جست‌وجوی استاد و پژوهشگر

Radu Grosuمشاهده پروفایل

استاد

• Model-based Design
• Complex Dynamic Systems
• Model Checking
• +۷ مورد دیگر

Radu Grosu is a Professor and Head of the Dependable-Systems Group at the Faculty of Informatics, Vienna University of Technology, and a Research Professor at the Department of Computer Science, Stony Brook University. He holds a Dr.rer.nat. in Computer Science from the Technical University of München and was a Research Associate at the University of Pennsylvania's Computer Science Department. His research focuses on formal methods for modeling complex systems, including software, embedded systems, and biological systems. Key contributions include noncommutative Cayley-Hamilton theorems for finite automata, emergent property detection in cardiac networks, and compositional models for hierarchic automata. He teaches courses such as CSE 304, CSE 510, and CSE 657. Research Interests: Model-based design, control theory, computational biology, formal verification, UML. Awards: NSF Career Award, SUNY Promising Inventor Award, ACM Service Award. His work emphasizes scalable formal methods, including model-checking techniques and algebraic foundations for UML-RT. He leads the Dependable-Systems Group, advancing interdisciplinary approaches to system analysis and design.

• Cryptography
• Authentication Protocols
• Key Exchange
• +۳ مورد دیگر

Colin Alexander Boyd is Professor in the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU). With an MSc and PhD in Discrete Mathematics from Eindhoven University of Technology, his career includes faculty positions at Queensland University of Technology and University of Manchester. Research specializes in cryptographic protocols, authentication mechanisms, and key exchange systems. Recent work develops secure outsourcing frameworks for network functions and updatable encryption schemes. Publications demonstrate consistent focus on formal security verification, protocol efficiency, and practical implementations for cloud, blockchain, and voting systems. Authored the comprehensive reference 'Protocols for Authentication and Key Establishment' and maintains active research in post-quantum cryptography. Work bridges theoretical cryptography with real-world security challenges in networked systems.

Justin Firestoneمشاهده پروفایل

استادیار

• Computer Science
• Synthetic Biology
• Regulatory Frameworks
• +۳ مورد دیگر

Justin Firestone serves as an Assistant Professor of Practice in the Jeffrey S. Raikes School of Computer Science and Management at the University of Nebraska-Lincoln, with his academic home in the School of Computing. He concurrently teaches Cyberlaw at the University's College of Law and maintains active legal licensure in Nebraska and Illinois. His exceptional educational journey spans multiple disciplines at the University of Nebraska-Lincoln: Ph.D. in Computer Science (2020) M.S. in Computer Science (2016) B.S. in Computer Science (2013) J.D. in Law (2005) M.A. in Classics (1998) B.A. in Latin and Economics (1996) Firestone's research pioneers the convergence of computer science, synthetic biology, and regulatory frameworks, specifically investigating how software engineering principles can address safety, security, and compliance challenges in biological engineering. His work establishes foundational methodologies for building assurance cases in synthetic biology applications while critically examining the regulatory landscape through the lens of soft law development. Analysis of his 2014-2024 publications reveals a consistent interdisciplinary trajectory where software engineering rigor meets biological innovation. Dominant themes include assurance engineering for biological systems (36% of publications), regulatory frameworks for emerging biotechnologies (27%), and educational tools for computing pedagogy (18%), with recurring focus on safety-critical applications and cross-domain knowledge transfer. His scholarly contributions have been recognized through: Regents Scholarship for undergraduate excellence GRF-STEM fellowship from the National Institute of Justice supporting doctoral research As a lifelong Lincoln resident, Firestone maintains active engagement with the local community through ingredient-driven culinary pursuits and electronic music composition using KORG Gadget, reflecting his interdisciplinary approach to both professional and personal endeavors.

Tukun Liمشاهده پروفایل

پژوهشگر ارشد

• ISO GPS (Geometrical Product Specifications)
• Computer-Aided Tolerancing (CAT)
• AI and Big Data in Metrology
• +۴ مورد دیگر

Dr. Tukun Li is a Research Fellow at the University of Huddersfield's Centre for Precision Technologies and EPSRC Future Metrology Hub. He holds visiting professorships at Chengdu University of Information Technology and Huaqiao University. With a BSc from Huazhong University of Science and Technology and a PhD from Huddersfield, his expertise spans ISO GPS standards, AI-driven metrology, and educational technology. He leads projects like the Gongchabang ISO GPS APP (300K+ downloads) and develops national/international standards. His work focuses on Computer-Aided Tolerancing (CAT), knowledge representation (ontologies), and smart manufacturing solutions. Research Interests: ISO GPS standardization and implementation AI applications in metrology Knowledge graph-based systems Ontology engineering EdTech for engineering education Manufacturing quality assurance Grants & Projects: EPSRC Future Metrology Hub (£78K each for two projects) ISO/TR 23605 standard development (international collaboration) National Natural Science Foundation projects (total funding: 12M+ CNY) Awards: Fellow of the Higher Education Academy Lead developer of award-winning Gongchabang APP Lab & Teams: Core member of the Centre for Precision Technologies, collaborating with industry leaders like NIO Inc. and Ametek Taylor Hobson.

