Rob Harkness is an Honorary Research Fellow affiliated with the School of Computing Science at the University of Glasgow. His work focuses on cybersecurity for safety-critical systems, particularly in industrial control environments and firmware protection. His research spans disciplines including cybersecurity, industrial control systems, and system safety. Recent publications analyze forensic approaches to cyber attacks and defensive strategies for embedded systems. Key publications highlight trends in firmware security (2017) and forensic analysis of industrial control system vulnerabilities (2016). No specific students, awards, or lab affiliations are mentioned in the provided data.
FRANCILLON Aurélien is a Full Professor at EURECOM within the Digital Security Department, specializing in embedded system security, software/hardware co-design vulnerabilities, and network security. He leads research on firmware analysis, side-channel attacks, and secure embedded architectures. His work includes developing frameworks like Avatar2 and SymCC, which advance dynamic analysis and symbolic execution techniques. Research Focus: His research bridges software and hardware domains, addressing critical security challenges in IoT, firmware, and embedded devices. Notable areas include electromagnetic side channels, covert channels (e.g., 'Screaming Channels'), and firmware vulnerability detection. He emphasizes practical defenses against exploitation of hardware-software interfaces. Awards & Grants: 2024 Test of Time Award (NDSS) for foundational work on SMART (2012) Google Faculty Research Award 2019-2020 (Security Category) Multiple best paper awards for contributions to covert channels (C5: Cross-cores cache) and firmware analysis (AVATAR framework) Teaching & Labs: Teaches system security at EURECOM and heads the Security and Privacy in the Digital World (S3) Lab. His teams develop open-source tools for embedded system security (e.g., LibAFL QEMU, Avatar²).
Mikael Asplund is an Assistant Professor in the Department of Computer Science at Linköping University, affiliated with the Software and Systems (SAS) division. His research focuses on the security and reliability of cyber-physical systems, particularly in intelligent transportation, smart grids, and autonomous systems. Research Interests: His work integrates formal reasoning, protocol design, and evaluation through simulations and real-world testbeds. Key areas include formal verification of security protocols, remote attestation, automated penetration testing, and vulnerability analysis in critical infrastructure such as EV charging and battery storage systems. Recent Research Trends: His recent publications (2023–2025) emphasize formal methods for security assurance, automated testing, and the application of these techniques to emerging technologies like 5G/6G networks, electric vehicles, and energy systems. There is a strong focus on provable security and practical implementations in embedded and distributed environments. Best paper award at ARES 2024. Nominated for best student paper at VEHITS 2025. Academic Leadership and Grants: He leads or co-leads major projects funded by WASP, ELLIIT, Vinnova, SSF, and Horizon Europe, including Automating Security Assurance using Formal Methods , Protocol Security Verification , and CyberSecDome . He is responsible for the Master’s Program in Cybersecurity and the IT program at LiU, and actively promotes innovation in computer science education. He also coordinates the Cybersecurity Lab at LiU, a dedicated facility for hands-on cybersecurity training.
Prof. Dr.-Ing. Matthias Hollick is a Full Professor of Computer Science at the Technical University of Darmstadt, leading the Secure Mobile Networking Lab (SEEMOO). He is co-affiliated with the Electrical Engineering and Information Technology Department. His research focuses on Security, QoS, and Resilience in Mobile Systems, Privacy in Cyber-physical Systems, and Cross-layer Optimization in Wireless Networks. He holds leadership roles as the Speaker of the emergenCITY Research Center (Hessian LOEWE initiative) and Deputy Speaker of the DFG Doctoral School on Privacy and Trust for Mobile Users. His work has been published at top venues including ACM MobiCom, ACM IMWUT, IEEE S&P, and USENIX Security, earning over 15 best paper/demonstration awards. Key contributions include protocols for secure IoT communication, privacy-preserving authentication (e.g., PrivateDrop), and resilient networking solutions for disaster scenarios (e.g., RESCUE framework). His lab’s open-source tools, such as FreeSpeaker and BTLEmap, advance smart home and Bluetooth security research. Scientific recognition includes awards at ACM MobiCom, ACM IMWUT, and IEEE DOCSS. His research bridges academia and industry, addressing real-world challenges like satellite communication security, 5G vulnerabilities, and anti-stalking protections via devices like AirTags. Current work emphasizes 6G resilience, UWB security, and decentralized crisis communication systems.
