Pascal Urien is a Professor at Télécom Paris, specializing in computer security with a focus on secure elements. He holds a PhD in computer science from École Centrale de Lyon and an HDR. His research spans cybersecurity, blockchain systems, IoT security, and network security. He leads the Cybersecurity and Cryptography (C²) research team and is affiliated with the Information Processing and Communication Laboratory (LTCI). Awards include the 2009 National Competition for Innovative Technology Companies and multiple industry accolades for smart card innovations. He co-founded EtherTrust, a startup rooted in his research. His work emphasizes secure elements in 6G, IoT, and blockchain, with over 100 publications and 15 patents. He collaborates with IETF on TLS/DTLS security modules and has contributed to standards like EAP-TLS smartcards. His teaching includes courses on cybersecurity, network security, and blockchain technologies.
Prof. Marten van Dijk is an Endowed Professor in Computer Science at the Faculty of Science, Vrije Universiteit Amsterdam (VU), with a focus on cybersecurity and cryptographic systems. He also holds an ancillary position as a researcher at the Centrum Wiskunde & Informatica (CWI) in Amsterdam since 2020. His research interests span cryptographic primitives, secure authentication mechanisms, differential privacy in machine learning, and cybersecurity in cyber-physical systems. He teaches the course 'Security and Machine Learning' at VU, emphasizing practical applications of security in modern computational systems. Recent work includes advancements in remote attestation protocols, cryptographic attack analysis on hardware security mechanisms, and optimization techniques for secure machine learning models. His contributions address critical challenges in data privacy, embedded system security, and federated learning frameworks. Prof. van Dijk collaborates internationally, focusing on interdisciplinary research at the intersection of cryptography, cybersecurity, and distributed systems.
Dr. Vladimir Sidorenko is a Senior Researcher at the Institute for Communications Engineering, Technical University of Munich (TUM), since January 2015, on leave from the Institute for Information Transmission Problems (IITP), Russian Academy of Sciences. His academic career includes roles at Ulm University (2003–2014) and the Computer Center of the Russian Health Ministry (1975–1983). He holds an M.S. in Electrical Engineering (1972) and a Ph.D. in Mathematics (1975) from the Moscow Institute for Physics and Technology. Research Interests: Coding theory, telecommunications, signal processing, cryptology, and applications. His work focuses on error correction, network security, quantum computing, and cryptographic protocols. He has authored over 140 papers in these areas. Recent Research Trends: Dr. Sidorenko’s recent work emphasizes quantum stabilizer codes decoding , network coding security , and PUF-based key agreement systems . His publications in 2023–2024 highlight advancements in polymorphic Gabidulin codes , data protection in networks , and minimal trellis structures for quantum codes . Awards: Recipient of the In-centi Award for Best Lecturing (Ulm University, 2003–2014). Collaborations: Invited researcher at institutions including Lund University (Sweden), Darmstadt TU (Germany), and Southwest Jiaotong University (China). Active in projects like Secure Key Agreement with PUFs and Concatenated Convolutional Codes .
Marvin Xhemrishi is a Researcher and PhD Candidate at the Technical University of Munich (TUM), affiliated with the Institute for Communications Engineering. He holds a M.Sc. in Communications Engineering (high distinction, TUM 2019) and a B.Sc. in Telecommunications Engineering from the Polytechnik University of Tirana (2017). His research focuses on federated learning security, privacy-preserving computing, and distributed systems. He has conducted visiting research at Chalmers University of Technology and AI Sweden (2022-2023). His work addresses challenges in secure aggregation, malicious client detection, and sparsity-privacy trade-offs in distributed computing frameworks. He contributes to advancements in coded computing, cryptography applications in machine learning, and hardware security through Physical Unclonable Functions (PUFs). Key research directions include federated learning security (e.g., FedGT framework for malicious client identification), privacy-preserving distributed algorithms, and cryptographic techniques for communication systems. His publications span IEEE Transactions, international symposia, and workshops, with recent focus on group testing applications in federated learning and secure distributed matrix operations.
