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Guo YikeView profile

Professor

• Machine Learning
• Data Mining
• Data Science
• +6 more

Guo Yike is the Provost of The Hong Kong University of Science and Technology (HKUST) and concurrently a Chair Professor in the Department of Computer Science and Engineering. He holds a PhD in Computational Logic from Imperial College London (1994) and a first-class honours degree in Computing Science from Tsinghua University (1985). His academic leadership roles include Director of the Data Science Institute at Imperial College London (2014–2020), Honorary Dean of Shanghai University's School of Computer Science (since 2020), and Vice President (Research and Development) at Hong Kong Baptist University (2020–2022). Research interests focus on machine learning, data mining, and large-scale applications in healthcare, finance, and environmental science. He pioneered informatics systems for biology, geophysics, and social media, emphasizing ethical AI and multimodal reasoning. Notable awards include Royal Academy of Engineering Fellowship (2018), ACM SIGMM Best Open Source Software Award (2017), and recognition as one of China's Top 10 Data Scientists (2016). Industry collaborations include leading startups like InforSense and TranSMART, working with GSK, Huawei, and BBC. Editorial roles include editor-in-chief of Annual Reviews of Data Sciences and deputy editor-in-chief of Machine Intelligence Research . His work bridges academia and industry, driving innovation in AI ethics, cloud computing, and interdisciplinary science.

• Database Systems
• Cloud Computing
• Software as a Service (SaaS)
• +7 more

Dean Jacobs is a prolific computer-science researcher whose career has focused on database systems, cloud computing, and software-as-a-service architectures. While no current institutional affiliation is provided in the dblp source, his continuous publication record in top-tier venues such as SIGMOD, VLDB, CIDR, IEEE CLOUD, and BTW from 1982 to 2013 attests to senior faculty status. Education PhD, Computer Science, Cornell University, USA, 1985 (Dissertation: General Correctness: A Unification of Partial and Total Correctness ) Research Interests Jacobs’ research spans the full lifecycle of data management in cloud and enterprise environments. Early work concentrated on type systems and formal semantics for database programming languages, contributing foundational insights into algebraic specifications, delayed updates, and active-database rule semantics. During the 2000s his focus shifted to multi-tenant architectures and SaaS, pioneering schema-mapping techniques, extensibility models, and performance-prediction methods for large-scale, elastic database clusters. More recently he has investigated operational business intelligence, real-time analytics, and SLA-aware resource management for cloud-hosted transactional and analytical workloads. Publication Trends Across three decades, Jacobs has produced a coherent body of work that moves from theoretical database-language design to applied cloud-data management. The 1990s papers revolve around formal methods—type theory, rule languages, and transaction semantics. From 2003 onward, the emphasis transitions to SaaS and cloud systems: multi-tenancy, elasticity, and service-level guarantees. His 2010–2013 output integrates these themes into practical systems for operational BI and robust tenant placement in in-memory clusters. Collaborations & Mentoring Although advisees are not listed in the dblp data, Jacobs has an extensive co-author network including Alfons Kemper, Stefan Aulbach, Jan Schaffner, Richard Hull, and Michael Seibold, indicating long-standing collaborative relationships and likely supervision of graduate students and post-docs.

Gregory L. WhitingView profile

Associate Professor

• Additive Manufacturing
• Printed Electronics
• Flexible Electronics
• +4 more

Gregory L. Whiting serves as an Associate Professor in the Department of Materials Science and Engineering within the College of Engineering and Applied Science at the University of Colorado Boulder. His office is located in SEEC N360E, and he can be reached at gregory.whiting@colorado.edu. Dr. Whiting leads the Boulder Experimental Electronics and Manufacturing Laboratory (BEEM Lab), where his research focuses on innovative manufacturing techniques for electronic systems. Dr. Whiting's research interests center on functional additive manufacturing, with particular emphasis on printed and flexible electronics, transient electronics, and distributed sensing systems. His work explores how printing technologies can be leveraged to fabricate unconventional electronic components that are customizable, mechanically flexible, large-area, widely distributed, biocompatible, and controllably transient. This research has significant implications across multiple sectors including healthcare, agriculture, energy systems, and space exploration. Analysis of Dr. Whiting's publication record reveals a consistent focus on printed electronics integration, with particular attention to creating functional systems that combine printed components with conventional electronics. His work spans materials development, device fabrication, and system integration, with increasing emphasis on practical applications in recent years. The research trajectory shows progression from fundamental device development toward more complex, application-oriented systems. Dr. Whiting has received numerous prestigious awards recognizing his contributions to the field: National Academy of Engineering US-EU Frontiers of Engineering symposium participant (2017) National Academy of Engineering US Frontiers of Engineering symposium participant (2016) PARC Exceptional Performance Awards for distributed methane sensing, interactive transient systems and electrostatic microparticle assembly (2015) DARPA Microsystems Technology Office Top Performer Award (2015) FLEXI Award for flexible electronics (2012) Wall Street Journal Technology Innovation Award runner up (2012) As the director of the BEEM Lab, Dr. Whiting oversees research efforts focused on developing new manufacturing approaches that enable more distributed production methods and provide pervasive functionality. His laboratory has secured significant funding, including DARPA support, and has produced innovative work in areas such as printed batteries, flexible sensors, and transient electronics systems. Current research directions include environmental monitoring applications (Envyro Sensors), as evidenced by recent recognition in the Venture Challenge.

