Find Academic Supervisors

• Network Security
• IoT Security
• Blockchain Technology
• +4 more

Dr. Georgios Spathoulas is a Teaching Professor at the University of Thessaly's Department of Computer Science and Biomedical Informatics since 2014, where he teaches undergraduate and postgraduate courses. He concurrently serves as a Postdoctoral Researcher at the Critical Infrastructures Security and Resilience group at NTNU CCIS. His research focuses on network security, IoT security, privacy-preserving techniques, and blockchain technology. He holds a Diploma in Electrical & Computer Engineering from Aristotle University of Thessaloniki (2002), an MSc in Computer Science from the University of Edinburgh (2005), and a PhD in Digital Systems from the University of Piraeus (2013). His research interests emphasize secure IoT systems, blockchain scalability, and privacy-preserving algorithms. Notable trends in his publications include advancements in blockchain state channels, cryptographic methods for IoT, and AI-driven cybersecurity solutions. Over 30 peer-reviewed publications highlight his contributions to cybersecurity, digital forensics, and distributed ledger applications. His work addresses challenges such as DNS exfiltration detection, lightweight commitment schemes for IoT, and defensive deception strategies against intruders. Collaborations include national/international projects and roles as a conference program committee member.

Wensheng ZhangView profile

Associate Professor

• Systems and Networking
• Cybersecurity
• Cloud Computing
• +4 more

Wensheng Zhang is an Associate Professor at Iowa State University's Department of Computer Science within the College of Engineering. His research focuses on systems and networking, particularly in cybersecurity, privacy-preserving technologies, distributed systems, and edge computing. He holds a Ph.D. from The Pennsylvania State University, an M.S. from the Chinese Academy of Sciences, and a B.S. from Tongji University. Dr. Zhang leads the Laboratory for Wireless Networks and Systems, exploring advanced topics like secure data aggregation, privacy in cloud computing, and IoT security frameworks. Recent research trends include investigating robustness of multimodal AI models, fair allocation in federated learning systems, and integrating cryptographic techniques (e.g., homomorphic encryption) with edge-based reinforcement learning for applications in traffic management and smart homes. His work emphasizes real-world deployable solutions for privacy and security challenges in distributed environments. No scientific awards are explicitly listed in the provided information. His advising and grant activities are not detailed here, though his prolific publication record suggests active participation in research funding and mentorship. The laboratory focuses on practical innovations in wireless networks, blockchain-IoT systems, and wearable sensor applications.

Jun UedaView profile

Professor

• Automation
• Mechatronics
• Robotics
• +5 more

Jun Ueda is a Professor at the Georgia Institute of Technology's School of Mechanical Engineering, affiliated with the Biorobotics and Human Modeling Lab. He joined Georgia Tech in 2008, previously serving as a Visiting Scholar/Lecturer at MIT (2002-2008) and Assistant Professor at Japan's Nara Institute of Science and Technology (2002-2008). He holds a Ph.D. (2002), M.S. (1996), and B.S. (1994) from Kyoto University, Japan. His research focuses on robotics, mechatronics, and bioengineering, emphasizing actuation technology for human-assistive systems. Key projects include bio-inspired actuators, MRI-guided robotics, and secure teleoperation systems. Education: Ph.D., Kyoto University, Japan (2002) M.S., Kyoto University, Japan (1996) B.S., Kyoto University, Japan (1994) Research Interests: Automation & Mechatronics: Robust control, bio-inspired actuators, and sensor integration Bioengineering: Medical robotics, neuromodulation, and tissue modeling Cybersecurity: Secure teleoperation and encrypted control systems Awards: 7th Nagamori Award (2021) IEEE Robotics and Automation Society Early Career Award (2009) FANUC FA and Robot Foundation Award (2005) Advising & Grants: Directed the Interdisciplinary Robotics PhD Program at Georgia Tech (2015-2017) Recipient of Helmsley Distinguished Fellowship (2016) Active grants in MRI-guided robotics and actuator development Labs/Teams: Leads the Biorobotics and Human Modeling Lab, collaborating on medical robotics, tactile sensing, and human-robot interaction.

