Find Academic Supervisors

Ang LiView profile

Assistant Professor

• Machine Learning
• Edge Computing
• Federated Learning
• +4 more

Dr. Ang Li is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Maryland, College Park (UMD), affiliated with the A. James Clark School of Engineering. He joined UMD in 2023 as a tenure-track faculty member, following a research associate position at Qualcomm AI Research. His research focuses on the intersection of machine learning and edge computing, emphasizing scalable, secure, and trustworthy intelligent systems. Dr. Li holds dual Ph.D.s in Electrical and Computer Engineering from Duke University (2022) and Computer Science from the University of Arkansas (2018), alongside an M.E. in Management of Innovation from Peking University and a B.S. in Computer Science from Henan University. His work addresses challenges in federated learning, edge AI deployment, and ethical AI systems. Notable achievements include the IEEE TCCPS Outstanding Ph.D. Dissertation Award, ACM KDD Best Student Paper Award (2020), and multiple recent recognitions such as the Qualcomm Innovation Fellowship (2025) and Cisco Research Award (2025). Dr. Li currently leads a research group exploring topics like counterfactual fairness, efficient LLM serving on edge devices, and medical image domain adaptation. Recent publications highlight contributions to federated learning frameworks (e.g., Hermes, FedHyper), edge computing optimizations (EdgeLoRA), and AI safety (Moderator). His group's work on MAPSeg achieved CVPR 2024 acceptance for medical image segmentation advancements. Grants include funding from Cisco for edge intelligence research. Dr. Li also serves on editorial and program committees, including IEEE TNNLS Associate Editor and ICLR 2025 Area Chair.

Maire O'NeillView profile

Professor

• Hardware Security
• Cryptography
• Approximate Computing
• +7 more

Maire O'Neill is a Professor at the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast, affiliated with the Secure Digital Systems (SDS) group and the Institute of Electronics, Communications & Information Technology (ECIT). Her research focuses on hardware security, cryptography, and secure embedded systems. She has held significant leadership roles and pioneered work in FPGA security, approximate computing, and post-quantum cryptography. Dr. O'Neill's academic journey includes over 20 years of contributions to secure digital systems, with a particular emphasis on cryptographic hardware, side-channel analysis, and IoT security. She has led major research projects such as the EU-funded 'TruDetect' initiative for hardware Trojan detection and the 'Secure IoT Processor Platform' project. Her work bridges theoretical research with practical applications, emphasizing real-world security challenges in electronics and computing. Education: Background in electrical engineering and computer science (details not explicitly stated in the text) Research Interests: Her work spans hardware security primitives, FPGA-based cryptographic solutions, and energy-efficient computing. She explores vulnerabilities like Rowhammer attacks and side-channel leaks while developing defenses through approximate computing and machine learning techniques. Her research addresses emerging threats in IoT, 5G networks, and post-quantum cryptography. Publications Trends: Recent work emphasizes machine learning applications in security (e.g., ML-KEM accelerator designs), hardware Trojan detection, and energy-efficient approximate computing. She frequently publishes in top-tier venues like IEEE Transactions and ACM conferences, with a focus on practical implementations and FPGA demonstrators. Awards: 2007: BFIIN ITEC Platinum Award, British Female Inventor of the Year, European Union Women Innovators 2015: Fellow of the Irish Academy of Engineering 2015: INVENT Award for collaborative innovation Advising & Grants: Supervised 8 PhD students (explicitly stated). Active in securing research grants (18 projects listed), including EU and industry collaborations. Engages in academic service roles like IEEE Distinguished Lecturer and international conference organization. Labs/Teams: Leads the Secure Digital Systems (SDS) group, collaborating with global partners on hardware security and IoT initiatives. Maintains strong ties with industry through projects like NIO New Deal Cyber Bid and TruDetect.

