Find Academic Supervisors

• Trustworthy Artificial Intelligence
• Software Verification and Validation
• Requirements Engineering
• +4 more

Prof. Lionel Briand is a distinguished academic and researcher in software engineering, holding the Tier 1 Canada Research Chair in Intelligent Software Dependability and Compliance at the University of Ottawa's School of Electrical Engineering and Computer Science (EECS Department). He is also the director of Lero, Ireland's Research Centre for Software, and affiliated with the Nanda Laboratory. His roles span technical leadership, research, and academia across institutions in seven countries. Educated with a PhD, Briand's research focuses on trustworthy AI, software verification/validation, requirements engineering, and regulatory compliance. He pioneered model-driven development and search-based techniques in software engineering. His work bridges academia and industry, collaborating with sectors like aerospace, automotive, and finance. Key research trends in his articles include AI-driven testing methodologies, regulatory compliance tools (e.g., GDPR), safety-critical systems, and metamorphic testing for autonomous systems. His recent work emphasizes large language models (LLMs) for consensus-based test generation and safety monitoring of AI agents. Awards: IEEE/ACM Fellowships, Harlan Mills Award, ACM SIGSOFT Research Award Grants: ERC Advanced Grant, Canada Research Chairs Tools: CompAI (GDPR compliance), TEASMA (DNN testing), Smarla (safety monitoring) Labs/Teams: Leads Lero and collaborates with interdisciplinary teams at the University of Luxembourg's SnT center and Fraunhofer Institute. His work emphasizes synergies between Canadian and Irish research ecosystems.

Benoit BaudryView profile

Professor

• Software Engineering
• Software Testing
• Blockchain Technology
• +4 more

Benoit Baudry is a Professor in Software Technology at KTH Royal Institute of Technology (KTH) within the Digital Futures Faculty and Division of Theoretical Computer Science. He also holds a professorship at Université de Montréal. His research focuses on software diversity, testing, and reliable web-based applications, emphasizing experimental approaches using large-scale open-source systems. He leads the CHAINS project (supported by SSF) and holds a Wallenberg AI, Autonomous Systems and Software Program (WASP) research chair. Previously, he directed the CASTOR software research center (2018–2022) and led the DiverSE group at INRIA Rennes, France. Research Interests Baudry's work spans software supply chain security, blockchain node reliability, and production monitoring for test suite improvement. He explores techniques like N-version design for blockchain nodes, binary diversification for WebAssembly, and coverage-based debloating of Java dependencies. His methods emphasize collaboration with industry and empirical validation of software systems. Grants & Projects - CHAINS project (SSF-funded) - WASP research chair - Former leadership of CASTOR and DiverSE groups. Labs & Collaborations Affiliated with Digital Futures, a cross-disciplinary center at KTH collaborating with Stockholm University and RISE. His work addresses societal challenges via digital technology innovation.

Haibo LiView profile

Professor

• Media Technology
• Digital Signal Processing
• Image and Video Compression
• +5 more

Haibo Li is a Full Professor of Media Technology at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and Digital Futures Faculty. His roles include leading the Media Lab in MID, directing Master’s programs in Media Technology, and teaching courses like Generative AI for Media Technology and Video Technology. He holds a Technical Doctorate from Linköping University (1993) and received the Nordic Best PhD Thesis Award (1994) and Docent title in Image Coding (1997). Research focuses on digital media technologies, including image/video compression, human-computer interaction, and AI-driven media systems. He pioneered the Digital Media Lab at Umeå University and co-founded UCIT. His work spans blind image deblurring, thermal comfort sensing, and multimodal emotion recognition. Over 250 publications and six patents highlight contributions to multimedia, AI, and signal processing. Key projects include developing contactless thermal measurement systems for smart buildings, real-time gesture recognition for wearable devices, and advanced neural networks for image restoration. Awards include the Nordic Best Thesis and leadership in EU projects. He chairs conferences and actively participates in MPEG standardization efforts.

