanaro's research output shows significant trends toward quantum algorithms for optimization problems, quantum computational supremacy, and bridging theoretical advances with practical implementation challenges. His publications span foundational quantum information theory to applied quantum algorithms, demonstrating a versatile research program that connects computer science with quantum physics. Among his professional activities, Montanaro served on the QIP steering committee (2016-2018) and was an editor for the Quantum journal until 2019. He has been active in conference organization, serving on program committees for ITCS 2018, AQIS 2017 and 2015, QIP 2015, and TQC 2014 and 2013, reflecting his standing in the quantum computing research community. He has supervised numerous PhD students including Josh Blake, Jorja Kirk, Sheila Perez Garcia, Sami Boulebnane, Jan Lukas Bosse, Lana Mineh, Joao F. Doriguello, Chris Cade, Sam Pallister, and Stephen Piddock. His teaching includes Quantum Computation (MATHM0023) which he has taught since 2014 and Advanced Quantum Information Theory which he taught in 2015 and 2016. As co-founder of Phasecraft, Montanaro is actively translating theoretical quantum computing advances into practical software solutions, positioning him at the intersection of academic research and quantum technology commercialization.
James McCann is an Associate Professor at the Carnegie Mellon Robotics Institute, where he leads the Carnegie Mellon Textiles Lab. He has been a faculty member since May 2017 after working at Disney Research Pittsburgh. McCann's academic journey includes a PhD from Carnegie Mellon advised by Nancy Pollard, followed by a postdoc at Adobe Research and a period developing video games. McCann's research focuses on building creative tools that operate in real-time and build user intuition, with particular emphasis on textiles fabrication and machine knitting. His work spans computer-aided fabrication, simulation, graphics, and creative tools development. He has pioneered systems for machine knitting design, including compilers for knitting instructions and tools for automatic conversion of 3D meshes to knitting patterns. His recent publications demonstrate a strong trend toward computational textiles, with significant contributions to knitting semantics, deployable textile structures, and applications of machine knitting in healthcare and robotics. McCann's work bridges computer science, robotics, and textile arts, creating practical systems for once-off manufacturing with industrial knitting machines. McCann actively mentors students, with current PhD candidates working on solid knitting machines, knit calibration, and assistive devices. His teaching portfolio includes courses on Real-Time Graphics, Algorithmic Textiles Design, and Game Programming. He has taught at CMU since 2017, developing innovative courses that blend computer science with physical fabrication. As director of the Textiles Lab, McCann oversees research projects spanning machine knitting, robotic painting, and real-time graphics systems. His lab develops practical tools for creators, emphasizing intuitive interfaces and real-time feedback that lower barriers to advanced fabrication techniques.
Gail E. Kaiser is a Professor of Computer Science and the Director of the Programming Systems Laboratory (PSL) in the Computer Science Department at Columbia University. She has been with Columbia University since 1985, becoming a full Professor in 1998. Prof. Kaiser's research spans software engineering, program analysis, software testing, and software security, with recent focus on addressing challenges in AI/ML systems testing and security. Prof. Kaiser received her PhD in Computer Science from Carnegie Mellon University in 1985 and her ScB in Computer Science and Engineering from MIT in 1979. Her dissertation at CMU was titled "Semantics for Structure Editing Environments" under advisor Nico Habermann, and at MIT she completed "Automatic Extension of an Augmented Transition Network Grammar for Morse Code Conversations" under advisor Al Vezza. Prof. Kaiser's research interests primarily focus on software engineering following a systems building approach, with recent emphasis on static and dynamic program analysis techniques to improve software reliability and security. Since 2005, she has investigated testing "non-testable" programs, particularly in machine learning, data mining, and scientific computing applications where traditional testing oracles are insufficient. She has developed novel techniques and tools for detecting bugs and verifying repairs in complex systems. Concurrently, she has worked on collaboration environments for computational scientists, creating knowledge sharing and domain-aware environments to support scientific workflows. Prof. Kaiser's recent publications demonstrate a strong focus on the intersection of software engineering and artificial intelligence. Her work addresses critical challenges in testing AI systems, code understanding through deep learning, vulnerability detection, and educational tools for computational thinking. There's a clear evolution from traditional software engineering topics toward AI/ML applications, with particular emphasis on metamorphic testing for non-testable systems, code similarity analysis, and educational applications. Prof. Kaiser has received numerous prestigious awards throughout her career: Distinguished Journal Award (10 Years) from 18th IEEE International Conference on Software Testing, Verification and Validation (ICST), April 2025 Best Research Paper Award