David J PaulView profile

Associate Professor

• Networks and distributed systems
• Internet and Cloud computing
• Security and Privacy
• +7 more

Associate Professor David J Paul is a computational scientist at the University of New England's School of Science and Technology within the Faculty of Science, Agriculture, Business and Law. With expertise spanning computer science, distributed systems, and applied technology, he has established himself as a multidisciplinary researcher bridging computer science with agriculture, sports science, and healthcare domains. His work consistently focuses on practical technology applications that address real-world challenges while maintaining data privacy and security. Dr. Paul earned his Bachelor's degrees in Mathematics and Computer Science from the University of Newcastle in 2004, followed by Honours in Computer Science in 2005, and completed his PhD titled "Deliberate Cooperation in Service-Oriented Environments: Dynamic Transactional Workflows for Web Services" in 2012. Prior to joining UNE in 2015, he worked at the Schizophrenia Research Institute from 2005-2015 and collaborated with the Health Behaviour Research Group starting in 2014. His research interests span networks and distributed systems (including Internet and Cloud computing), security and privacy, computer science education, sports science, agriculture, and e-Health. Dr. Paul has demonstrated particular strength in developing systems that integrate multiple disparate datasets while maintaining privacy, as evidenced by his work on the Australian Schizophrenia Research Bank and agricultural applications like ASKBILL and RamSelect. His recent work shows increasing focus on cybersecurity applications for SMEs, women's sports analytics, and precision agriculture technologies. Dr. Paul has secured multiple research grants including University of New England SABL Teaching & Learning Grants (2023), an Australian Council of Deans of ICT grant (2022-2023), SheepCRC projects (2015-2016), and a NeCTAR grant (2012). His extensive publication record demonstrates consistent productivity across both theoretical computer science and applied domains. School of Science and Technology Teaching Award (2017) for Computer Science curriculum redesign School of Science and Technology Development Award for COSC110 introductory programming unit With over 20 research students supervised across PhD, Master's, and Honours programs, Dr. Paul has established himself as a dedicated mentor. His current research portfolio spans cybersecurity for SMEs, precision agriculture technologies, sports analytics (particularly women's rugby league), and advanced cryptographic techniques. His work on QuON, ASKBILL, and RamSelect demonstrates his ability to create practical technological solutions that address specific industry challenges while maintaining rigorous academic standards.

• Hardware Security
• Cryptographic Circuits
• Formal Methods
• +3 more

Devanshi Upadhyaya is a Researcher at the Institute of Computer Architecture and Computer Engineering under the University of Stuttgart. Her work focuses on hardware security, cryptographic circuit protection, and formal methods for analyzing vulnerabilities in embedded systems. She is actively engaged in advancing secure hardware design principles through research in side-channel attacks, fault injection, and logic locking mechanisms. Her research interests include cryptographic circuit security, formal verification of hardware systems, and securing neural network accelerators. She has contributed to developing frameworks that assess vulnerabilities in mixed-signal and approximate computing architectures, emphasizing practical defenses against both software and hardware-based attacks. Notable projects include optimizing waveform accurate fault attacks and enabling power side-channel simulations for AI hardware accelerators. Her work bridges theoretical security models with real-world hardware implementation challenges. Devanshi holds a M.Sc. degree and is affiliated with the Hardware Oriented Computer Science department at the University of Stuttgart, where she collaborates on interdisciplinary projects involving cryptography, embedded systems, and machine learning hardware.

Panos TrahaniasView profile

Professor

• Robotics
• Computer Vision
• Humanoid Robots
• +1 more

Panos Trahanias is Professor and Vice Chair in the Department of Computer Science at the University of Crete, and heads the Computational Vision and Robotics Laboratory (CVRL) at Foundation for Research and Technology - Hellas (FORTH). His research bridges computational vision, robotics, and embedded systems, with specific focus on humanoid robot navigation, real-time SLAM implementations, and adaptive control systems. Recent investigations include developing climbing quadruped robots, robust grip-lifting mechanisms, and probabilistic contact estimation methods for dynamically challenging terrains. Medical applications feature prominently through neural network approaches for ventilator waveform analysis in critical care settings. Hardware innovation is demonstrated through FPGA-accelerated visual SLAM architectures and reconfigurable embedded systems designed for resource-constrained robotic platforms. His work consistently advances the integration of probabilistic methods, deep learning, and adaptive control in autonomous systems.

