Security-Assured Reinforcement Learning Professors and Supervisors

Firat Aydemirمشاهده پروفایل

دانشیار

Dumlupınar UniversityTurkey

Assoc. Prof. Dr. Firat Aydemir holds a position as Associate Professor at the Department of Computer Engineering, Faculty of Engineering, Dumlupınar University. He completed his bachelor's in Electrical-Electronic Engineering from Gazi University (2001-2005), a non-thesis master's in Electrical Engineering from the University of Rochester (2007-2009), and a PhD in Electrical-Electronics from Dumlupınar University (2012-2017). His academic career includes roles such as Department Head (2024–present), Deputy Director of Research and Application Centers (2020–2024), and head of multiple academic committees. His research focuses on IoT, embedded systems, sensor technologies, and AI applications in healthcare and industrial automation. Notable projects include developing a lung cancer diagnosis system via breath analysis, license plate recognition using deep learning, and IoT-based smart systems for education continuity during pandemics. He has authored over 24 peer-reviewed publications and holds an award for the 5th LED Competition (2014). Aydemir teaches courses on microcontrollers, embedded systems, circuit theories, and IoT applications. His work spans academic leadership, grant coordination, and interdisciplinary research collaborations.

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Pierluigi Nuzzoمشاهده پروفایل

دانشیار

University of California, BerkeleyUnited States

Pierluigi Nuzzo is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on high-assurance design of cyber-physical systems, including methodologies for AI, autonomous systems, secure hardware, and electronic design automation. He holds faculty positions at Berkeley and previously at the University of Southern California, where he co-directed the Center for Autonomy and Artificial Intelligence. Education: Ph.D. (UC Berkeley, 2015), M.S./B.S. (University of Pisa and Sant'Anna School) Affiliations: Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Wireless Research Center (BWRC), Industrial Cyber-Physical Systems Center (iCyPhy) Research interests span compositional methods, system design, and certification frameworks. His work integrates formal verification, optimization, and machine learning to ensure safety and trustworthiness in complex systems. Recent projects include DeFacto (cyber-physical production systems) and secure logic locking for ICs. Awards include the NSF CAREER Award, DARPA Young Faculty Award, and multiple best paper awards. His teaching includes EECS C249B on cyber-physical system design principles.

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Marzia Cesconمشاهده پروفایل

استادیار

University of HoustonUnited States

Dr. Marzia Cescon is an Assistant Professor at the Department of Mechanical and Aerospace Engineering, College of Engineering, University of Houston. She holds a Ph.D. in Control Engineering from Lund University and has held postdoctoral and research roles at Harvard, the University of Melbourne, and the Sansum Diabetes Research Institute. Ph.D. in Control Engineering, Lund University (2014) Tech. Lic. in Control Engineering, Lund University (2011) M.Sc. in Automation Engineering, University of Padova (2008) B.Sc. in Information Engineering, University of Padova (2005) Her research focuses on learning-based control systems for biomedical applications, aerospace, and multi-agent systems. Key areas include data-driven modeling , reinforcement learning , and safe autonomous systems . Her recent publications emphasize predictive control algorithms for diabetes management (e.g., artificial pancreas systems) and adaptive control in aerospace applications. Keywords include machine learning , control engineering , and biomedical systems . NSF CAREER Award (2024) Cornelis Drebbel Faculty Fellowship (2023-2024) Diabetes Technology Society Awards (Bronze, Silver, Gold as advisor) Dr. Cescon has secured over $1.3 million in grants from the National Science Foundation , NASA , AFRL , and industry partners. She leads projects on real-time assurance for autonomous systems and digital twins for physiological modeling.

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Bandaru Sunithمشاهده پروفایل

دانشیار

University of SkövdeSweden

Bandaru Sunith is an Associate Professor at the University of Skövde's School of Engineering Science, with previous affiliation to The Virtual Systems Research Centre (closed May 2017). His research spans multi-objective optimization, digital twins, and knowledge-driven decision support systems for manufacturing applications. He actively leads the Virtual Factories with Knowledge-Driven Optimization (VF-KDO) research profile and contributes to the ADOPTIVE project focused on vehicle ergonomics optimization. Dr. Sunith's research interests include: Multi-objective optimization and evolutionary algorithms Digital twin frameworks for manufacturing systems Knowledge discovery and visualization for decision support Anomaly detection in industrial processes Factory layout optimization integrating human well-being metrics His recent publications (2023-2025) demonstrate a clear trend toward integrating advanced AI techniques with traditional optimization methods. The research increasingly focuses on practical industrial applications, particularly in automotive manufacturing, with strong emphasis on translating theoretical advances into usable tools. His work bridges the gap between computational optimization and real-world manufacturing challenges, often incorporating human factors considerations. Dr. Sunith has secured significant research funding from Swedish innovation agencies: Virtual Factories with Knowledge-Driven Optimization (VF-KDO) funded by Knowledge Foundation (Grant 2018-0011) Integrated Manufacturing Analytics Platform for Predictive Maintenance (IMAP) funded by Vinnova (Grant 2021-02537) LITMUS project on human-centric sustainable production funded by Knowledge Foundation He has supervised multiple researchers including Henrik Smedberg and Mahesh Kumbhar, resulting in collaborative publications and the development of practical tools like Mimer - a web-based platform for knowledge discovery in multi-criteria decision support. His research group maintains strong industry connections, particularly with automotive manufacturers in Sweden.

