جست‌وجوی استاد و پژوهشگر

MIGUEL CARRERO PAZOSمشاهده پروفایل

استاد

• Computational Archaeology
• GIS and Spatial Statistics
• Megalithic Landscapes
• +۳ مورد دیگر

Miguel Carrero-Pazos is a permanent faculty member and Professor at the University of Santiago de Compostela, affiliated with the Department of History within the Faculty of Geography and History. He is a core member of the GEPN-AAT research group (Grupo de Estudios para la Prehistoria del Noroeste Ibérico. Arqueología, Antigüedad y Territorio) and associated with the Centro de Investigación Interuniversitario das Paisaxes Atlánticas Culturais (CISPAC). His doctoral thesis (2017) explored megalithic tomb patterns in Galicia using GIS and spatial statistics. His research focuses on computationally-informed landscape archaeology, emphasizing GIS applications, spatial statistics, and 3D imaging techniques. Key areas include megalithic landscape analysis, rock art documentation (using photogrammetry and digital enhancement), and Neolithic settlement patterns. He has published extensively on topics like burial mound distribution modeling, visibility analysis of megaliths, and digital methods for preserving epigraphic heritage. Recent work highlights include developing hybrid machine learning algorithms for detecting archaeological tumuli, analyzing the 'Coast of Death' dolmen topographies, and advancing open-source computational tools in archaeology. He collaborates internationally, contributing to projects like Epigraphica 3.0, which digitizes Roman inscriptions, and the IFRAO 2018 rock art congress proceedings. His research integrates cutting-edge technologies with traditional archaeological questions, prioritizing reproducibility and interdisciplinary methods. He actively engages in heritage preservation efforts, particularly in Galicia, addressing challenges posed by rural depopulation and demographic changes. Carrero-Pazos is a leading voice in applying computational approaches to understand prehistoric and early medieval human decision-making in landscape contexts.

• Digital Circuit Design
• Embedded Systems
• Microelectronics
• +۳ مورد دیگر

Manuel Jesús Bellido Díaz is a Full Professor at the University of Seville's Department of Electronic Technology. His research focuses on digital circuit design, embedded systems, microelectronics, and FPGA implementations. He leads the 'Investigación y Desarrollo Digital' (Digital Research and Development) group and has been involved in numerous projects, including the SEPIC project on embedded systems for critical infrastructure and the USECHIP microelectronics chair. He has advised doctoral students such as Germán Cano (2022), David Guerrero (2012), and Alejandro Millán (2008). Education: Doctorate in Electronic Engineering (University of Seville). His research interests span power optimization, timing models, and hardware/software co-design. Recent projects include secure IoT boot mechanisms (IRIS), FPGA-based time synchronization cores, and energy-efficient CMOS gate implementations. He has authored over 100 publications, including books and journal articles on topics like trigonometric computation algorithms and embedded file systems. Grants and projects include funding from TSI-020100-2008, SOL2024-31823, and OTRI/08-FCIE13. His work also involves patents for trigonometric function calculators and FPGA-oriented file systems. Labs/Teams: Active in the Digital Technology Research Group, collaborating on hardware-oriented solutions for IoT and embedded systems.

• Microelectronics
• Embedded Systems
• IoT (Internet of Things)
• +۴ مورد دیگر

Enrique Ostúa Arangüena is an Associate Professor in the Department of Electronic Technology at the University of Seville. He is part of the Digital Research and Development group and has been involved in numerous research projects focused on microelectronics, embedded systems, and FPGA-based solutions. His work includes contributions to IoT security, real-time systems, and hardware-oriented file systems. He has led or participated in projects such as USECHIP (Microelectronics Chair), Advanced Initiation Systems for IoT, and Hardware Vorbis CODEC development. His research spans digital circuit design, low-power electronics, and FPGA implementation, with emphasis on applications like time synchronization (SNTP), cryptographic hardware (E-LUKS), and embedded system optimization. He has authored chapters in books such as Grid Computing and Program of Teaching Teams for the Training of Novice Teachers , and has presented at conferences like the IBERCHIP Workshop and IEEE Symposium on Industrial Embedded Systems. Key contributions include patents on trigonometric function calculators and hardware security modules. He has collaborated extensively with researchers like David Guerrero Martos and Julián Viejo Cortés, focusing on methodologies for teaching digital electronics and FPGA-based SOC design. His work emphasizes open-source technologies, hardware-software co-design, and practical applications in industrial control systems.

