جستجوی استاد راهنمای دانشگاهی

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.

• Database Systems
• Information Systems
• Conceptual Modeling
• +۵ مورد دیگر

Ivan Lukovic is a Professor at the Faculty of Technical Sciences, University of Novi Sad, specializing in database systems, information systems, and conceptual modeling. His research spans over two decades with consistent publication output, demonstrating expertise in domain-specific languages, business process modeling, and data science education. His educational background isn't explicitly detailed in the provided sources, but his extensive publication record and editorial roles suggest advanced academic training in computer science. His research primarily focuses on database systems, information modeling, and applications of these technologies in various domains including education and industry. Lukovic's research interests center around database systems, conceptual modeling, and domain-specific languages. His work demonstrates particular expertise in form-based modeling approaches, integrity constraints, and model-driven development methodologies. He has made significant contributions to the understanding of business application modeling, production process modeling in Industry 4.0 contexts, and educational approaches in computer science. His recent publications show a strong focus on practical applications of database and information systems research, particularly in the areas of production process modeling, educational technologies, and data integration. The articles demonstrate increasing sophistication in methodology and application scope, with recent work addressing contemporary challenges in AI-powered literature review assistance and explainable AI methods. Lukovic has served as guest editor for multiple journal issues, including special editions on computer science applications in management and sustainability, and explainable AI methods. His editorial work reflects recognition of his expertise by the academic community. His collaborative work spans numerous projects with colleagues at the University of Novi Sad and international partners. He has contributed to educational initiatives in data science, particularly through the development of academic study programs at his institution. His work on students' preferences in computer science education indicates active engagement with educational research and curriculum development. Lukovic's research has practical applications in industry settings, particularly in manufacturing and business process domains. His recent work on production process modeling for Industry 4.0 demonstrates how his theoretical research translates into real-world applications that address contemporary industrial challenges.

Aditya Johriمشاهده پروفایل

استاد

• AI Ethics
• Educational Technology
• Computer-Supported Collaborative Learning
• +۳ مورد دیگر

Aditya Johri is a tenured Professor of Information Sciences & Technology in the College of Engineering & Computing at George Mason University (GMU). He directs the Technocritical Research on AI, Learning & Society Lab (trailsLAB) and is an affiliate faculty in multiple departments, including Computer Science and Civil Engineering. His research focuses on how technology shapes learning in formal and informal settings, with a strong emphasis on AI ethics, algorithms, and data ethics in engineering education. Affiliations: School of Computing, College of Engineering & Computing; GMU's Institute for Digital InnovAtion (IDIA). Educational Background: PhD (Learning Sciences & Technology Design, Stanford University), MS (Georgia Tech), MA (University of Georgia), BE (Delhi College of Engineering). Research Contributions: Editor of the International Handbook of Engineering Education Research (2023) and co-editor of the Cambridge Handbook of Engineering Education Research (2014), which won a 2015 Best Book Award. His work is funded by NSF grants, including an Early CAREER Award and projects on AI ethics and generative AI in education. Awards & Recognition: 2022 University Teaching Excellence Award (High Impact Teaching), Fulbright-Nokia Distinguished Chair (2021–2022), and multiple best paper awards. He also led the NSF-funded project creating role-play case studies for AI ethics education. Grants: Over $20M in NSF/USDA funding for the AI Institute for Resilient Agriculture (AIIRA), and leadership in projects like COALESCE (context-aware cyber-agricultural systems). Advising & Mentorship: Supervised over 20 PhD and MS students, with notable advisees including Dr. Yinan Sun (Postdoc) and graduates like Dr. Hon Jie Teo (CUNY). His mentorship earned the 2022 OSCAR Mentoring Excellence Award. Labs & Collaborations: trailsLAB explores AI ethics, learning technologies, and societal impacts. Collaborations include Aalto University (Finland), Technical University of Munich, and the Leverhulme Center for the Future of Intelligence (Cambridge).

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

• Software Engineering
• Formal Methods
• Program Analysis
• +۲ مورد دیگر

Maria Christakis is a Full Professor at TU Wien's Faculty of Informatics where she leads the Rigorous Software Engineering Group. Her research develops methods and tools for building reliable software through formal methods, automated test generation, and program verification. She directs several projects including Sherlock (a framework for testing program analyzers), Minotaur (constraint-based program generator), and SmartACE (compositional verifier for smart contracts). Her group focuses on improving software robustness while enhancing developer productivity. Awards include the Distinguished Paper Award at ICSE 2016 and Best Presentation Award at ESEC/FSE 2020. She currently advises 5 PhD students and teaches courses in Advanced Software Engineering and Software Engineering 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.

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.