Jana HofmannView profile

Researcher

• Information Security
• Trust in Security Systems
• Microarchitectural Side Channels
• +3 more

Jana Hofmann is a tenure-track faculty member at the Max Planck Institute for Security and Privacy (MPI-SP), leading the Foundations of Information Security and Trust group. Previously, she was a postdoctoral researcher at Microsoft Azure Research and completed her PhD at Saarland University (CISPA Helmholtz Center). Her research focuses on establishing strong security guarantees for systems through formal methods, with an emphasis on microarchitectural side channels and information leakage prevention. Education : PhD, Saarland University (2022), advised by Bernd Finkbeiner M.Sc., University of Edinburgh (2016–2017) B.Sc., Saarland University (2013–2016) Research Interests : Hofmann’s work combines formal methods, programming languages, and systems approaches to address information leakage in hardware/software systems. Key areas include: Microarchitectural side-channel detection/prevention Formal leakage models and verification techniques Smart contract synthesis and hyperproperties Publications : Her recent work emphasizes practical applications of formal methods to cloud CPU security, smart contracts, and automated testing. Notable contributions include Gaussian elimination-based memory coloring and isolation techniques for cloud CPUs. Awards : Dr.-Eduard-Martin Prize (2022) for best PhD thesis in computer science Distinguished Paper Awards at CCS’24 and CSF’23 Advising & Grants : Hofmann has advised five students on theses in formal verification and security. She actively contributes to program committees for conferences like CAV, CCS, and CSF. Labs/Teams : She leads the MPI-SP Foundations group, focusing on advancing trust in security-critical systems through interdisciplinary research.

Aïna Linn GeorgesView profile

Researcher

• Algorithms
• Theory & Logic
• Programming Languages & Verification
• +3 more

Aïna Linn Georges is a researcher affiliated with the Max Planck Institute for Software Systems (MPI-SWS) in Saarbrücken, Germany. Her work focuses on formal methods, programming languages, and system security. She specializes in program verification, capability-based systems, and secure compilation. Her research addresses challenges such as concurrency, memory safety, and security in distributed and cyber-physical systems. Her research interests include separation logic, modular verification frameworks, and the mechanization of proofs for systems like WebAssembly and capability machines. She explores robust security properties under diverse attacker models and contributes to foundational aspects of capability-based architectures. Recent work highlights include advancements in non-frame-preserving updates for separation logic, formal verification of compartmentalized C programs, and secure compilation techniques. Her publications reflect a strong emphasis on practical formal methods for real-world systems, with applications in both theoretical and applied computer science.

Mahmoud SalamatiView profile

Research Fellow

• Formal Methods in Control
• Learning in Control Systems
• Cyber-Physical Systems
• +2 more

Dr. Mahmoud Salamati is a Research Fellow at the Max Planck Institute for Software Systems, working in the Rigorous Software Engineering group under Professor Rupak Majumdar. His research bridges formal methods, control theory, and machine learning with applications in cyber-physical systems, energy grids, and autonomous vehicles. Salamati's work develops formal verification techniques for stochastic systems and data-driven control synthesis methods. Recent publications focus on regret-free reinforcement learning for temporal logic specifications, metastable failure analysis in distributed systems, and abstraction-based controller synthesis. His research addresses fundamental questions about decidability and scalability in formal control synthesis while maintaining practical applications in energy management and autonomous systems. Key contributions include novel approaches to time-bounded reachability problems in continuous-time Markov chains, symbolic control methods for multi-agent systems, and hybrid optimization techniques for power plant control. Salamati's work demonstrates consistent integration of theoretical computer science foundations with practical engineering challenges in critical infrastructure systems.

