جستجوی استاد راهنمای دانشگاهی

• Computer Vision
• Computer Graphics
• Machine Learning
• +۵ مورد دیگر

Hanbyul Joo is an Assistant Professor in the Department of Computer Science and Engineering at Seoul National University (SNU). Prior to joining SNU, he was a Research Scientist at Facebook AI Research (FAIR) in Menlo Park. He completed his Ph.D. in the Robotics Institute at Carnegie Mellon University, working with Yaser Sheikh, and received his M.S. in Electrical Engineering and B.S. in Computer Science from KAIST, Korea. Dr. Joo's educational journey began at KAIST, where he earned both his Bachelor's and Master's degrees. He then pursued his Ph.D. at Carnegie Mellon University's Robotics Institute, completing his dissertation titled "Sensing, Measuring, and Modeling Social Signals in Nonverbal Communication." His doctoral work focused on developing the Panoptic Studio, a unique sensing system with over 500 synchronized cameras for capturing social interactions. Dr. Joo's research primarily focuses on endowing machines and robots with the ability to perceive and understand human behaviors in 3D . His goal is to build "social Artificial Intelligence" that can interact with humans using social signals (body languages). He pursues this direction using data-driven methods where data is collected by measuring the wide spectrum of social signals transmitted during interpersonal social interaction. His research spans computer vision, machine learning, computer graphics, and robotics , with particular emphasis on 3D human pose estimation, human-object interaction, and social signal processing. His recent publications demonstrate a clear trend toward leveraging diffusion models for 3D reconstruction and generation tasks, with a focus on human-centric applications. His work bridges the gap between 2D image understanding and 3D scene reconstruction, often utilizing pre-trained models to overcome data limitations. The research consistently addresses fundamental challenges in understanding human behavior, interaction with objects, and social dynamics in 3D space. Dr. Joo is a recipient of several prestigious awards including the Samsung Scholarship and the CVPR Best Student Paper Award in 2018 . His paper "Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies" received this honor at CVPR 2018. His research has been widely recognized in top computer vision and AI conferences, with multiple oral presentations at venues like CVPR, ICCV, and ECCV. Dr. Joo actively mentors a large group of students, with approximately 15 current students working toward MS/PhD degrees under his supervision. His lab, the SNU VCLab, focuses on cutting-edge research in computer vision and graphics. He has secured significant research funding through his work, though specific grant details aren't provided on his website. Dr. Joo frequently serves as an area chair for major conferences including CVPR, ICCV, and NeurIPS, demonstrating his standing in the academic community. Dr. Joo leads the SNU VCLab, which has developed several notable datasets and tools including SNU ParaHome, FrankMocap, and the CMU Panoptic Studio Dataset. His lab maintains strong industry connections, with students interning at leading companies like Meta. The lab's research focuses on building the infrastructure and algorithms needed for social AI, with an emphasis on practical applications that can be deployed in real-world settings.

Paul Pu Liangمشاهده پروفایل

استادیار

• Multimodal Machine Learning
• Human-AI Interaction
• Generative AI
• +۳ مورد دیگر

Paul Pu Liang is an Assistant Professor at the Massachusetts Institute of Technology (MIT) Media Lab and Department of Electrical Engineering and Computer Science (EECS). He directs the Multisensory Intelligence research group, focusing on building AI systems that integrate diverse sensory inputs to enhance human-AI symbiosis. His work spans theoretical foundations, large-scale resources, and neural architectures for multisensory learning. Education: PhD in Machine Learning (Carnegie Mellon University), MS in Machine Learning (Carnegie Mellon), BS with University Honors in Computer Science and Neural Computation (Carnegie Mellon) Research Interests: Multimodal machine learning, human-AI interaction, clinical AI, generative models, and responsible deployment of AI systems Key Contributions: MultiBench, HEMM evaluation framework, CLIMB clinical data foundations, and multimodal transformer architectures Recent publications emphasize multimodal foundation models , clinical applications , and socially responsible AI . His work has been recognized with multiple best paper awards and fellowships from Siebel, Facebook, and other institutions. Scientific Awards Siebel Scholars Award Waibel Presidential Fellowship Facebook PhD Fellowship Center for ML and Health Fellowship Rising Stars in Data Science Four best paper awards Paul teaches courses on machine learning and multimodal AI at MIT and CMU. He mentors students across multiple programs including Media Arts & Sciences, EECS, and IDSS, with former advisees now at institutions like OpenAI, UC Berkeley, and Princeton.

