Find Academic Supervisors

Manel AbdellatifView profile

Professor

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +7 more

Manel Abdellatif is a Professor in the Department of Software Engineering and IT at École de Technologie Supérieure (ETS) in Montreal, Canada. Her research spans trustworthy AI, service computing, and software maintenance/evolution, with significant contributions to software engineering conferences including ASE (Tool Demo PC Member 2025), ICSE (Awards Chair 2025), and DeepTest (Committee Member 2026). She actively publishes in top venues like IEEE TSE and ACM TOSEM with recent work on deep learning safety and microservice modernization. Dr. Abdellatif holds an M.Sc. from ÉTS and a Ph.D. from Polytechnique Montréal. Her doctoral research focused on service identification approaches for legacy system migration to SOA, establishing her expertise in software modernization. She maintains strong industry connections through applied projects addressing real-world challenges in system migration and digital transformation. Her research program centers on AI system reliability and software modernization , with three interconnected thrusts: (1) Safety monitoring and testing of deep reinforcement learning systems (SMARLA framework), (2) Microservice migration including antipattern detection and service identification, and (3) Legacy system modernization using machine learning and semantic analysis. She combines empirical studies with practical tool development, often collaborating with industry partners on real systems. Analysis of her 15 most recent publications reveals a clear trajectory toward ensuring trustworthy behavior in AI systems while addressing architectural challenges in modern software. Her work bridges theoretical advances in deep learning testing with practical solutions for microservice adoption, showing increasing focus on formal methods for safety guarantees in autonomous agents. Dr. Abdellatif supervises a dynamic research group with 9 current graduate students working on cutting-edge topics including federated learning anti-patterns (Amirhossein Roudgar), AI-to-microservice migration (Hakim Ghlissi), and deep learning test optimization (Hatem Feki). Her supervision spans both theoretical research (master's theses) and industry-applied projects addressing concrete modernization challenges. Her work is conducted within ETS's Software Systems, Multimedia and Cybersecurity research axis, where she collaborates with colleagues on software quality, IoT systems, and empirical engineering methods. The department's strong industry connections enable her team to validate approaches on real-world systems while maintaining academic rigor.

Huaming ChenView profile

Senior Lecturer

• Software Engineering
• AI
• Cybersecurity
• +3 more

Huaming Chen is a Senior Lecturer at the School of Electrical and Computer Engineering, The University of Sydney (equivalent to Associate Professor in the US system). He specializes in trustworthy AI, software security, and computational biology. His research focuses on integrating AI techniques into software engineering and scientific domains to enhance system reliability and ethical compliance. Currently, he advises PhD and master's students on projects involving AI in intelligent building systems, blockchain-driven governance frameworks, and open-source AI security. He holds roles as Area Chair for ACM MM, PC member for venues like ACM CCS and IJCAI, and Guest Editor for Computers & Security . His service extends to IEEE/ACM memberships and discipline expertise in Software Engineering and Electrical & Computer Systems. Awards include the 2020 IEEE CIS Student Grant and 2017 travel fellowships for bioinformatics and services computing conferences. Research interests span adversarial machine learning, AI ethics, computational biology applications, and secure AI system design. Key contributions include work on federated learning security, adversarial attacks on vision transformers, and privacy-aware STI/HIV risk prediction models. He actively promotes responsible AI through workshops and interdisciplinary collaborations.

• Music Processing
• Music Information Retrieval
• Audio Signal Processing
• +1 more

Prof. Dr. Meinard Müller is a Professor at Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), leading the Chair of Semantic Audio Signal Processing within the International Audio Laboratories Erlangen (a joint institute with Fraunhofer IIS). He specializes in Music Processing, Music Information Retrieval, Audio Signal Processing, and Motion Processing. His work bridges fundamental research and practical applications in music technology, including audio decomposition, source separation, and computational musicology. Research Interests: Focus on algorithmic methods for music analysis, audio feature extraction, and real-time audio processing. Teaching: Supervises a large group of doctoral and master’s students, offering courses on audio processing and music technology. Awards: Recognized as an IEEE Fellow (2020) and ACM Senior Member (2020). His research group develops open-source tools like the FMP Notebooks and libfmp, and maintains datasets such as the Wagner Ring Dataset and Dagstuhl ChoirSet. He actively contributes to conferences like ISMIR and ICASSP, with over 200 publications in top-tier journals and conferences.

