Find Academic Supervisors

• Conceptual Modeling
• Business Process Management
• Digital Transformation
• +5 more

Yves Wautelet serves as an Associate Professor at the Faculty of Economics and Business at KU Leuven, where he conducts research in conceptual modeling, business process management, and digital transformation. His work addresses critical challenges at the intersection of information systems engineering and business strategy, with particular focus on sustainability-driven modeling approaches and IT governance frameworks. His primary research interests include: Conceptual modeling methodologies and frameworks Business process management and information systems design Digital transformation strategies and implementation IT governance and business-IT alignment Sustainability-driven modeling for circular economy Agile software development practices and methods Requirements engineering with user stories Wautelet's recent publication record demonstrates significant scholarly productivity with numerous 2024-2025 publications spanning conceptual modeling frameworks for sustainability (Circulise), tools for identifying ambiguity in user stories (AmbiTRUS), and approaches to align strategic and operational agility. His work bridges theoretical foundations with practical applications across diverse domains including healthcare, circular economy, software development, and organizational transformation. His research consistently applies model-driven approaches to solve complex real-world problems, often integrating sustainability considerations into information systems engineering. As a promotor and co-promotor, Wautelet currently supervises multiple doctoral research projects including: Automatic generation of conceptual models from textual descriptions (2024-2028) Sustainability-Driven Modeling Assistant for Twin Transition in Vietnam (2024-2028) Home Care Business Process Management using Distributed Ledger Technologies (2024-2028) Teaching Modeling Skills in BPMN formalism (2021-2025) His research is conducted through the Information Systems Engineering Research Group (LIRIS) at KU Leuven's Brussels campus, where he contributes to advancing model-driven approaches for addressing contemporary business and technological challenges.

• Formal Methods
• Software Engineering
• Software Verification and Validation
• +7 more

Carlo Alberto Furia is an Associate Professor and Vice Dean at the Faculty of Informatics, Università della Svizzera italiana (USI). He is affiliated with the Software Institute, where he leads the ATOM research group. His academic journey includes prior roles as an Associate Professor at Chalmers University of Technology and a Senior Researcher at ETH Zurich’s Chair of Software Engineering. PhD in Computer Science, Politecnico di Milano Master of Science in Computer Science, University of Illinois at Chicago Laurea in Computer Science and Engineering, Politecnico di Milano His research centers on formal methods for software engineering, aiming to enhance software correctness, reliability, and quality through rigorous techniques. Key areas include automated program verification, contract-based development, loop invariant inference, and empirical evaluation using Bayesian data analysis. He emphasizes practical applicability and automation in formal methods. His recent publications reflect a strong focus on program analysis at the bytecode level, multilingual software analysis, automated repair of Android security issues, and empirical methodologies. These works span topics such as JVM substitutability, exception behavior in Java bytecode, and information flow security, demonstrating a consistent thread in improving software robustness through formal and automated techniques. He is actively involved in the software engineering research community as an Associate Editor of the Empirical Software Engineering (EMSE) journal and as a Program Committee member for major conferences including FASE, FM, ASE, ICSE, and CauSE. Carlo Furia has advised multiple research projects and supervised student theses. He has led and contributed to funded research initiatives, particularly in program analysis and verification. His group has developed tools such as AutoProof and other software artifacts available through the ATOM software page. He regularly teaches courses such as Software Analysis, Programming Fundamentals, and Software Design & Modeling. He leads the ATOM research group, which focuses on advancing automated techniques for software testing, analysis, and verification. The group develops practical tools and conducts empirical studies to validate research outcomes.

• Machine Learning
• Artificial Intelligence
• Explainability of AI
• +3 more

Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University and serves as Director of the Stanford AI Lab (SAIL) and Senior Fellow at the Institute for Human-Centered AI (HAI). He holds dual roles as Chief Scientist at Visual Layer and Virtue AI. His research focuses on machine learning methods, explainability, fairness, and ethics of AI, alongside systems for scalable AI deployment. Education details are not explicitly provided, but his work spans foundational contributions to machine learning systems (e.g., XGBoost) and explainable AI frameworks like Anchors and LIME. He emphasizes ethical AI through projects like CheckList for model testing and Model Equality Testing for API transparency. His scientific contributions include advancing optimization techniques (AdaScale SGD, TVM compiler) and ethical benchmarks for generative AI. He has been recognized as a Member of the National Academy of Engineering for his transformative impact on AI systems and their societal applications. Guestrin leads interdisciplinary initiatives at SAIL and HAI, fostering collaboration between technical innovation and human-centered design. His work bridges theory and practice, addressing challenges in healthcare (diabetes management systems) and AI security.