Percy LiangView profile

Associate Professor

• Machine Learning
• Natural Language Processing
• Foundation Models
• +4 more

Affiliations Associate Professor at Stanford University's Computer Science Department within the School of Engineering. Director of the Center for Research on Foundation Models (CRFM) and core member of Stanford HAI . Also affiliated with the Artificial Intelligence Lab, Natural Language Processing Group, and Machine Learning Group. Education Ph.D. in Computer Science, UC Berkeley (2011), advisors: Michael Jordan and Dan Klein MEng in Computer Science, MIT (2005), advisor: Michael Collins B.S. in Computer Science, MIT (2004) Research Focuses on foundational aspects of AI, including: Foundation models and their societal implications (e.g., copyright, data attribution) Building scalable and interpretable AI systems Reproducible research via CodaLab Worksheets Formal methods for ensuring correctness in ML systems Key Contributions Pioneered work in: Automated reasoning for Olympiad-style problems Data Programming framework for rapid dataset creation Executable papers and experiment reproducibility AI ethics and model interpretability Grants & Awards Presidential Early Career Award (2019) IJCAI Computers and Thought Award (2016) Microsoft Research Faculty Fellowship (2014) Multiple patents and industry collaborations Labs & Teams Leads the CRFM team developing open foundation models. Collaborates with industry (e.g., Google, Meta, OpenAI) and co-founded xAI initiatives. Active in open-source projects like sfig for presentation tools.

• Artificial Intelligence
• Planning and Scheduling
• Reinforcement Learning
• +5 more

Sheila A. McIlraith is a Professor in the Department of Computer Science at the University of Toronto, where she leads research at the intersection of artificial intelligence, knowledge representation, and formal methods. With an extensive publication record spanning over three decades, she has made significant contributions to planning, reinforcement learning, and epistemic reasoning in AI systems. Her research interests focus on developing formal frameworks for AI planning and decision-making, with particular emphasis on interpretable AI, reward specification in reinforcement learning, and multi-agent systems. She has pioneered work in reward machines for reinforcement learning, epistemic planning, and the application of formal methods to ensure safety and fairness in AI systems. Her recent work bridges symbolic AI with deep learning approaches to create more transparent and controllable intelligent agents. Analysis of her recent publications reveals a strong trend toward integrating formal specification languages with machine learning, particularly in reinforcement learning where she develops methods for specifying complex tasks using linear temporal logic and related formalisms. Her work increasingly addresses ethical considerations in AI, including fairness in sequential decision making and the impact of ethics education in computer science curricula. McIlraith has mentored numerous PhD students who have become prominent researchers in AI, including Rodrigo Toro Icarte, Toryn Q. Klassen, and Andrew C. Li. Her collaborative network spans major AI research institutions worldwide, with frequent collaborations with researchers at institutions including the Vector Institute and international universities. Her research group explores the theoretical foundations of AI planning while developing practical applications in areas including robotic assistance, ethics-aware AI, and interpretable decision systems. Current projects focus on language-guided reinforcement learning, multi-agent verification, and the development of tools for responsible AI development and deployment.

Julien MusolinoView profile

Professor

• Cognitive Science
• Developmental Psychology
• Linguistics
• +4 more

Julien Musolino is a Professor at Rutgers University, holding dual appointments in the Department of Psychology and the Rutgers Center for Cognitive Science (RuCCS). He is also a member of the Philosophy Department's graduate faculty. His research explores the foundations of human cognition, focusing on language, rationality, agency, memory, and belief formation, with a particular interest in their societal implications. Musolino has been recognized with multiple teaching awards for his outreach efforts, and his work has been supported by NIH and NSF grants. Education: University of Geneva, Switzerland University of North Wales, Bangor, UK University of Maryland University of Pennsylvania Research Interests: Musolino's work bridges cognitive science, linguistics, and philosophy. Key areas include belief dynamics, temporal binding mechanisms, supernatural concept memorability, and the cognitive underpinnings of religious thought. He actively engages in public discourse through lectures, podcasts, and media appearances, addressing topics like free will and the intersection of science and society. Recent Activities: Recent talks include presentations to employment lawyers, physics students, and humanist networks. He debated theologian Brian Brock (2025) and participated in Swiss media interviews (2023). His research has been featured in leading journals and interdisciplinary conferences. Grants & Funding: NIH and NSF grants support his studies on belief formation, temporal binding, and cognitive science outreach. Labs/Teams: Affiliated with RuCCS and collaborates with interdisciplinary teams on projects like the memorability of supernatural concepts and rethinking temporal binding.