• Aerospace Engineering
• Structural Engineering
• Materials Science
• +6 more

Gunnar Tibert is an Associate Professor at KTH Royal Institute of Technology's School of Aerospace, Moveability, and Naval Architecture. He specializes in deployable structures for aerospace applications, with a focus on bistable composites, tensegrity systems, and space deployment mechanisms. His roles include examiner and course responsible for courses like Spacecraft Dynamics and Project in Aerospace Engineering . Education: Ph.D. in Deployable Tensegrity Structures for Space Applications (KTH, 2002), Licentiate in Numerical Analyses of Cable Roof Structures (KTH, 1999). Research Interests: Structural dynamics, composite materials, space system design, and vibration suppression in deployable systems. His work spans both peer-reviewed articles and experimental studies, including sounding rocket experiments for space web deployment. Recent publications focus on planetary sunshade systems for climate engineering, metamaterials for vibration control, and additive manufacturing in satellite components. He collaborates on projects like the Suaineadh space web experiment and B2D2 composite boom deployment. Notable contributions include form-finding methods for tensegrity structures and material characterization for bistable tape springs. His research combines experimental testing, numerical simulations, and aerospace engineering principles to advance deployable space technologies.

Costas VassilakisView profile

Professor

• Information systems
• Semantic web technologies
• Service-oriented architectures
• +4 more

Costas Vassilakis is a Professor in the Department of Informatics and Telecommunications at the University of Peloponnese, serving as Dean of the Faculty of Economics and Technology since 2019. He holds a PhD from the University of Athens (1995) and has held academic roles since 2005, including Associate Professor and Visiting positions. His research focuses on information systems, semantic web technologies, and cultural informatics, with contributions to projects like Virtual Museums and SmartGov. He leads initiatives in digital cultural heritage, cybersecurity, and smart agriculture. Notable projects include Experimedia Blue for personalized cultural experiences and Cyber-Trust for IoT security. Active in teaching, he has instructed courses on operating systems, software engineering, and real-time systems. He contributes to academic governance, including roles in the University Council and strategic planning units. His work bridges technology and cultural preservation, emphasizing interdisciplinary innovation. Education: PhD in Informatics, University of Athens (1995); MSc equivalent in Informatics, University of Athens (1990). Research Interests: Focuses on recommenders systems, collaborative filtering optimization, cybersecurity frameworks, and AI applications in agriculture. Active in cultural heritage digitization, museum technologies, and VR systems. Recent Projects: Includes TripMentor (tourism platform), PaloAnalytics (market analytics), and Rehabotics (rehabilitation robotics). Leads EN.I.R.I.S.S.T., a national infrastructure for maritime and logistics research. Awards: No explicit awards listed, but recognized through extensive project leadership and academic roles.

• Software Engineering
• Computer Security
• Machine Learning
• +5 more

Yun Lin is a researcher affiliated with Shanghai Jiao Tong University, with a former position at the National University of Singapore's School of Computing. Their work focuses on software engineering, computer security, and machine learning. Key research interests include formal methods for program verification, adversarial machine learning, phishing detection, and deep learning applications in security. Lin has collaborated extensively with institutions like NUS and the Institute of High Performance Computing, producing over 70 publications since 2014 across venues such as ICSE, USENIX Security, and NeurIPS. Research interests span security (e.g., phishing detection, smart contract vulnerabilities), program analysis (bug localization, delta debugging), and AI-driven software development (code editing recommendations, LLM integration). Their contributions address challenges in software reliability, adversarial robustness, and scalable security solutions. Prominent publications include works on automated program refinement with LLMs, phishing webpage identification via deep vision models, and conflict resolution in version control systems. Lin’s collaborations emphasize interdisciplinary approaches blending formal methods with machine learning. No awards or grants are explicitly listed in the provided text.