• Computer Vision
• Artificial Intelligence
• Multimodal Learning
• +۷ مورد دیگر

Dr. Hilde Kuehne is a Professor at the University of Tuebingen and a key researcher at the Tuebingen AI Center. She holds affiliations with MIT-IBM Watson AI Lab and Goethe University Frankfurt, with a focus on computer vision, multimodal learning, and explainable AI. Her work bridges vision-language models, audio-visual alignment, and self-supervised methods. Co-organizer of the New Frontiers in Associative Memories workshop @ ICLR 2025 Member of the Scientific Advisory Board of the Carl-Zeiss-Foundation Contributor to CVPR 2025's UTD dataset for unbiased video benchmarks Her research addresses critical challenges in: Explainability for Vision Transformers (LeGrad) Fine-grained audio-visual alignment (CAV-MAE Sync) Zero-shot visual recognition automation (Meta-Prompting) Spatio-temporal grounding without annotations Recent collaborative work spans 15+ publications across CVPR, NeurIPS, ICCV, and ICLR, with emphasis on multimodal foundation models, dataset bias mitigation, and differentiable logic networks. She actively contributes to workshop organization and peer review as evidenced by her involvement in CVPR 2025 and ICLR 2025 program committees.

Joseph Sifakisمشاهده پروفایل

پژوهشگر

• Model Checking
• Concurrent Systems Specification and Verification
• Component-Based Design
• +۳ مورد دیگر

Joseph Sifakis is a CNRS Research Director and founder of Verimag Laboratory in Grenoble, France. He holds the INRIA-Schneider endowed industrial chair since 2008 and has been instrumental in advancing concurrent systems specification and verification. Education: Electrical Engineering (Technical University of Athens), Computer Science (University of Grenoble) Research interests focus on component-based design , real-time systems , and correct-by-construction techniques . He pioneered the development of the BIP framework and contributed to model checking, a cornerstone of industrial system verification. Recent publications emphasize component-based modeling, formal verification, and distributed system design, reflecting his work's impact on embedded systems and critical applications like aerospace and telecommunications. Scientific awards include: Turing Award (2007) CNRS Silver Medal (2001) Test-of-Time Award (2012) Multiple honorary doctorates (2008-2011) Member of prestigious academies Industry collaborations span Airbus, ST Microelectronics, and the European Space Agency, with applications in aeronautics, telecommunications, and industrial software standards. He leads the ARTIST2 Network of Excellence and directs the CARNOT Institute 'Intelligent Software and Systems'.

Luca Iocchiمشاهده پروفایل

استاد

• Cognitive Robotics
• Task Planning
• Multi-Robot Coordination
• +۶ مورد دیگر

Luca Iocchi is a Full Professor at Sapienza University of Rome, where he teaches in the Master in Artificial Intelligence and Robotics program. He is affiliated with the Department of Computer, Control, and Management Engineering and the Faculty of Engineering of Information, Computer Science and Statistics. Iocchi serves as an Associate Editor for Artificial Intelligence Journal and has been the scientific coordinator of Spoke of PNRR project FAIR (Future AI Research). His educational background includes: Master in Engineering in Computer Science (Laurea in Ingegneria Informatica) cum Laude, Sapienza Università di Roma, 1995 PhD in Engineering in Computer Science (Dottorato in Ingegneria Informatica), Sapienza Università di Roma, 1999 Professor Iocchi's research focuses on cognitive robotics, task planning, multi-robot coordination, robot perception, robot learning, human-robot interaction, and social robotics. His work has significant applications in security, surveillance, and environmental monitoring. He has published over 200 referred papers with an h-index of 46 (Google Scholar). His research bridges theoretical AI with practical robotic systems operating in real-world environments, with a particular emphasis on developing intelligent systems that can interact effectively with humans. His recent publications show a strong trend toward multi-agent reinforcement learning, trust modeling in human-AI teams, UAV coordination, and planning systems. There's a clear focus on making robotic systems more reliable, efficient, and capable of operating in complex real-world scenarios like healthcare facilities and smart cities. His work increasingly integrates formal planning approaches with machine learning techniques. Professor Iocchi has received numerous scientific awards: 1999 Top Paper Award WebNet'99 2006 Best Paper Award RoboCup 2006 2008 Best Robotics Demo Award AAMAS 2008 2014 Best Paper Award For Engineering Contribution RoboCup 2014 2017 RoboCup@Home SSPL 2017 - 3rd place 2018 Canada-Italy Innovation Award 2019 Best Paper Award For Engineering Contribution RoboCup 2019 As an academic advisor, Iocchi has directed the PhD Program in Engineering in Computer Science from 2020 to 2023. He has been Principal Investigator for numerous research projects including SciRoc (European Robotics League), AI4EU (European AI project), BUBBLES, AIPlan4EU, ROSITA, Trust Your Agents, and FAIR. His research has been supported by EU H2020 programs, national grants, and industry collaborations, demonstrating strong connections between academia and practical applications. Professor Iocchi is actively involved with the Cognitive Cooperating Robots Lab (LabRoCoCo) and is a key member of the RoboCup Federation, having served as Vice-President from 2019 to 2024. He has played a significant role in benchmarking domestic service robots through RoboCup@Home and the European Robotics League Service Robots (ERL-SR), which he helped establish. His leadership in organizing international scientific robot competitions has been instrumental in advancing the field of service robotics.