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Alessandro Savinoمشاهده پروفایل

دانشیار

Polytechnic University of TurinItaly

Alessandro Savino is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) of Politecnico di TORINO. He serves as an academic advisor for Bachelor’s and Master’s degree programs in Computer Engineering (Ingegneria Informatica) and contributes to PhD programs in Artificial Intelligence and Computer Engineering. Research Interests: Approximate computing, Cybersecurity (including automotive systems), Dependability, Parallel computing, Reliability analysis, and Neuromorphic architectures. Key Projects: Leads RESCHIP4EU (2024-2028), NEUROPULS (2023-2027), and commercial contracts focused on real-time OS validation and avionics design. Publications: Recent work spans hardware security (e.g., VeriSide for leakage assessment), spiking neural networks (SpikeExplorer, SpikingJET), and automotive cybersecurity (CARACAS, CAN-MM). Teaching: Instructs courses on Parallel and Distributed Computing, Hardware & Wireless Security, and System Programming across Politecnico di TORINO and Scuola IMT Alti Studi - LUCCA. Research Group: Leads the SMILIES group, focusing on resilient computer architectures and life sciences.

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Constandinos Mavromoustakisمشاهده پروفایل

استاد

University of NicosiaCyprus

Constandinos Mavromoustakis is a Professor at the Department of Computer Science, University of Nicosia (School of Sciences and Engineering). He leads the Mobile Systems Lab (MOSys Lab) and holds leadership roles in IEEE, including Vice-Chair of the Cyprus Section and Chair of the Computer Society Chapter. He actively contributes to IEEE Communications Society committees and standardization groups like IEEE-SA SCC42 WG2040. Education includes: Dipl.Eng in Electronic and Computer Engineering from Technical University of Crete MSc in Telecommunications from University College London PhD from Aristotle University of Thessaloniki His research integrates Mobile Systems, IoT, and Wireless Communications, with emphasis on: 5G/6G network security using AI-driven strategies Resource optimization in cloud-edge ecosystems UAV-assisted networks for critical infrastructure Healthcare applications via IoMT and data analytics Recent publications (2024-2025) demonstrate strong focus on securing next-gen networks (O-RAN, 6G), optimizing edge computing for VR/IoT, and advancing smart healthcare/cities. Dominant themes include Deep Reinforcement Learning, quantum-inspired optimization, and latency minimization techniques. He participates in EU-funded initiatives including FP7, H2020, Eureka, and national projects. Industrial collaborations include consultancy for Intel Corporation. He directs the MOSys Lab, which researches mobile systems, IoT interoperability, and network security, with projects spanning UAV communications, medical IoT, and sustainable computing.

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Dragos Truscanمشاهده پروفایل

استاد

Abo Akademi UniversityFinland

Prof. Dragos Truscan is a Professor of Computer Science at the Faculty of Natural Sciences and Engineering within Åbo Akademi University , focusing on Information Technology and Mission Critical Software Systems . Research interests include validation and verification , automated test generation using model-based technology and AI , test left-shifting , and runtime monitoring of industrial software systems Active in Security Requirements Engineering , DevOps , Deep Learning , and Software Component analysis Recent work explores: 2025 - Reinforcement learning for security testing and visual fault localization 2024 - Explainability in network attack detection and hyperparameter optimization 2023-2024 - Model-based requirements engineering in large collaborative projects Scientific Recognition Best Paper Award (2024) Best Paper Award (2013) Academic Activities Organizing member of IEEE International Conference on Software Services Engineering (2025) Hosted scholars like Victoria Zsók and Dr. Dumitru Iulian Nastac

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Antonio Filieriمشاهده پروفایل