• Global Navigation Satellite Systems (GNSS)
• Avionics Systems
• Wireless Communication
• +7 more

René Jr Landry is a full Professor in the Department of Electrical Engineering at École de technologie supérieure (ETS), Université du Québec, specializing in Global Navigation Satellite Systems (GNSS), avionics, and wireless communication technologies. His academic journey includes a B.Ing. from Polytechnique Montréal, M.Sc. from University of Surrey (UK), and Ph.D. from SupAréo in Toulouse. He maintains active research leadership through two key laboratories: LASSENA (Laboratory of Space Technologies, Embedded Systems, Navigation and Avionics) and LACIME (Communications and Microelectronic Integration Laboratory). His research spans critical aerospace navigation domains including GNSS signal processing, inertial navigation systems, software-defined radio for avionics, radio frequency interference mitigation, and indoor positioning technologies. Landry's work addresses real-world challenges in satellite navigation robustness, precision positioning in urban/denied environments, and next-generation avionic system security. His current projects focus on blockchain-enhanced IoT security, AI-driven GNSS disruption analysis, and adaptive RF front-ends for multi-band avionics applications. Analysis of his recent publications reveals strong emphasis on resilient positioning systems through multi-constellation integration (particularly Iridium-NEXT), blockchain applications for navigation security, and explainable AI techniques for GNSS signal quality assessment. His work increasingly bridges traditional navigation engineering with cutting-edge security and machine learning paradigms. 2014 Prix d'excellence du c.a. pour les services à la collectivité Landry has supervised over 100 graduate students across doctoral, master's, and research projects since 2005, with current supervision extending through Summer 2025. His research funding supports multiple industry partnerships focused on avionics certification, software-defined radio implementations, and next-generation navigation systems. The LASSENA laboratory under his leadership develops certified avionic products from open-source SDR platforms and advances multi-sensor fusion techniques for challenging navigation environments. His research infrastructure includes specialized facilities for GNSS signal simulation, avionics hardware testing, and multi-sensor integration. Current work emphasizes flight-tested validation of RF front-end technologies, blockchain-secured navigation data, and real-time interference mitigation systems for aviation applications.

• Software Testing
• Mutation Testing
• Model-Based Testing
• +7 more

Jeff Offutt is a Professor and Chair of the Department of Computer Science at the University at Albany, College of Nanotechnology, Software, & Engineering. Previously, he was a Full Professor with Tenure in Software Engineering at George Mason University since 2005. He received his PhD in Information & Computer Science from the Georgia Institute of Technology in 1988. His research spans software testing, mutation testing, model-based testing, automatic test data generation, web application testing, and software engineering education. He has led significant projects such as the NSF-funded integration of CS into K-5 classrooms and the Google-funded SPARC project for scalable CS1/CS2 instruction. The 15 most recent articles reflect a continued focus on mutation testing cost reduction, model-based testing oracles, educational innovations, and security aspects of web applications. Trends include empirical validation, industrial applicability, and bridging theory with practice in software testing and engineering education. John Toups Presidential Medal for Excellence in Teaching (2020) George Mason University’s Alumni Association Faculty Member of the Year (2020) Outstanding Faculty Award from the State Council of Higher Education for Virginia (2019) Best Paper Award at ICST 2021 10-Year Most Influential Paper Award at MODELS 2020 George Mason University Teaching Excellence Award (2013) ACM Notable Article Award (2013) Jeff Offutt has mentored numerous graduate students including Upsorn Praphamontripong, Nan Li, and Yu-Seung Ma, and has led major grant-funded projects such as the SPARC educational model and NSF initiatives on K-5 CS integration. His textbook Introduction to Software Testing (with Paul Ammann) is widely adopted globally. He led the MS in Software Engineering program at GMU and developed several new courses in software testing, web engineering, and usability. He pioneered innovative teaching methods using web technologies and asynchronous learning models. He also co-founded the IEEE International Conference on Software Testing, Verification and Validation (ICST) and served as Editor-in-Chief of Software Testing, Verification and Reliability from 2007 to 2019.

