Find Academic Supervisors

Wei-Shinn KuView profile

Professor

• Data management systems
• Data analytics
• Cybersecurity
• +3 more

Wei-Shinn Ku is a Professor in the Department of Computer Science and Software Engineering at Auburn University . He earned his Ph.D. in Computer Science from the University of Southern California in 2007, following M.S. degrees in Computer Science (2003) and Electrical Engineering (2006), and a B.S. in Information and Computer Education from National Taiwan Normal University (1999). As a program director at the National Science Foundation (2019–2022), he shaped research directions in data science and cybersecurity. Dr. Ku leads the Data Science and Engineering Laboratory (DSE Lab) at Auburn University, focusing on data management systems , data analytics , cybersecurity , and mobile computing . His recent work includes privacy-preserving techniques for IoT networks, spatio-temporal modeling for traffic prediction, and federated learning frameworks for mobile social networks. He has secured multiple NSF grants for projects like Indoor Spatial Query Evaluation and Data Integrity in Clouds . Dr. Ku's 15 most recent articles (2024–2025) span domains including traffic prediction , air quality modeling , secure cloud computing , and IoT security , with subfields like graph neural networks, quadtree-based optimization, and homomorphic encryption. His research has been recognized with awards such as the Auburn Alumni Engineering Council Research Award (2018, 2012) and the AFRL Summer Research Associate (2016). He has advised 18 current and graduated students in Ph.D. and M.S. programs, including Wenlu Wang, Ting Shen, and Kazuya Sakai. His professional service includes chair roles at IEEE/ACM conferences (ICDE, CIC, ICLR) and editorial positions at journals like IEEE Transactions on Knowledge and Data Engineering .

• Collective Intelligence
• Multiagent Systems
• Knowledge Diffusion
• +3 more

Marcin Maleszka is a researcher at the Faculty of Information and Communication Technology at Wrocław University of Science and Technology, specializing in collective intelligence , multiagent systems , knowledge diffusion , and knowledge integration . His work bridges computational models with social dynamics, focusing on group learning, recommendation systems, and secure data sharing. Current affiliation: Department of Applied Informatics, Wrocław University of Science and Technology Email: marcin.maleszka@pwr.edu.pl Office hours: Wednesday 13:30–15:00, Thursday 13:00–15:00, Saturday 16:45–17:45 His research explores how agent-based systems can model social collectives, optimize knowledge transfer, and enhance educational and healthcare technologies. Recent publications highlight applications in digital heritage , medical diagnostics , and urban mobility , with methodologies spanning GANs , ANNs , and agent modeling . Key publication trends include: Integration of Generative Adversarial Networks and Graph Neural Networks for heritage data Development of agent-based models for group learning Security mechanisms in peer-to-peer systems using attribute-based encryption Applications in medical imaging and traffic modeling Contact details: Address: ul. Z. Janiszewskiego 11/17, 50-372 Wrocław Room: C-3, 15 (Wrocław) and D-1, 30 (during Saturday office hours) Phone: +48 71 320 2938

Qing YangView profile

Professor

• Computer Architectures
• Hardware/Software Designs for AI
• Machine Learning
• +2 more

Qing Yang is a Professor at the University of Rhode Island's College of Engineering , specializing in Electrical, Computer and Biomedical Engineering. His research spans Computer Architectures , Hardware/Software Designs for AI , Machine Learning , Data Storage , and Computer Networks . Ph.D., Computer Engineering, University of Louisiana, Lafayette, 1988 M.A., Electrical Engineering, University of Toronto, 1985 B.S., Computer Science, Huazhong University of Science and Technology, China, 1982 Yang's work focuses on advanced computing systems, including FPGA optimization, in-sensor processing for ADAS, and secure die-to-die communication architectures. His publications highlight innovations in storage systems, graph processing acceleration, and thermal monitoring for data centers. Recent research trends emphasize AI hardware acceleration , runtime security mechanisms , and smart storage solutions , with articles published in venues like FPGA, IEEE NAS, and ACM Transactions on Storage. Yang has secured multiple National Science Foundation grants for projects such as "Introducing a New In-Sensor Computing Architecture for Intelligent 3-D Imaging Systems." His inventions in data recovery and cache coherence have resulted in 14 U.S. patents , several of which were commercialized through a startup.