• Computational Linguistics
• Natural Language Processing
• Syntax-Pragmatics Interface
• +۳ مورد دیگر

Antoine Venant is an Adjunct Professor in the Department of Linguistics and Translation at the Université de Montréal's Faculty of Arts and Sciences. He serves as Deputy Director of the OLST (Observatoire de Linguistique Sens-Texte). His expertise spans computational linguistics, syntax-pragmatics interface, and syntax-semantics interface. He teaches advanced courses in semantics, pragmatics, and natural language processing. Research interests include discourse analysis, game-theoretic pragmatics, and NLP applications. He leads or co-leads multiple research projects funded by agencies like FRQSC and SSHRC, focusing on lexical analysis, model evaluation, and Bayesian reasoning in discourse. Recent projects include assessing latent structure models using artificial datasets and measuring lexical aptitude of generative language models. Supervised Theses : Three Master's theses (2023-2024) addressing pragmatic models, discourse coherence, and danmaku comment classification. Grants : Over $1M in research funding from Canadian federal and provincial agencies. Affiliations : Deputy Director at OLST; collaborator in interdisciplinary projects bridging linguistics, computer science, and cognitive neuroscience. His work integrates formal linguistic theory with computational methods, producing tools for evaluating NLP systems and advancing discourse theory through Bayesian modeling frameworks.

Christopher David Vigerمشاهده پروفایل

دانشیار

• Philosophy of Mind
• Philosophy of Language
• Cognitive Science
• +۳ مورد دیگر

Christopher David Viger is an Associate Professor in the Department of Philosophy at Western University, holding a dual affiliation with the Rotman Institute of Philosophy. Based in the Western Interdisciplinary Research Building in London, Ontario, he serves as Assistant Department Chair and contributes to the Philosophy of Mind and Cognitive Science research domain within the Rotman Institute. His educational background includes foundational training in mathematics and logic, followed by retraining in philosophy at Carleton University under Andrew Brook. He completed his PhD in Philosophy at McGill University supervised by Paul Pietroski, after which he held postdoctoral positions at Tufts University (with Daniel Dennett), Dalhousie University, and the CUNY Graduate Center/Rutgers Center for Cognitive Science. Dr. Viger's research centers on the language-thought relationship , where he developed the Acquired Language of Thought Hypothesis (ALOT) challenging traditional LOT models. He argues conceptual structure interfaces specialized processing modules, with compositionality emerging from natural language rather than innate cognition. His newer work investigates neural connectivity to explain implicit bias, dual process theory failures, and limitations in brain training applications, aiming to improve critical thinking pedagogy. His publication trajectory shows consistent focus on foundational cognitive questions, with recent work (2018-2024) examining language ontology and interpretive effects, while maintaining engagement with core themes like conceptual compositionality and the binding problem. Early influential contributions addressed LOT critiques and philosophical logic puzzles. Key recognitions include: Marilyn Robinson Teaching Award (2008) SSHRC Postdoctoral Fellowship Rotman Faculty Fellowship in Philosophy and Neuroscience (2012-2014) Administratively, he served as Undergraduate Chair (2010-2014) while maintaining active research collaborations across institutions. His work bridges philosophy, cognitive science, and neuroscience through the Rotman Institute's interdisciplinary framework, with recent scholarship increasingly addressing educational applications of cognitive theory. Dr. Viger maintains strong institutional presence through the Rotman Institute's Philosophy of Mind and Cognitive Science domain, collaborating with researchers like Tim Bayne and Mel Goodale. His postdoctoral work with Daniel Dennett at Tufts established enduring connections to major cognitive science centers, reflected in ongoing engagement with CUNY and Rutgers research networks.