• Databases
• Programming Languages
• Compilers
• +7 more

Amir Shaikhha is an Associate Professor (Reader) in the School of Informatics at the University of Edinburgh. He was previously an Assistant Professor (Lecturer) at the same institution from 2020 to 2024 and a Departmental Lecturer at the University of Oxford until August 2020. He is also a Junior Research Fellow at University College, Oxford. His academic journey began with a Ph.D. from EPFL in 2018, where he was awarded the Google Ph.D. Fellowship in structured data analysis and a Ph.D. thesis distinction. His research centers on the design and implementation of data-analytics systems, drawing upon techniques from databases, programming languages, compilers, and machine learning. He develops high-performance systems such as SDQL.py, StructTensor, and VecHT, focusing on the compilation of data science workloads and optimization of tensor operations. His work bridges the gap between high-level abstractions and efficient execution, particularly in sparse and probabilistic computing domains. The recent publications highlight a strong trend in compiler-driven optimizations for data-intensive applications, including automatic differentiation, loop fusion, probabilistic programming, and domain-specific language (DSL) restaging. His research integrates machine learning for systems decisions and emphasizes reproducibility and performance. He has published consistently in top venues like PLDI, OOPSLA, SIGMOD, and CGO, reflecting sustained impact in programming languages and database systems. Dahl-Nygaard Junior Prize, 2025 Google Research Scholar Award, 2025 Most Influential Paper Award, GPCE 2024 Best Paper Award, GPCE 2017 Most Reproducible Paper Award, SIGMOD 2017 Google Ph.D. Fellowship, 2017 Amir Shaikhha has advised PhD students including Hesam Shahrokhi and has been nominated for Best Supervisor of the Year at the University of Edinburgh. He leads research projects that have received recognition and support through awards and grants, including the Google Research Scholar Award. He actively serves the community through program committees (e.g., GPCE, DBPL, DRAGSTERS), editorial roles, and peer review for premier journals. His leadership in organizing workshops and conferences underscores his role as a central figure in the programming languages and databases research communities. He leads a research group focused on compiler and database systems, with recent open-source releases such as StructTensor and VecHT. His team collaborates with researchers from institutions like MIT, EPFL, and TU Berlin, and he co-chairs workshops like Sparse@PLDI and DRAGSTERS. His lab emphasizes innovation in how data-intensive programs are compiled and executed efficiently across modern hardware.

Ruixiang TangView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +4 more

Ruixiang Tang is an Assistant Professor at Rutgers, The State University of New Jersey. His research focuses on artificial intelligence, machine learning, and natural language processing, with an emphasis on multimodal learning, model security, and ethical AI. He explores topics such as adversarial robustness, bias mitigation, and applications in healthcare and robotics. Key research interests include developing robust algorithms for vision-language models, analyzing model vulnerabilities like backdoors and hallucinations, and designing trustworthy AI systems. His work bridges theoretical advancements and practical applications, addressing challenges in healthcare data augmentation, copyright infringement detection, and cognitive reasoning. His recent publications highlight contributions to multimodal in-context learning, counterfactual reasoning benchmarks, and secure model optimization. Tang's research also intersects with fairness in AI, such as mitigating bias in NLP models and ensuring equitable outcomes in medical applications.