at 24th IEEE International Conference on Source Code Analysis & Manipulation (SCAM), October 2024 Distinguished Reviewer Awards for ASE 2024 and FSE 2024 ACM SIGSOFT Distinguished Paper Award for "CONCORD: Clone-aware Contrastive Learning for Source Code", July 2023 Best Student Paper Award at ICCE 2021 Multiple ACM SIGSOFT Distinguished Paper Awards dating back to 2014 Presidential Young Investigator in Software Engineering and Software Systems from NSF (1988-1993) Prof. Kaiser has chaired Columbia's doctoral program since 1997 and served on editorial boards including IEEE Internet Computing and as a founding associate editor of ACM Transactions on Software Engineering and Methodology. Her lab has been continuously funded by major agencies including NSF, NIH, DARPA, ONR, NASA, and numerous companies. Current grants include significant NSF funding for secure containers architecture, learning semantics of code for software assurance, and finding semantic security bugs. As Director of the Programming Systems Laboratory (PSL), Prof. Kaiser leads research in software systems, program analysis, and software testing. The lab has developed numerous tools and techniques for software reliability and security, with recent focus on challenges in AI/ML systems. Her work bridges theoretical foundations with practical applications, often resulting in deployable tools that address real-world software engineering challenges.
Andrew Head is an Assistant Professor at the University of Pennsylvania in the Computer and Information Science department. His research focuses on human-computer interaction, programming, and reading, particularly in developing technology for interactive reading and reasoning. He advises PhD students Alyssa Hwang, Hita Kambhamettu, Litao Yan, Jeffrey Tao, and Jessica Shi, and co-leads the Penn Human-Computer Interaction (Penn HCI) group with Danaé Metaxa. His work is published in top venues like ACM CHI, UIST, and ICSE. Research Interests: Andrew's work bridges interactive systems with programming environments, aiming to enhance how scientists and programmers interact with their tools. Key areas include AI-assisted code understanding, math notation accessibility, and medical note interpretation through interactivity. He employs user studies to identify needs and builds interactive systems to address them. Recent Article Trends: His publications emphasize systems-centric HCI approaches, integrating AI into code and document interfaces. Topics span code explanation (e.g., Ivie), property-based testing (e.g., Tyche), math notation augmentation (e.g., FreeForm), and medical informatics (e.g., Explainable Notes). Recent work also explores notebook environments (e.g., Bolt-on, Tyche) and literate programming (e.g., Colaroid). Scientific Awards: Distinguished Paper Award, ICSE 2024 Best Paper Awards at CHI (2024, 2023, 2022, 2019), UIST (2023, 2018), and others. Nominated for Best Paper at CHI 2023 and VL/HCC 2015. Advising & Grants: Andrew advises multiple PhD students and has secured significant grants, including a $1M NSF award for 'Property-based Testing for the People' (2024). His group collaborates with Penn’s MindCORE center and teams like PLClub and PennNLP. Labs & Teams: He co-leads the Penn HCI group, which works closely with other Penn research teams and labs. The group focuses on creating interactive tools that enhance scientific and programming workflows, supported by grants and cross-institutional partnerships.
Gerard A. Ateshian is the Andrew Walz Professor of Mechanical Engineering and Professor of Biomedical Engineering at Columbia University, where he has been a faculty member since 1991. He also served as Chair of the Department of Mechanical Engineering from 2011 to 2014 and is the founding director of the Musculoskeletal Biomechanics Laboratory (MBL), established in 1996. Education: BS (1986), MS (1987), MPhil (1990), and PhD (1991) in Mechanical Engineering from Columbia University. Research Interests: Professor Ateshian's research focuses on the mechanics of soft biological tissues , particularly articular cartilage . His work integrates theoretical, experimental, and computational approaches to understand cartilage lubrication, tissue engineering, and growth and remodeling in living tissues. He has extended continuum mechanics frameworks to model complex biological phenomena such as mass transport, osmotic effects, and reactive mechanics in tissue mixtures. His recent efforts include developing open-source computational tools (FEBio) and translating research into clinical treatments for osteoarthritis . Scientific Awards: H.R. Lissner Medal, ASME (2017) OARSI Basic Science Award (2013) Columbia Engineering Alumni Association Distinguished Faculty Teaching Award (2012) Great Teacher Award, Society of Columbia Graduates (2002) YC Fung Young Investigator Award, ASME (1997) Fellow: ASME, BMES, AIMBE Advising and Mentorship: Professor Ateshian has advised numerous PhD students, including recent graduates Vince Sise and Katherine Spack , who completed their dissertations on cartilage fatigue and osteochondral allografts, respectively. His lab actively mentors students from both Mechanical and Biomedical Engineering programs. Laboratory and Collaborations: He directs the Musculoskeletal Biomechanics Laboratory (MBL) , which collaborates closely with the Cellular Engineering Laboratory of Prof. Clark Hung and with Dr. Jeffrey Weiss at the University of Utah on computational modeling (FEBio). He also collaborates with Columbia University Medical Center faculty on clinical translation of cartilage research.
