جست‌وجوی استاد و پژوهشگر

Maximilian R. Odenbrettمشاهده پروفایل

پژوهشگر

• Formal Methods
• Parallel Computing
• Model Checking
• +۳ مورد دیگر

Maximilian R. Odenbrett was a PhD student at Eindhoven University of Technology (2010–2011), collaborating with RWTH Aachen University's MOVES group. His research focused on Efficient Multi-Core Model Checking , leveraging NVIDIA's CUDA for parallel processing on GPGPUs. He specialized in formal methods, stochastic model checking, and parallel programming for multi-core systems. Education : Diplom-Informatiker (Computer Science) from RWTH Aachen University (2004–2010), with a minor in Psychology. Research Interests : Development of parallel algorithms for formal verification, application of GPUs in bioinformatics (e.g., biological network analysis), and model-based analysis of software systems. His work bridges theoretical formal methods with practical parallel computing solutions. Publications : Focused on applying GPGPUs to model checking and bioinformatics problems, as seen in his BMC Bioinformatics paper (2012) and conference contributions on AADL specification analysis (2010). His research emphasizes scalability and efficiency in computational systems. Affiliations : Collaborated with Prof. Joost-Pieter Katoen's MOVES group at RWTH and Prof. P.A.J. Hilbers' BMI group at TU Eindhoven. His work contributed to the EMCMC project on multi-core model checking.

Alexandre Joannouمشاهده پروفایل

پژوهشگر

• Computer Architecture
• Hardware Security
• Memory Safety
• +۴ مورد دیگر

Dr. Alexandre Joannou is a Senior Research Software Engineer at the Department of Computer Science and Technology, University of Cambridge. His research focuses on secure hardware-software co-design, particularly in the CHERI (Capability Hardware Enhanced RISC Instructions) project. He specializes in computer architecture, memory safety, and RISC-V processors, with a focus on integrating security into low-level system components. His work spans theoretical contributions to capability-based systems and practical implementations in accelerators, GPUs, and embedded devices. Key areas include secure speculation, memory protection, and formal verification of hardware designs. Joannou's research has advanced temporal safety in heaps, compressed capabilities for efficiency, and architectural contracts to prevent vulnerabilities. His publications emphasize hardware security innovations, including CHERI-RISC-V integration, GPGPU memory protection, and randomized testing methodologies. Collaborations involve both academic and industry partners in secure computing ecosystems.

• Machine Learning
• Graph Neural Networks
• Algorithmic Reasoning
• +۳ مورد دیگر

Dobrik Georgiev is a Lecturer in the Department of Computer Science and Technology within the School of Technology at the University of Cambridge. His research centers on bridging algorithmic reasoning with neural architectures, focusing on how neural networks can execute and generalize algorithmic processes. His primary research interests include: Neural algorithmic reasoning and its applications to combinatorial problems Graph neural networks and hypergraph learning systems Explainable AI through concept-based interpretability Deep equilibrium models for algorithmic execution Biological data analysis using neural architectures Georgiev's publication record demonstrates consistent innovation in neural execution models, with recent work exploring bottlenecks in algorithmic reasoning (2025), multi-solution reasoning frameworks (2024), and generalization beyond synthetic graph models (2023). His research shows strong interdisciplinary connections between theoretical computer science, machine learning, and computational biology. While no formal awards are documented in available sources, his work has established significant contributions to neural algorithmic reasoning frameworks. Georgiev maintains active research collaborations through the Department of Computer Science and Technology's initiatives, particularly in the areas of machine learning and neural architectures. His technical leadership is evident in software contributions like the LENs library for logic-explained networks.

• Human-Computer Interaction (HCI)
• User Experience (UX)/Usability
• Domain Analysis
• +۳ مورد دیگر

Michaela Bačíková is an Assistant Professor at the Faculty of Electrical Engineering and Informatics (FEI) of the Technical University of Košice (TUKE). Her research focuses on Human-Computer Interaction (HCI), domain usability, and domain analysis, with an emphasis on graphical user interfaces (GUIs), domain-specific languages (DSLs), and gesture-driven interaction. She leads the development of the DEAL tool, a domain analysis framework for extracting domain models from software systems. Her teaching includes courses on component-based programming, web technologies, and user interface design. Research Projects: DEAL (Domain Extraction ALgorithm) : A tool for analyzing GUIs to generate DSLs, ontologies, and usability metrics. EU Project: 'Evolving Architectural Knowledge in the Edge-to-Cloud Continuum' (participant). Educational initiatives: Integrating gesture-driven IDEs and social networks for mentoring in programming courses. Research Interests : Automated domain usability evaluation using DEAL. DSL-driven GUI generation and feature modeling. Innovations in teaching software development and user experience design. Grants & Labs : Recipient of FEI TUKE Grant no. FEI-2015-16 for domain usability metrics research. Active in the FEI lab developing DEAL and related tools.