Riccardo Lazzeretti serves as Associate Professor at Sapienza University of Rome since September 2022, following progression from Assistant Professor roles (RTD-A/B) at the same institution from 2017-2022. His academic trajectory includes post-doctoral research at the University of Siena's VIPP group, a research grant at the University of Padua, and industry experience at Italian startup Cynny s.p.a. His educational foundation comprises: MSc in Computer Science Engineering, University of Siena (2007) PhD in Information Engineering, University of Siena (2012) Lazzeretti's research centers on privacy-preserving technologies with emphasis on Secure Multi-Party Computation for encrypted signal processing. His work spans biometric security (including multi-biometric authentication), IoT security protocols, blockchain applications in healthcare, and social network misinformation detection. Recent publications demonstrate deep integration of cryptographic techniques with machine learning for resource-constrained devices and critical infrastructure protection. Analysis of his 15 most recent publications reveals dominant trends in IoT/drone security (32% of works), browser/API vulnerabilities (13%), and privacy-preserving architectures (27%). His research consistently bridges theoretical cryptography with practical system implementations, particularly for emerging technologies like NDN and programmable data planes. His scholarly recognition includes: SIGMM Test of Time Paper Honourable Mention (2021) for Multimedia Security contributions IET Biometrics Premium Best Paper Award (2021) Lazzeretti has supervised graduate students at the University of Siena and secured research funding through collaborations with NATO's CMRE, Digital Catapult (UK), and IMT Lucca. Current projects include privacy-preserving consensus algorithms for IoT and blockchain-based health applications. He maintains active partnerships with European research entities including Centre for Maritime Research and Experimentation and Digital Catapult. His laboratory affiliations encompass the VIPP research group at University of Siena and ongoing collaborations with Sapienza's cybersecurity initiatives, focusing on cross-disciplinary teams integrating signal processing, cryptography, and machine learning expertise.
Di Ma is Associate Professor and Associate Dean for Graduate Education and Research in the College of Engineering and Computer Science at the University of Michigan-Dearborn. She also serves as Director of the Cybersecurity Center for Education, Research, and Outreach (CCERO) and the Security and Forensics rEsearch (SAFE) Lab, actively leading institutional cybersecurity initiatives since CCERO's founding in 2016. Her educational background includes: Ph.D. in Computer Science, University of California-Irvine (2009) Master of Engineering, Nanyang Technological University Bachelor of Engineering, Xi-an Jiaotong University Dr. Ma's research centers on security, privacy, and applied cryptography with emphasis on connected/autonomous vehicle systems (ECUs, infotainment), smartphone security (malware prevention, acoustic channels), and RFID/sensor protection. She investigates demographic inference from Wi-Fi metadata and develops location-aware cryptographic protocols, focusing on real-world vulnerabilities in transportation and mobile ecosystems. Her work bridges theoretical cryptography with practical safety-critical systems. Analysis of her 15 most recent publications reveals sustained leadership in automotive cybersecurity since 2012, with increasing focus on vehicle privacy (2019-2020), secure firmware updates, and cross-domain cryptographic access control. Key trends include adapting smartphone security techniques to automotive use cases and pioneering ambient sensor-based defenses against relay attacks, consistently published in IEEE flagship journals. Her scientific recognition includes: Trevor O. Jones Outstanding Paper Award (SAE, 2019) CECS Faculty Excellence Award for Research (2017) Tan Kah Kee Young Inventor's Award (Silver Medal, 2004) NSF-TRUST Fellowship (2009) Graduate Dean’s Dissertation Fellowship (UC Irvine, 2009) Air Force Summer Faculty Fellowship (AFOSR, 2011) Dr. Ma has advised PhD students including Ahmad Nasser (2019) and Nevrus Kaja (2019). Her research is funded by major grants from NSF (DGE-1419280 as PI, 2014), NHTSA (2015, 2017), AFOSR (2011), Ford (2016), Intel, and Research in Motion. Notably, she co-led a $7M Department of Energy project (2020) protecting EV charging infrastructure. As SAFE Lab Director, she develops security frameworks for mobile/IoT systems while CCERO coordinates university-wide cybersecurity education and outreach. Her teams include Vertically Integrated Project (VIP) groups designing automotive intrusion detection systems, and she organized the inaugural Dearborn Cybersecurity Day (2019) with 130+ industry participants.