Chilukuri K. Mohan is a Professor at Syracuse University, affiliated with the Syracuse Evolutionary and Neural Systems Exploration (SENSE) Lab. He holds a Ph.D. from the State University of New York at Stony Brook and a B.Tech. from the Indian Institute of Technology, Kanpur. His research focuses on neural networks, evolutionary algorithms, bioinformatics, reinforcement learning, and anomaly detection. Current projects include automated object design optimization, networked autonomous systems analysis, explainable reinforcement learning, and code stylometry for authorship detection. He collaborates extensively with scientists in bioinformatics and cybersecurity domains. Notable awards include the IEEE Region 1 Technological Innovation Award (2019) and the International Society of Applied Intelligence's Distinguished Scholar Award (2011). His work spans theoretical algorithm development and applied domains like quantum chemistry, sensing-communication systems, and biometric security.
Dr. Abid Khan is a Senior Lecturer in Cyber Security at the School of Computing and Engineering, University of Derby, England. Previously, he held roles at Aberystwyth University and COMSATS University Islamabad. His research focuses on applied cryptography, privacy in IoT, blockchain, and secure smart grid systems. He is a Senior Member of IEEE and serves as an Associate Editor for IEEE Access. Education: PhD in Computer Science (Harbin Institute of Technology, China), MS in Computer Science (Quaid-i-Azam University, Pakistan), BS in Maths and Physics (University of Punjab, Pakistan). His postdoctoral research at Politecnico di Torino, Italy, focused on e-security. Research interests include secure data aggregation in smart grids, quantum-resistant distributed ledgers, and privacy preservation in vehicular networks. Recent work emphasizes blockchain applications for healthcare data provenance and IoT security frameworks. Teaching responsibilities include modules on Communication & Security Protocols, Security Management, and Enterprise Security Management. His publications span privacy-preserving techniques, blockchain-based solutions, and VANET security. He has contributed frameworks like BCALS (blockchain-based log management) and MobChain (collusion-resistant location proof systems). His work addresses challenges in decentralized systems, fog computing, and federated learning privacy.
Zhiyuan Yan is a Professor and Associate Chair in the Department of Electrical and Computer Engineering at Lehigh University. He holds a B.E. from Tsinghua University and M.S./Ph.D. degrees from the University of Illinois at Urbana-Champaign. His research focuses on coding theory, VLSI design, wireless communications, and embedded systems. He is a Senior Member of the IEEE and received the NSF CAREER Award in 2011. Educational Background: B.E., Electrical Engineering, Tsinghua University M.S., Electrical Engineering, University of Illinois at Urbana-Champaign Ph.D., Electrical Engineering, University of Illinois at Urbana-Champaign Research Interests: His work spans coding theory (including polar codes and LDPC codes), VLSI implementations for communication systems, and cybersecurity through physical unclonable functions (PUF). He explores neuromorphic computing, memristive neural networks, and hardware-efficient error correction techniques. His contributions include efficient decoder architectures and distributed storage solutions. Scientific Awards: 2011: NSF CAREER Award Advising & Grants: While specific student names aren't listed, his research has been supported by multiple grants. His lab focuses on collaborative projects between academia and industry, emphasizing practical implementations of theoretical advancements. Lab & Teams: His research team develops cutting-edge VLSI systems, neural network accelerators, and secure communication protocols. Collaborations include work on memristive hardware and neuromorphic computing architectures.
Francesco Da Ros is an Associate Professor at the Technical University of Denmark , Department of Electrical and Photonics Engineering. His work focuses on machine learning applications in photonic systems, particularly in optical communication and photonic computing. Active in silicon photonics and nonlinear optics Key contributor to optical machine learning and digital signal processing Research Interests: Da Ros explores the intersection of machine learning and photonics , with specific projects involving: Photonic reservoir computing End-to-end optimization of optical communication systems Neuromorphic photonic circuits Channel equalization techniques Quantum communication systems Thermal crosstalk compensation in integrated photonics Scientific Contributions: His research has produced over 245 publications and 23 projects , including significant work on: Raman amplifier optimization Machine learning for optical matrix multipliers Frequency comb phase noise characterization Security systems using distributed acoustic sensing Awards: Recipient of prestigious awards such as: Best Young Italian Researcher in Denmark (2019) DOPS Prize (2017) Horizon Prize for Breaking Optical Transmission Barriers (2016) Academic Leadership: Actively supervises PhD students in projects related to: End-to-end learning for multi-core fiber systems Nonlinear fiber-optic channel optimization Quantum communication lasers Integrated quantum photonic reservoir computing
Amine Bermak is a Professor at the Department of Electrical and Electronic Engineering, Hong Kong University of Science and Technology. His research focuses on biomedical circuits, machine learning applications, and hardware security. Key contributions in CMOS sensors for bioluminescence and bacterial monitoring Pioneering work in IoT security and physical unclonable functions (PUFs) Developed edge computing frameworks for healthcare and industrial applications Significant publications in IEEE Transactions on Biomedical Circuits and Systems His recent articles highlight advancements in deepfake detection, energy-efficient ADCs, and wearable strain sensors. Collaborations span institutions in Hong Kong, Qatar, and Japan.