• Artificial Intelligence
• Multi-Agent Systems
• Robotics
• +۴ مورد دیگر

Stefania Monica is an Associate Professor at the Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia. She specializes in Artificial Intelligence, Multi-Agent Systems, Robotics, and Programming Languages. Her research focuses on topics such as agent interaction protocols, indoor positioning algorithms, and agent-oriented programming (Jadescript). She teaches courses on Artificial Intelligence, Data Science, Web Technologies, and IoT. Recent work includes studies on stigmergic interactions in multi-agent systems and 3D indoor positioning using optimization algorithms. Her contributions span theoretical frameworks (e.g., kinetic models) and practical implementations (e.g., Jadescript language enhancements for robustness and interoperability). Research interests emphasize synergy between AI and robotics, with applications in localization, swarm intelligence, and neural-symbolic learning. Teaching responsibilities include managing engineering courses on data science, information systems, and IoT development. She actively contributes to academic conferences and journals, curating special issues on computational logic and agent-based systems.

• Artificial Intelligence
• Formal Verification
• Probabilistic Systems
• +۳ مورد دیگر

Jan Křetínský is a Professor at the Department of Computer Science, Faculty of Informatics, Masaryk University. His research focuses on formal verification, probabilistic systems, and artificial intelligence. He leads projects funded by the European Union and Masaryk University, including initiatives on robust AI and controller synthesis. Key roles include membership in academic boards for theoretical computer science and doctoral supervision. Awards include the 2023–2028 MUNI Award in Science and Humanities. His work bridges theoretical foundations with practical applications in autonomous systems and safety-critical technologies. Education: Habilitation in Informatics (Masaryk University, 2019), PhD (Dr. rer. nat.) Projects: EU Horizon Europe grants (2025–2030), MUNI-funded research on verification and generative AI Labs/Teams: Live-Lab at FI MU Research emphasizes scalable verification techniques for probabilistic models, integration of neural and symbolic AI, and ethical AI frameworks. Current grants address explainability and robustness in generative AI systems.

• Natural Language Processing
• Formal Language Theory
• Machine Learning
• +۳ مورد دیگر

Gail Weiss is a Lecturer and Post-Doctoral Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Natural Language Processing Laboratory (NLP) within the School of Computer and Communication Sciences. She holds dual roles in research and teaching, contributing to both academic research and coursework in natural language processing and formal methods. Her research focuses on the intersection of formal language theory and machine learning, particularly exploring the capabilities of neural networks such as recurrent neural networks (RNNs) and transformers in recognizing formal languages. Recent work examines the vulnerability of higher education to AI tools and advances methods for extracting formal models from neural architectures. Teaching includes courses like 'Topics in Natural Language Processing' and contributes to the EDIC academic program. Her research has been published in top venues, addressing theoretical foundations of neural networks and practical applications in knowledge representation. She is based at INR 240 in Lausanne.

• Robotics
• Human-Robot Interaction
• Swarm Robotics
• +۵ مورد دیگر

Michael A. Goodrich is a Professor in the Department of Computer Science at Brigham Young University, with an extensive research career spanning over two decades in robotics, human-robot interaction, and swarm intelligence. His work bridges theoretical foundations with practical applications, particularly in autonomous systems and multi-agent coordination. His research interests focus on Robotics , Human-Robot Interaction , Swarm Robotics , Artificial Intelligence , and Autonomous Systems . Goodrich's work explores how humans and robots can effectively collaborate, with particular emphasis on proficiency assessment, resilience, and communication in human-robot teams. His research has significant applications in search and rescue operations, swarm robotics, and autism therapy. Analysis of his recent publications reveals a strong focus on robot self-assessment capabilities, swarm behavior optimization, and resilience in multi-agent systems. His work increasingly integrates formal methods with practical robotics applications, creating frameworks for robots that can assess their own capabilities and communicate this information effectively to human teammates. Among his notable contributions are frameworks for robot proficiency self-assessment using assumption-alignment tracking, methods for designing resilient swarm behaviors, and formalizations of resilience for goal-oriented agents. His work has been published consistently in top venues including IEEE Transactions on Robotics, ACM Transactions on Human-Robot Interaction, and International Journal of Robotics Research. Dr. Goodrich has advised numerous students who have become active researchers in the field, including Aadesh Neupane, Daqing Yi, Xuan Cao, and Puneet Jain. His collaborative work spans multiple institutions and has practical applications in wilderness search and rescue, autism therapy, and multi-robot coordination systems.