Christos-Efthymios KotselidisView profile

Associate Professor

• Computer Architecture
• Parallel and Distributed Computing
• High-Performance Computing
• +4 more

Christos-Efthymios Kotselidis is an Associate Professor in the Department of Computer Science at the University of Manchester. His work focuses on runtime systems, heterogeneous computing, and high-performance computing. He leads research in optimizing Java programs using frameworks like TornadoVM and explores GPU acceleration for cryptography (FHE). His contributions include advancements in vectorization for RISC-V architectures and memory analysis of Java benchmarks. Education: PhD in Computer Science (2011) from the University of Manchester, supervised by Prof. Steve Furber and Dr. Chris Kirkham, with a thesis on Distributed Software Transactional Memory. Research Interests: Runtime environments, parallel programming models, compiler optimization, and hardware-software co-design. His work aligns with UN SDG 9 (Industry, Innovation, and Infrastructure) through digital futures research. Notable Achievements: Recipient of the Best Paper Award (2017) Principal Investigator in the Centre for Digital Trust and Society (2021–present) Over 49 peer-reviewed publications, including books and conference contributions on topics like FHE acceleration and runtime systems Lab/Team Affiliations: Member of the Advanced Processor Technology group and collaborates on the Digital Futures research beacon at the University of Manchester.

Aloni CohenView profile

Assistant Professor

• Cryptography
• Privacy
• Law
• +3 more

Aloni Cohen is an Assistant Professor of Computer Science and Data Science at the University of Chicago. Previously, he served as a Postdoctoral Associate at Boston University, with a joint appointment at the Hariri Institute for Computing and the School of Law. He is also a former affiliate at the Berkman Klein Center for Internet & Society and a Fellow at the Aspen Tech Policy Hub. Aloni earned his PhD in Electrical Engineering and Computer Science from MIT, advised by Shafi Goldwasser, supported by a Facebook Fellowship and an NSF Graduate Student Research Fellowship. His research bridges theoretical cryptography, privacy, law, and policy, focusing on real-world applications like differential privacy, encryption, and legal frameworks such as GDPR and the Fifth Amendment. Key research themes include privacy-preserving data analysis, cryptographic watermarking, and ethical implications of AI. He explores tensions between cryptographic theory and legal regulations governing data privacy and surveillance. His recent publications address topics such as the societal impacts of differential privacy, legal challenges in compelled decryption, and policy questions around multiparty computation. Notable awards include the 2024 Outstanding Paper Award at the International Conference on Algorithmic Learning Theory and the 2022 Distinguished Paper Award at USENIX Security Symposium. Awards: 2024 Outstanding Paper Award, International Conference on Algorithmic Learning Theory 2022 Distinguished Paper Award, USENIX Security Symposium 2014 NSF Graduate Student Research Fellowship Aloni’s work involves interdisciplinary collaborations, addressing both technical and policy dimensions of modern computational challenges. Details on grants or specific advising roles are not explicitly listed in the provided texts.

• Wireless Communications
• Deep Learning
• Signal Processing
• +7 more

Thien Huynh-The is a Professor in the Department of Electrical Engineering at Chungnam National University's College of Engineering, where they lead research in wireless communications, deep learning applications, and IoT systems. With over 150 publications spanning from 2014 to 2025, their work demonstrates sustained academic productivity with significant contributions to 5G/6G networks, spectrum sensing, and federated learning architectures. Research interests focus on the intersection of deep learning and wireless communications, particularly in automatic modulation classification, semantic segmentation for remote sensing, and energy-efficient communication protocols. Their innovative approaches include developing specialized CNN architectures like SRNet for spectrum sensing and CosPoint Transformer for 3D semantic segmentation, addressing critical challenges in 5G/6G systems and metaverse infrastructure. Recent work explores STAR-RIS-aided networks, waveform classification for integrated radar-communication systems, and privacy-preserving federated learning for healthcare applications. Analysis of publication trends reveals increasing focus on metaverse technologies and 6G communications since 2022, with significant contributions to IEEE Communications Surveys & Tutorials and IEEE Internet of Things Journal. Key research areas include federated learning optimization, channel estimation techniques using attention networks, and semantic communication frameworks for edge-assisted metaverse applications. Collaborative research spans multiple institutions with frequent co-authorship with Dong-Seong Kim, Quoc-Viet Pham, and Won-Joo Hwang. Current projects address critical challenges in wireless power transfer, spectrum efficiency, and computational resource allocation in next-generation networks.