Chaona ChenView profile

Lecturer

• Computational Modelling of Social Signals
• Human-Centred AI
• Social Robotics
• +3 more

Dr. Chaona Chen is a Lecturer in Robotics at the University of Sheffield's School of Computer Science, specializing in computational modeling of social signals, cultural influence on communication, and human-centered AI/social robotics. She holds a Ph.D. and B.Sc. from the University of Glasgow and Wuhan University respectively. Her research emphasizes culturally adaptive social robots and mental health applications. Education: Ph.D. in Psychology & Neuroscience, University of Glasgow (UK) B.Sc. in Computer Science, Wuhan University (China) Research Interests: Dr. Chen's work focuses on: - Mathematical modeling of facial expressions, gaze, and body movements - Cultural and social conventions influencing human communication - Developing AI/social robots for social skill training in mental health contexts Awards & Funding: Leverhulme Early Career Fellowship (£100k, 2021-2024) Lord Kelvin/Adam Smith Fellowship (£90k, 2021-2024) Best Workshop Paper Award (IEEE ICRA 2021) Grants & Projects: European Research Council-funded FaceSyntax project (2017-2021) Multiple grants from EPSRC, ESRC, and international collaborations Teaching & Supervision: Supervises 2-4 Masters and 3-6 Undergraduate research projects annually. Teaches courses in Social Robotics and Advanced Cognition. Labs & Collaborations: Active in Sheffield Robotics and the Complex Systems Modelling research group. Collaborates with MIT Media Lab and Carnegie Mellon University on social robotics.

Eleni VasilakiView profile

Professor

• Bioinspired Machine Learning
• Neuromorphic Computing
• Computational Neuroscience
• +3 more

Professor Eleni Vasilaki holds the Chair in Bioinspired Machine Learning at the University of Sheffield's School of Computer Science, where she serves as Head of the Machine Learning research group and member of the Complex Systems Modelling research group. She joined as Lecturer in 2009 and became Professor in 2016. Education includes: Bachelor's in Informatics and Telecommunications from University of Athens Master's in Microelectronics from University of Athens DPhil in Computer Science and Artificial Intelligence from University of Sussex Research focuses on developing novel machine learning techniques inspired by biological principles, particularly in reinforcement learning and reservoir computing methods. Her team collaborates with material scientists and engineers to design neuromorphic computing hardware. Publications demonstrate strong emphasis on neuromorphic computing, machine learning applications in neuroscience, reservoir computing systems, and computational modeling of biological processes. Recent work addresses device-agnostic modeling, physical neural networks, and stochastic computing platforms. Significant grants include: MARCH: Magnetic Architectures for Reservoir Computing Hardware (£936,815, Co-PI) ActiveAI - active learning and selective attention for robust AI (£953,584, Co-PI) Modeling probabilistic reinforcement learning in Drosophila (Google, £50,769, PI) CausalXRL: Causal explanations in Reinforcement Learning (£309,915, PI) Brains on Board: Neuromorphic Control of Flying Robots (£2,128,934, Co-PI) Leads the Machine Learning research group and collaborates with the Complex Systems Modelling group, focusing on brain-inspired computing architectures and neuromorphic hardware design.

Ozgur SinanogluView profile

Professor

• Hardware Security
• Integrated Circuit Design
• VLSI Circuits
• +7 more

Ozgur Sinanoglu is a Professor of Electrical and Computer Engineering at New York University Abu Dhabi (NYUAD) and a Global Network Professor at NYU Tandon School of Engineering. He holds a B.S. in Electrical and Electronics Engineering and Computer Engineering from Bogazici University (1999), and an M.S. and Ph.D. in Computer Science and Engineering from UC San Diego (2001, 2004). His research focuses on hardware security, design-for-trust, and VLSI circuit reliability. Education: B.S., Electrical and Electronics Engineering & Computer Engineering, Bogazici University, 1999 M.S., Computer Science and Engineering, UC San Diego, 2001 Ph.D., Computer Science and Engineering, UC San Diego, 2004 Research Interests: Design-for-Security in VLSI circuits Logic locking and hardware obfuscation Hardware Trojans and side-channel attacks Secure chip manufacturing and testing His work has led to over 200 publications and 20 patents. He directs the NYUAD Center for Cybersecurity and the Design-for-Excellence Lab, funded by NSF, DoD, Intel, and Mubadala Technology. Awards include the NYUAD Distinguished Research Award (2021) and induction into the UAE's Mohammed bin Rashid Academy of Scientists (2021). Grants and Leadership: Lead PI on NSF and DoD-funded projects Directed ACM ASIACCS 2017 in Abu Dhabi Associate Editor for IEEE TIFS, TETC, and JETC Labs and Teams: Center for Cybersecurity (CCS) Design-for-Excellence Lab (DfX)

Aakash TyagiView profile

Professor

• Hardware Verification
• Secure Computing
• High Performance Computing Architectures
• +3 more

