جست‌وجوی استاد و پژوهشگر

Elizabeth Williamsمشاهده پروفایل

مدرس ارشد

• Nuclear Physics
• Artificial Intelligence Ethics
• Policy Analysis
• +۳ مورد دیگر

Dr Elizabeth Williams is a Senior Lecturer at the ANU Centre for European Studies and an Honorary Senior Lecturer in the ANU Department of Nuclear Physics. She holds a B.S. (Intensive) in Physics from Yale University (2004) and a Ph.D. in Experimental Nuclear Structure from Yale (2009). Her research bridges nuclear physics and applied ethics, focusing on AI governance and responsible innovation. As a Research Fellow at the 3A Institute, she develops frameworks for managing data, AI, and cyber-physical systems in societal contexts. Her work emphasizes anticipatory governance, ethical AI implementation, and the societal impacts of emerging technologies. Her academic journey includes postdoctoral roles at Yale and CSIRO, an ARC DECRA fellowship at ANU (2012–present), and leadership in transdisciplinary research programs. Key interests include fusion-fission dynamics in superheavy elements, trustworthy autonomous systems, and policy around AI ethics. She has contributed to projects like the AI Observatory for the Nuclear Sector and studies on rehabilitation robotics in healthcare. Williams has published extensively on nuclear physics phenomena (e.g., quasifission, fission dynamics) and AI-related topics such as voice data documentation, trust in human-machine systems, and defense technology implications. Her research highlights the intersection of science and policy, advocating for proactive approaches to technological development's societal consequences.

Shekoufeh Kolahdouz Rahimiمشاهده پروفایل

پژوهشگر

• Software Engineering
• Model-Driven Engineering
• Artificial Intelligence
• +۳ مورد دیگر

Shekoufeh Kolahdouz Rahimi is a researcher affiliated with the Centre for Research in Arts and Creative Exchange under the School of Arts, Humanities, and Social Sciences . Her work bridges academic disciplines through innovative research collaborations and publications. Research Focus: Integration of Large Language Models (LLMs) into software engineering practices, including Agile methodologies, Model-Driven Engineering (MDE), and formal verification of BPMN models. Conference Contributions: Active participation and organization in workshops like Agile MDE 2024 and Agile MDE 2025, with peer-reviewed publications in CEUR Workshop Proceedings. Academic Leadership: Peer-review roles for journals like Frontiers in Computer Science , and supervision of PhD research initiatives. Publication Trends: Recent work highlights the application of LLMs to automate tasks in software requirement engineering, game development, and MDE workflows. Her research emphasizes interdisciplinary collaboration, combining computational methods with creative arts frameworks.

Salvatore ORLANDOمشاهده پروفایل

استاد

• Computer Science
• Machine Learning
• Data Mining
• +۴ مورد دیگر

Salvatore Orlando is a Full Professor and Director of the Department of Environmental Sciences, Computer Science and Statistics at Ca' Foscari University of Venice. He holds academic roles including membership in the University Scientific Instrumentation Service Center (CSA) Management Committee and the Academic Senate. His research focuses on machine learning, information retrieval, and data mining, with notable contributions to learning-to-rank algorithms, decision tree ensembles, and adversarial machine learning. Key areas include efficient algorithms for large-scale systems, model interpretability, and fairness in AI. Orlando’s work bridges theoretical advancements with practical applications in database technology and cybersecurity. His recent publications emphasize optimizing ranking models, enhancing algorithm resilience, and addressing ethical considerations in AI systems. He is actively involved in organizing international conferences and serves on program committees, further contributing to the academic community.

