جست‌وجوی استاد و پژوهشگر

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.

• Cryptography
• Secure Multi-Party Computation
• Privacy-Preserving Techniques
• +۳ مورد دیگر

Oleksandr Tkachenko holds a PhD from the Technical University of Darmstadt (2022) and is actively engaged in research at the intersection of cryptography, secure computation, and data privacy. His work focuses on advancing practical applications of multi-party computation (MPC), privacy-preserving techniques, and front-end security in web technologies. Key contributions include the development of frameworks like MOTION and FUSE for efficient MPC implementations, as well as secure aggregation methods for federated learning (ScionFL). He has also explored polynomial maximization techniques for statistical estimation and privacy-preserving genomic analysis. Collaborations with institutions like TU Darmstadt and co-authors such as Thomas Schneider and Christian Weinert highlight his interdisciplinary approach. His publications span venues like ACM Transactions on Privacy and Security, IEEE ARES, and the Automation conference series, reflecting expertise in both theoretical and applied aspects of cryptography.

Margo Seltzerمشاهده پروفایل

استاد

• Computer Systems
• Data Management
• Machine Learning
• +۵ مورد دیگر

Margo Seltzer is the Canada 150 Research Chair in Computer Systems and Cheriton Family Chair in Computer Science at the University of British Columbia (UBC), where she is a Professor in the Department of Computer Science, Faculty of Science. She currently serves as co-Head of the UBC Computer Science Department, managing its academic and operational affairs. Her research focuses on systems, provenance, machine learning, and security, with notable contributions to graph databases, program synthesis, and reproducibility frameworks. She holds prestigious awards including election to the Royal Society of Canada (2023) and was nominated for the ACM Athena Lecturer Award. Her work emphasizes interdisciplinary applications, such as healthcare anomaly detection and interpretable AI models for datasets with missing values. Dr. Seltzer leads the Systopia research group, which explores systems and machine learning intersections. Recent projects include Velosiraptor (code synthesis for memory translation), HyperBrain (anomaly detection in brain networks), and NetShaper (differentially private network security). She actively publishes in top conferences like NeurIPS, ICML, and USENIX. Her lab develops tools like Provenance-Aware Storage Systems (PASS) and frameworks for reproducible research. She mentors postdoctoral researchers but is not currently admitting new graduate students, emphasizing alignment between student interests and her research areas.

Collin Capanoمشاهده پروفایل

عضو هیئت علمی

• Gravitational Wave Astronomy
• Black Hole Physics
• Neutron Star Dynamics
• +۳ مورد دیگر

Collin Capano is a Research Associate Professor in the Physics department at Syracuse University. He holds a Ph.D. and B.S. in Physics from Syracuse University (2011 and 2005 respectively). His research focuses on gravitational wave astronomy, black hole spectroscopy, and computational methods for analyzing astrophysical data. He has led projects involving black hole ringdown analysis, neutron star equation of state studies, and multimessenger astronomy. Dr. Capano has secured significant grants including a National Science Foundation award for developing black hole spectroscopy methods and desktop cluster infrastructure (2024-2026) and a Defense University Instrumentation Initiative grant (2022). He currently directs the Open Source Project Office at Syracuse University's Research Computing division. His work has been published in top journals like Physical Review D , Nature Astronomy , and The Astrophysical Journal . Notable contributions include testing general relativity with GW170817 observations and constraining neutron star radii through combined gravitational-wave and electromagnetic data analysis. Awarded the Max-Planck-Gesellschaft performance bonus (2020), he serves as a reviewer for leading journals including Physical Review Letters , Monthly Notices of the Royal Astronomical Society , and Classical and Quantum Gravity . He actively participates in international workshops and conferences, presenting on topics ranging from black hole spectroscopy to the future of gravitational wave astronomy.

• Biomedical Circuits
• Ultra-Wideband Communication
• Robotics
• +۳ مورد دیگر

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.