Vijay Krishna is a Distinguished Professor of Economics at Penn State University and serves as the Job Market Placement Director in the Department of Economics. His academic career includes a Ph.D. in Economics from Princeton University (1983), an M.A. in Economics from Delhi University (1978), and a B.A. in Mathematics from Delhi University (1976). His research focuses on Economic Theory and Industrial Organization , with recent interdisciplinary contributions in electronics and computer engineering. Notable areas include ferroelectric devices, memory storage technologies, and hardware security. He has also explored neural network architectures and energy-efficient computing systems. Recent publications (2023–2025) highlight innovations in cryogenic logic gates, nonvolatile memory systems, and hybrid neural networks. His work often bridges theoretical economics with applied engineering, reflecting cross-disciplinary collaboration. Awards and grants are not explicitly listed in the provided materials, but his extensive publication record underscores sustained academic engagement. Professor Krishna advises on job market placements for economics PhD students and maintains an active role in academic administration. His research group explores cutting-edge technologies in both traditional economic theory and emerging computational fields.
Hakan USTA is a Professor of Materials Science and Chemistry in the Department of Materials Science and Nanotechnology Engineering at Abdullah Gül University (AGU) in Kayseri, Turkey. He leads the USTALAB research group, which focuses on the theoretical design and synthetic development of new organic molecular, macromolecular, and polymeric π-conjugated materials for new-generation (opto)electronics and nanotechnology applications. Dr. USTA received his B.S. in Chemistry in 2004 from Bilkent University (Ankara, Turkey) with a Cumulative-GPA of 3.7/4.0 (Ranked 1st, Honors in Chemistry). He then obtained his Ph.D. in Chemistry from Northwestern University (Evanston, IL) under the supervision of Prof. Tobin J. Marks in 2008, with a Cumulative-GPA of 3.9/4.0. His doctoral research focused on 'Solution-Processable Molecular and Polymeric Semiconductors for Ambient-Stable Organic Field-Effect Transistors.' Dr. USTA's research spans multiple areas of materials science and nanotechnology, with particular focus on developing high-performance functional organic materials for optoelectronic applications. His work encompasses p-/n-/ambipolar semiconductors, low band gap donor-acceptor polymers, low-LUMO π-systems, hot-exciton fluorescent molecules, green-solvents soluble high-performance semiconductors, semiconductors with scalable synthesis, and thermally responsive molecules. These materials form nano-/micro-structures in thin-films that lead to applications in organic surface enhanced Raman spectroscopy (o-SERS), thin-film transistors (OTFTs), photovoltaics (OPVs), light-emitting diodes/transistors (OLEDs and OLETs), and multiplex encoded surfaces (physically unclonable functions-PUFs). Analysis of Dr. USTA's recent publications reveals a strong trend toward developing novel organic semiconductor materials with specific molecular designs for targeted applications. His research has particularly focused on BTBT derivatives, BODIPY-based materials, and indenofluorene systems, with applications spanning from high-performance transistors to advanced sensing platforms and security technologies. The interdisciplinary nature of his work bridges chemistry, materials science, and electrical engineering to address challenges in organic electronics. 2020 Tubitak Science Incentive Award in Materials Science and Nanotechnology 2015 The Young Scientists Award (TÜBA-GEBİP) 2015 The Young Scientist of the Year (Science Heroes Association) 2014 BAGEP Distinguished Young Scientist Award Dr. USTA has received significant research funding, with his group reporting >1.1 million USD in grants received. His research group has produced over 70 journal articles and 14 international patents, with his work accumulating over 5000 citations. He actively collaborates with researchers both nationally and internationally, including with Prof. Myung-Gil Kim's group at Sungkyunkwan University in South Korea on NRF-TUBITAK joint projects. Dr. USTA leads the USTALAB research group, which has established itself as a leading center for the development of novel organic semiconductor materials. The lab's work on organic SERS platforms has received notable recognition, including coverage in C&EN News. The group maintains active research directions in multiple areas of organic electronics, with ongoing projects focused on n-type BTBT development, organic encoded surfaces, and new materials for optoelectronic applications.