Jianhui YueView profile

Assistant Professor

• Computer Architecture
• Operating Systems
• System Optimization of Big Data Processing
• +3 more

Jianhui Yue is an Assistant Professor in the Department of Computer Science at Michigan Technological University. His research focuses on computer architecture, operating systems, and system optimization for big data processing. He specializes in memory systems, persistent memory technologies, and hardware acceleration for graph processing and machine learning workloads. His work addresses challenges in optimizing performance, energy efficiency, and reliability in modern computing systems. Key research areas include hybrid memory architectures, in-storage accelerators (e.g., FlashGNN), and crash consistency mechanisms for non-volatile memory. His publications emphasize innovations in cache optimization, NAND flash management, and graph algorithms for dynamic data mining. His recent work (e.g., Cheetah, P3DC) highlights advancements in reducing latency and improving scalability in high-performance computing environments. Dr. Yue’s contributions span hardware-software co-design, with a focus on practical applications of computer architecture principles to real-world systems. His research bridges theoretical concepts with deployable solutions, addressing critical bottlenecks in data-centric computing.

Linda DeBrunnerView profile

Professor

• Digital Signal Processing Hardware
• Low Power Implementations
• Special-Purpose Digital Systems
• +3 more

Dr. Linda DeBrunner is a Professor of Electrical and Computer Engineering at the FAMU-FSU College of Engineering, Florida State University since 2006. Previously, she was a Professor at the University of Oklahoma (1990-2006). She holds a Ph.D. (1991), M.S. (1986), and B.E.E. (1984) from Virginia Tech and Auburn University, respectively. Her research focuses on digital signal processing hardware, low-power implementations, and special-purpose digital systems. Notable projects include low-power DSP designs, multiplier optimization, and bridge displacement estimation using modal analysis. She has secured over $6 million in external funding from NSF, ONR, FHWA, and industry. DeBrunner has published >50 refereed papers and serves on editorial boards (e.g., Journal of Circuits, Systems, and Computers) and conference committees (Asilomar). She received 'Favorite Professor' accolades twice and is a Senior IEEE Member. Her work emphasizes FPGA-based systems, multiplier architectures, and real-time signal processing applications. Recent projects explore the discrete Hirschman transform for convolution optimization and bridge health monitoring via accelerometer data analysis.

Erdal OrukluView profile

Associate Professor

• Reconfigurable computing
• Advanced computer architectures
• Hardware/software co-design
• +8 more

Erdal Oruklu is an Associate Professor of Electrical and Computer Engineering and Associate Chair at the Department of Electrical and Computer Engineering, Illinois Institute of Technology (IIT). He joined IIT in 2005 after completing his Ph.D. in Computer Engineering at IIT. His research focuses on reconfigurable computing, advanced computer architectures, VLSI design, and embedded systems, with applications in ultrasonic imaging, signal processing, and machine learning. Education: B.Sc. in Electronics and Communications Engineering from Technical University of Istanbul (1995), M.Sc. in Electrical Engineering from Bogazici University (1999), and Ph.D. in Computer Engineering from IIT (2005). Research interests include real-time ultrasonic embedded systems, system-on-chip (SoC) design, high-speed computer arithmetic, and sensor technologies. He leads the VLSI and SoC Research Laboratory and co-directs the ECASP Laboratory. His work has been supported by grants from NSF, DOE, and industry partnerships. Notable contributions include reconfigurable architectures for ultrasonic flaw detection, polymorphic computing architectures, and FPGA-based implementations of signal processing algorithms. He has supervised numerous Ph.D. and M.S. students, contributing to advancements in hardware/software co-design, embedded systems, and cybersecurity. He holds senior membership in IEEE and serves on editorial boards and technical committees for major conferences. Awards include Best Paper Awards at MWSCAS and EIT, and recognition as an outstanding professor.

Hans-Joachim WunderlichView profile

Professor

• Computer Architecture
• Hardware Reliability
• Fault Tolerance
• +7 more

Prof. Hans-Joachim Wunderlich is a Professor at the University of Stuttgart's Institute of Computer Architecture and Computer Engineering, within the Faculty of Computer Science, Electrical Engineering, and Information Technology. His research focuses on hardware reliability, fault tolerance, and testing methodologies for VLSI circuits and embedded systems. He specializes in areas such as delay fault testing under PVT variability, approximate communication, and aging-aware design. Key research interests include robust testing techniques for small delay faults, error-tolerant communication protocols (e.g., RAPPER and Gray code-based approaches), and GPU-accelerated simulation of faults. His work addresses challenges in functional safety, interconnect reliability, and securing reconfigurable architectures against security violations. He explores energy-efficient iterative solvers for approximate computing environments and develops methods for predicting device aging and early life failures. Prof. Wunderlich’s contributions span academic and industrial applications, with a focus on real-time systems and dependable multi-processor systems-on-chip (MPSoCs). His research integrates formal verification, machine learning for defect detection, and hybrid protection schemes for reconfigurable scan networks. Recent work emphasizes stress-aware testing strategies and optimization of sensor data streaming in resource-constrained environments.