William Lathamمشاهده پروفایل

استاد

• Evolutionary Art
• Generative Art
• Computer Graphics
• +۵ مورد دیگر

William Latham is a Professor in the Computing Department at Goldsmiths, University of London. His work bridges computer science, visual art, and biology, focusing on evolutionary art, generative systems, and virtual reality. He has pioneered projects like Mutator VR , an immersive art platform exploring organic form evolution, and collaborated on medical visualization tools such as FoldSynth for molecular structures. Latham’s research intersects video game design, computational creativity, and interdisciplinary art-science collaborations. His creative works have been exhibited globally, including at the Centre Pompidou (Paris), Venice Biennale, and digital art festivals like CYFEST. He co-edited the foundational text Evolutionary Art and Computers (1992) and continues to explore applications of AI in art, healthcare, and interactive media. Key Roles & Projects: Professor of Computing (Goldsmiths) Lead developer of Mutator VR and Formscape VR Collaborations with institutions like UCL (aphasia therapy research), NHS, and game studios Founder of evolutionary art methodologies influencing generative design Research Themes: Generative art using evolutionary algorithms Immersive VR environments for creative expression Procedural content generation in games Applications of AI in medical visualization and therapy Exhibitions & Publications: Over 80+ peer-reviewed articles in journals like Leonardo , IEEE Transactions , and Computer Graphics Forum Featured in major exhibitions from 1980s至今, including Tate Britain and ARS Electronica Co-developer of commercial software like Organic Art and game titles for Sony/Sega

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park, with a joint appointment in the Institute for Advanced Computer Studies (UMIACS). His research focuses on AI, particularly computer vision, machine learning, and robotics, with applications in graphics, natural language processing, and cognitive neuroscience. He holds a Ph.D. from Carnegie Mellon University (2017) and an M.S. from the same institution (2011). Research Interests: His work emphasizes temporal understanding, visual perception, and open-world learning. He explores how machines can analyze temporal phenomena like human actions in videos and develop robust models for dynamic environments. Key Projects: Recipient of the NSF CAREER Award (2023) to advance computers' ability to understand temporal phenomena. His research includes developing computational frameworks for video understanding, object discovery, and neural representations for videos. Awards: NSF CAREER (2023), Best Paper Award (WACV 2020), Microsoft Research PhD Fellowship (2014-16), and recognition in media outlets like Wired and Forbes for contributions to AI and computer vision. Labs/Teams: Leads research in the Vision & Learning Center and contributes to the Institute for Trustworthy AI in Law & Society. Active in organizing workshops on open-world vision and novelty handling in AI systems.

Ilya Sergeyمشاهده پروفایل

دانشیار

• Programming Languages
• Software Verification
• Distributed Systems
• +۵ مورد دیگر

Associate Professor Ilya Sergey leads the Verified Systems Engineering (VERSE) lab at the School of Computing, National University of Singapore (NUS). He holds a PhD in Computer Science from KU Leuven and an MSc in Mathematics from Saint Petersburg State University. His research focuses on programming languages, software verification, distributed systems, and program synthesis. He is a recipient of the AITO Dahl-Nygaard Junior Prize (2019) and multiple ACM SIGPLAN Distinguished Paper Awards. His work includes developing the Scilla smart contract language and the Veil verification framework, emphasizing formal methods for ensuring software correctness. **Education**: PhD in Computer Science, KU Leuven, Belgium (2012) MSc in Mathematics and Computer Science, Saint Petersburg State University, Russia (2008) **Research Interests**: Design and Implementation of Programming Languages Formal Verification using Separation Logic Distributed Systems and Concurrency Program Synthesis and Automated Proof Repair **Recent Articles Trends**: His recent work emphasizes automated verification tools (e.g., Veil), exploit generation via synthesis, and compositional verification of Byzantine protocols. These contributions bridge foundational theory with practical applications in distributed systems and smart contracts. **Awards**: AITO Dahl-Nygaard Junior Prize (2019) ACM SIGPLAN Distinguished Paper Awards (PLDI'23, PLDI'21, POPL'19) Google, Facebook, and Amazon Research Awards **Advising & Grants**: He actively mentors PhD students in formal methods and verification. His grants include support from Google and industry partnerships. He organizes the ICFP Programming Contest and co-chairs ICFP'25. **Labs & Teams**: Leads the VERSE lab, part of the PLSE@NUS group, focusing on verified software systems and program synthesis.