Kevin Sullivanمشاهده پروفایل

دانشیار

• Software Engineering
• Cybersecurity
• Human Factors
• +۵ مورد دیگر

Kevin Sullivan is an Associate Professor of Computer Science at the University of Virginia, School of Engineering and Applied Science. He has been at UVa since 1994, transitioning from Assistant to Associate Professor. His research focuses on software evolvability, cyber-social learning systems, and software engineering economics. Sullivan holds degrees from Tufts University (B.S., 1987) and the University of Washington (M.S. and Ph.D., 1990/1994). Research interests include human factors in software systems, cybersecurity, programming languages, and formal methods. Notable areas of work involve reconciling modularity principles with aspect-oriented programming, and establishing cyber-social learning systems across healthcare, education, and defense sectors. Recent publications emphasize ultra-large-scale systems, data warehousing, and AI perception security. His work with John Knight on real-world types and interpreted formalisms has advanced software assurance methodologies. Award-winning former student Hridesh Rajan (Ph.D. advisee) now leads data science programs at Iowa State University. Sullivan has also been involved in projects like the SQOTA system qualities ontology and the SEHC workshop series. His current focus includes learning health systems and formal synthesis techniques for software architectures.

Eerke Boitenمشاهده پروفایل

استاد

• Cyber Security
• Privacy
• Formal Methods
• +۱۵ مورد دیگر

Eerke Boiten is Professor of Cyber Security and Head of the School of Computer Science and Informatics at De Montfort University (DMU), where he also leads the Cyber Technology Institute (CTI). He previously served as Director of the Research Centre in Cyber Security at the University of Kent from 2012 to 2017 and has been a senior academic since 1995. His work bridges formal methods, cybersecurity, and privacy, with significant contributions to refinement in Z and Object-Z, and current research in anonymisation, privacy impact assessment, and AI ethics. Full Name: Eerke Boiten Institution: De Montfort University School: School of Computer Science and Informatics Academic Rank: Professor Email: eerke.boiten@dmu.ac.uk Boiten's research interests are centered on ensuring software correctness and data privacy. He began his career in formal methods, publishing extensively on refinement, program transformation, and state-based systems. His later work shifted toward cybersecurity and privacy, with current projects including privacy-preserving unified communication, synthetic data trade-offs, and cyber intelligence sharing under GDPR. He is particularly interested in the societal implications of computing, especially in health data governance and AI policy. The recent publications highlight a strong trend in privacy and security, particularly in communication systems, data anonymisation, and regulatory compliance. His work increasingly focuses on real-world applications, such as smart toilets and NHS data sharing, reflecting a commitment to socially responsible computing. He also critically engages with emerging technologies like blockchain and large language models, emphasizing technical realism over hype. Boiten has received significant external funding from EPSRC, Alan Turing Institute, EU H2020, and InnovateUK for projects on ransomware, synthetic data, and railway cybersecurity. He has supervised PhD students and served as a principal investigator on multiple grants. His leadership roles include chairing the BCS-FACS Refinement Workshop and serving on EPSRC and NWO grant panels. He is actively involved in public discourse, having authored over 39 articles for The Conversation on topics like NHS data sharing, AI regulation, and online age verification. His media engagement underscores his role as a public intellectual in cybersecurity and data ethics. Boiten leads the Cyber Technology Institute, which held NCSC/EPSRC Academic Centre of Excellence in Cyber Security Research status (2019–2023) and NCSC Academic Centre of Excellence in Cyber Security Education (Gold) status since 2021. He also contributes to professional organizations including BCS and the Higher Education Academy.

• High-Performance Computing
• Data Governance
• Big Data Analytics
• +۴ مورد دیگر

Anna Queralt Calafat is an Associate Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Services and Information Systems Engineering at the Barcelona School of Informatics. Her research focuses on High-Performance Computing (HPC), distributed systems, and data governance, with notable contributions in knowledge graphs, cloud-edge continuum management, and parallel workflow optimization. She leads projects funded by European and national grants, including contributions to strategic research agendas like ETP4HPC. Queralt has supervised doctoral students like Jonathan Marti and Rizkallah Touma, and her work spans over 100 publications in top venues such as Future Generation Computer Systems and the International Semantic Web Conference. She actively participates in conference committees and has received a Best Student Paper Award for collaborative research. Her educational background includes a degree in Computer Engineering and a doctorate in Software. She is part of research groups inSSIDE and DTIM, advancing areas like HPC integration with big data analytics. Key projects include automated data lifecycle management and fog-to-cloud distributed processing. Her work bridges theoretical models with practical systems like DataClay and PyCOMPSs, emphasizing scalable and efficient computing solutions.