Kui WuView profile

Professor

• Computer Networks
• Wireless and Mobile Networking
• Mobile Computing
• +5 more

Prof. Kui Wu is a Professor in the Department of Computer Science at the University of Victoria, affiliated with the Faculty of Engineering and Computer Science. His research focuses on computer networks, wireless and mobile networking, mobile computing, and network security. He is part of the Parallel, Networking and Distributed Computing (PANDA) research group. Key areas of expertise include distributed learning frameworks, autonomous systems, IoT anomaly detection, and edge computing architectures. His work integrates machine learning techniques with network optimization, addressing challenges in real-time systems, security, and resource allocation. Notable contributions include advancements in federated learning, privacy-preserving distributed systems, and UAV-based monitoring solutions. Prof. Wu's research also explores edge computing innovations, such as smart contract-aided IoT resource sharing and energy-efficient edge data centers. He has contributed to over 50 peer-reviewed publications, with recent work emphasizing AI-driven network design, anomaly detection in IoT, and reinforcement learning applications in autonomous driving safety. His research has practical implications for improving the reliability and efficiency of next-generation communication and computing infrastructures.

Sabur H BaidyaView profile

Assistant Professor

• Autonomous Systems
• Cyber-Physical Systems
• Internet-of-Things (IoT)
• +4 more

Sabur H Baidya is an Assistant Professor in the Department of Computer Science and Engineering at the University of Louisville's J.B. Speed School of Engineering. He leads the Autonomous Intelligent Mobile Systems Lab (AIMS Lab) and is affiliated with the Louisville Automation & Robotics Research Institute (LARRI). His research focuses on autonomous systems, cyber-physical systems, IoT, edge computing, and distributed intelligence. Education: B.S. in Communication Engineering, West Bengal University, 2007 M.S. in Computer Science, University of Texas at Dallas, 2013 Ph.D. in Computer Science, University of California, Irvine, 2019 Research Interests: His work integrates sensing, communication, and computing systems to develop intelligent distributed systems. Key areas include IoT security, optimization of edge computing architectures, and autonomous drone/robotics applications. He explores machine learning techniques for resource-constrained environments and cybersecurity in containerization platforms. Awards & Recognition: No specific awards listed, though his research has been published in top-tier venues across autonomous systems, IoT, and cybersecurity domains. Advising & Grants: While no student names or grant details are provided, his lab actively engages in collaborative projects with industry partners like Nokia Bell Labs and Huawei, and academic collaborations at Rutgers WINLAB and UC San Diego. Labs & Teams: Directs the AIMS Lab, collaborating with the Louisville Automation & Robotics Research Institute (LARRI) to advance robotics and automation technologies. His work also intersects with the Jacobs School of Electrical and Computer Engineering (UCSD) and WINLAB (Rutgers).

Sean ChoiView profile

Assistant Professor

• Computer Networking
• Programmable Hardware
• Machine Learning Infrastructure
• +3 more

Sean Choi is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University. He holds a Ph.D. in Electrical Engineering from Stanford University (2020), M.S. in Computer Science from Stanford (2013), and a dual B.S. in Computer Science and Business Economics & Management from Caltech (2011). His research focuses on programmable network hardware, including smartNICs, and their applications in machine learning, blockchain, and cloud computing. Education: Ph.D. in Electrical Engineering, Stanford University (2020) M.S. in Computer Science, Stanford University (2013) B.S. in Computer Science & Business Economics, California Institute of Technology (2011) Research Interests: Programmable Network Switches, SmartNIC Acceleration, Cloud Infrastructure, Serverless Computing, Blockchain Optimization, and Privacy-Preserving Machine Learning . Recent Work: Explores leveraging programmable hardware for low-latency machine learning inference, blockchain scalability via NIC-assisted consensus, and privacy-preserving federated learning through homomorphic encryption offload. His NSF-funded project develops ultra-low-latency ML systems using programmable networks. Awards: NSF CRII 2023 Award Hewlett Packard Enterprise Grant Labs/Teams: Leads research on network programmability and hardware acceleration within Santa Clara University's School of Engineering. Collaborates with industry on data center network optimization and cloud infrastructure projects.