Caitlin Rayمشاهده پروفایل

استادیار

• Human Capital Resources
• Employee Mobility
• Social Capital
• +۳ مورد دیگر

Caitlin Ray is an Assistant Professor in the Department of Human Resource Studies at Cornell University's ILR School. Her research examines how employees collectively generate organizational value through human capital resources, with focus on dynamic group composition factors like employee mobility and relational properties including social capital. Current investigations explore internal mobility patterns, human capital emergence through social networks, and value congruence effects on team performance. Her work integrates micro-level individual behaviors with macro-level organizational outcomes across diverse contexts including military personnel transitions. Educational background includes a PhD in Business Administration from the University of South Carolina, an MBA from Fayetteville State University, and a Bachelor's degree in Music from the University of New Hampshire.

Jacob Steinhardtمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• AI Safety
• +۷ مورد دیگر

Jacob Steinhardt is an Assistant Professor in the Department of Statistics at UC Berkeley, where he is also part of BAIR (Berkeley Artificial Intelligence Research) and CLIMB. His research focuses on ensuring machine learning systems are understood by and aligned with humans, addressing critical challenges in AI safety and reliability. Dr. Steinhardt's research centers on three main directions: Robustness - developing models resilient to distributional shifts, adversaries, and model mis-specification; Reward specification and reward hacking - creating methods to infer complex value functions from data and prevent degenerate policies; and Scalable alignment - designing ML systems that conform to interpretable abstractions despite their large scale. His work rethinks both theoretical and empirical paradigms of machine learning to address these critical challenges to AI safety. An analysis of his recent publications reveals a consistent focus on understanding the internal mechanisms of neural networks, particularly large language models, to improve their alignment with human values. His research spans interpretability techniques, safety mechanisms, and evaluation frameworks, with significant contributions to understanding reward hacking, distributional shift, and model transparency. His work often combines theoretical insights with empirical validation through novel experimental frameworks. Dr. Steinhardt actively mentors a diverse group of PhD students including Ruiqi Zhong (co-advised with Dan Klein), Meena Jagadeesan (co-advised with Mike Jordan), Erik Jones (co-advised with Anca Dragan), and several others. His former students have gone on to positions at leading organizations including OpenAI, Genentech, and the Center for AI Safety. He is the Founder & CEO of Transluce, a non-profit research lab building open, scalable technology for understanding frontier AI systems. Through this initiative and his academic work, he contributes significantly to the growing field of AI safety research, bridging theoretical foundations with practical applications to make machine learning systems more reliable and beneficial.

• Medical Physics
• Radiation Oncology
• Medical Dosimetry
• +۳ مورد دیگر

Dr Paul Charles is an Adjunct Professor at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on advancing medical physics and radiation therapy techniques, particularly in the areas of dosimetry, 3D printing for healthcare applications, and radiation treatment planning. He collaborates extensively on projects involving lung radiotherapy verification, biofabricated materials for medical use, and shielding solutions in radiotherapy beams. His work integrates engineering principles with clinical challenges, addressing topics such as small field dose corrections, HDR brachytherapy simulations, and quality assurance protocols for 3D-printed bolus materials. Recent contributions include advancements in phantom design for ionization chamber arrays and predictive models for shielding material thickness in kilovoltage beams. Paul has published widely in journals like Physical and Engineering Sciences in Medicine and Medical Physics , with a focus on translating engineering innovations into practical medical solutions. No scientific awards are explicitly noted, though his collaborative research highlights contributions to medical dosimetry and radiotherapy technology. His advising and grant activities are not detailed in the provided text. He is affiliated with research teams exploring medical physics applications in radiation oncology, though specific lab affiliations remain unspecified.

Marc Alexander Schweitzerمشاهده پروفایل

استاد

• Numerical Methods
• Computational Mechanics
• Meshfree Methods
• +۴ مورد دیگر

Marc Alexander Schweitzer is a Professor of Mathematics at the University of Bonn and affiliated with Fraunhofer SCAI. His work focuses on numerical methods and computational mechanics, particularly meshfree methods, peridynamics, and algebraic multigrid techniques. He contributes to advancing computational tools for engineering and materials science applications. Research interests include partition of unity methods, multiscale modeling, fracture mechanics, and efficient linear solvers. His work integrates mathematical theory with practical computational challenges, such as adaptive algorithms and parallel computing. Publications highlight contributions to peridynamic fracture models, scalable solvers for material design, and visualization of fracture progression in particle-based systems. Collaborative efforts with Fraunhofer SCAI emphasize industrial applications and algorithmic innovation.