• Machine Learning
• Healthcare Informatics
• Explainable AI
• +6 more

Kai-Chih Pai is an Associate Professor at China Medical University's College of Information Science and Technology, Department of Computer Science, with a distinguished research career spanning over 14 years. His work bridges computer science and healthcare, focusing on practical AI applications that address critical medical challenges. Dr. Pai's research interests center around Machine Learning applications in healthcare , Explainable AI systems , Natural Language Processing for Chinese language , and Educational Technology . His work demonstrates a strong commitment to developing AI solutions that are not only technically sophisticated but also interpretable and clinically useful. His research trajectory shows a clear evolution from educational technology applications to increasingly sophisticated healthcare AI systems. His publication record reveals significant contributions to medical decision support systems , particularly in acute kidney injury prediction , pneumonia diagnosis , and mortality prediction in critical care settings. More recently, he has been exploring cutting-edge applications of large language models for industrial knowledge management. His work consistently emphasizes the importance of model interpretability in medical contexts. Federated machine learning approaches for multi-institutional medical research Privacy-preserving healthcare technologies using homomorphic encryption Adaptive learning systems for Chinese language education Predictive analytics for critical care medicine Dr. Pai has established a productive research program with consistent publication output in high-impact venues, demonstrating expertise that spans both theoretical AI development and practical healthcare applications. His collaborative work with medical professionals across Taiwan highlights the interdisciplinary nature of his research and its real-world impact.

• Cloud Computing
• Blockchain Technology
• Data Security
• +5 more

Xiaojun Zhang is a Professor actively contributing to cloud computing, blockchain technology, data security, and educational technology. His work spans cybersecurity, signal processing, and wireless systems. Key Research Areas: Privacy-preserving data aggregation, machine learning for biomedical imaging, blockchain-based integrity auditing, and educational metacognition studies. Recent Article Trends (2022–2025): Focus on secure federated learning, data denoising algorithms, and blockchain applications in smart grids, healthcare, and education. Collaborations include institutions in China and international researchers.

• Biometric Security
• Cybersecurity
• Internet of Things (IoT)
• +3 more

Song Wang is a Senior Lecturer in the Department of Engineering at La Trobe University, Melbourne, Australia. She holds a B.Eng. from Xi'an Jiaotong University and a Ph.D. in Control Theory from the University of Melbourne. Her research focuses on biometric security, IoT security, and privacy-preserving authentication systems, with a particular emphasis on applying machine learning and deep learning to solve challenges in healthcare, cybersecurity, and multimedia security. Dr. Wang's academic journey includes roles as a design engineer at NEC Australia (2000–2004) and as a Lecturer (2005–2008) before becoming a Senior Lecturer at La Trobe University (2009–present). Her work has explored cancellable biometrics, secure IoT authentication, and model inversion attacks in AI systems. She has collaborated extensively on projects involving medical imaging (e.g., Alzheimer’s disease prediction via structural MRI) and privacy-preserving frameworks for EEG and fingerprint authentication. Her recent publications highlight advancements in graph convolutional networks for driver drowsiness detection, homomorphic encryption for biometric data, and lightweight authentication systems for resource-constrained IoT devices. These contributions underscore her expertise in bridging theoretical research with practical applications in cybersecurity and healthcare. No scientific awards are explicitly mentioned in the provided materials. Her research has been supported by collaborations with institutions like the University of Melbourne and industry partners, though specific grants are not detailed.

Ivan Bjerre DamgårdView profile

Professor

• Cryptography
• Secure Communication
• Multi-Party Computation (MPC)
• +3 more

Professor Ivan Bjerre Damgård is affiliated with the Department of Computer Science at Aarhus University, where he holds a full academic rank of Professor. His primary research focus lies in cryptography, secure communication, and privacy-preserving technologies. Key areas include multi-party computation (MPC), homomorphic encryption, and differential privacy. He has contributed to foundational work on secure protocols for dynamic networks, threshold cryptosystems, and resilient distributed systems. His recent publications (2023–2024) emphasize advancements in MPC protocols with optimized communication and round complexity, secure computation in unstable networks, and privacy-preserving techniques. Notable work includes 'Phoenix' (2023) addressing network instability and 'Broadcast-Optimal MPC' frameworks minimizing setup requirements. Professional activities include organizing conferences like TCC 2009 and ICALP 2008, as well as delivering lectures on cryptography and IT security. No scientific awards are explicitly listed in the provided information. His research collaborates extensively with global institutions, reflected in co-authored papers with researchers like Agarwal, Braun, and Ciampi. Ongoing work appears to prioritize practical implementations of theoretical cryptographic constructs in real-world distributed environments.

Alain PasselègueView profile

Researcher

• Cryptography
• Fully Homomorphic Encryption
• Cryptographic Applications

Alain Passelègue is a junior researcher (chargé de recherches) at Inria within the AriC team at École Normale Supérieure de Lyon, currently focusing on fully homomorphic encryption (FHE) and its applications. His research spans diverse areas of cryptography including obfuscation techniques and multilinear maps. He completed his PhD in cryptography at École Normale Supérieure under Michel Abdalla (2013-2017), followed by postdoctoral research at UCLA under Amit Sahai (2017-2018). He currently supervises PhD students Calvin Abou Haidar and Mahshid Riahinia and serves on program committees for leading cryptography conferences including Crypto, PKC, and Asiacrypt.