• Cyber-security
• Digital Forensics
• Networks
• +4 more

Rich Macfarlane is an Associate Professor at the School of Computing Engineering and the Built Environment , Edinburgh Napier University, UK. He specializes in Cyber-security , Digital Forensics , and Network Security , with extensive experience in ransomware detection, forensic tool evaluation, and cloud-based security training. Research Centres: Centre for Distributed Computing, Networking and Security Projects: Directed KTP initiatives funded by Innovate UK, Data Lab, and Scottish Enterprise Research Interests include: Ransomware attack detection and mitigation Digital forensic tool validation Cloud infrastructure security Entropy-based file analysis Deception techniques for threat identification Article Trends show technical focus on ransomware detection, entropy analysis, digital forensic datasets, and wireless network monitoring. The work combines mathematical modeling , cloud-based platforms , and machine learning applications in forensics. Education & Supervision : Second Supervisor for Matthew Thomson (2023) - Digital Media & AI Forensics Second Supervisor for Philip Penrose (2013-2017) - Large Storage Device Forensics Grants : Managed industry-funded projects totaling £874,436 including KTP with Morgan Stanley (£136,306), Fragment Finder (£228,757), and vSoC development (£119,363).

• Cybersecurity
• IoT Security
• Threat Intelligence Sharing
• +4 more

Dr. Nick Pitropakis is an Associate Professor at the School of Computing Engineering and the Built Environment (Edinburgh Napier University). He actively contributes to cybersecurity research through 39 publications, focusing on threat intelligence sharing , cloud security , IoT security , and privacy-preserving technologies . CyberHunt - Automated Threat Hunting for Critical Infrastructures (Funder: Norway RC) TRUST Platform - Crossborder Data Federation (Funder: UKRI) AI-based Biometric Authentication (Funder: Data Lab) His research combines graph theory for attack path analysis and blockchain for secure threat intelligence sharing. Key publication areas include: LLMs in threat hunting Chaotic encryption schemes Adversarial machine learning Distributed ledger applications Scientific contributions span 19 journal articles, 15 conference proceedings, and 2 book chapters. Collaborates with Prof Bill Buchanan and Dr Pavlos Papadopoulos on cybersecurity standardization and trusted computing .

• Computer Vision
• Machine Learning
• Image Processing
• +5 more

Dr. Xin Lin is a Professor at the School of Computer Science and Technology, University of Science and Technology of China in Hefei. With an extensive publication record spanning computer vision, machine learning, and artificial intelligence, Dr. Lin leads a research group focused on solving challenging problems in image processing, robotics, and wireless communications. His work bridges theoretical advancements with practical applications across healthcare, autonomous systems, and industrial manufacturing. Dr. Lin's research interests encompass computer vision, machine learning, image processing, and artificial intelligence, with particular expertise in image restoration, 3D object detection, and human pose estimation. His laboratory develops innovative approaches to handle multiple image degradations simultaneously and create lightweight, efficient vision systems suitable for real-world deployment. The research demonstrates strong interdisciplinary connections, applying computer vision techniques to medical imaging, satellite communications, and industrial IoT applications. Analysis of Dr. Lin's recent publications reveals a strong focus on multi-task learning approaches that address multiple image degradation problems simultaneously. His work shows increasing sophistication in handling complex real-world scenarios, from low-light conditions to rain interference, while maintaining computational efficiency. The research trajectory demonstrates a clear path from fundamental image processing techniques to practical applications in autonomous driving, healthcare, and industrial systems. Dr. Lin has received recognition for his contributions to the field through numerous publications in top-tier venues including CVPR, IEEE Transactions, and ACL. His work on image restoration, particularly the Dual Degradation Representation framework, has gained significant attention in the computer vision community. Dr. Lin actively supervises graduate students and collaborates with researchers worldwide. His laboratory works on cutting-edge projects involving digital twins for manufacturing, satellite communications, and medical imaging applications. Current research directions include developing more robust and efficient models for real-world deployment scenarios, with particular attention to resource-constrained environments.