• Cybersecurity
• Code Obfuscation
• Malware Analysis
• +۳ مورد دیگر

Sebastian Schrittwieser is a Researcher in the Research Group Security and Privacy, part of the Faculty of Computer Science. His work focuses on cybersecurity, code obfuscation, malware analysis, and machine learning applications in security. He leads and contributes to projects like INODES (Cyber Defense Strategies) and EMRESS (Resilience Evaluation Models). His research bridges theoretical foundations and practical applications, addressing challenges in software protection and threat detection. Key research interests include: Code Obfuscation Techniques and Resistance Adversarial Machine Learning and Risk Assessment Malware Analysis and Program Simulation User Behavior in Cybersecurity Contexts Recent publications emphasize empirical studies on IT/OT infrastructure security, graph neural network vulnerabilities, and quantum-inspired machine learning. He actively collaborates with institutions like SBA Research and presents at international conferences. Grants include Research Funding for projects on optimal cyber defense strategies (INODES) and software resilience evaluation (EMRESS). His work aligns with interdisciplinary efforts in security engineering and privacy-preserving technologies.

Anton Burtsevمشاهده پروفایل

دانشیار

• Operating Systems
• Computer Security
• Formal Verification
• +۳ مورد دیگر

Anton Burtsev is an Associate Professor at the Kahlert School of Computing, University of Utah. His research focuses on redefining operating system architectures to address modern challenges like security attacks, data center workloads, and heterogeneous hardware. He leads the Mars Research Group, developing systems like the formally verified Atmosphere microkernel in Rust/Verus, and the high-performance DRAMHiT hash table. Key projects include Rust for Linux kernel integration, verified drivers (Veld), and isolation mechanisms like RedLeaf OS. Research interests include kernel isolation, formal verification, language safety (Rust), and overcoming the memory wall. Current work emphasizes clean-slate OS designs and retrofitted security solutions for existing kernels. Collaborative projects include Horizon (secure scientific cloud computing) and RedLeaf OS verification efforts. Advising undergraduate to PhD students interested in OS research. Notable grants include NSF CAREER (NgOS) and collaborative NSF grants on verified systems. Active in the MARS reading group and open-source contributions via repositories.

• Compilers
• Software Testing
• Formal Verification
• +۳ مورد دیگر

John Regehr is a Professor in the School of Computing at the University of Utah. His research focuses on compilers, formal verification, software testing, and embedded systems. He has contributed to tools like Alive2, YARPGen, and ARMor, which address compiler correctness, fuzz testing, and secure isolation. His work emphasizes uncovering compiler bugs, optimizing low-level code, and ensuring system reliability. Key collaborations include Eric Eide, Yang Chen, and Nuno P. Lopes. His articles span compiler verification, fuzzing techniques, and embedded system safety, reflecting a strong commitment to both academic rigor and practical impact. Research interests include compiler optimization validation, undefined behavior analysis, and program synthesis. His work on peephole optimizations and formal methods has influenced LLVM and industry practices. He also explores scheduling algorithms for real-time systems and memory safety in constrained environments like TinyOS. Notable contributions include foundational papers on compiler bug detection (e.g., 'Finding and understanding bugs in C compilers') and tools like Minotaur for SIMD optimization. His lab's work often bridges theory and practice, with applications in security, performance, and embedded software reliability.