Aakash Tyagi is a Professor of Practice in Computer Science & Engineering at Texas A&M University, affiliated with the Multidisciplinary Engineering program. His roles include academic leadership, teaching, and industry collaboration. He holds a Ph.D. in Computer Engineering from the University of Louisiana (1993), an M.S. in Electrical and Computer Engineering (1989), and a B.S. in Electronics & Communication from Kamla Nehru Institute of Technology, India (1987). His research focuses on hardware verification, secure computing, and high-performance computing architectures. He has over two decades of industry experience at Intel, contributing to CPU design and managing projects like the Knights Landing processor. Teaching interests include computer architecture, data structures, and project management. His publications emphasize innovative applications of machine learning in hardware verification, formal methods for processor security, and optimization techniques for EDA tools. Notable works include HyPFuzz (2023), MinBLoG (2024), and Spec2Assertion (2025), advancing automated verification and vulnerability detection. Tyagi has received over 15 teaching and service awards from Texas A&M, including multiple Distinguished Achievement Awards (2015–2024) and grants for instructional innovation. He actively bridges academia and industry through research partnerships and mentorship.

Martin WeyssowView profile

Researcher

• Deep Learning
• Natural Language Processing
• Software Engineering
• +5 more

Martin Weyssow is a Research Scientist (Postdoc) at Singapore Management University (SMU), affiliated with the Centre for Research on Intelligent Software Engineering (RISE). His work focuses on advancing large language models (LLMs) for software vulnerability detection, code generation, and continual learning. He holds a Ph.D. in Computer Science from Université de Montréal, where his thesis explored efficient alignment of LLMs for code generation, earning the highest distinction with an exceptional mention. Weyssow has been awarded prestigious scholarships including the Google Scholarship for Excellence and the FRQ Merit Scholarship for Foreign Students. Education : Ph.D. in Computer Science, Université de Montréal (2020–2024) M.Sc. in Computer Science & Data Science, University of Namur (2018–2020) B.Sc. in Computer Science & Mathematics, University of Namur (2015–2018) Research Interests : LLM alignment for code analysis, vulnerability detection, parameter-efficient fine-tuning, and lifelong learning systems. His work bridges deep learning, NLP, and software engineering to enhance LLM adaptability in dynamic coding environments. Grants & Awards : NSERC Alliance International Catalyst Grant (2022–2023) Google Scholarship for Excellence (2021–2022) FRQ Merit Scholarship for Foreign Students (2022–2024) Current Projects : Leading research on LLM-driven vulnerability detection and parameter-efficient techniques for code generation at RISE. Supervising students on topics like self-supervised learning and neural interface design. Active in grant-writing and international collaborations. Labs & Teams : Member of the SOftware Analytics Research (SOAR) Lab at SMU and collaborator with Prof. David Lo’s team.

Nguyen TungView profile

Associate Professor

• Software Engineering
• Mobile Application Development
• Code Analysis and Optimization
• +3 more

Nguyen Tung is an Instructional Associate Professor in the Department of Computer Science & Engineering at Texas A&M University. His research focuses on software engineering, mobile application development, and computational tools for improving code quality and developer productivity. He has developed frameworks like ALPACA for linguistic analysis of software corpora and ExAssist for exception handling recommendations. His work spans topics including API misuse correction, automated response systems for app reviews, and deep learning approaches to UI design patterns. Key research areas include: Code analysis and optimization Statistical methods for software migration Machine learning in IDEs Human-computer interaction in educational technologies He has published extensively on topics such as defect prediction, code recommendation systems, and mining visual logs of software behavior. His contributions include tools like DRC for detecting dangling references in web applications and GraPacc for graph-based code completion.