• Machine Learning
• Deep Learning
• Network Security
• +5 more

Ayman Yafoz is an active Associate Professor in the Department of Computer Science at Alexandria University's College of Engineering. His research bridges computer science and engineering with a focus on practical security and optimization applications. His scholarly output includes 60 publications with an h-index of 12, demonstrating consistent contributions to his fields of expertise. Dr. Yafoz's research interests center on the intersection of optimization algorithms and machine learning/deep learning techniques. His work spans network security, intrusion detection systems, medical image analysis, Arabic language processing, and IoT security applications. He frequently develops novel hybrid models that combine metaheuristic optimization approaches with neural network architectures to solve complex real-world problems. His publication portfolio reveals strong trends in security applications, with approximately 60% of his recent work focused on various aspects of cybersecurity including intrusion detection, botnet detection, and encryption. Another significant portion addresses medical imaging and accessibility technology, demonstrating his commitment to applying computational methods to healthcare challenges. Dr. Yafoz maintains active research collaborations with scholars across the Middle East, particularly with co-authors Raed Alsini, Omar Alghushairy, and Khaled Tarmissi. His work appears primarily in engineering journals with notable publications in Alexandria Engineering Journal, IEEE Access, and Sustainability.

• Machine Learning
• Compressed Sensing
• Privacy-Preserving Techniques
• +4 more

Jia Liang is a researcher at Henan Polytechnic University's School of Electrical Engineering and Automation, with a focus on Machine Learning , Compressed Sensing , and Privacy-Preserving Techniques . His work bridges Computer Science and Signal Processing , particularly in Radar Imaging and Medical Image Analysis . Key Collaborations: Di Xiao, Ying Luo, Qun Zhang, Hui Huang Technical Expertise: Federated Learning, SAR Imaging, Compressive Sensing, Adversarial Learning His research emphasizes secure data processing in IoT and cloud environments, with recent innovations in cross-disciplinary applications like biosignal analysis for cysticercosis diagnosis . Publications span top venues including IEEE Transactions on Aerospace Systems and Remote Sensing . Notable trends include privacy-preserving machine learning for federated systems and 3D radar imaging of rotating targets, alongside medical imaging solutions for chest radiographs and optical coherence tomography .

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +5 more

Övgü Öztürk Ergün is an Assistant Professor in the Department of Software Engineering at Muğla Sıtkı Koçman University's Faculty of Engineering. She holds a Ph.D. from The University of Tokyo's Department of Frontier Informatics. Her research focuses on computer vision, machine learning, and AI applications in areas such as augmented reality, cultural heritage digitization, and human behavior analysis. She has led multiple TÜBİTAK-funded projects including 'Customer Tracking and Interest Measurement Technologies for Retail Sector Sales Performance Improvement' and '3D Modeling of Cultural Heritage Sites.' Her work spans academic publications, industry collaborations, and innovative teaching in software engineering and interactive multimedia systems. Education: Middle East Technical University, B.Sc. Electrical & Computer Engineering (2003) University of Tokyo, Ph.D. Frontier Informatics (2010) Research Interests: Her interdisciplinary research integrates computer vision with real-world applications, including privacy-preserving biometrics, smart environment monitoring, and cultural heritage preservation. She has pioneered ontology-based systems for Turkish cuisine and developed AR tools for virtual try-on. Her work emphasizes ethical AI and context-aware systems, with recent focus on food recognition using CLIP models and churn prediction in telecom sectors. Awards & Grants: TÜBİTAK Undergraduate Software Projects Competition (2013) Robot Days 3rd Place (2013) Completed TÜBİTAK 1001/1505 projects totaling over 500k USD funding Advising & Labs: Supervised BENGÜ-ÖZTÜRK's thesis on food image recognition. Active in the University's Research & Application Centers for Computer Vision and AR/VR development. Collaborates with ISTKA and Ministry of Development on cultural heritage digitization projects.