دانشیار مهمان

Max Planck Institute for Security and PrivacyGermany

Antonio Filieri is a Senior Applied Scientist at Amazon Web Services (AWS) and holds a Visiting Associate Professor position at the Department of Computing, Imperial College London. Previously, he was a tenured Associate Professor at Imperial College London (2022-2024) and Assistant Professor (2016-2022), and served as Assistant Professor at the University of Stuttgart between 2013 and 2015. His academic career spans over a decade with significant contributions to software engineering research. Dr. Filieri's research focuses on formal mathematical methods for software design, verification, self-adaptation, and security. His primary research areas include static analysis, privacy, and automated test generation for security; exact and approximate methods for probabilistic program analysis; control theory for adaptive software; quantitative verification and model checking; and runtime-efficient and incremental verification. His work bridges theoretical foundations with practical applications in industry settings, particularly in cloud computing and security domains. His recent publications demonstrate a strong focus on probabilistic methods for software analysis, security testing, and performance modeling. The research trends show increasing integration of formal methods with machine learning techniques, particularly in test oracle generation and neural network analysis. There's also a clear emphasis on scalability and practical applicability of verification techniques to real-world systems like serverless computing and microservices architectures. Dr. Filieri has received numerous prestigious awards for his contributions: Best Student Paper Award (2025) for 'Robust Probabilistic Model Checking with Continuous Reward Domains' ACM Distinguished Paper Award (2023) for 'Sibyl: Improving Software Engineering Tools with SMT Selection' Best Paper Award (2022) for 'Enhancing Performance Modeling of Serverless Functions via Static Analysis' Best Artifact Award (2017) for 'Self-adaptive video encoder: comparison of multiple adaptation strategies made simple' Most Influential Paper Award (awarded at SEAMS 2025) for 'Software Engineering Meets Control Theory' ACM SigSoft Distinguished Paper Award (2011) for 'Run-time Efficient Probabilistic Model Checking' Dr. Filieri has advised several PhD students including Donato Clun (2024), Runan Wang (2024), and Xiaotong Ji (expected 2025). His advising focuses on probabilistic program analysis, automated testing, and security verification. His research has been supported by significant grants from both academic and industry sources, enabling collaborations across multiple institutions and contributing to advancements in software engineering practices. While specific lab information isn't prominently featured in the provided materials, Dr. Filieri's work suggests strong connections with research groups focused on formal methods, software verification, and adaptive systems at both Imperial College London and AWS. His research often involves interdisciplinary collaboration between theoretical computer science and practical software engineering challenges.

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Janaka Senanayakeمشاهده پروفایل

مدرس

Robert Gordon UniversityUnited Kingdom

Dr. Janaka Senanayake is a Lecturer in Cybersecurity at Robert Gordon University (RGU), attached to the School of Computing, Engineering and Technology. He is affiliated with the Cybersecurity Research Group and contributes to the university's 'Living in a Digital World' research theme. Dr. Senanayake completed his PhD in Cybersecurity at RGU, specializing in software security. He earned a Bachelor of Science (Special) in MIT with First Class Honors from the University of Kelaniya, Sri Lanka in 2017. Prior to joining RGU, he worked as a Software Engineer at DirectFN Pvt. Ltd. and NDB Bank Information Technology and R&D Department for approximately three years, and also served as a lecturer and researcher at several international universities. His research focuses on cybersecurity, software security, mobile computing, artificial intelligence, and software engineering. Dr. Senanayake has published extensively on Android security, AI-powered vulnerability detection, and network security solutions. His recent work demonstrates a strong emphasis on practical security applications using advanced AI techniques while addressing privacy concerns through approaches like federated learning. Dr. Senanayake has produced 23 publications from 2021-2025, with a significant concentration of high-impact work in 2024-2025. His research output shows a clear trajectory toward integrating cutting-edge AI technologies with practical cybersecurity applications, particularly in mobile and network security domains. His notable contributions include: MADONNA: A browser-based malicious domain detection system using optimized neural networks FedREVAN: Real-time detection of vulnerable Android source code through federated neural networks with XAI DroidKey: A framework for API key security in Android applications Research on integrating large language models for automated vulnerability scanning Dr. Senanayake is actively involved in PhD supervision with research topics including Software Security, Cybersecurity, Security and Artificial Intelligence, Mobile Security, and Law and AI. His industry background combined with academic expertise provides a valuable practical perspective to his research and teaching. He maintains active research collaborations, as evidenced by his numerous co-authored publications with researchers from different institutions, particularly focusing on cybersecurity challenges in both Western and Sri Lankan contexts.