Pooja RaniView profile

Research Fellow

• Empirical Software Engineering
• Green Software Engineering
• Natural Language Processing
• +4 more

Pooja Rani is a Senior Researcher at the Software Evolution and Architecture Lab (SEAL), Department of Informatics, University of Zurich. She holds a PhD from the University of Bern (2022) and an M.E. in Software Systems from BITS Pilani (2017). With industry experience at Samsung, VMware, and People Interactive Ltd., she bridges theoretical and practical software engineering. Her research focuses on: Empirical Software Engineering, particularly developer practices in code comprehension and maintenance. Green Software Engineering, including energy anti-patterns and sustainability tooling. NLP and machine learning applications in software evolution, documentation quality, and polyglot environments. AI-driven approaches for debugging, testing, and optimization. Her publications emphasize energy efficiency in software systems, AI-assisted development tools, code comment analysis, and metamorphic testing. Recent work explores LLMs for sustainable coding, object-centric debugging, and autonomous system validation. She actively advises students, with ongoing supervision of 15+ BSc/MSc theses on topics like comment quality, energy anti-patterns, and polyglot development. She collaborates internationally with institutions including TU Delft, Inria, and the University of Victoria.

Balder ten CateView profile

Associate Professor

• Logic in computer science
• Data management
• Knowledge representation
• +5 more

Balder ten Cate is an Associate Professor at the University of Amsterdam, where he leads the Theoretical Computer Science research unit within the Institute for Logic, Language and Computation (ILLC). His academic journey includes previous positions at INRIA, UC Santa Cruz, Stanford, LogicBlox, and Google. He maintains active collaborations across institutions including the University of Bergen (co-advising PhD students) and Tsinghua University. Primary Affiliation: Theoretical Computer Science (TCS) Secondary Affiliation: Mathematical & Computational Logic (MCL) Office: Room L6.38, LAB42, Science Park 900, Amsterdam Dr. ten Cate's research spans diverse applications of logic in computer science and AI, with particular emphasis on data management, knowledge representation, and machine learning. His work bridges theoretical foundations with practical applications, focusing on how logical frameworks can enhance data systems and AI capabilities. He has developed significant contributions in finite model theory, database theory, and computational learning theory, with recent work exploring connections between universal algebra and learning theory. His publication record shows a strong trajectory in theoretical computer science, with recent papers focusing on query algorithms, interpolation in logical fragments, and the learnability of database queries. The research demonstrates consistent contributions at top venues including PODS, ICDT, IJCAI, and ACM Transactions journals. His work on 'Extremal Fitting Problems for Conjunctive Queries' received the ACM PODS 2023 Best Paper Award, while 'SAT-Based PAC Learning of Description Logic Concepts' earned an IJCAI 2023 Distinguished Paper Award. 2024 ETAPS Best Paper Nomination 2023 ACM SIGMOD Research Highlights Award 2023 Alberto Mendelzon Test-of-Time Award ICDT 2012 Best Paper Award 2006 EACSL Ackermann Prize for best dissertation Dr. ten Cate actively supervises PhD and MSc students, with recent theses covering topics like modal formula characterization, conjunctive queries, and temporal logic. He serves on numerous program committees including PODS 2025 and ICALP 2024, and has organized workshops on learning and logic. His current MSCA European Re-Integration Fellowship 'LLAMA: Logic and Learning: an Algebra and Finite Model Theory Approach' (2021-2025) demonstrates ongoing research leadership. He also contributes to academic service through committee memberships including the ASL Committee on Education and the editorial board of the Journal of Logic, Language and Information.

Thaddeus RoppelView profile

Associate Professor

• Robotics
• Sensor Fusion
• Artificial Intelligence
• +7 more

Thaddeus Roppel is an Associate Professor in the Department of Electrical and Computer Engineering at Auburn University’s College of Engineering. He conducts research in robotics, sensor fusion, artificial intelligence, and MEMS sensors, and actively mentors graduate students in the Sensor Fusion Laboratory. He teaches courses in electronics, IC fabrication, wireless design, and advanced mobile robotics, and contributes to outreach programs including K-12 robotics and Engineers Without Borders. Ph.D., M.S., B.S. in Electrical Engineering — Michigan State University Dr. Roppel’s research focuses on integrating data from heterogeneous sensors to enhance robotic perception and decision-making. His work spans cooperative robotics, neural network-based odor classification, and real-time sensor fusion inspired by biological systems. He emphasizes hands-on learning and innovation in both research and teaching. His recent scholarly output centers on intelligent systems, including neural processing of sensor data, robotics for space and agriculture, and educational technologies. These works reflect a strong interdisciplinary focus on AI, embedded systems, and practical engineering solutions for real-world challenges. Recurrent neural networks for odor recognition Cooperative and mobile robotics Technology Readiness Level (TRL) frameworks in academic projects MEMS and sensor integration Capstone design and international collaborations Educational tools for electronics and simulation Dr. Roppel has advised multiple graduate students, including Ph.D. and Master’s candidates, and leads the Sensor Fusion Laboratory at Auburn. He has secured academic and project-based funding for student research in robotics and sensor systems. His outreach includes organizing robotics competitions and promoting engineering education through digital and physical initiatives. The Sensor Fusion Laboratory, under his direction, explores advanced integration of sensor data for applications in environmental monitoring, defense, and autonomous systems. The lab supports graduate research, senior design projects, and collaborative initiatives with national and international institutions.