Shakil M. Khanمشاهده پروفایل

استادیار

• Knowledge Representation
• Causal Reasoning
• Multi-Agent Systems
• +۳ مورد دیگر

Dr. Shakil M. Khan is the SaskPower Assistant Professor in Artificial Intelligence at the University of Regina's Department of Computer Science. His primary research develops formal models for causal reasoning in artificial intelligence, focusing on knowledge representation and rational agent systems. He holds a Ph.D. from York University and leads the PRACToR lab. Education: Ph.D. Computer Science, York University (2018) B.Sc. Computer Science, University of Windsor Dr. Khan's research program develops logical frameworks for actual causation in nondeterministic domains, with applications to explainable AI and multi-agent systems. His work combines situation calculus with game-theoretic approaches to model causal relationships and responsibility attribution. Current projects investigate abstraction techniques for concurrent game structures and root cause analysis in hybrid dynamic systems. His publications demonstrate strong theoretical foundations in logic-based AI, with recent articles exploring the semantics of causality, knowledge representation, and agent modeling. Research keywords frequently include situation calculus, formal verification, and multi-agent systems. Dr. Khan supervises graduate students working on causal reasoning projects and has received NSERC Discovery funding (2022-2027). He serves on program committees for leading AI conferences including AAAI, IJCAI, and KR. Dr. Khan teaches courses in discrete structures, artificial intelligence, and knowledge representation.

Vincenzo Deufemiaمشاهده پروفایل

استاد

• Cybersecurity
• IoT Security
• Data Analytics
• +۷ مورد دیگر

Vincenzo Deufemia is a Full Professor at the Department of Computer Science of the University of Salerno . His work focuses on cybersecurity, IoT security, data analytics, machine learning, and end-user development in smart environments. He has been actively involved in interdisciplinary research spanning database systems, human-computer interaction, and ethical AI applications. Key research interests include: Automation rule security and coherence Unsupervised learning for IoT interference detection End-user customization of smart systems Functional dependency discovery in data streams Social media analysis for geopolitical perception studies Recent work has explored leveraging generative AI for ethical phishing defense mechanisms and developing tools like EFESTO-5W for non-expert security rule specification. His contributions also include visualization frameworks for dependency analysis and spatial integrity constraints verification. No formal awards are explicitly listed in the provided material, though his extensive publication record indicates significant contributions. His research has practical applications in both academic and industrial contexts, particularly in improving end-user security awareness and system usability in smart environments.

Apurva Narayanمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Dr. Apurva Narayan is an Affiliate Professor in Computer Science and Data Science, and an Associate Member in Mechanical Engineering at the University of British Columbia's Irving K. Barber Faculty of Science. His research focuses on artificial intelligence, machine learning (particularly explainable AI and quantum ML), data mining, cybersecurity of cyber-physical systems, and decision-making under uncertainty. He holds a PhD from the University of Waterloo (Systems Design Engineering) and a Bachelor's in Electrical Engineering from Dayalbagh Educational Institute. Education: PhD in Systems Design Engineering, University of Waterloo (2015) Bachelor of Engineering in Electrical Engineering, Dayalbagh Educational Institute (2008) Research Interests: Explainable AI/ML and quantum machine learning Data analytics and mining Safety/security of cyber-physical systems Graph-theoretic analysis of complex systems Decision-making under uncertainty Reverse engineering complex software systems Awards: Systems Society of India Young Scientist Award (under 40) MITACS Accelerate Award (2013) Dr. T.E. Unny Memorial Award (2011) Multiple teaching nominations at University of Waterloo Advising & Grants: Supervises graduate students in AI/ML and cybersecurity. Secured grants including MITACS Accelerate and Waterloo scholarships. Active in patent filings related to timed regular expressions. Labs/Teams: Engaged in collaborations within the UBC Department of Computer Science and cross-disciplinary projects with Mechanical Engineering on system reliability and quantum computing applications.