• Computational Neuroscience
• Brain Disease Modeling
• Neural Simulation
• +۶ مورد دیگر

William W. Lytton is a Professor in the Department of Physiology and Pharmacology at SUNY Downstate Medical Center's College of Medicine, with additional affiliations in Neurology and Biomedical Engineering. His research focuses on computational neuroscience with applications to various brain diseases including epilepsy, stroke, schizophrenia, Parkinson's, and Alzheimer's. Dr. Lytton leads The Neurosimulation Laboratory, which develops computational models to bridge the gap between molecular-level brain function and observable behavior. His work addresses the challenge of integrating findings across different spatial and temporal scales in neuroscience. He has been instrumental in developing biomimetic models that can interface with biological brains and control robotic systems. His research has evolved from basic neural simulation techniques to clinical applications of computational neuroscience. Analysis of his recent publications shows a strong focus on the credibility and reproducibility of computational models in neuroscience, with increasing attention to clinical applications and the integration of computational approaches with clinical practice. His work spans from molecular pathways of neuronal ischemia to large-scale brain network modeling, demonstrating a comprehensive approach to understanding brain function and dysfunction. Dr. Lytton serves on the Program Committee for the Computation and Neural Systems Meeting and acts as a reviewer for various scientific journals and funding agencies, contributing significantly to the advancement of computational neuroscience as a discipline.

• Artificial Intelligence
• Data Science
• Information Systems
• +۳ مورد دیگر

Dr. Aldeniz Enverov Rashidov is an Associate Professor at the Technical University of Gabrovo , affiliated with the Faculty of Electrical Engineering and Electronics and the Department of Automation, Information and Control Systems . With a Ph.D. in Industrial Information Systems (2006), he has led the Information Support Sector since 2005 and directed the Center for E-Learning and Distance Education since 2012. Education: M.Sc. in Systems and Management (1996), Ph.D. in Industrial Information Systems (2006) Research interests: AI applications in education, intelligent databases, e-voting systems, web-based information architectures, and industrial cybersecurity His recent 15 publications focus on AI-driven manuscript review algorithms, e-voting security frameworks, and ChatGPT-assisted research methodologies. Notable achievements include IEEE Senior Membership (2024) and developing the UMIS 1.0 university management system. Scientific Awards Senior Member, IEEE Computer Society (2024) Senior Member, IEEE Robotics & Automation Society (2024) He has led 9 internal TU-Gabrovo projects on AI applications and contributed to international collaborations like MechMate (2016-2017). His work emphasizes system intelligence evaluation methodologies and hybrid data structuring models.

Amleto Di Salleمشاهده پروفایل

استادیار

• Software Engineering
• Model-Based Software Engineering
• Distributed Systems
• +۷ مورد دیگر

Amleto Di Salle is an Assistant Professor at the Gran Sasso Science Institute in Italy, specializing in Software Engineering , with a focus on Software Architecture , Model-Based Engineering , and Technical Debt . He previously served as a Research Fellow at the European University of Rome and earned a Ph.D. in Computer Science from the University of L’Aquila in 2015. Research Interests : Software Architecture Model-Driven Software Engineering Distributed Systems Composition Technical Debt and Code Smells Self-Adaptive Systems Digital Twins Recent Publications show a trend toward Deep Learning for Code Analysis , Model-Driven Architecture , and Distributed System Compliance . He has collaborated extensively on projects like CHOReVOLUTION and MATISSE . Teaching spans courses on Software Engineering , Model-Driven Development , and Java/J2EE Programming at institutions including the University of L’Aquila and European University of Rome.

Eric Larsonمشاهده پروفایل

استاد

• Software Engineering
• Programming Languages
• Concurrency and Parallel Computing
• +۴ مورد دیگر

Eric Larson is a Professor and Associate Chair in the Department of Computer Science at Seattle University's College of Science & Engineering. He holds a PhD in Computer Science & Engineering from the University of Michigan and maintains an active research program focused on software engineering tools with educational applications. His research interests include: Software testing and verification Concurrency and parallel programming Educational tools for computer science education Static and dynamic program analysis Mobile security and Android applications Computer architecture and simulation Dr. Larson's publication record shows a consistent research trajectory from low-level architectural simulation (MASE, 2001) to educational concurrency tools (MDAT, 2013) and specialized analysis tools (EGRET, 2016). His work demonstrates strong practical applications in computer science education, with tools designed specifically to help students grasp challenging concepts in programming and systems. Notable contributions include: EGRET for regular expression testing MDAT for multithreading education Permeate for Android security analysis ANNA for computer architecture education SUDS for bug detection infrastructure MASE for microarchitectural simulation Dr. Larson actively collaborates with students, as evidenced by numerous co-authored publications, and maintains laboratory space for software analysis and educational tool development. His personal website (last updated September 2022) serves as a repository for his research tools and publications, reflecting his commitment to open dissemination of educational resources.