Qingwei MaView profile

Professor

• Hydrodynamics
• Fluid-Structure Interaction
• Offshore Engineering
• +12 more

Professor Qingwei Ma is a full Professor of Hydrodynamics at City, University of London, where he also serves as the Director of the Research Centre for Fluid-Structure Interaction. He holds a PhD in Ocean Engineering & Naval Architecture from University College London and has built a distinguished career in fluid dynamics and offshore engineering. His academic journey includes positions at Ocean University of Qingdao, University College London, Robert Gordon University, and City, University of London, where he has been since 2002. PhD, Ocean Engineering & Naval Architecture, University College London, 1998 MEng, Naval Architecture & Ocean Engineering, Harbin Engineering University, 1984 BEng, Naval Architecture & Ocean Engineering, Harbin Engineering University, 1982 Professor Ma’s research focuses on hydrodynamics of marine structures , nonlinear wave-structure interaction , and advanced numerical methods such as QALE-FEM, MLPG_R, and SPH. His recent work emphasizes multi-scale simulations for offshore wind turbines, wave energy converters, and floating platforms. He has developed and applied hybrid numerical models to simulate extreme wave events, vortex-induced vibrations, and fluid-structure interactions under complex environmental conditions. The 15 most recent publications reflect a strong trend toward hybrid computational methods , machine learning integration (e.g., GNN, CNN), and multi-physics coupling in offshore and renewable energy systems. His work spans journal articles, conference proceedings, and books, with a consistent focus on improving accuracy and efficiency in simulating violent wave interactions. His scientific recognitions include: CH Kim Award, ISOPE (2016) Vice-chancellor’s Award for Excellence in Learning and Teaching (2015) Chang Jiang Scholarship, China Professor Ma has supervised numerous PhD and master’s students, led major research projects, and contributed to editorial boards of journals such as Ocean Systems Engineering (Editor-in-Chief), Computer Modeling in Engineering & Sciences , and Journal of Ocean Engineering and Marine Energy . He is actively involved in international collaborations, particularly with Harbin Engineering University, where he holds a Visiting Chair Professorship. He leads the development of innovative simulation tools for offshore renewable energy systems, with applications in floating wind turbines, wave energy devices, and hybrid platforms. His lab focuses on high-fidelity numerical modelling using particle and finite element methods, with strong ties to experimental validation and industrial applications.

• Static Program Analysis
• Abstract Interpretation
• Programming Languages
• +9 more

Caterina Urban is a Research Scientist at INRIA, affiliated with École Normale Supérieure in Paris, where she is a member of the ANTIQUE research team. She holds a PhD in Computer Science from École Normale Supérieure and completed her undergraduate and graduate studies at Università degli Studi di Udine with summa cum laude honors. She previously conducted postdoctoral research at ETH Zurich. PhD in Computer Science, École Normale Supérieure, Paris, France (2015) MSc in Computer Science, Università degli Studi di Udine, Italy (2011) BSc in Computer Science, Università degli Studi di Udine, Italy (2009) Her research spans formal methods, abstract interpretation, and static analysis, with a strong emphasis on ensuring reliability, fairness, and robustness in machine learning and data science software. She leads projects such as Lyra (on data science software), Libra (fairness certification), ForML, and SAIF. Her work bridges theoretical foundations with practical tool development, targeting real-world software challenges in AI safety. Her recent publications reveal a consistent focus on applying abstract interpretation to machine learning, data leakage, fairness, and program termination. Key themes include certified robustness, input usage analysis, and perfectly parallel verification, demonstrating both theoretical depth and practical impact. She has developed open-source tools like Libra and FuncTion. Best Poster Award at SOFSEM 2011 Artifact Evaluation Badges at OOPSLA 2020 Mentored award-winning undergraduate research (Global Undergraduate Awards 2022) She advises multiple PhD and postdoctoral researchers and has supervised numerous master’s and bachelor’s students. She is actively involved in the academic community, serving on program committees for PLDI, OOPSLA, and CAV, and is the general chair of iFM 2025. She teaches advanced courses at MPRI and international summer schools on abstract interpretation and formal methods. She leads research on data science software analysis, fairness in ML, and static verification of neural networks, often in collaboration with institutions across Europe. Her lab, part of the ANTIQUE team, fosters interdisciplinary work combining formal methods with AI and software engineering.

Ravi TandonView profile

Professor

• Information Theory
• Machine Learning
• Security and Data Privacy
• +12 more

Ravi Tandon is a Professor in the Department of Electrical and Computer Engineering at the University of Arizona, where he holds a Craig M. Berge Faculty Fellowship and a courtesy appointment in Applied Mathematics. He joined the university in 2015 after serving as a Research Assistant Professor at Virginia Tech. Education: PhD in Electrical and Computer Engineering, University of Maryland, College Park, 2010 B.Tech in Electrical Engineering, Indian Institute of Technology (IIT) Kanpur, 2004 Research Interests: His research spans information theory and its applications to wireless networks, machine learning, security, and privacy. He focuses on trustworthy machine learning, privacy-preserving AI, federated learning, secure communications, and private information retrieval. His work aims to develop foundational theories and practical systems for secure, fair, and efficient AI and communication systems. Recent Research Trends: His most recent publications (2023–2025) emphasize trustworthy AI, including fairness-accuracy tradeoffs, robustness, uncertainty quantification, and privacy in generative models and causal inference. He also continues to advance information-theoretic foundations of secure communications and distributed learning. Scientific Awards: NSF CAREER Award (2017) Keysight Early Career Professor Award (2018) Best Paper Award, IEEE GLOBECOM 2011 Advising and Grants: Dr. Tandon actively supervises PhD and MS students, with former students placed at Apple, Google, Meta, NXP, and other leading tech firms. He has led significant research projects, including an NSF SaTC grant on differential privacy in graph mining. His editorial service includes roles at IEEE Transactions on Information Theory, IEEE Transactions on Wireless Communications, and IEEE Transactions on Communications. Labs and Teams: He leads a research group focused on information theory, machine learning, and privacy, collaborating with researchers in computer science, applied mathematics, and industry. His work bridges theoretical foundations with real-world AI and communication system design.