• Structural Engineering
• Earthquake Engineering
• Bridge Safety
• +3 more

Georgios Balomenos is an Adjunct Assistant Professor in Civil Engineering at McMaster University. His academic work focuses on structural reliability, infrastructure resilience, and probabilistic modeling of structural systems under extreme loads. With over 50 publications in the last decade, he maintains an active research profile in civil engineering mechanics. Dr. Balomenos's research interests span several key areas of structural engineering: Earthquake engineering and seismic vulnerability assessment Bridge safety and infrastructure durability modeling Probabilistic analysis of structural systems Multi-hazard impact assessment Structural component behavior under extreme loads Infrastructure management using advanced computational methods His work consistently addresses complex challenges in structural reliability through innovative analytical frameworks. Analysis of Dr. Balomenos's recent publications reveals a strong focus on developing computational frameworks for infrastructure vulnerability assessment. His work frequently employs probabilistic methods, finite element analysis, and machine learning techniques to model structural behavior under diverse hazard scenarios including earthquakes, blasts, fires, and coastal hazards. The publications demonstrate consistent application of fragility modeling across different infrastructure systems. In teaching, Dr. Balomenos has instructed several core civil engineering courses including: Engineering Risk and Reliability (CIVENG 707, 2020-2022) Engineering Mechanics: Dynamics (CIVENG 2Q03, 2020-2022) Structural Dynamics and Seismic Design (CIVENG 4DD4, 2021-2022) Specialized Studies in Civil Engineering (CIVENG 704, 2020) Seismic Design of Structures (CIVENG 4ED4, 2019) This demonstrates substantial involvement in both fundamental mechanics and specialized structural engineering instruction.

Eva RakovskáView profile

Assistant Professor

• Knowledge Management
• Artificial Intelligence
• Expert Systems
• +3 more

Eva Rakovská is an Assistant Professor at the Department of Applied Informatics , Faculty of Economic Informatics , University of Economics in Bratislava . Her work spans Knowledge Management , Artificial Intelligence , and E-learning in public administration contexts. Academic Affiliation: Faculty of Economic Informatics, University of Economics in Bratislava Research Focus: Knowledge Management, Expert Systems, Fuzzy Logic, E-learning Platforms Key Grants: VEGA 1/0261/10, VEGA 1/0373/18, KEGA 015EU-4/2021 Her publications since 2004 reveal a consistent focus on Knowledge Management and Artificial Intelligence in public sector contexts, with recent trends emphasizing Fuzzy Logic for software usability and Explainable AI . She has contributed to LMS system design and inclusive education technologies for disabled students. While no explicit awards are listed, her work has been cited in international journals (e.g., International Journal of Applied Mathematics and Computer Science ) and conferences. She has collaborated extensively in academic editing and conference organization for events like AIESA and DisCo .