Sander Stuijkمشاهده پروفایل

دانشیار

• Embedded Systems
• Signal Processing
• Multi-Core Architectures
• +۳ مورد دیگر

Sander Stuijk is an associate professor at the Department of Electrical Engineering of Eindhoven University of Technology, chairing the Electronic Systems (ES) group. His research focuses on design methodologies for embedded signal processing applications in high-tech systems like industrial manufacturing, automotive, and healthcare. He develops high-level compilation strategies for heterogeneous multi-core platforms, with interests in efficient code generation and resource allocation. Prof. Stuijk holds leadership roles including chairing the ES group, serving on the board of 4TU.NIRICT (a Dutch ICT research consortium), and coordinating the Embedded Systems master program. He contributes to national ICT initiatives through ICT Next Generation, a network for mid-career academics. He also chaired the Department Council (2016-2018) and participates in TPC activities. His research spans streaming applications, real-time systems, and multi-processor architectures. Notable projects include the PROMES initiative (programming embedded multi-media systems) and the MNEMEE project (automated MPSoC design). His work emphasizes predictable and energy-efficient computing, with applications in healthcare monitoring (e.g., remote PPG, thermal imaging) and reconfigurable systems. Prof. Stuijk has advised numerous PhD/Master students and led projects like FORSEE, VSM, and SenSafety. His publications include over 70 journal/conference papers, covering topics like neuromorphic computing, GPU optimization, and medical signal processing. He actively engages in academic service, including organizing SCOPES workshops and serving on editorial boards.

Flavius Gruianمشاهده پروفایل

مدرس ارشد

• Embedded Systems
• Real-Time Systems
• Hardware-Software Co-Design
• +۵ مورد دیگر

Flavius Gruian is a Senior Lecturer and Reader Project Manager at the Department of Computer Science, Lund University (LTH). He holds affiliations with ELLIIT (Linköping-Lund initiative on IT and mobile communication) and the LTH Profile Area: AI and Digitalization. His research focuses on resource-constrained embedded systems, real-time scheduling, and energy-efficient hardware-software co-design, with applications in IoT, radar systems, and radio astronomy. Education and Academic Roles: No explicit education details provided, but his roles include senior lecturer, project manager, and principal investigator (PI) in multiple research projects. He advises PhD students (Boulasikis, Callanan, Waldemarson) and contributes to initiatives like GPAI (AI hardware for computing) and NordicIoT (Nordic IoT hub). Research Interests: His work spans embedded systems modeling, real-time algorithms, custom hardware architectures, Java in embedded systems, and machine learning for industrial applications. He explores energy-efficient designs and reliability in distributed embedded systems. Key Projects: Active projects include GPAI (2021–2026), TaLMiRRA (machine learning in radar/astronomy, 2019–2022), and NordicIoT (2018–2023). He also leads research on scalable actor networks and GPU programming for visual systems. Grants and Funding: PI roles in TaLMiRRA (STINT-funded) and NordicIoT (Nordic collaboration). Projects involve collaboration with international teams and industry partners. Labs/Teams: Affiliated with ELLIIT and the LTH AI profile area. Collaborates on hardware-software co-design and embedded systems optimization.

• Formal Methods
• Software Engineering
• Machine Learning
• +۳ مورد دیگر

Eva Schenner is a Researcher at TU Wien's Department of Software Engineering, affiliated with both the Office Services and eduLAB teams. She specializes in formal methods, program analysis, and machine learning applications in software engineering. Her work focuses on testing tools, smart contract certification, and probabilistic models for material design. She has led multiple high-profile projects, including the FWF-funded 'Types4Strings' (2024–2027), WWTF's 'Effective Formal Methods for Smart-Contract Certification' (2023–2027), and an Amazon Research Award project on testing Dafny (2024–2027). Her research also spans metamorphic testing of machine-learning models and probabilistic hybrid models for material design. Education: Mag.a phil. (Master of Philosophy) Her awards include Amazon and Google Research Awards, and grants from the WWTF and Max-Planck-Gesellschaft. She actively contributes to initiatives like Bebras, promoting computational thinking education since 2007. Labs/Teams: Involved in the Software Engineering group within the Faculty of Informatics, collaborating with industry partners like Amazon and Google.