Anwar HasanView profile

Professor

• Cryptography
• Post-Quantum Cryptography
• Secure Multiparty Computation
• +5 more

Anwar Hasan is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He specializes in cryptography, post-quantum cryptography, and secure computation, with a focus on algorithm optimization, cryptographic hardware implementation, and energy efficiency. His work spans theoretical foundations and applied systems, including blockchain technology, fault-tolerant cryptosystems, and secure multiparty computation. His research interests emphasize developing quantum-resistant cryptographic protocols, improving efficiency in cryptographic algorithms (e.g., EdDSA signature verification, CSIDH optimization), and designing secure systems against adversarial attacks (e.g., poisoning in federated learning, covert adversary models). He also explores energy consumption analysis of cryptographic algorithms, particularly in the context of NIST PQC standards and elliptic curve-based systems. Recent work includes contributions to threshold cryptography (isogeny-based schemes), decentralized oracle networks (CountChain), and privacy-preserving blockchain applications (AdChain). His publications frequently address hardware-software co-design for cryptographic primitives, emphasizing both theoretical advancements and practical implementation challenges. Dr. Hasan’s research also extends to applied areas such as fraud detection systems, digital rights management using blockchain, and energy efficiency in embedded cryptographic systems. He collaborates on projects involving secure communication in cloud computing and resilient key management for mobile applications.

Christopher NielsenView profile

Professor

• Artificial Intelligence in Healthcare
• Control Systems
• Medical Imaging
• +3 more

Christopher Nielsen is a Professor and Associate Chair for Graduate Studies in the Department of Electrical and Computer Engineering at the University of Waterloo. His affiliations include the Executive Committee, Full-Time Faculty, and other academic groups. He is reachable at cnielsen@uwaterloo.ca and EIT 4106. His research interests span Artificial Intelligence in Healthcare, Control Systems, and Robotics. Key focuses include medical imaging (e.g., retinal disease analysis, federated learning for healthcare), adaptive control systems, and cybersecurity in AI applications. He also explores spinal surgery outcomes and human-robot interaction. Recent work emphasizes AI-driven solutions for retinal age prediction, cybersecurity in medical AI, and advanced control methodologies for robotics and automotive systems. His publications reflect interdisciplinary collaboration between engineering, medicine, and data science. Notable technical trends in his articles include: 1) AI integration into medical diagnostics, 2) adaptive control for complex systems, 3) privacy-preserving federated learning architectures, and 4) human-centered robotics navigation. No scientific awards are explicitly listed. His advising record and grant activities remain unspecified in available texts. His professional webpage (ece.uwaterloo.ca/~cnielsen/) provides further technical details on ongoing research.

• Cryptography
• Machine Learning
• Database Systems
• +3 more

Michael Brand is an Adjunct Professor at RMIT University's School of Computing Technologies. His research focuses on cryptography, privacy-preserving technologies, machine learning, and algorithm design. Key areas include zero-knowledge proofs, secure financial systems like FinTracer, and computational theory. His work bridges theoretical foundations with practical applications in data security and real-time processing systems. Research interests span advanced cryptographic protocols, privacy-enhancing machine learning techniques, and distributed computing paradigms. Notable contributions include innovations in blockchain tracing mechanisms and foundational work in statistical estimation theory. He actively publishes in top-tier venues, addressing challenges in secure computation, network analysis, and database systems. Collaborations emphasize interdisciplinary solutions to modern computational and security challenges.

Ivan DamgårdView profile

Professor

• Cryptography
• Data Security
• Quantum Information
• +3 more

Ivan Damgård is a Professor at the Computer Science Department of Aarhus University. His research focuses on cryptography, data security, quantum information, and algorithms. He co-authored the book Information Theoretically Secure Multiparty Computation and Secret Sharing with Ronald Cramer and Jesper Buus Nielsen. He is head of the Cryptography Group at Aarhus University and co-founder of companies Cryptomathic and Partisia, specializing in data security and secure multiparty computation. He teaches courses such as Introductory Cryptography, Cryptologic Protocol Theory, and Quantum Information Processing. His work involves major research projects like CACE, CFEM, and ITSCI. He has contributed to debates on provable security in cryptography, including a position paper clarifying misunderstandings in the field. Outside academia, Damgård is an active folk musician, playing fiddle and guitar in multiple bands and releasing several albums.