Amar Hadzihasanovicمشاهده پروفایل

استادیار

• Category Theory
• Higher-Dimensional Algebra
• Quantum Computing
• +۳ مورد دیگر

Amar Hadzihasanovic is an Assistant Professor at Tallinn University of Technology, affiliated with the Compositional Systems and Methods group, and a Scientific Advisor at Quantinuum. He specializes in category theory, higher-dimensional algebra, and their applications in quantum computing and formal systems. His recent work includes organizing the 110th Peripatetic Seminar on Sheaves and Logic (PSSL) and completing a book on higher-categorical diagrams with Cambridge University Press. Hadzihasanovic has been awarded grants from ARIA (Safeguarded AI programme) and the Estonian Research Council, supporting his research on diagrammatic rewriting and homotopy theory. His research interests span categorical foundations, diagrammatic sets, and computational aspects of higher-dimensional structures. Key contributions include model structures for (∞, n)-categories, acyclicity conditions in pasting diagrams, and formal axiomatizations of quantum systems. He has advised PhD students Alkis Ioannidis and Clémence Chanavat, and collaborates widely with institutions like the University of Cambridge and Quantinuum. Hadzihasanovic has delivered invited talks at conferences such as Category Theory 2024, the Nordic Congress of Mathematicians, and the Geometric and Topological Methods in Computer Science (GETCO). His teaching includes a course on category theory and diagrammatic reasoning at Kyoto University. His software, including the rewalt library, supports topologically sound higher-dimensional diagram rewriting.

• Knowledge Representation and Reasoning
• Data Engineering
• Database Theory
• +۴ مورد دیگر

Eleonora Laurenza is a PostDoc Researcher at the Department of Databases and Artificial Intelligence, Vienna University of Technology (TU Wien). Her research focuses on the intersection of deductive logic-based and inductive methods in Artificial Intelligence, with a special emphasis on Knowledge Graphs, Data Engineering, and Reasoning Systems. She contributes to projects like KnowledgeGraph (2020–2028), DeConquer (2023–2027), and SustainGraph (2023–2025). Her work spans topics such as scalable reasoning in knowledge graphs, probabilistic reasoning frameworks, and applications in distributed ledger technology. Recent publications address FATEful smart contracts and model-independent knowledge graph design. She teaches courses like 'Knowledge Graphs' and 'Research and Career Planning for Doctoral Students.' Laurenza collaborates on interdisciplinary projects involving blockchain ethics, regulatory compliance systems, and uncertainty modeling in data systems. Her research bridges foundational theory with practical applications in AI-driven data management.

António Casimiroمشاهده پروفایل

دانشیار

• Dependable adaptive systems
• Middleware for distributed embedded real-time systems
• Cyber-Physical Systems
• +۴ مورد دیگر

António Casimiro is an Associate Professor at the Department of Informatics of the Faculty of Sciences, University of Lisbon. He holds a PhD in Computer Science (2003) from the same institution. His research focuses on dependable adaptive systems, middleware for distributed real-time applications, and cyber-physical systems. He leads the Cyber-Physical Systems research line at the LASIGE research unit and coordinates EU projects like ADMORPH and VEDLIoT, as well as the national AQUAMON project for water monitoring. Education: PhD in Computer Science, University of Lisbon (2003) Research Interests: Middleware for embedded real-time systems Fault-tolerant distributed systems IoT and environmental monitoring Cybersecurity in autonomous systems Key Projects: KARYON (FP7): Safe cooperative automotive/avionics systems VEDLIoT (H2020): Accelerated AIoT systems AQUAMON: Wireless sensor networks for water monitoring Awards: None explicitly listed, but active in editorial roles like Editor-in-Chief of the Ada User Journal. Teaching & Leadership: Coordinates the Master in Information Security program Former Vice-Director of LASIGE and member of Faculty Scientific Council Labs/Teams: LASIGE research unit, Navigators group, involvement in GULLIVER testbed for vehicular systems.

Wen Yeمشاهده پروفایل

استادیار

• User Experience Design
• Empathy-Driven Design
• Wearable Technology
• +۳ مورد دیگر

Wen (Vivian) Ye is an Assistant Professor of Practice in the Department of Design at the University of Texas at Austin. She holds dual Master of Fine Arts degrees from the University of California–Davis and the Marangoni Institute in Italy. Her academic role combines teaching design thinking and advanced design studio courses with industry experience from roles as a fashion designer at VERO MODA and a global fashion buyer for Armani. Her research focuses on user experience, empathy-driven design, and wearable technology, emphasizing cross-disciplinary collaboration. Ye actively participates in National Science Foundation (NSF) projects aimed at improving transportation and power systems through electric vehicles and clean energy solutions. She designs educational materials for these initiatives, bridging academic and industrial applications. Her work highlights innovation in sustainable technology and human-centered design principles. Her recent publications span data science code generation, neural network adaptation, and uncertainty quantification in machine learning. These contributions address foundational challenges in AI and computational methodologies, reflecting her dual expertise in design and technical systems.