Yiorgos Tsiatouhas is a Professor in the Department of Computer Science and Engineering at the University of Ioannina. He holds a B.Sc. in Physics (University of Athens, 1990), M.Sc. in Informatics and Telecommunications (University of Athens, 1993), and Ph.D. in Informatics and Telecommunications (University of Athens, 1999). His research focuses on VLSI circuit design, reliability engineering, and secure computing. He teaches courses such as 'Electronics' (MYY404), 'VLSI Circuits' (MYE018), and 'Reliable Integrated Systems' (Y2), emphasizing topics like CMOS technology, radiation-hardened electronics, and testing methodologies. His research interests span secure computing architectures, fault-tolerant systems, and aging monitoring in integrated circuits. Recent work includes developing radiation-hardened latches, PUF-based security solutions for SoCs, and novel testing algorithms for phase-change memories. He has also contributed to visible light communication (VLC) systems, addressing challenges like anti-reflective obstacle detection and adaptive signal decoding in non-line-of-sight (NLOS) environments. Teaching activities include supervising student projects, maintaining an active lab for SPICE-based circuit simulation, and integrating industry-standard tools like Cadence into coursework. His lab focuses on designing and testing analog/digital circuits, emphasizing practical skills in SPICE simulation and layout design.
Thai Son Mai is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science. He actively contributes to research in Machine Learning, Data Mining, and High Performance Computing, with applications in Medicine, Neuroscience, Environment, and Transportation. He leads and collaborates on projects such as 'Drought AI' and 'RELAX: Relaxed Semantics Across the Data Analytics Stack'. His work focuses on scalable algorithms, cybersecurity, and environmental systems. Research Interests: Dr. Mai's research spans algorithm development for data-driven decision-making, including clustering algorithms, scalable machine learning, and applications in hydrology and urban flood management. He emphasizes interdisciplinary approaches, combining computer science with environmental science and healthcare. Recent Trends in Articles: Recent publications address challenges in low-power device optimization, physical unclonable function security, and ensemble models for hydrological forecasting. His work bridges theoretical advancements with real-world implementations in flood management systems and embedded computing. Awards: Best Poster Award - Marco D'Antonio (2025) Grants & Projects: Principal Investigator (PI) for 'Drought AI' (2020-2021) and 'Calculating The Human Proteoform Maximum Theoretical Peptide Index' (2023-2023) Co-Investigator (CoI) for 'RELAX' (active since 2022) and multiple reaction prediction projects Labs/Teams: Engages in interdisciplinary teams focusing on AI for health, disaster resilience, and high-performance computing.
Dr Son T Mai is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science. His research focuses on high performance machine learning, data mining, and their applications in medicine, transportation, and environmental science. He leads projects such as Drought AI and RELAX, exploring AI-driven solutions for climate resilience and data analytics efficiency. He actively publishes in top venues like AAAI and IEEE Transactions, and has received the Best Poster Award for contributions to hardware security research. Research Interests: Machine Learning, Data Mining, High Performance Computing, AI applications in medicine/transportation/environmental science. Selected Projects: RELAX: Relaxed Semantics Across the Data Analytics Stack (PI/CoI) Drought AI: Enhancing Livelihood Resilience in Mekong Delta (PI) Active Learning for Reaction Prediction (CoI) Key Achievements: 63+ publications, 13 datasets deposited, and leadership in interdisciplinary collaborations spanning cybersecurity, hydroinformatics, and approximate computing.
Prof Leandros Maglaras is a Professor at the School of Computing, Engineering and the Built Environment, Edinburgh Napier University. He has published extensively on cybersecurity topics including critical infrastructure protection, IoT security, and machine learning applications in network defense. Recent Research: Focuses on post-quantum cryptography, blockchain-based security systems, and digital twin applications for attack detection. Publications: 15 most recent works span journals and conferences in cybersecurity, with emphasis on 2024-2025 outputs. Current Collaborations: Works with researchers including Naghmeh Moradpoor, Berk Canberk, and Bill Buchanan on emerging threats in autonomous vehicles, electric vehicle security, and Linux ransomware analysis. Key methodologies include TLS fingerprinting, federated learning, and augmented reality security systems Active in developing visualization tools for incident response playbooks PhD Supervision: Serves as second supervisor for Yagmur Yigit's research on smart attack detection in autonomous networks.