• Software Verification
• Automated Reasoning
• Code Synthesis
• +۳ مورد دیگر

Ruzica Piskac is a Professor of Computer Science at Yale University, leading the Rigorous Software Engineering (ROSE) group. She previously held an Independent Research Group Leader position at the Max Planck Institute for Software Systems (2012–2013) and earned her PhD from EPFL (2011), awarded the Patrick Denantes Prize for her thesis on decision procedures for program synthesis and verification. Her research focuses on improving software reliability and trustworthiness through formal methods, encompassing software verification, automated reasoning, applied cryptography, and code synthesis. Notable honors include multiple Amazon Research Awards, the Yale Ackerman Award, and the Microsoft Research Award. She currently serves as Program Chair for the International Conference on Computer Aided Verification (CAV 2024) and on the Steering Committee for Formal Methods in Computer-Aided Design (FMCAD). Education: PhD in Computer Science (EPFL, 2011), MSc in Computer Science (Saarland University, 2005) Key Projects: ROSE group’s work on legal accountability for automated decisions (soid tool), privacy-preserving model checking, and quantum computing security Professional Activities: Editorial roles for conferences like LPAR, FMCAD, and VMCAI Her research group has graduated five PhD students, four now holding assistant professor roles. Current projects include formal XAI via syntax-guided synthesis, privacy-preserving cryptographic protocols, and quantum circuit vulnerability analysis.

• Theoretical Computer Science
• Formal Methods
• Programming Languages
• +۵ مورد دیگر

Luís Cruz-Filipe is an Associate Professor at the Department of Mathematics and Computer Science (IMADA) within the Faculty of Engineering at the University of Southern Denmark. His research spans theoretical computer science with a focus on formal methods, programming languages, and distributed systems. He maintains an active research profile with numerous publications in top venues. His research interests primarily center around choreographic programming, formal verification, sorting networks, and logic programming. Cruz-Filipe has made significant contributions to the theoretical foundations of choreographic programming, developing formal frameworks for specifying and verifying distributed communication protocols. His work on sorting networks has advanced the understanding of optimal network structures, while his research in approximation fixpoint theory has provided formal foundations for logic programming semantics. Analysis of his recent publications reveals a strong trend toward formal methods applied to distributed systems, particularly in choreographic programming where he has developed theoretical frameworks, verification techniques, and compilation approaches. His work bridges theoretical computer science with practical implementation concerns, often using proof assistants like Coq to ensure correctness. More recently, he has expanded into sustainability topics with work on green software development. As an academic, Cruz-Filipe has contributed significantly to the theoretical foundations of choreographic programming, producing a cohesive body of work that has helped establish this field. His publications demonstrate a consistent focus on formal methods to address challenges in distributed systems, with increasing attention to practical implementation and verification.

• Plasma Physics
• Atomic and Molecular Physics
• Condensed Matter Physics
• +۴ مورد دیگر

Ronald D. White is a Professor at James Cook University (JCU), known for his extensive research in plasma physics, electron and positron scattering, and computational modeling of charged particle transport. His work spans fundamental studies of electron dynamics in gases and liquids, including applications in organic electronics (e.g., OLEDs) and plasma-liquid interactions. He has collaborated globally, contributing to cross-section databases critical for plasma simulations. His research emphasizes theoretical and computational methods, with a focus on non-equilibrium systems and material interfaces. Key interests include: Electron and positron scattering cross-sections Transport phenomena in plasmas and condensed matter Organic light-emitting diodes (OLED) performance modeling Machine learning applications in plasma diagnostics Publications highlight contributions to: Boltzmann equation solutions for structured materials Positron track modeling in biological systems Nanostructured organic semiconductor dynamics Electron swarm behavior in RF fields His work bridges fundamental physics and applied technologies, with implications for radiation dosimetry, energy materials, and advanced optoelectronics.