Andrea D'Ambrogioمشاهده پروفایل

دانشیار

• Software and Systems Engineering
• Systems and Software Performance Engineering
• Model-Driven Systems Engineering
• +۳ مورد دیگر

Prof. Andrea D'Ambrogio is an Associate Professor of Software and Systems Engineering at the Department of Enterprise Engineering, University of Roma 'Tor Vergata' (Italy). He previously held roles such as Assistant Professor of Computer Science and Research Associate at the same university, and contributed to projects in Europe and internationally. His research focuses on software/systems performance, model-driven engineering, business process management, and distributed simulation. He has led over 20 international conferences and initiated workshops like CoMetS and Mod4Sim. He currently serves as President of the Society for Modeling & Simulation International (SCS), and is a member of IEEE, ACM, and INCOSE. Research Contributions: Prof. D'Ambrogio has authored over 150 publications in journals and conferences. His work emphasizes simulation engineering processes, MSaaS (Modeling & Simulation as a Service), and the integration of IoT with business processes. Recent studies address distributed simulation interoperability, pandemic impact analysis on healthcare systems, and low-code simulation frameworks. Leadership & Editorial Roles: He has served as General Chair for major events like SpringSim'19 and SummerSim'18. He is an Associate Editor of the SIMULATION journal and editorial board member of IAENG International Journal of Computer Science. His leadership in SCS advances global standards in modeling and simulation practices. Teaching: Teaches courses such as Service-Oriented Software Engineering, Software Systems, and Internet-Based Distributed Simulation. His academic contributions span both theoretical advancements and practical applications in enterprise systems engineering.

• Software Engineering
• Formal Methods
• Model-Driven Development
• +۳ مورد دیگر

Alexander Raschke is a Professor at the University of Ulm, affiliated with the Institute of Software Engineering and Compiler Construction. His research focuses on formal methods, model-driven engineering, software safety and security, and abstract state machines. He has authored/co-authored over 50 publications, including works on collaborative modeling frameworks, security analysis techniques, and programming by demonstration. He holds a PhD in Computer Science from the University of Ulm (2009). As an academic, he has organized and chaired conferences such as ABZ (2020, 2021), demonstrating leadership in the formal methods community. His work bridges theoretical foundations with practical applications in software systems, emphasizing safety-critical and self-adaptive systems. Notable contributions include the development of EditQL—a textual query language for evolving models—and frameworks for attack-tree generation in cybersecurity.

Grumberg Ornaمشاهده پروفایل

استاد

• Model checking
• Automated verification of hardware and software systems
• Abstraction, refinement, and counterexamples
• +۶ مورد دیگر

Orna Grumberg is a Professor at the Computer Science Department of the Technion-Israel Institute of Technology. She holds the Leumi Chair in Science since 2011 and has been a full professor since 2005. Her work focuses on model checking and automated verification of hardware and software systems. Leumi Chair in Science (2011–present) Full Professor, Technion (2005–present) Steering Committee, CAV Conference (2010–present) Editorial Board, Journal on Formal Methods in System Design (2003–present) Dr. Grumberg’s research interests span automated verification, abstraction-refinement techniques, compositional and distributed model checking, and their application to security vulnerabilities and temporal logics. She is a co-author of the seminal book Model Checking , which serves as a foundational reference in the field. Her recent work explores security in network protocols via parameterized systems, SAT-based approaches for hardware verification, and interpolation-based model checking. These contributions integrate abstraction refinement, lazy abstraction, and reachability analysis. Collaboration with IBM on UML verification Collaboration with Rafael on security vulnerabilities in protocols Dr. Grumberg has mentored students who now lead verification teams at Intel, IBM, and Israeli universities. She previously served as Associate Dean for Graduate Studies at the Technion.

Michael Weharمشاهده پروفایل

مدرس

• Computer Science
• Algorithms
• Computational Art
• +۷ مورد دیگر

Michael Wehar is a Lecturer in Computer Science at Bryn Mawr College , where he teaches Introductory Programming Courses. He is a multidisciplinary researcher and developer with a focus on algorithms , computational art , and software engineering , creating innovative tools that bridge computer science with creative applications. Co-founder of AlgoArt.org , a platform for algorithmic art creation and exhibition Creator of Word of The Hour , a multilingual vocabulary learning platform Co-developer of Treegle Dictionary , a structured definition platform His research spans computational complexity, human-computer interaction, and educational technology. Key areas include: Algorithmic Art : Generative design systems, interactive visualizations Pattern Matching : Matrix algorithms, Voronoi diagrams, formal verification Language Technology : Multilingual dictionaries, crowdsourced translation systems Educational Tools : Git repository analysis, interactive learning platforms His 15 most recent publications focus on topics ranging from 2D pattern matching to ETH-based complexity lower bounds , with significant contributions to automata theory and algorithm design. He has received academic recognition including the Best Faculty Poster at CCSC:EA 2022 and Honorable Mention at IFoRE 2022 . As a dedicated mentor, he has guided over 100 students across institutions like Swarthmore College, Temple University, and University at Buffalo in projects spanning: Web development frameworks AI applications in art and education Mobile productivity tools Language learning platforms Game development projects