• Computer Engineering
• Hardware Security
• Artificial Intelligence
• +3 more

Jaan Raik is a Tenured Full Professor and Head of the Centre for Dependable Computing Systems at Tallinn University of Technology (TalTech), Department of Computer Systems. His research focuses on hardware reliability, fault tolerance in embedded systems, and secure computing architectures. Key areas include fault-resilient deep neural network accelerators, approximation methods for DNNs, and vulnerability assessment in hardware-software systems. He leads the Centre for Dependable Computing Systems, which explores advanced reliability engineering techniques for modern computing systems. His work integrates formal verification, machine learning, and hardware design to address challenges in embedded systems, RISC-V processors, and edge AI chips. Notable contributions include frameworks like Saffira for DNN accelerator reliability assessment and innovative methods for assertion-based verification. Research interests span fault diagnosis, approximation computing, and security-aware architectures. He frequently publishes at top venues such as IEEE European Test Symposium and DDECS, with recent focus on resilient neural networks, cache leakage detection (CLD), and hardware Trojan mitigation. Collaborations include industry partnerships for practical deployment of fault-tolerant systems and academic initiatives to standardize reliability assessment methodologies. His work bridges theoretical computer engineering with applied solutions for real-world systems.

Gabriela NicolescuView profile

Professor

• Cybersecurity for Avionics
• Security by Design for Cyber-Physical Systems
• Advanced Programming Models for Avionic Multi and Many-Core Systems
• +11 more

Gabriela Nicolescu is a Professor at Polytechnique Montréal , affiliated with the Department of Computer Engineering and Software Engineering and the LAMA-WeST (Web, Semantics and Text) research group. Her work focuses on design methodologies , programming , and security for advanced systems integrating 3D SoCs , optical networks , and heterogeneous technologies . Education: B.Sc. and M.Sc., Politehnica Bucharest Ph.D., Institut National Polytechnique de Grenoble (2002), awarded Best Thesis in Microelectronics Research Interests include cybersecurity for avionics , security by design for cyber-physical systems , advanced programming models for multi-core systems , and modeling/simulation of heterogeneous systems . She has contributed to fields like optical networks on chip , real-time embedded systems , and edge computing security . Recent Publications highlight her leadership in all-optical neural networks , formal verification , and security for avionic systems . Her work spans machine learning-driven intrusion detection , photonic integrated circuits , and ontology-based cybersecurity frameworks . Scientific Awards: Best Thesis in Microelectronics, Institut National Polytechnique de Grenoble (2002) Student Supervision encompasses doctoral and master’s theses in areas such as multi-core RTOS , security in autonomous systems , AI-based penetration testing , and performance optimization in avionics . Current advisees include Felipe Magalhaes , Julien Carayol , and Kacem Khaled , while completed supervision includes A. S. Shahnejat Bushehri and Rami Zrelli . She is actively involved in collaborative research projects addressing national cybersecurity priorities and safer aircraft design , as highlighted in media outlets like The Globe and Mail and Science-Presse .

Aastha MehtaView profile

Assistant Professor

• Systems Security
• Data Privacy
• Operating Systems
• +3 more

Aastha Mehta is a Tenure-Track Assistant Professor in the Computer Science Department at the University of British Columbia (UBC), affiliated with the Systopia Lab and the Security and Privacy Group. Her research focuses on systems security, data privacy, and distributed systems, particularly in cloud and edge platforms. She teaches courses such as CPSC 538M (Systems Security) and CPSC 317 (Introduction to Computer Networking). Research Interests: Her work spans systems security, network privacy, ICS security, and privacy-preserving technologies. Key projects include compliance tools for serverless applications (Growlithe), network side-channel mitigation (NetShaper), and epidemic risk mitigation systems leveraging Bluetooth beacons. Received NCC Funding for ICS security research (2024) Faculty Teaching Awards (UBC) Advising: Currently supervises PhD and MSc students like Yayu Wang, Angela DeMarco, and Satvik Vemuganti. Collaborates with researchers from MPI-SWS, UofT, and UBC ECE on security and privacy projects. Service: Serves on program committees for CCS 2025, ASPLOS 2025, and Oakland S&P. Organized mentoring programs at SOSP and OSI.