Davide Anconaمشاهده پروفایل

دانشیار

• Formal Methods
• Runtime Verification
• Programming Languages
• +۳ مورد دیگر

Davide Ancona is an Associate Professor at the University of Genoa, Italy, in the Department of Computer Science, Bioengineering, Robotics, and Systems Engineering (DIBRIS). He holds key governance roles: Member of the School Council, School of Mathematical, Physical and Natural Sciences Member of the Department Board, DIBRIS His teaching portfolio spans Internet of Things, Healthcare IoT, Wearable Devices, Programming Languages, and Pervasive Computing across Computer Science and Bioengineering degree programs. His research concentrates on: Formal Methods and Programming Languages (specializing in corecursive streams and type systems) Runtime Verification for software correctness and multi-agent systems Internet of Things applications in healthcare contexts His work bridges theoretical foundations with practical verification tools for critical systems. Analysis of his 2023-2024 publications reveals: Foundational advances in corecursive stream equivalence and expressivity Novel applications of logic programming to runtime monitoring Techniques for verifying mutable object behavior in Java Extensions to robotic multi-agent frameworks like JaCaMo These contributions demonstrate a cohesive trajectory from theoretical programming language research to real-world IoT and healthcare validation systems.

Pei Liمشاهده پروفایل

استادیار

• Transportation Engineering
• Artificial Intelligence
• Digital Twins
• +۵ مورد دیگر

Dr. Pei Li is an Assistant Professor in the Department of Civil and Architectural Engineering and Construction Management at the University of Wyoming. Her research focuses on advancing safety and mobility through the development of digital, intelligent transportation systems that can sense traffic, predict future conditions, and make decisions. She bridges transportation engineering with cutting-edge technologies including digital twins, AI, and V2X communication systems. Dr. Li's educational background includes: B.Eng in Logistics Engineering from Tongji University (2015) M.Eng in Communication and Transportation Engineering from Tongji University (2018) M.S. in Smart Cities from University of Central Florida (2020) Ph.D. in Civil Engineering from University of Central Florida (2021) Dr. Li's research spans multiple domains in intelligent transportation systems. Her primary interests include Digital Twins, Artificial Intelligence, Transportation Safety, and Human Factors. She develops models that leverage deep learning, computer vision, and sensor technologies to create innovative solutions for traffic management and crash prevention. Her work often addresses the challenges of real-time decision making in complex transportation environments, with particular emphasis on pedestrian safety, autonomous vehicle systems, and connected infrastructure. Dr. Li's publication record shows a progression from basic vehicle maneuver detection using smartphone sensors to sophisticated digital twin applications and explainable AI for autonomous systems. Her recent work (2024-2025) demonstrates expertise in large language models for transportation, federated digital twin frameworks, and physics-informed trajectory planning. She frequently employs advanced techniques including deep reinforcement learning, attention mechanisms, and natural language processing to address complex transportation safety challenges. Dr. Li maintains an active research presence through multiple platforms including ResearchGate, LinkedIn, GitHub (as PeiLi-Sandman), and Google Scholar. Her GitHub profile shows contributions to self-driving car projects, including implementations of particle filters, MPC controllers, and other autonomous vehicle technologies from the Udacity Self-Driving Car Engineer Nanodegree program. Dr. Li is actively recruiting students for her research group, with opportunities available for those interested in digital twins, artificial intelligence, transportation safety, and human factors research. She appears to be establishing a robust research program at the University of Wyoming focused on intelligent transportation systems, with particular emphasis on making transportation safer through advanced computing technologies.