Mainack Mondalمشاهده پروفایل

پژوهشگر

• Algorithms
• Theory & Logic Programming Languages
• Verification
• +۵ مورد دیگر

Mainack Mondal is affiliated with the Max Planck Institute for Software Systems (MPI-SWS), where he focuses on research areas including algorithms, theory, logic programming languages, verification of cyber-physical systems, distributed and networked systems, security, privacy, and social information systems. His work bridges theoretical foundations with practical applications in software systems and distributed computing. Education details and specific academic qualifications are not explicitly provided in the text. Research interests emphasize interdisciplinary approaches to system design and security, leveraging formal methods and distributed computing principles. No specific publications, awards, or grant information are listed here. The text primarily outlines institutional career opportunities and general research themes rather than personal achievements. Mainack is part of the MPI-SWS community, contributing to its mission in advancing software systems research.

• Time Series Analysis
• Blockchain Technologies
• Business Process Management
• +۸ مورد دیگر

Roman Vaculín is a Researcher at IBM Research, focusing on interdisciplinary domains where artificial intelligence, blockchain technologies, and data-centric workflows converge. His work spans automated machine learning, time series analysis, and secure computation via cryptographic methods like homomorphic encryption. Affiliation: IBM Research Key research areas: Time Series Analysis, Blockchain, AI Explainability, Business Process Management Across his publications, Vaculín explores: Time Series Modeling: Developing robust frameworks like TsSHAP and end-to-end architectures for forecasting and imputation. Blockchain Applications: Designing trusted AI systems, secure multi-party computation, and verifiable simulations. Automated Machine Learning: Creating toolkits for industrial AI explainability and automation. Privacy-preserving Techniques: Optimizing encrypted inference and secure decision tree protocols. His methodology often integrates formal verification with practical implementations, emphasizing efficiency and interpretability in complex systems. While no formal awards or students are documented in the provided data, his collaborative publications with institutions like IBM Research and academic partners highlight his role in advancing applied AI research.

Sepehr Amir-Mohammadianمشاهده پروفایل

دانشیار

• Cybersecurity
• Programming Languages
• Formal Software Security Assurance
• +۵ مورد دیگر

Sepehr Amir-Mohammadian is an Associate Professor in the Department of Computer Science at the University of the Pacific, part of the School of Engineering and Computer Science. His research focuses on cybersecurity, programming languages, and formal software security assurance, particularly applying linguistic approaches to ensure properties in security-critical applications. He earned his PhD in Computer Science from the University of Vermont (2017), focusing on in-depth security policy enforcement, following an MS in Information Security Engineering (Amirkabir University of Technology, 2011) and a BS in Information Technology Engineering (Amirkabir University of Technology, 2009). Teaching interests include computer networking, reliable software design, programming languages, and theoretical computer science. He has held leadership roles such as Engineering & Computer Science Council Chair (2020-2021) and currently represents his school in the Technology in Education Committee. His research bridges theory and practice, with contributions to audit logging correctness, quantitative information flow analysis, and cybersecurity in cyber-physical systems. Recent work emphasizes concurrent audit logging in distributed systems, timing attack analysis in cyber-physical systems, and leveraging digital twins for security quantification. Notable collaborations include the OpenMRS medical records system and PRISM-Leak tool development for probabilistic program analysis. His publications span conferences like ACM CPSS, IEEE COMPSAC, and IEEE ICNC, addressing challenges in microservices, concurrency, and formal methods. While no specific awards are noted, his contributions to security frameworks and practical implementations reflect significant scholarly impact. Research groups and projects include work on hybrid-dynamic systems, concurrent logging, and network protocol testing with large language models.

Pedro Henriquesمشاهده پروفایل

استاد

• Programming Languages
• Compiler Design
• Software Analysis
• +۲ مورد دیگر

Pedro Henriques is Professor of Computer Science at University of Minho, where he coordinates the Language Processing group at Algoritmi Research Center. With a PhD in Formal Languages and Attribute Grammars, he teaches compiler design and programming language engineering. His research develops: Formal methods for software analysis Educational tools for programming pedagogy Ontology-driven computational thinking frameworks AI applications in agriculture and healthcare Recent work includes neuroeducation-informed frameworks (OntoCnE) and Programming Cocktails methodology for optimizing cognitive load in code instruction. He has supervised 14 PhD dissertations and authored the foundational text "XML & XSL: da teoria a prática". Current EU projects explore VR cognitive rehabilitation and olive cultivar identification using deep learning.