• Mobile Crowd Sensing
• Visual Information Processing
• Medical Imaging Analysis
• +5 more

Huihui Chen is an Associate Professor in the Department of Computer Science and Engineering at Northwestern Polytechnical University, specializing in mobile crowd sensing, medical imaging analysis, and control systems for autonomous vehicles. With over 50 publications spanning from 2013 to 2025, Dr. Chen has established a significant research presence in interdisciplinary areas combining computer science, engineering, and medical applications. Dr. Chen's research interests focus on mobile crowd sensing systems , particularly visual crowdsensing applications for smart cities and transportation. Their work extends to medical imaging analysis for conditions like Fragile X syndrome and granulosa cell tumors, as well as control systems for unmanned surface vehicles. The research demonstrates a progression from fundamental crowd sensing frameworks to sophisticated applications in medical diagnostics and autonomous systems. Key methodological approaches include sensor fusion techniques, neural network modeling, and optimization algorithms for dynamic environments. Recent publications reveal a growing emphasis on interdisciplinary applications, particularly at the intersection of computer vision and medical diagnostics. The 15 most recent articles demonstrate expertise across multiple domains including transportation systems optimization, neural network synchronization, and medical imaging analysis. This diverse portfolio shows a research trajectory that bridges theoretical computer science with practical applications in healthcare and autonomous systems. Dr. Chen actively collaborates with researchers including Bin Guo (31 joint publications), Zhiwen Yu, Chundi Zheng, and Aiguo Wang, forming a productive research group focused on mobile sensing technologies. The collaborative network extends across multiple Chinese institutions, with significant contributions to IEEE journals and international conferences in computer science and engineering.

Chana Weil-KennedyView profile

Researcher

• Formal Verification
• Distributed Systems
• Petri Nets
• +3 more

Chana Weil-Kennedy is a Researcher at CEA List in Paris, focusing on formal verification of distributed systems, Petri nets, and theoretical computer science. Previously, she was a Postdoctoral Researcher at the IMDEA Software Institute (2023–2024) and completed her PhD at the Technical University of Munich (TUM), supervised by Javier Esparza. Her PhD thesis, Observation Petri Nets , explores parameterized verification and Petri net reachability under the ERC-supported PaVeS project. Education: PhD in Computer Science, TUM (2023) Master’s in Computer Science, École Normale Supérieure Paris-Saclay (2018) Bachelor’s in Mathematics, Université Paris-Sud (formerly Orsay) Research Interests: Runtime verification, distributed systems, formal methods, Petri nets, and mathematical reasoning in computer science. Her work emphasizes parameterized verification of protocols and systems, with applications to population protocols and distributed computations. Teaching & Supervision: Tutored courses at TUM including Automata and Formal Languages, Petri Nets, and Fundamental Algorithms. Co-supervised three Bachelor’s theses and a Master’s thesis on distributed systems and verification tools. Awards: Best Paper Award at Petri Nets 2019 for Parameterized Analysis of Immediate Observation Petri Nets . Labs/Teams: Active at CEA List, collaborating on runtime verification and distributed system analysis. Formerly part of Pierre Ganty’s group at IMDEA and Javier Esparza’s team at TUM.