Eran Yahav is a Professor in the Computer Science Department at the Technion, Israel Institute of Technology, and serves as CTO at Tabnine. His research bridges programming languages, software engineering, program analysis, and machine learning, focusing on program synthesis, verification, and code intelligence. Research Interests: His work spans program synthesis , abstract interpretation , verification of concurrent systems , binary analysis , and AI for code . He leads the PRIME project, which uses machine learning and static analysis to enable programming with millions of examples, improving code completion, search, and prediction. The recent publications reflect a strong trend toward integrating neural models with program analysis—using structured representations of code (e.g., AST paths) for property prediction, generating sequences from code (code2seq), learning distributed representations (code2vec), and interpreting neural networks via automata extraction. His work consistently appears in top-tier venues such as POPL, PLDI, ICSE, OOPSLA, and ICLR. Scientific Awards: Best paper award at ISSTA'07 Best paper award at ISSTA'06 Advising and Grants: He has advised numerous PhD and Master’s students, many of whom have published in premier conferences and now hold academic or industry positions. While specific grants are not mentioned, his sustained high-impact research and leadership in major projects (e.g., PRIME, Fender, SAFE) imply significant funding support. He has served on program committees for PLDI, POPL, CAV, OOPSLA, and VMCAI, reflecting his standing in the programming languages and verification communities. Labs and Teams: He leads a research group focused on program analysis and synthesis, with strong collaborations, particularly with Martin Vechev and others, on concurrency, synthesis, and machine learning for code. The group has developed influential tools such as PRIME, code2seq, code2vec, TRACY, and SAFE.
Vincent Naessens is a researcher at Ghent University, Belgium, with a focus on data privacy, cybersecurity, and IoT systems. His work spans privacy-preserving data publishing, security analysis of industrial control systems, and secure IoT applications. Research Interests: Data Privacy, Cybersecurity, IoT, eHealth Systems, Machine Learning. His recent articles (2023-2025) explore advanced anonymization pipelines, Linux-based IoT benchmarks, and reverse engineering of IoT ecosystems. These works emphasize balancing privacy utility, detecting firmware vulnerabilities, and improving location masking in colluding environments. Dr. Naessens has collaborated extensively with researchers like Jorn Lapon, Jan Vossaert, and Michiel Willocx, contributing to conferences such as ARES, SEC, and WOOT. His projects include cloud pricing analysis, IoT security seminars, and eHealth system privacy mechanisms.
Kanad Basu is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds the title of Fellow, Eugene McDermott Distinguished Professor. Previously, he worked at IBM and Synopsys, and was an Assistant Research Professor at New York University. His research focuses on hardware security, AI hardware, quantum computing, and VLSI testing. Basu has authored over 130 peer-reviewed publications, 2 patents, and received the Best Paper Award at the 2011 International Conference on VLSI Design. Education: PhD and MS in Computer Engineering from the University of Florida (2012), BE in Electronics and Telecommunication Engineering from Jadavpur University, India (2007). Research Interests: His work spans hardware security (e.g., side-channel attacks, hardware Trojans), functional safety in automotive systems, quantum computing EDA tools, and AI-driven verification techniques. Recent projects include secure assertion generation using LLMs, radiation hardening for edge AI, and quantum circuit equivalence checking. Recent Achievements: Won NSF CAREER Award (2025), served on TPC for IEEE DATE 2026 and IEEE Quantum Week 2026, and led the TIES lab to win NSF CAREER funding. His lab has produced over 15 PhD graduates, many now in industry roles at Intel, NXP, and Oak Ridge National Lab. Awards: Includes NSF CAREER (2025), IEEE Top Picks (2024/2023), and Blavatnik nomination (2019). Academic service roles include associate editorships for IEEE Design & Test and IET Computers. Lab & Collaborations: The Trustworthy & Intelligent Embedded Systems (TIES) lab collaborates with NSF CHEST and TxACE. Ongoing projects involve quantum simulation frameworks (QuaSi), secure DNN acceleration (MENDNet), and radiation-aware functional safety.