Anand Bhattadمشاهده پروفایل

استادیار

• Computer Vision
• Computer Graphics
• Generative Modeling
• +۲ مورد دیگر

Anand Bhattad is an Assistant Professor in the Department of Computer Science at Johns Hopkins University, starting Fall 2025. Previously, he held positions as a Research Assistant Professor at the Toyota Technological Institute at Chicago (TTIC) and a visiting scholar at UC Berkeley. His research focuses on the intersection of computer vision, generative modeling, and physical reasoning, aiming to develop perception-driven and physics-aware visual models. His academic journey includes a PhD in Computer Science from the University of Illinois Urbana-Champaign under David Forsyth, with mentorship from Derek Hoiem, Svetlana Lazebnik, Greg Shakhnarovich, and Shenlong Wang. Prior to his PhD, he earned dual master’s degrees in Computer Science and Civil and Environmental Engineering at UIUC and a bachelor’s in Civil Engineering from NITK Surathkal, India. Research interests center on how generative models encode physical and perceptual knowledge, with key contributions in intrinsic image emergence, projective geometry limitations, and physics-aware relighting techniques. His work bridges classical computer vision concepts with modern deep learning, producing state-of-the-art methods for 3D scene synthesis and image editing. Articles span topics like 3P Vision , diffusion models, and 360° video datasets, reflecting interdisciplinary approaches in computer graphics and computational photography. Scientific awards include Outstanding Reviewer at ICCV 2023, CVPR 2022 Best Paper Finalist, and multiple conference service roles as workshop organizer and area chair. He designed the TTIC course Past Meets Present: A Tale of Two Visions , teaching connections between historical and modern computer vision research.

Laura Ascenzi-Morenoمشاهده پروفایل

استاد

• Bilingual Education
• Translanguaging
• Biliteracy
• +۷ مورد دیگر

Laura Ascenzi-Moreno serves as Professor of Bilingual Education and Bilingual Program Coordinator in the Childhood, Bilingual and Special Education Department at Brooklyn College, City University of New York (CUNY), School of Education. Her academic leadership focuses on developing educators capable of serving linguistically diverse student populations through equity-centered pedagogical frameworks. Her educational foundation includes a B.A. in Anthropology and Education from Swarthmore College (1994), an M.A. in Individualized Studies in Education from Harvard Graduate School of Education (1999), New York State Permanent Certification for PreK-6 (2002), a Bilingual ESL and Teacher Leadership Program from Bank Street College (2004), and a Ph.D. in Urban Education from CUNY Graduate Center (2012). Dr. Ascenzi-Moreno's research pioneers translanguaging applications across literacy instruction, assessment, and computational learning environments. She investigates how emergent bilinguals' linguistic repertoires can transform reading development, teacher knowledge construction, and multimodal assessment practices. Her work consistently centers equity through frameworks like syncretic reasoning and accompáñamiento, challenging deficit ideologies while promoting asset-based approaches to multilingual education. Analysis of her 2020-2024 publications reveals an accelerating interdisciplinary trajectory where translanguaging principles increasingly intersect with computer science education. This evolution demonstrates strategic expansion from foundational literacy research into computational literacies, with growing emphasis on co-design methodologies and teacher agency in developing multilingual CS curricula. Her scientific recognition includes the 2024 NCTE Outstanding Elementary Educator Award, Language Arts Distinguished Article Award, and major NSF grants totaling $1.3 million for computational literacy projects. Additional honors encompass Fulbright Scholarship work in Colombia, PSC-CUNY research awards, and Westinghouse Science Talent Search distinction. As Principal Investigator of NSF-funded PiLa-CS and former CUNY-NYSIEB Associate Investigator, she secures substantial grant support while mentoring teacher candidates. Her service includes NCTE Elementary Steering Committee leadership (2022-2026), journal reviewing, and Brooklyn's New York Teacher Table participation addressing educator recruitment/retention. Dr. Ascenzi-Moreno co-directs the Participating in Literacies in Computer Science project and maintains active collaboration with NYC schools through translanguaging professional development initiatives. Her work with the CUNY-NYSIEB project established foundational frameworks now implemented across New York bilingual programs.

David Hsuمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Machine Learning
• +۲ مورد دیگر

David Hsu is Provost's Chair Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing, where he founded and directs the NUS Artificial Intelligence Laboratory (NUSAIL) and leads the Smart Systems Institute. His academic leadership includes chairing major conferences such as Robotics: Science & Systems (2015) and IEEE ICRA (2016), alongside editorial roles in IEEE Transactions on Robotics and the Journal of Artificial Intelligence Research. He earned a B.Sc. in Computer Science & Mathematics from the University of British Columbia and a Ph.D. in Computer Science from Stanford University. His research spans robotics, AI, and computational biology, with recent focus on robot planning under uncertainty and human-robot collaboration. Current work integrates machine learning with decision-theoretic planning to enable robust human-robot co-existence in unstructured environments. Analysis of his 2023-2025 publications reveals dominant trends in deformable object manipulation (e.g., clothes handling via semantic keypoints), open-world navigation using scene graphs, and LLM-driven multi-agent reasoning for complex tasks. Key innovations include perspective-aware visual grounding for human-centric interaction and functional object arrangement through compositional generative models, reflecting a strong emphasis on real-world applicability. His scientific contributions have earned prestigious recognition: IJCAI-JAIR Best Paper Prize (2022) for foundational AI research Robotics: Science & Systems Test of Time Award (2021) IEEE Fellowship (2018) for contributions to robotic planning RSS Best Systems Paper Award (2017) RoboCup Best Paper Award at IROS (2015) Humanitarian Robotics Award at ICRA (2015) As director of the Adaptive Computing Laboratory, Hsu drives research on fundamental computational frameworks for human-robot interaction. The lab's work on uncertainty-aware decision-making has secured significant research funding through grants from Singapore's National Research Foundation and industry partnerships with robotics firms. While specific student names aren't publicized, his leadership in the NUSAIL indicates extensive mentorship of doctoral candidates in AI and robotics.

Marcelo Mattarمشاهده پروفایل

استادیار

• Computational Neuroscience
• Decision-Making
• Reinforcement Learning
• +۳ مورد دیگر

Marcelo Mattar is an Assistant Professor of Psychology and Neural Science at New York University, leading the Mattar Lab. His research focuses on the neural computations underlying memory, decision-making, and reinforcement learning. He holds a Ph.D. in Psychology from the University of Pennsylvania and has held academic positions at NYU, UC San Diego, and postdoctoral roles at Princeton University and the University of Cambridge. His work bridges computational neuroscience and artificial intelligence, aiming to model how the brain uses internal models for planning and decision-making. Education: Ph.D. in Psychology (Computational and Cognitive Neuroscience), University of Pennsylvania, 2016 M.A. in Statistics, University of Pennsylvania, 2016 B.A. in Electronics Engineering, Instituto Tecnologico de Aeronautica, Brazil, 2010 Research Interests: The lab develops mathematical models of learning and decision-making, leveraging reinforcement learning, Bayesian statistics, and neural networks. Experiments involve human behavioral studies and neuroimaging, with collaborations in animal electrophysiology and computational psychiatry. Key Contributions: His work explores how episodic memory and hippocampal replay support flexible decision-making. Recent studies highlight parallels between human cognition and AI systems, such as language models' metacognitive abilities and brain-inspired algorithms. Awards: Newton International Fellowship, Royal Society (2018–2019) Lab Team: The lab includes postdocs, PhD students, and undergraduates from diverse fields like cognitive science, neuroscience, and computer science. Current members are listed on the lab's website. Lab Location: Meyer Hall, 6 Washington Place, New York, NY 10003.