Ataberk OlgunView profile

Lecturer

• Computer Architecture
• Memory Systems
• DRAM Reliability
• +5 more

Ataberk Olgun is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich, where he conducts research in the SAFARI Research Group under Professor Onur Mutlu. His work focuses on computer architecture with particular emphasis on memory systems, DRAM reliability, and security vulnerabilities such as RowHammer. His educational background includes BSc and MSc degrees from TOBB ETÜ (Ankara University of Economics and Technology) under Professor Oğuz Ergin's supervision. Prior to his doctoral work at ETH Zürich, he worked at Kasirgalabs until 2021, leading the design of a RISC-V system-on-chip manufactured using SKY130 technology, and previously developed authentication methods based on behavioral biometrics with Professor Kemal Bicakci. Olgun's research interests center on designing reliable and energy-efficient memory systems. He has developed FPGA-based testing platforms for DDR3/4 and HBM2 DRAM chips, created end-to-end systems for processing-in-memory techniques, and made significant contributions to understanding and mitigating the RowHammer problem. His work often combines hardware design with system-level considerations to address fundamental challenges in modern memory technology. His publication record shows consistent output in top-tier venues including ACM TACO, USENIX Security, ISCA, MICRO, and HPCA, with a clear trend toward increasingly sophisticated investigations of DRAM vulnerabilities and innovative mitigation strategies. Recent work focuses on fine-grained DRAM architectures, scalable RowHammer mitigation techniques, and experimental characterization of emerging memory technologies. As part of the SAFARI Research Group at ETH Zürich, Olgun collaborates extensively with Professor Onur Mutlu and other researchers on memory systems research. His technical expertise spans FPGA-based prototyping, DRAM characterization, hardware security, and memory controller design. Olgun maintains an active research presence with numerous ongoing projects investigating next-generation memory technologies, security vulnerabilities in modern DRAM, and energy-efficient memory system designs. His work bridges theoretical understanding with practical implementation through FPGA-based experimental platforms.

• Computer Security
• Formal Verification
• Networking Protocols
• +3 more

Dr. Christoph Sprenger is a Lecturer at the Department of Computer Science at ETH Zürich, specializing in formal verification of security protocols and networking systems. His work focuses on ensuring the correctness and security of distributed systems, databases, and network infrastructure through rigorous mathematical methods. His research addresses challenges such as verified isolation guarantees for database transactions, formal verification of router implementations, and secure data plane protocols in next-generation internet architectures. He has contributed to frameworks like IsaNet and N-Tube, emphasizing practical applications of formal methods in real-world systems. Key themes in his publications include protocol design, cryptographic security, and compositional verification techniques. Recent work explores the intersection of formal verification with path-aware networking and distributed system reliability. His research maintains a strong emphasis on bridging theoretical foundations with deployable solutions. Current projects involve advancing formal verification tools for critical infrastructure components, such as transactional memory managers and secure service architectures. His work consistently bridges gaps between abstract protocol specifications and their secure, interoperable implementations.

Viktor KuncakView profile

Associate Professor

• Formal Verification
• Automated Reasoning
• Programming Languages
• +3 more

Viktor Kuncak is an Associate Professor and head of the Laboratory for Automated Reasoning and Analysis (LARA) at EPFL. He joined EPFL in 2007 after completing his PhD at MIT. His research focuses on formal verification, automated reasoning, programming languages, and program synthesis. He has led significant initiatives such as the European COST Action on automated reasoning formats and secured a 5-year ERC grant (2012–2017). Education: PhD in Computer Science from MIT (2007), MSc in Computer Science and Physics from MIT (2001), BSc in Computer Science from University of Novi Sad (2000). Research Interests: Formal methods, automated theorem proving, program analysis, and tools for software verification. His work bridges theoretical foundations and practical systems, with contributions to verifiers like Leon and Stainless. Recent work emphasizes verified compilers, set theory formalisms, and education tools like Lisa. His publications address challenges in SMT solving, equivalence checking, and functional programming. Scientific Awards: ERC Starting Grant (2012), ACM SIGSOFT Distinguished Paper (2010), CAV Best Paper (2010). Advising: Supervised 16 completed PhD theses, with current students working on proof assistants and formal verification frameworks. Grants include SNSF and EU-funded projects. Labs/Teams: Directs LARA lab at EPFL, collaborating with global researchers in formal methods and programming languages.

• Biomedical Circuits
• Ultra-Wideband Communication
• Robotics
• +3 more

Marco Crepaldi is an active researcher with a focus on biomedical circuits, wearable systems, and machine learning applications. His work spans collaborations with institutions and researchers globally, contributing to fields such as multisensory rehabilitation devices, ultra-wideband communication, and neuromorphic computing. Research Interests: Biomedical circuits for rehabilitation, wireless sensing, machine learning hardware, and human-robot interaction. Recent publications highlight his innovation in: Transformer and graph neural networks for fall detection Reservoir computing for seizure control Low-power 8-bit neural processors Wearable multisensory systems for visually impaired infants He has co-authored 73+ peer-reviewed publications since 2007, with a technical focus on ultra-wideband systems, asynchronous logic, and embedded biomedical devices.