Mark SandlerView profile

Professor

• Digital Signal Processing
• Machine Learning
• Deep Learning
• +6 more

Mark Sandler is a Professor of Signal Processing and Director of the Centre for Digital Music at Queen Mary University of London. He leads the School of Electronic Engineering and Computer Science and contributes to the Centre for Multimodal AI. His work spans Digital Signal Processing, Machine Learning, and their applications in Audio and Music Technology, including source separation, neural audio synthesis, and physics-informed models. He has pioneered research in music informatics, sound synthesis, and AI-driven creativity. Awards include Fellowships from the Royal Academy of Engineering (FREng), IEEE (FIEEE), IET (FIET), and Audio Engineering Society (FAES). Research interests include audio segmentation, harmonic analysis, and interdisciplinary projects with computer vision. He has authored numerous publications on topics like neural synthesizers, knowledge distillation, and spatial audio systems. His contributions extend to semantic web technologies for music production and interactive audio applications. Current projects focus on low-latency synthesis, machine learning for music tagging, and adaptive audio systems. Awards and grants highlight leadership in digital music innovation. His academic roles include supervising research groups and directing interdisciplinary initiatives. The labs under his leadership include the Centre for Digital Music and collaborations with industry partners on audio engineering and immersive technologies.

• Machine Learning
• Computational Neuroscience
• Data Science
• +3 more

UnivProf. Dr. Fabian Sinz is a Professor at the Institute of Computer Science, University of Göttingen, heading the Machine Learning group. His research focuses on interdisciplinary applications of machine learning in computational neuroscience, data science, and neuroscience. He teaches courses such as Data Science I and leads seminars on Machine Learning and Computational Neuroscience. His work bridges theoretical machine learning with empirical neuroscience, particularly in modeling neural activity and connectomics. Research interests include neural population coding, generative models for neural data, and applying machine learning to understand brain function. Notable contributions involve developing methods like TRACE and NEURD for analyzing neural datasets and predicting visual cortex responses. Recent publications emphasize foundational models for neural activity prediction and cross-modal learning. Dr. Sinz collaborates on large-scale projects like the Dynamic Sensorium Competition, advancing reproducibility in predictive modeling of brain activity. He has advised on machine learning applications in health informatics and biomechanical reconstruction. His lab’s work frequently intersects with computer vision, computational biology, and neuroimaging technologies.

• Image Compression
• Cryptography
• Privacy-Preserving Systems
• +5 more

Ja-Ling Wu is a distinguished Professor in the Department of Electrical Engineering at National Taiwan University's College of Electrical Engineering and Computer Science. With an extensive publication record spanning over four decades (1984-2025), Professor Wu has established himself as a leading researcher in multimedia systems, image processing, and security technologies. His academic career demonstrates consistent productivity with 255 indexed publications showing no signs of slowing down, with numerous papers published in 2024 and 2025. Professor Wu's research interests center on image compression, cryptography, privacy-preserving systems, and machine learning applications. His work bridges theoretical foundations with practical implementations, particularly in multimedia security and efficient data representation. Over his career, he has developed innovative approaches to image and video coding, data hiding techniques, and secure computation methods that have influenced both academic research and industry applications. Analysis of his recent publications (2023-2025) reveals a strategic evolution of his research toward contemporary challenges in AI security, with increasing focus on learned image compression, privacy-preserving machine learning, and the integration of cryptographic techniques with deep learning systems. His work demonstrates a consistent ability to adapt to emerging technological landscapes while maintaining core expertise in multimedia processing. Professor Wu has mentored numerous students who have become active researchers in their own right, with many continuing to collaborate with him on cutting-edge projects. His leadership in the field is evident through his sustained publication output across top venues including IEEE Transactions, ACM Multimedia, and specialized cryptography conferences.