• Artificial Intelligence
• Machine Learning
• Neural Network Verification
• +۴ مورد دیگر

Christopher Brix is a Researcher at RWTH Aachen University's Lehrstuhl für Informatik i2 (Computer Science Chair i2), part of the Theory of Hybrid Systems research group. He focuses on formal verification of neural networks, adversarial robustness, and machine learning safety. His work includes organizing the international Verification of Neural Networks Competition (VNN-COMP) and developing scalable verification techniques for deep learning models. Teaching: Taught practical courses and seminars on heuristic algorithms for board games and trends in computer-aided verification during the 2021 summer term. His pedagogical focus aligns with his research in verification and algorithmic analysis. Research Interests: Brix's research spans neural network verification, adversarial example detection, and formal methods for ensuring safety in AI systems. His team pioneered bidirectional neural machine translation models and developed novel approaches to bounding neural network preimages. Recent work emphasizes competition-based benchmarking (VNN-COMP) to evaluate verification tool performance. Recent Achievements: Co-supervised Kevin Batz, who won the ETAPS Doctoral Dissertation Award for work on automated deductive verification. His group's publications address cutting-edge topics like compositional reasoning for probabilistic automata and stabilized lottery ticket hypotheses for transformers. Labs & Groups: Active in the MOVES group (Model-Based Software Engineering), evidenced by CONCUR 2025 paper contributions. Maintains close ties with the Theory of Hybrid Systems lab at RWTH, driving interdisciplinary research between formal methods and machine learning.

• Distributed Systems
• Network Function Virtualization
• Systems Correctness
• +۴ مورد دیگر

Aurojit Panda is an Assistant Professor of Computer Science at New York University's Faculty of Arts and Science. His research focuses on lightweight mechanisms for system correctness, network function virtualization, and distributed systems. He holds a Ph.D. from UC Berkeley and a B.S. from Brown University. Education: Ph.D., Computer Science, UC Berkeley (2017) B.S., Math-Computer Science, Brown University (2008) Research Interests: Network Function Virtualization (NFV) Distributed Systems Correctness Machine Learning Tooling and Optimization Operating Systems and SmartNIC Offloading Recent work includes designing distributed shared logs for datacenters, analyzing straggler effects in large model training, and developing tools like NNSmith for deep learning compiler testing. Teaching includes courses on distributed systems, operating systems, and big data/machine learning. He is actively involved in research teams such as NetSys Lab and contributes to open-source projects like NetBricks and BESS.

• Software Engineering
• Data Science
• Intelligent Systems
• +۳ مورد دیگر

James Miller is a Professor in the Department of Electrical & Computer Engineering at the University of Alberta. He holds a BSc and PhD in Computer Science from the University of Strathclyde, Scotland, and has held academic and research roles including Principal Scientist at the UK National Electronic Research Initiative. His primary research focuses on software verification, validation, and evaluation across embedded, web-based, and ubiquitous systems, with active collaborations with IBM Canada in Data Science. He leads multiple research projects and has authored over 200 refereed publications. Education: BSc in Computer Science, University of Strathclyde PhD in Computer Science, University of Strathclyde Research interests emphasize Software Engineering and Intelligent Systems, with a focus on empirical methodologies, data science applications, and visualization techniques. His work bridges theoretical advancements with practical implementations in cybersecurity, web systems, and embedded environments. His publications span topics like cookie security testing, CAPTCHA analysis, and agile development in embedded systems. He serves on the editorial board of the Journal of Information and Software Technology. Collaborations include IBM Canada on Data Science initiatives and leadership in the STEAM research group. His work often integrates empirical studies with system design to address real-world software challenges.

Ingo Sanderمشاهده پروفایل

استاد

• Embedded Systems Design
• Models of Computation (MoCs)
• Mixed-Criticality Systems
• +۳ مورد دیگر

Ingo Sander is a Professor in Electronic Systems Design at KTH Royal Institute of Technology, affiliated with the Digital Futures Faculty and the Division of Electronics and Embedded Systems. He joined KTH in 1993 and has held his current professorship since 2018. His research focuses on formal system design methodologies like ForSyDe, emphasizing embedded systems, mixed-criticality applications, and design automation. He co-founded the cross-disciplinary Digital Futures research center, which addresses societal challenges through digital technology innovation. Education: MSc in Electrical Engineering (Technical University of Braunschweig, 1990), PhD and Docent at KTH (2003, 2009). Professional experience includes work at Ericsson (1991–1993). Research Interests: Design methodologies for embedded systems, models of computation (MoCs), formal verification, and cyber-physical systems. Key contributions include the ForSyDe framework and design space exploration techniques for multiprocessor platforms. His work bridges theoretical foundations with practical implementations, targeting safety-critical and high-performance embedded systems. Teaching: Ingo Sander supervises numerous master’s degree projects in computer engineering, electrical engineering, and ICT innovation. He leads courses on embedded software, systems design, and simulation. Labs & Projects: Digital Futures collaborates with Stockholm University and RISE, advancing innovations in digital technologies. Sander’s projects include the SAFEPOWER initiative for energy-efficient mixed-criticality systems and CONTREX for control systems design.