Tom Schrijversمشاهده پروفایل

استاد

• Programming Languages
• Functional Programming
• Type Systems
• +۶ مورد دیگر

Tom Schrijvers is a Professor at the Department of Computer Science in the Faculty of Engineering Science at KU Leuven, Belgium. He leads the Programming Languages Group within the Declarative Languages and Artificial Intelligence (DTAI) research group. His research focuses on programming languages, particularly functional and logic programming, with special emphasis on Haskell, type systems, and algebraic effects. His research interests include: Functional Programming, especially Haskell Type Systems and Type Theory Algebraic Effects and Handlers Logic Programming, particularly Prolog Constraint Programming Domain-Specific Languages Programming Language Theory Prof. Schrijvers' recent research has focused on effect systems, staged programming, and language composition. His work on algebraic effect handlers has been particularly influential, providing new insights into how effects can be modularly composed and handled in functional languages. He has also made significant contributions to the understanding of type classes and their implementation in Haskell. His publications demonstrate a consistent focus on practical applications of programming language theory, with work spanning from foundational type theory to applied domain-specific languages for areas like fluorescence microscopy. His research often bridges the gap between theoretical programming language concepts and practical implementation concerns. Prof. Schrijvers has supervised numerous PhD students to completion, including Pieter Wuille, Benoit Desouter, George Karachalias, Steven Keuchel, Amr Saleh, Alexander Vandenbroucke, and Ruben Pieters. He currently supervises PhD students Klara Mardirosian, César Santos, Gert-Jan Bottu, Koen Pauwels, Birthe van den Berg, and Roger Bosman. His research group has received funding from various sources including EU projects like GRACeFUL. The Programming Languages Group at KU Leuven, which he leads, focuses on functional (Haskell) and logic (Prolog, Datalog, CLP) programming languages, as well as general programming language theory. The group has been active in numerous research projects and collaborations across Europe.

Changyou Chenمشاهده پروفایل

دانشیار

• Multi-Modal Learning
• Vision and Language
• Foundation Models
• +۷ مورد دیگر

Dr. Changyou Chen is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York. His research focuses on Multi-Modal Learning Foundation Models Deep Generative Models Large-scale Bayesian Sampling with applications in document understanding, music-AI integration, and molecular representation learning. Research Trends revealed through his recent publications include Optimizing Multimodal Large Language Models Developing Novel Retrieval-Augmented Generation Frameworks Creating Benchmark Datasets for Visual Text Understanding Advancing Diffusion Models with Domain-Specific Constraints across domains from music sheets to biomedical documents. Scientific Contributions : UB Young Investigator Award (2020) Architect of LoCAL Framework for Long Document Understanding Co-developer of MusiXQA Benchmark Pioneering Work in Probability Contrastive Learning Academic Leadership includes mentoring 10+ graduate students and serving as Area Chair for major AI conferences (ICML, NeurIPS, AAAI, IJCAI). His Labs develop scalable solutions for multimodal reasoning, with recent work demonstrating practical GPU memory optimization through LoRA adapter sharing.

• Multi-agent Systems
• Artificial Intelligence
• Computational Creativity
• +۵ مورد دیگر

Mark d'Inverno is a Professor in the Department of Computing at Goldsmiths, University of London, where he has established himself as a leading researcher at the intersection of artificial intelligence, multi-agent systems, and creative applications. His academic journey began with foundational work in formal methods and agent-based systems, culminating in his 1998 PhD thesis 'Agents, Agency and Autonomy: A Formal Computational Model' from University College London, and has evolved toward practical applications in music technology and ethical AI systems. Professor d'Inverno's research interests span multiple interconnected domains, with a particular focus on computational creativity, multi-agent systems, and the application of AI in musical contexts. His work explores how artificial intelligence can enhance creative processes, particularly in music composition and performance, while maintaining ethical considerations in social AI systems. He has made significant contributions to understanding how agents can interact meaningfully in social contexts, how ethical frameworks can be embedded in online systems, and how technology can support creative learning experiences. His recent scholarly output demonstrates a clear trajectory toward applied research with social impact, as evidenced by his 2021-2024 publications which increasingly address ethical considerations in AI, human-AI collaboration in creative domains, and educational applications of technology. These works reveal a researcher deeply engaged with both theoretical foundations and practical implementations, bridging the gap between abstract computational models and real-world creative and educational applications. Professor d'Inverno maintains an extensive collaborative network, frequently working with Matthew Yee-King on music technology applications, with Pablo Noriega on ethical AI frameworks, and with Jon McCormack on computational creativity. His research has been supported through various projects that connect theoretical computer science with practical creative applications, particularly in the development of systems that facilitate human-AI creative collaboration.