Roni Katzirمشاهده پروفایل

دانشیار

• Linguistics
• Computational Linguistics
• Cognitive Science
• +۴ مورد دیگر

Prof. Roni Katzir is an Associate Professor in the Department of Linguistics and a member of the Sagol School of Neuroscience at Tel Aviv University. His research focuses on the intersection of mathematical/computational methods and linguistic cognition, investigating how humans represent, learn, and use language knowledge for inference and discourse. He holds a BSc in Mathematics from Tel Aviv University and a PhD in Linguistics from MIT. Education: BSc in Mathematics, Tel Aviv University PhD in Linguistics, Massachusetts Institute of Technology (MIT) His key research areas include computational linguistics, cognitive science, and formal semantics. Notable contributions address the computational modeling of grammar induction, the evaluation of large language models against human linguistic cognition, and the application of Minimum Description Length principles in neural networks. He leads the Computational Linguistics Lab and undergraduate program at TAU and teaches in the Adi Lautman Interdisciplinary Program for Outstanding Students. Recent work critically examines Large Language Models (LLMs), arguing they fail to replicate core aspects of human linguistic competence. His research also explores neural network generalization for grammar induction and the typological stability of logical operators. Prof. Katzir has advised numerous collaborative projects and leads initiatives in computational phonology and semantics. His labs focus on explanatory adequacy in language learning and neurosymbolic AI integration.

• Computer Architecture
• Heterogeneous Parallel Systems
• GPU Computing
• +۴ مورد دیگر

Juan Luis Aragon Alcaraz is a Full Professor in Computer Architecture at the University of Murcia (Spain), affiliated with the Faculty of Informatics and the Department of Computer Engineering and Technology. He obtained his PhD in Computer Engineering from the University of Murcia in 2003 and held a postdoctoral position as a Visiting Assistant Professor and Researcher at the University of California, Irvine. He has held visiting researcher positions at EPFL (2013) and Princeton University (2015–2022). His research focuses on computer architecture, emphasizing heterogeneous parallel systems, GPUs, memory hierarchies, and energy-efficient microarchitecture design. He has advised 7 PhD theses and co-authored over 60 publications in top-tier conferences and journals. His work includes contributions to GPU-accelerated medical imaging, energy-efficient graphics pipelines, and hardware-software co-design for parallel systems. Key research trends in his articles include optimizing GPU performance for real-time applications, energy efficiency in graphics rendering, and scalable latency tolerance in manycore systems. His patents include innovations in presbyopia correction devices leveraging GPU and FPGA technologies. Dr. Aragón has taught numerous courses in computer architecture and design, including Computer Architecture and Organization, Advanced Aspects in Multicore Architectures, and Embedded Systems Technologies. His labs and collaborations span academic-industrial partnerships, emphasizing practical applications of his theoretical contributions.

Guo Yikeمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Data Science
• +۶ مورد دیگر

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)
• +۷ مورد دیگر

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. Whitingمشاهده پروفایل

دانشیار

• Additive Manufacturing
• Printed Electronics
• Flexible Electronics
• +۴ مورد دیگر

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 Hofmannمشاهده پروفایل

پژوهشگر

• Information Security
• Trust in Security Systems
• Microarchitectural Side Channels
• +۳ مورد دیگر

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 Georgesمشاهده پروفایل

پژوهشگر

• Algorithms
• Theory & Logic
• Programming Languages & Verification
• +۳ مورد دیگر

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 Salamatiمشاهده پروفایل

پژوهشگر ارشد

• Formal Methods in Control
• Learning in Control Systems
• Cyber-Physical Systems
• +۲ مورد دیگر

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 Liangمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing
• Foundation Models
• +۴ مورد دیگر

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
• +۵ مورد دیگر

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.