Agustín Zaballos DiegoView profile

Assistant Professor

• Smart Grids
• Internet of Things (IoT)
• Sensor Networks
• +3 more

Agustín Zaballos Diego is an Assistant Professor in the Department of Computer Engineering at University Ramon Llull (URL), Barcelona, Spain, since 1999. He serves as Research Coordinator in the Department of Engineering at La Salle Campus Barcelona and leads the R&D Networking and Security Area since 2002. His academic background includes a PhD in Data Networks and Internet Technologies (2012), an International MBA (2014), and an M.S. in Electronic Engineering (2000). University: University Ramon Llull (URL) Department: Department of Computer Engineering Research Group: GRITS Research Focus: Real-time QoS-aware routing protocols in Smart Grids, Ubiquitous Sensor Networks, and IoT communications. His work bridges telecommunications, computer science, and energy systems through projects like OPERA (FP6), INTEGRIS (FP7), and FINESCE (FP7). Publication Trends: Recent articles highlight advancements in HF communications for Antarctic research, hybrid genetic algorithms for traffic engineering, IPv6 testing, and Industry 4.0-related networking solutions. Keywords span Smart Grids, IoT, Sensor Networks, and QoS optimization. Collaborative Projects: Key initiatives include the Antarctica Project , ATHIKA (ICT in healthcare), ENVISERA (environmental sensor networks), HOTSUP (online teaching innovation), PLANET4 (AI/ML in industry), and XIoT (IoT scalability challenges).

Victor VianuView profile

Professor

• Computational Logic
• Database Systems
• Database Theory
• +4 more

Victor Vianu is a Professor of Computer Science and Engineering at the University of California, San Diego and holds the INRIA International Chair at INRIA-Saclay in Paris. He has maintained continuous faculty status at UC San Diego since 1983 while developing extensive international collaborations, particularly with French research institutions including INRIA, ENST-Paris, ENS-Paris, and the University of Paris. His academic credentials include a Ph.D. in Computer Science from the University of Southern California (1983) and undergraduate studies in Mathematics and Informatics at the University of Bucharest (1974-1977). Professor Vianu's research spans computational logic, database systems and theory, and automatic verification. His work uniquely bridges theoretical foundations with practical applications in XML processing, workflow systems, and data-driven applications. He has made seminal contributions to understanding the theoretical underpinnings of database query languages and their expressive power, particularly in the context of XML technologies and workflow systems. His research demonstrates a consistent trajectory from theoretical computer science to practical database systems applications. His publication record reveals significant contributions to database theory spanning over three decades, with particular emphasis on XML technologies, workflow systems, and formal methods for data-driven applications. His work shows a clear evolution from foundational theoretical work to practical applications in business processes and web technologies. INRIA International Chair (2013) Fellow of the American Association for the Advancement of Science (AAAS) (2013) ACM PODS Alberto O. Mendelzon Test-of-Time Award (2010) Fellow of the Association for Computing Machinery (ACM) (2006) Professor Vianu has held significant leadership roles including Editor-in-Chief of the prestigious Journal of the ACM, numerous program committee chair positions for major database conferences (PODS, ICDT, ASIAN), and General Chair for ACM SIGMOD conferences. He has served on the executive committees of SIGMOD (1998-2000) and PODS (1993-2004), and was a member of the ICDT Council (1997-2007), demonstrating sustained influence in the theoretical database community. His extensive invited talks at major conferences including College de France, ACM PODS, and International Conference on Database Theory highlight his international recognition.

Michael StevensView profile

Senior Lecturer

• Biomedical Engineering
• Advanced Manufacturing
• Digital Manufacturing
• +6 more

Dr. Michael Stevens is a Senior Lecturer at University of New South Wales (UNSW) Canberra , where he focuses on advanced manufacturing and biomedical device control systems . His work bridges digital manufacturing for SMEs with smart artificial heart technologies , emphasizing industry collaboration and translational research. Specializes in physiological control systems for rotary blood pumps Develops unobtrusive fall detection systems for dementia patients Leads international projects on total artificial heart development Education : B.Eng (Medical - First Class Honours), Queensland University of Technology (2010) PhD in Physiological Control for Biventricular Assist Devices, University of Queensland (2014) Research Trends show consistent focus on: Machine learning for biomedical diagnostics (2018–2025) mmWave radar and thermal sensors in patient monitoring (2021–2024) Computational fluid dynamics in artificial heart modeling (2016–2024) Physiological control algorithms for rotary blood pumps (2011–2025) Scientific Awards : UNSW Scientia Education Award (2021) for contextual teaching Heart Foundation Runner-up for "Smart Artificial Hearts" pitch (2021) ARC PGC Supervisor Award (2017) for mentoring Grants & Supervision : Holds over $6 million in competitive funding including MRFF and ARC grants. Currently supervises 4 PhD students while maintaining industry partnerships with VitalCare and BiVACOR. Labs & Facilities : Works across UNSW Engineering labs and Graduate School of Biomedical Engineering platforms, including mock circulation loops and high-performance computing clusters for CFD simulations.