Alexander Kollerمشاهده پروفایل

استاد

• Computational Linguistics
• Neural-Symbolic Models
• Semantic Parsing
• +۵ مورد دیگر

Professor Alexander Koller is a leading academic in Computational Linguistics at Saarland University's Department of Language Science and Technology. He holds a courtesy appointment in Computer Science and contributes to the Saarland Informatics Campus - one of Europe's premier computer science research centers. He leads the Computational Linguistics group and serves as speaker for the DFG-funded Research Training Group 'Neuroexplicit Models of Language, Vision, and Action'. PhD in Computer Science (Saarland University) Former positions: University of Potsdam, Columbia University, University of Edinburgh Sabbatical experiences: Meta AI (Paris), Allen Institute for AI (Seattle) His research focuses on computational modeling of meaning and reasoning in NLP, combining neural and symbolic approaches. Key contributions include semantic parsing systems like the AM parser and Alto, neurosymbolic models, and the GIVE Challenge for NLG evaluation. His recent work explores LLMs' limitations in problem-solving and compositional generalization. Recent publications highlight diverse applications across semantic parsing, dialogue systems, and LLM evaluation. Awards include ACL 2020 Best Theme Paper and multiple Outstanding Paper recognitions at ACL conferences. 2025 - AI Action Summit keynote speaker 2023 - ACL Outstanding Paper Awards 2022 - ELLIS Faculty appointment He maintains the DialogOS system for spoken dialogue development and teaches advanced computational linguistics topics. His group includes multiple postdocs and PhD students working across LLMs, dialogue systems, and semantic modeling.

Kenneth D. Forbusمشاهده پروفایل

استاد

• Qualitative Reasoning
• Analogical Reasoning
• Spatial Reasoning
• +۶ مورد دیگر

Ken Forbus is the Walter P. Murphy Professor of Computer Science and Professor of Education at Northwestern University. He earned his Ph.D. in Artificial Intelligence from MIT in 1984, along with S.M. and S.B. degrees in Computer Science from the same institution. Current research focuses on qualitative reasoning , analogical reasoning , spatial reasoning , sketch understanding , and the Companion cognitive architecture . He has made foundational contributions to qualitative physics , compositional modeling , and cognitive simulation through systems like CyclePad and Companions . His work spans AI, cognitive science, and education technology with applications in intelligent tutoring systems , educational software , and interactive entertainment . Awards and Fellowships: Humboldt Research Award AAAI Fellow Cognitive Science Society Fellow ACM Fellow AAAS Fellow Herbert A. Simon Prize recipient Research trends in recent publications include analogical reasoning frameworks, normative modeling, pretense simulation, qualitative spatial representations, and applications in education and cognitive systems. Articles frequently address intersections between AI, cognitive science, and human-computer interaction. Teaching activities include core courses like Cognitive Science 207 , Design of Problem Solvers , and Conversational AI . He co-developed the open-source Freeciv game framework for AI research in strategy games.

• Software Engineering
• Formal Methods
• Model-Driven Engineering
• +۴ مورد دیگر

Pierre KELSEN is a Full Professor in the Department of Computer Science at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM). His research focuses on Software Engineering, Formal Methods, Model-Driven Engineering, and Algorithmic Graph Theory. He leads the LASSY Laboratory for Advanced Software Systems, emphasizing model decomposition, regulatory compliance, and formal verification techniques. Education: PhD in Computer Science (1993, University of Illinois at Urbana-Champaign), M.Sc. (1989, UIUC), and Diploma (1986, University of Karlsruhe). Postdoctoral work at the University of British Columbia and Max-Planck-Institut für Informatik. Research Interests: - Development of formal modeling languages (e.g., VCL, F-Alloy) - Model transformation and validation frameworks - Algorithms for compliance and complexity challenges - Visual and modular design methodologies Funding: - ASINE (FNR Pearl, 2013–present): Architecture-based service innovation - MaRCo (FNR Core, 2010–2013): Business-centric regulatory compliance Publications span model-driven engineering, formal methods, and algorithmic foundations, with recent work exploring AI integration in domain modeling and compliance analysis. Labs/Teams: LASSY Laboratory, collaborating on tools like Lightning and Democles for executable modeling frameworks.