Karthik R. Narasimhan is a Professor at Princeton University's School of Engineering and Applied Science in the Department of Computer Science. Previously, he earned his PhD from MIT under Regina Barzilay and served as a visiting research scientist at OpenAI during 2017-18. His research focuses on the intersection of language and decision-making, building autonomous agents that learn from both experience and human knowledge. His research spans multiple high-impact areas including language agents (Text-DQN, CALM, ReAct, Tree of Thoughts), reinforcement learning (h-DQN, Multi-Objective RL), and AI safety (Toxicity in ChatGPT, DataMUX). He has developed critical datasets and benchmarks such as WebShop, InterCode, SWE-bench, and SILG that have become standard evaluation tools in the field. Current work emphasizes agent capabilities, software engineering automation, and multimodal interaction. His publication trends show strong focus on practical agent deployment (SWE-agent, Tree of Thoughts), safety evaluation (Probing AI Safety), and efficiency improvements (DataMUX). Recent work increasingly addresses real-world challenges in software engineering, security, and human-AI collaboration through rigorous benchmarking. Co-author of foundational GPT (2018) paper Key developer of Text-DQN (2015), CALM (2020), ReAct (2022), Tree of Thoughts (2023) Creator of influential benchmarks: WebShop (2022), SWE-bench (2023), InterCode (2023) He actively advises students through Princeton's computer science program, with research supported by multiple grants focused on autonomous agent development and language-based decision systems. His GitHub repositories (nlp-datasets, text-world-player) demonstrate strong community engagement in open-source research tools. Current projects include advancing language agent capabilities through Reflexion (2023) and Tree of Thoughts (2023) frameworks while addressing critical safety and efficiency challenges.
Ceren Budak is an Associate Professor at the University of Michigan School of Information and holds a joint appointment as Associate Professor of Electrical Engineering and Computer Science in the College of Engineering. Her work bridges computer science, statistics, and social sciences through computational social science approaches. Her educational background includes a PhD in Computer Science from the University of California, Santa Barbara (2012) and a Bachelors degree in Computer Science from Bilkent University in Turkey (2007). Prior to joining the University of Michigan faculty, she was a Postdoctoral Researcher at Microsoft Research New York. Professor Budak's research centers on computational social science, with particular emphasis on analyzing large-scale datasets to address questions with social, political, and policy implications. Her work spans several interconnected domains: News Media Production & Consumption (examining bias in news outlets and reader preferences), Social Movements & Media (using social media data to study collective action), Social Networks (understanding information diffusion processes), and Measuring and Promoting the Quality of Online Discussions (developing tools to improve online conversations). She teaches SI 608 (Networks) and SI 618 (Data Manipulation and Analysis) at the School of Information. Her publication record demonstrates consistent contributions to understanding how online information ecosystems operate, with recent work focusing on AI-human collaboration, misinformation dynamics, social movement framing, and the application of computational methods to political communication. Her research shows a clear trajectory from foundational work on social network diffusion to increasingly sophisticated analyses of contemporary information challenges. Among her service activities, she has served as Registration chair for COSN (ACM Conference on Online Social Networks) 2015 and as Program Committee Member for numerous prestigious conferences including WWW, ICWSM, WebSci, AAAI, and others. She has also been involved in organizing the MSR NYC Data Science Seminar Series and instructing the Microsoft Research Data Science Summer School.