Jinhan KimView profile

Researcher

• Software Engineering
• Mutation Testing
• Fault Localization
• +4 more

Jinhan Kim is a Postdoctoral Researcher at the Università della Svizzera italiana (USI) in the Faculty of Informatics, working in the TAU lab under Prof. Paolo Tonella. He earned his Ph.D. from KAIST under Prof. Shin Yoo, focusing on software engineering research in mutation testing, fault localization, and deep learning system testing. His work bridges traditional software engineering techniques with AI-driven methodologies, emphasizing AI4SE and SE4AI paradigms. Education: Ph.D. in Software Engineering, KAIST, 2023 Research Interests: Mutation Testing Deep Learning System Testing Autonomous Systems Testing Adversarial Attack Detection Empirical Software Engineering Service and Leadership: Organized SBFT 2026 and DeepTest 2026 (co-located with ICSE 2026) Program Committee Member for ASE, ISSTA, Mutation, and DeMeSSAI Board of Distinguished Reviewers for TOSEM (2024–2025) Labs and Teams: Active contributor to the TAU Lab at USI, focusing on advanced software testing and AI integration.

• Autonomous Vehicles
• Intelligent Transportation Systems
• Robotics
• +7 more

Miguel Ángel Sotelo Vázquez is a full Professor at the University of Alcalá, leading the INVETT Research Group (Intelligent Vehicles and Traffic Technologies). He holds the Department of Automatic Control and specializes in autonomous systems, particularly in path planning, sensor fusion, and human-vehicle interaction. His research integrates machine learning, robotics, and control theory to address challenges in intelligent transportation systems. He earned his Ph.D. in 2001 with a thesis on autonomous vehicle navigation in partially known environments. His work emphasizes real-world deployment, explainable AI, and safety-critical systems. Recent projects focus on lane change prediction, pedestrian behavior modeling, and cybersecurity for autonomous systems. Key contributions include neuro-symbolic frameworks for decision-making, real-time multi-physics field reconstruction, and cross-cultural studies of pedestrian interactions. He collaborates internationally on urban mobility resilience and hydrogen refueling infrastructure. Research Highlights : Development of knowledge graph-based prediction architectures Experimental validation of human-vehicle interaction in VR environments Creation of the SCOUT trajectory prediction framework

Gero MühlView profile

Professor

• Distributed systems
• Distributed algorithms
• Event-based systems
• +7 more

Prof. Dr.-Ing. habil. Gero Mühl is a W2-Professor at the University of Rostock, where he holds the chair for "Architecture of Application Systems" since October 2009. His academic journey includes positions as a Heisenberg Fellow at the Technical University of Berlin (2009), postdoctoral research at TU Berlin (2002-2009), and doctoral studies at TU Darmstadt where he received his Dr.-Ing. degree with distinction in 2002. He completed dual Diplomas in Computer Science (Dipl.-Inform.) and Electrical Engineering (Dipl.-Ing.) from FernUniversität in Hagen in 1998. Prof. Mühl's research focuses on Self-Organizing Distributed Systems , with particular expertise in distributed systems, distributed algorithms, event-based systems, middleware, energy-efficient systems, organic computing, sensor networks, web services, and electronic commerce. His work bridges theoretical foundations with practical implementations in real-world distributed environments. His recent publications show a strong trend toward time-sensitive networking, content-based publish/subscribe systems, and P4 programmable data planes. These works address critical challenges in industrial communication, real-time systems, and network reliability. His research group has made significant contributions to making distributed systems more autonomous, reliable, and efficient. Scientific awards and recognitions include: Nomination for the Berlin Science Award for Young Scientists (2008) Heisenberg Fellowship by the German Research Foundation (DFG) (2008) Best paper award in System Software and Security at SAC 2015 Prof. Mühl has been actively involved in numerous research projects and collaborations, particularly focusing on self-organizing and self-stabilizing systems. His work on the REBECA publish/subscribe middleware represents a significant contribution to autonomous distributed systems. He has supervised numerous students and researchers, contributing to the development of the next generation of computer scientists specializing in distributed systems. His laboratory at the University of Rostock focuses on practical implementations of self-organizing distributed systems, with current projects investigating time-sensitive networking, publish/subscribe systems, and energy-efficient distributed computing. The team combines theoretical analysis with practical system development to address real-world challenges in industrial and commercial applications of distributed systems.