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Michael Pradelمشاهده پروفایل

استاد

Max Planck Institute for Biology TübingenGermany

Michael Pradel is a full professor in the Computer Science Department at the University of Stuttgart and faculty member at CISPA Helmholtz Center for Information Security (effective September 2025), where he leads the Software Lab. He is also affiliated with the International Max Planck Research School for Intelligent Systems and the Stuttgart ELLIS Unit, reflecting his interdisciplinary research approach. His research interests focus on software engineering, particularly program analysis, bug detection, and the application of machine learning to developer tools. Pradel's recent work increasingly explores LLM-based approaches for program repair, code analysis, and automated software development, as evidenced by projects like RepairAgent and ExecutionAgent. Pradel's publication record shows a clear trend toward integrating AI techniques with traditional software engineering methods, with recent papers focusing on LLM applications for program repair, change validation, and quantum software analysis. His work bridges theoretical foundations with practical tool development, as seen in frameworks like DyLin for Python analysis and LintQ for quantum programs. Ernst-Denert Software Engineering Award Emmy Noether grant (1.3 million Euro) by the DFG ERC Starting Grant (1.5 million Euro) Multiple ACM SIGSOFT Distinguished Paper Awards ACM Distinguished Member recognition Pradel actively mentors PhD students, with recent graduates including Matteo (specializing in quantum software) and Luca (focusing on software evolution). His group has received significant funding and maintains strong industry connections, including past sabbaticals at Facebook. He serves in leadership roles for major conferences, including PC co-chair for FSE 2027, demonstrating his standing in the software engineering community. The Software Lab maintains active collaborations with institutions worldwide, including CMU, Google, KAIST, and several European universities.

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Zhou Yangمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Zhou Yang is an Assistant Professor at the University of Alberta and Fellow at the Alberta Machine Intelligence Institute (Amii), with research focusing on the intersection of software engineering and artificial intelligence. His academic journey includes a Ph.D. from Singapore Management University, an M.Sc. in Software System Engineering from University College London, and undergraduate studies at Yangzhou University. His research interests span Software Engineering, Artificial Intelligence, AI for Software Engineering (AI4SE), Software Engineering for AI (SE4AI), Large Language Models, and Cybersecurity. Yang's work explores how human and AI collaboration can improve code writing, how AI impacts open-source communities, and how developers build software in emerging environments like VR/AR. His recent publications demonstrate strong trends in code language models, with significant contributions to ASE, ICSE, ISSTA, and top journals like TOSEM and TSE. His research addresses critical challenges including token efficiency in code generation, user perception of AI coding assistants, privacy preservation in code models, and fairness in AI systems. 2024 IEEE Computer Society Best Paper Award (1 out of 183 submissions) ACM Distinguished Paper Award from ISSTA 2024 Distinguished Reviewer Award from Internetware 2024 SMU Research Staff Excellence Award (1 of 4 university-wide) SMU Presidential Fellowship Award SMU Dean's List Award 1st Place in ACM Student Research Competition at ICSE 2024 Yang actively mentors graduate students with regular one-on-one meetings, constructive feedback, and support for top-venue publications. He provides full funding through teaching assistantships and research grants, including travel support for conferences. His lab focuses on responsible research conduct and societal implications of AI work. Though early-career, he actively builds professional networks for students through collaborations and supports diverse career paths in academia and industry. He leads research in the Alberta Machine Intelligence Institute, focusing on practical applications where software engineering principles enhance AI systems and where AI techniques solve real-world software engineering challenges.

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Luciano Baresiمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Luciano Baresi is a Full Professor at the Polytechnic University of Milan (Politecnico di Milano), Italy, affiliated with the Department of Electronics, Information and Bioengineering. He earned his laurea (MSc) and PhD in Computer Science from the same institution and has held visiting positions at the University of Oregon (USA), Tongji University (China), and the University of Paderborn (Germany). His research spans software engineering, with current focuses on self-adaptive systems, edge computing, and AI/ML-based software. His work integrates formal methods with practical applications, emphasizing autonomous systems, cloud-edge continuum, and federated learning. Recent publications highlight AI-driven advancements in software testing, resource optimization, and educational tools. Key research themes include: AI/ML for autonomous driving testing and data augmentation Serverless computing at the edge Federated learning system architectures Containerization and cloud resource management Awarded for impactful contributions: RE 2020 Most Influential Paper ICSOC 2020 Best Paper SEAMS 2022 Best Paper He advises 14+ PhD students and leads projects like Ketonet (health app), WHO's Essential Items Estimator, and dynaSpark. As Editor-in-Chief of Proceedings of the ACM on Software Engineering and senior editor for multiple journals, he shapes academic discourse in adaptive systems and software engineering.

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