Arvind Sharma is Associate Professor (Førsteamanuensis) at the Department of Information Security and Communication Technology , Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU) , Gjøvik, Norway. His core competences bridge hardware security & reverse engineering and techno-economic optimisation of photovoltaic-based micro-grids . Education PhD in Engineering, 2021 – University of Agder, Norway Dissertation: Techno-Economic Performance Evaluation of Photovoltaic based Micro-Grid with Energy Management Strategies Research Interests Dr Sharma pursues two intertwined research agendas. First, he develops quantitative frameworks for hardware security assurance , focusing on reverse-engineering techniques that uncover vulnerabilities in PCBs and system-level architectures of IoT devices and smart-grid equipment. Second, he performs techno-economic modelling and optimisation of renewable-energy micro-grids, integrating PV generation, battery and hybrid storage, demand-side management, and dynamic pricing schemes to enhance reliability and cost-effectiveness. Across both domains he employs methodologies such as finite-state-machine control, life-cycle cost analysis, Monte-Carlo simulation, and physical assurance inspection protocols. Publication Trends From 2017 to 2025 he has authored or co-authored more than 50 refereed works. His recent hardware-security papers (2023-2025) advance quantitative metrics for resilience and demonstrate PCB-level reverse engineering to combat supply-chain attacks. Simultaneously, his energy-system output (2019-2025) delivers detailed techno-economic studies of PV-battery micro-grids, addressing voltage-quality constraints, subsidy impacts, and storage sizing across institutional and building-integrated contexts. Scientific Awards & Recognition Invited keynote: Hardware Security Assurance Framework , European Symposium on Reliability of Electron Devices, 2024 Featured presenter: NORCICS workshops on cyber-security in cyber-physical energy grids, 2022-2023 Teaching & Supervision Course coordinator & lecturer: IMT4114 Introduction to Digital Forensics Course coordinator & lecturer: IIK4100 Hardware Security in Embedded Systems Supervising master and PhD projects at NTNU and University of Agder in hardware security and micro-grid optimisation. Laboratory & Teams He conducts experimental work in NTNU Gjøvik’s hardware-security lab equipped with high-resolution microscopy, PCB milling, and side-channel measurement benches. He collaborates with the Smart Grid Security research group and the Nordic Centre for Cyber-Security in Critical Infrastructure (NORCICS) , fostering cross-disciplinary projects between cybersecurity and sustainable energy systems.
Michele Valsesia is a Research Fellow at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino . His work is affiliated with the SOFTENG - Software Engineering Group , focusing on software certification and IoT security. His research interests lie at the intersection of Software Engineering and Cybersecurity , particularly in Internet of Things (IoT) systems. Key areas include automated firmware analysis , formal verification , and code quality metrics for secure software development. Recent publications highlight advancements in IoT supply chain security and Rust-based automated tools for code assessment. Contributions span journal articles in IEEE IoT Journal and conferences like DCOSS-IoT and ARES , emphasizing embedded systems and security analysis . He actively contributes to the SOFTENG research group , developing methodologies for software reliability and security through code metrics and certification frameworks.
Dr. Masudul Haider Imtiaz serves as an Assistant Professor in the Department of Electrical and Computer Engineering at Clarkson University, teaching core courses including Embedded Systems, Digital Design, and Machine Learning for Biomedical Signals while actively contributing to curriculum development through multiple university committees. Education: Bachelor’s and Master’s in Applied Physics, Electronics, and Communication Engineering from University of Dhaka, Bangladesh PhD in Summer 2019 from University of Alabama for research on wearable sensor systems studying cigarette smoking behavior His research program centers on developing wearable sensor technologies for preventive healthcare, diagnostic applications, and assistive biometric systems, with emphasis on sensor design, firmware development, and machine learning analysis of physiological signals in real-world settings. Dr. Imtiaz currently mentors 4 PhD candidates, 3 Master’s students, and 23 undergraduates, with research funding secured through Clarkson University Team Science Grants, NSF CITeR, and private industry partnerships. He directs the AI Vision, Health, Biometrics, and Applied Computing (AVHBAC) laboratory and the Center for Advanced PCB Design and Manufacture (CAPDM), while maintaining active collaborations with the Center for Identification Technology Research (CITeR) and Center for Rehabilitation Engineering, Science, and Technology (CREST).