Fengjun LiView profile

Professor

• Computer Security
• Network Security
• Privacy
• +5 more

Fengjun Li is a Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas. She leads the KU Privacy and Security (KUPS) Lab, which is part of the High Assurance and Secure System Center at the Institute for Information Sciences. Dr. Li also serves as the director of the Midwest VICEROY Institute (MVI) and is the point-of-contact for the CAE-CD and CAE-R Centers at the University of Kansas. Dr. Li's research spans a broad spectrum of topics related to computer and network security, with current focus on AI security and privacy (adversarial ML attacks and defenses, privacy-preserving federated learning), and cyber-physical system security including IoT and smart grid security. Her work bridges theoretical security concepts with practical applications across emerging technologies. She has published extensively in top security venues including ACM CCS, NDSS, ESORICS, and IEEE transactions. Her recent publications (2022-2025) demonstrate leadership in cutting-edge security challenges, particularly in adversarial machine learning, AI-generated content detection, and privacy-preserving deep learning techniques. Analysis of her publication trends shows increasing focus on AI security as these systems become more integrated into critical infrastructure. Scientific Recognition Best Paper Award at ICPR 2022 for 'Aggregating global features into local vision Transformer' Distinguished Paper Award at ACSAC 2021 for 'Two Souls in an Adversarial Image' Best Paper Award at SecureComm 2015 for 'Enhancing Traffic Analysis Resistance for Tor Hidden Services with Multipath Routing' Research Leadership and Mentoring Dr. Li has advised numerous PhD and Master's students, with current PhD candidates including Zhaohui Wang, Ye Wang, Pramil Paudel, Sumit Bhattarai, and Liangqin Ren. Her former students include Sana Awan (now Assistant Professor at University of Wisconsin Whitewater), Krushi B. Patel (Research Scientist at Spectral AI), and Lei Yang (Research Scientist at Amazon). Her research has been supported by NSF, NSA, and Ripple University Initiatives funding. Research Infrastructure As director of the KU Privacy and Security Lab, Dr. Li oversees research on privacy and security problems in distributed systems dealing with large-scale information collection and sharing. The lab investigates security challenges across computer networks, AI systems, cyber-physical systems, and online social networks. Through the Midwest VICEROY Institute, she facilitates broader cybersecurity research collaboration across regional institutions.

• Artificial General Intelligence (AGI)
• Data Mining
• Social Networks
• +2 more

Jie Tang is a Professor at the Department of Computer Science, Tsinghua University , and a Fellow of ACM, AAAI, IEEE . His research focuses on Artificial General Intelligence (AGI) , with significant contributions to large pre-trained models like GLM-130B, ChatGLM, CogView, CogVideo, and CodeGeex. Research Trends : Jie Tang's work spans AGI development with human-like reasoning Graph Neural Networks for network representation Social network mining and influence modeling Academic knowledge graph construction (AMiner system) Advancing foundation models for cross-modal tasks Scientific Awards : SIGKDD Test-of-Time Award SIGKDD Service Award NSFC Distinguished Young Scholar 2nd National Award for Science & Technology Advising and Grants : He mentors highly-motivated students and postdocs in AGI research. His work has received extensive funding and recognition, including over 400 publications in top conferences (IJCAI, AAAI, NeurIPS, KDD) and journals (TPAMI, TKDE).

Luca SpalazziView profile

Associate Professor

• Computer Science
• Cybersecurity
• Blockchain Technology
• +5 more

Luca Spalazzi is an Associate Professor at the Department of Information Engineering , Università Politecnica delle Marche , Italy. His research spans multiple domains including cybersecurity , blockchain technology , machine learning , and telerehabilitation systems for Parkinson's disease. He applies formal methods to software verification and security analysis, with a focus on real-time systems and distributed architectures . Key research areas: Cybersecurity, Blockchain, Machine Learning, IoT, Formal Verification Recent work: Blockchain-based sustainable supply chains, Zero-Knowledge Proofs, Smartphone health monitoring His publications (2013-2025) demonstrate expertise in malware detection , smart contract verification , and AI-driven health solutions . Articles include BRAIN 2024 workshop organization and RAPIDO system for Parkinson's telerehabilitation.

• Parasitology
• Applied Ecology
• Environmental Parasitology
• +4 more

Dr. Milen Nachev is a Scientific Assistant at the Faculty of Biology , University of Duisburg-Essen , specializing in Aquatic Ecology . His research spans Environmental Parasitology , Ecotoxicology , and Biodiversity , focusing on host-parasite interactions and contaminant uptake by parasites. Key research areas include: Parasite diversity, evolution, and transmission dynamics Bioaccumulation and bioindication of heavy metals Stable isotope analysis for trophic structure studies Assessment of pollution impacts on aquatic ecosystems He employs advanced analytical techniques such as ICP-MS , AAS , IRMS , and molecular barcoding to investigate metal contamination in food webs (e.g., Rhine River, Orange-Vaal Basin) and parasite roles in pollution monitoring. His publications highlight multi-stressor effects on fish, parasite-specific accumulation patterns, and methodological innovations in isotope analysis.