Mark Greenمشاهده پروفایل

مدرس ارشد

• Metaprogramming
• Software Systems Development
• Agent-Oriented Programming
• +۳ مورد دیگر

Dr. Mark Green is a Senior Lecturer in the School of Engineering, Computing and Mathematics at Oxford Brookes University. He holds a PhD in Computer Science from the University of Reading, focusing on visualization techniques for formal specifications of communicating systems. His expertise spans metaprogramming, software systems development, and cybersecurity, with over 10 years of teaching experience in higher education. Research interests include Agent-Oriented Programming (via the CAOPLE language), game technologies (including procedural generation and gamification), and semantic systems. He is affiliated with the Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute and the Cloud Computing and Cybersecurity Group (CCC). Notable achievements include the 2017-18 Brookes Union Award for Outstanding Contribution to the Student Experience. Teaching modules include Secure Systems Architecture, Game Technologies and Development, and Software Development with C and C++. He has supervised MSc projects in experimental game development, web UI design, and AI applications. His research outputs include contributions to microservices development (CIDE IDE) and formal methods in system analysis. Awards: Brookes Union Award (2017-18) Professional Affiliations: mBCS member Consultancy: Developed a configurable Fruit Machine simulator for psychological research

Berry Gérardمشاهده پروفایل

استاد

• Models of computation
• Programming language design
• Synchronous programming
• +۵ مورد دیگر

Berry Gérard is a distinguished Professor at Collège de France, holding the Chair in Algorithms, Machines, and Languages since 2012. He previously served as Director of Research at INRIA Sophia Antipolis and Chief Scientist at Esterel Technologies. His academic journey includes roles at École des Mines de Paris and École Polytechnique. Gérard specializes in models of computation, programming language design, and synchronous systems. He has contributed significantly to the development of the Esterel language for embedded systems and formal verification techniques. Education: Docteur d’Etat in Mathematics, Université Paris VII (Computer Science option), 1979 Ingénieur des Mines, Corps National des Ingénieurs des Mines, 1970 École Polytechnique, 1967 Research Interests: Focuses on computational models (e.g., lambda-calculus, synchronous concurrency), programming languages (Esterel, Hop/HipHop), circuit synthesis, and formal verification. His recent work explores diffuse programming and web orchestration. Key Awards: 2014: Médaille d'or du CNRS 2005: Member, Académie des technologies 1993: Member, Academia Europaea 1979: Bronze Medal of CNRS Professional Roles: President of the Council of Education and Research at École Polytechnique Member of the Scientific Council of IRCAM Former President of the INRIA Evaluation Committee Labs/Teams: Active in INRIA’s Indes project on diffuse programming and collaborates with IRCAM on real-time music systems.

• Approximation Theory
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Feng Dai is affiliated with Xidian University's National Laboratory of Radar Signal Processing in China. His research spans approximation theory, machine learning, computer vision, and optimization algorithms. He has collaborated extensively with institutions like the National Laboratory and co-authored over 140 publications since 2002. Key research interests include polynomial approximation on multivariate domains, deep learning for computer vision tasks (e.g., object detection, semantic segmentation), and optimization techniques for engineering systems. His work bridges mathematical theory with practical applications in signal processing and imaging systems. Recent publications focus on advancing polynomial mesh theory, developing algorithms for panoramic imaging, and improving federated learning frameworks for IoT applications. Notable contributions include work on universal discretization methods and boundary handling in oriented object detection. His interdisciplinary approach integrates computational mathematics with modern AI techniques, addressing challenges in both theoretical and applied domains such as autonomous systems and medical imaging.

Thomas Schneiderمشاهده پروفایل

استاد

• Cryptography
• Privacy Engineering
• Secure Computation
• +۲ مورد دیگر

Thomas Schneider is Professor of Cryptography and Privacy Engineering at TU Darmstadt. His research develops privacy-preserving cryptographic protocols for applications including private set intersection, mobile contact discovery, genomic privacy, and secure computation compilers. He leads the Engineering Cryptographic Protocols Group (ENCRYPTO) and has received both ERC Starting (2019) and Consolidator Grants (2023). Schneider's current projects include: Developing efficient private set intersection protocols for mobile applications Privacy-preserving machine learning frameworks Hardware-assisted cryptographic protocols Secure computation for cloud environments