Raef Aidibi serves as Adjunct Faculty in the Department of Electrical and Computer Engineering at Lawrence Technological University, teaching undergraduate and graduate courses in embedded systems, digital electronics, and real-time control while leveraging 25+ years of industry experience in automotive and power electronics firmware development. His academic credentials include: Ph.D. in Electrical & Computer Systems Engineering from Oakland University Dual M.S. degrees in Electrical & Computer Engineering and Embedded Systems from Oakland University B.S. in Electrical & Computer Engineering from Lawrence Technological University Dr. Aidibi's research centers on real-time embedded systems for electrification applications, with recent work applying Hybrid Dynamic Systems and Dynamic Programming algorithms to Intelligent Transportation Systems. He focuses on bridging industrial control challenges with adaptive signal processing techniques for EV charging infrastructure and inverter systems. His technical expertise spans DIN/ISO 15118/CHAdeMO/GB/T protocol implementations, high-power DC-AC inverter control, and RTOS-based embedded architectures for automotive communication and driver awareness systems, emphasizing model-based development and security in production environments. No scientific awards were documented in available sources. Regarding academic mentoring, the source material contains no specific information about student advising or research grant administration. Details about dedicated research laboratories or collaborative engineering teams were not provided in the institutional documentation.
Engin Kirda is a Professor at Northeastern University, holding appointments in both the Khoury College of Computer Sciences and the Department of Electrical and Computer Engineering within the College of Engineering. He directs the Information Assurance Program and was awarded the inaugural Sy and Laurie Sternberg Interdisciplinary Chaired Professorship. His research focuses on malware analysis, web security, reverse engineering, and intrusion detection, with notable contributions to tools like Anubis, FIRE, and Pixy. Education: PhD in Computer Science from the Technical University of Vienna (2002). Prior to Northeastern, he held positions at Eurecom and the Technical University of Vienna. Research Interests: Malware Analysis & Detection Web Application Security Embedded Systems Security Privacy-Preserving Technologies Automated Vulnerability Discovery (e.g., fuzzing, differential testing) Recent Work Trends: Recent articles (2023–2025) emphasize web application firewall bypasses, embedded system hardening, and privacy-enhancing technologies. Collaborative projects like FLANKER (NSF-funded) address lateral movement detection in enterprise networks. Awards: Consistently ranked in Stanford's Top 2% Cited Scientists since 2021, reflecting impactful contributions to cybersecurity research. Grants & Labs: Principal Investigator on NSF/DARPA grants totaling $2.5M+, including projects on firmware analysis (FIRMALICE), Android marketplace security (DARKDROID), and graph-based lateral movement detection. Leads the International Secure Systems Lab (ISS Lab).
Aravind Machiry is an Assistant Professor at the Elmore Family School of Electrical and Computer Engineering at Purdue University. His research focuses on system and software security, particularly in securing embedded systems, firmware, and leveraging modern tools like static analysis and fuzzing to mitigate vulnerabilities. He leads the Purdue Systems and Software Security Lab (PurS3) , which emphasizes principled yet practical security solutions. Research interests include embedded systems security (e.g., Rust adoption in embedded contexts), vulnerability detection in firmware and network stacks, and securing continuous integration workflows. He has explored topics like language model integration in Android apps, fault injection in API error handling, and mitigating data poisoning in federated learning. His work bridges hardware and software domains, with contributions to secure execution environments (e.g., ARM TrustZone via Tarnhelm) and binary analysis tools like BinTrimmer. While no formal awards are listed, his grant-funded projects (e.g., CAREER award) reflect recognition of his research impact. Advising focuses on graduate students in cybersecurity and embedded systems domains. Key projects include rehosting embedded applications as Linux apps (LEMIX), automated firmware analysis (KARONTE), and feedback-driven binary generation (Cornucopia). His research often addresses scalability challenges in vulnerability detection across large codebases.