Daniel FremontView profile

Associate Professor

• Formal Methods
• Automated Reasoning
• Cyber-Physical Systems
• +6 more

Daniel Fremont is an Associate Professor of Computer Science and Engineering at the University of California, Santa Cruz, where he conducts research at the intersection of formal methods and autonomous systems. His work focuses on developing mathematical techniques to improve the reliability of software, hardware, and cyber-physical systems through precise specification, formal verification, automatic synthesis, and principled testing approaches. Dr. Fremont's research interests center on applications of logic in computer science, particularly using automated reasoning to enhance system reliability. His work spans formal methods for cyber-physical systems (CPS), especially autonomous systems that incorporate machine learning. Key research areas include algorithmic improvisation for creating systems with controlled randomness, probabilistic programming through the Scenic language for environment modeling, and formal verification techniques applicable to safety-critical autonomous systems. His group has successfully applied these methods to autonomous vehicles, aircraft systems, and robotics, with significant contributions to both theoretical foundations and practical implementations. The publication record reveals a strong trajectory from theoretical foundations of control improvisation toward practical applications in autonomous systems verification. Early work established the theoretical framework of control improvisation, while recent publications focus on applying these techniques to real-world challenges in autonomous driving, aircraft systems, and AI-based autonomy. A consistent theme across his research is the integration of formal methods with machine learning to address the verification challenges posed by complex, learning-based systems operating in uncertain environments. Best Paper Award at IoTDI 2016 for 'Control Improvisation with Probabilistic Temporal Specifications' Dr. Fremont leads a research group focused on formal methods for autonomous systems, with significant contributions to the development of tools like Scenic (a probabilistic programming language for scenario specification) and VerifAI (a toolkit for formal design and analysis of AI-based systems). His work bridges theoretical computer science with practical engineering challenges in safety-critical autonomous systems, receiving funding from various sources supporting research at the intersection of formal methods and artificial intelligence. The group's approach combines theoretical algorithm development with practical implementation and testing, often collaborating with industry partners working on autonomous vehicle technology. The research group maintains active development of several open-source tools, including the Scenic language for scenario specification and VerifAI for formal analysis of AI systems. They have demonstrated applications across multiple domains including autonomous vehicles, aircraft systems, and robotics, with particular emphasis on simulation-based testing and verification approaches that can provide formal guarantees about system behavior.

• Power Electronics
• Building Automation
• Smart Grids
• +7 more

Dr. Andrzej Ożadowicz is a University Professor at the Department of Power Electronics and Automation of Energy Conversion Systems within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His office is located in room 510, building C-1, with contact details including phone +48 12 617 50 11 and email ozadow@agh.edu.pl. He holds PhD, DSc, and Engineering degrees, reflecting his dual expertise in academic research and practical engineering applications. His research spans Power Electronics, Building Automation, Smart Grids, and IoT-driven energy systems. Key interests include energy efficiency optimization through digital twins and BIM, distributed energy resource integration , and AI-enhanced demand management . Notably, he pioneers applications of deep reinforcement learning in home energy systems and develops frameworks for Smart Readiness Indicator implementation. His work bridges theoretical innovation with practical case studies in building thermal modeling and dynamic façade systems. Recent publications (2021-2025) reveal three dominant trends: (1) Convergence of digital twin technology with building automation for real-time energy management; (2) Critical analysis of IoT security and interoperability in smart infrastructure; (3) Pedagogical innovations in engineering education through blended learning methodologies post-COVID-19. His scholarly output demonstrates consistent focus on energy transition challenges and smart grid evolution. Professor Ożadowicz actively contributes to the Discipline Council for Automation, Electronics, Electrical Engineering and Space Technologies at AGH. He is instrumental in the AutBudNet initiative —a network of certified laboratories for energy efficiency assessment that implements "learning by doing" principles in building automation education. His work with this consortium emphasizes practical validation of smart grid technologies and demand response systems.