Lin Tan is a Professor of Computer Science at Purdue University , holding the Mary J. Elmore New Frontiers Professorship . She joined Purdue in 2019 after serving as a Canada Research Chair and associate professor at the University of Waterloo. She is an ACM Distinguished Member and IEEE Senior Member . Education: PhD in Computer Science, University of Illinois Urbana-Champaign BS in Computer Science and Technology, Zhejiang University Research Interests: Professor Tan’s research lies at the intersection of software engineering , artificial intelligence , and security . Her work focuses on software-AI synergy , software dependability , defect detection & repair , and software text analytics . She leverages machine learning and natural language processing to enhance software reliability, and conversely uses software techniques to improve the dependability of AI systems. Her recent projects include building binary foundation models (Nova), evaluating large language models for code generation and repair, and developing interactive debugging tools that reduce debugging time by one-third. She also explores robot task planning with LLMs and automated front-end development . Awards & Honors: Best Paper Award Finalist, ICRA 2025 ELATES Fellow, 2024-2025 ACM SIGSAC Distinguished Paper Award, CCS 2024 J.P.Morgan AI Faculty Research Awards (2020, 2021, 2022) ACM SIGSOFT Distinguished Paper Awards (ASE 2020, MSR 2018, FSE 2016) Canada Research Chair (2017) Ontario Early Researcher Award (2015) NSERC Discovery Accelerator Supplements Award (2015) Google Faculty Research Awards (2010, 2014) IEEE Micro Top Picks (2006) Advising & Funding: Professor Tan currently advises eight PhD students and has graduated 20+ PhD and Master’s students now thriving in academia (York University, Concordia University, University of Alberta) and industry (Microsoft, Meta, Amazon, Google). Her group is generously supported by NSF , Meta/Facebook Research Awards , J.P.Morgan AI Faculty Awards , and NSF REU programs. Labs & Teams: She leads the Software Reliability & AI Lab at Purdue, recruiting postdocs, PhD, MS, and undergraduate researchers year-round. Lab interests span binary recovery , LLM-based program repair , testing deep-learning libraries , and data-free model extraction .
Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.
Dr. Edyta Chlebowska is an Assistant Professor at the Cyprian Norwid Research Center, John Paul II Catholic University of Lublin. Specializing in the visual art and literary legacy of Cyprian Norwid, she explores intersections between Polish Romanticism, art history, and cultural studies. Her work extends to album art analysis and interdisciplinary connections with music (e.g., Pink Floyd, Czesław Niemen). Her research focuses on Norwid's self-portraits, eschatological themes in his drawings, and comparative studies with 19th-century Warsaw painting. She reconstructs lost artworks, documents private collections (e.g., Konstancja Górska, Victor Gomulicki), and examines Norwid's reception in Florence and Odessa. Key projects include translating Studia Norwidiana and critical editing of Norwid's complete works. As editor-in-chief of multiple On Cyprian Norwid. Studies and Essays volumes and organizer of conferences like Niemen Non Stop and Colloquia Norwidiana , she bridges academic rigor with public engagement. Her publications span peer-reviewed books, journal articles, and popular science pieces, emphasizing Norwid's unpublished works and symbolic programs.
Tom Rainforth is an Associate Professor of Statistical Machine Learning at the University of Oxford's Department of Statistics, leading the RainML Research Lab (rainml.uk). He holds a Tutorial Fellowship at Mansfield College and is Principal Investigator of the ERC Starting Grant 'Data-Driven Algorithms for Data Acquisition' (2024–2029). Previously, he held roles including a postdoc under Yee Whye Teh (2017–2019), Junior Research Fellow at Christ Church College (2019–2019), and Florence Nightingale Bicentennial Fellow (2020–2024). He earned his MEng in Mechanical Engineering from the University of Cambridge and his D.Phil from Oxford under Frank Wood and Michael Osborne, focusing on probabilistic programming and Monte Carlo methods. He briefly worked in Ferrari's Formula 1 team. Research Interests : Bayesian experimental design, probabilistic and data-efficient machine learning, active learning, deep learning (with a focus on probabilistic approaches), probabilistic programming, and Monte Carlo methods. His work emphasizes statistical efficiency and adaptive algorithms. Publications : Recent contributions span modern Bayesian experimental design, adaptive importance sampling (Daisee), and applications of probabilistic methods in LLMs and generative models. His research bridges theory and practice, addressing challenges in scalability and robustness. Awards : ERC Starting Grant (2024–2029), highlighting his leadership in foundational AI research. Advising & Grants : Supervises 15 graduate students, including work on Bayesian neural networks, generative flows, and experimental design. His ERC grant supports cutting-edge data-driven algorithm development. Labs & Teams : Directs the RainML Lab, which develops scalable Bayesian methods and probabilistic AI systems.
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.