• Image Processing
• Biomedical Signal Analysis
• Medical Imaging
• +3 more

Dr. Gizem DURSUN DEMİR is a Researcher at the Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, Suleyman Demirel University. She holds a Ph.D. in Electrical and Electronics Engineering from the same institution (2023). Her research focuses on image and biomedical signal processing, with applications in medical diagnostics and cybersecurity. Education: Bachelor's: Suleyman Demirel University, Electronics and Communication Engineering (2014) Master's: Suleyman Demirel University, Electronics and Communication Engineering (2017) Ph.D.: Suleyman Demirel University, Electronics and Communication Engineering (2023) Research Interests: Image/video processing, biomedical signal analysis, medical imaging, cryptography, machine learning, and neurodegenerative disease diagnostics. She specializes in developing AI-driven tools for automated analysis of biomedical signals and medical imaging. Recent Work Trends: Recent publications emphasize dental radiography segmentation using generative models, automated calcium imaging analysis for neurodegenerative diseases (e.g., ALS), and AI applications in pathology. She also explores secure image encryption techniques. Grants & Projects: EU H2020 Project: Automated Functional Screening of IgGs for Neurodegenerative Disease Diagnostics (2018–2023) National Project: Image Encryption via Lossless Transform Methods (2016–2017) Labs/Teams: Collaborates with teams at the University of Belgrade and Suleyman Demirel University on neuroglial structure analysis and biomedical signal processing.

Xiaoxiao Shaun LiView profile

Assistant Professor

• Trustworthy AI
• Machine Learning
• Federated Learning
• +4 more

Xiaoxiao Shaun Li is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), with an adjunct position at Yale School of Medicine. She holds positions as faculty at the Vector Institute and is a Canada CIFAR AI Chair and Canada Research Chair (Tier II) in Responsible AI. Her research focuses on developing trustworthy and efficient machine learning systems, particularly in federated learning, medical imaging, and agentic AI. Dr. Li earned her Ph.D. from Yale University and completed postdoctoral work at Princeton University. Education: B.S. (Honors), Zhejiang University, China, 2015 Ph.D., Yale University, 2019 Research Interests: Trustworthy AI foundational theories and algorithms Federated learning frameworks for privacy-preserving applications Agentic AI systems with robust decision-making Medical imaging analysis and fairness benchmarking Interpretable neuroimaging models for brain disorder prediction Grants & Awards: Canada Foundation for Innovation Grant (PI, 2023) UBC Green Lab Fund (PI, 2023) Canada Research Chair Tier II (2022–2027) Labs & Teams: Dr. Li leads the Trusted and Efficient AI (TEA) Lab at UBC, focusing on next-generation AI systems. The lab collaborates with industry partners like Vector Institute and academic institutions globally to bridge research and clinical applications.

Melek ÖnenView profile

Professor

• Applied Cryptography
• Cloud Security
• Security and Privacy for Big Data and Machine Learning
• +3 more

Melek Önen is a Full Professor at EURECOM's School of Digital Security, specializing in applied cryptography, cloud security, and privacy-preserving technologies. Her work focuses on cybersecurity challenges in federated learning, big data, and IoT systems. She leads research on secure aggregation techniques, privacy-preserving machine learning, and cryptographic protocols for real-world applications like healthcare. Key research interests include federated learning security, homomorphic encryption, and privacy-aware data analytics. She has developed frameworks such as PAPAYA for privacy-preserving data analytics and UPRISE-IoT for IoT privacy solutions. Her projects often address regulatory compliance (e.g., GDPR) in cloud and distributed systems. Recent research trends show a strong focus on mitigating adversarial attacks in distributed systems, optimizing secure computation efficiency, and balancing privacy with functionality in machine learning. Notable contributions include secure biometric authentication protocols ( Nomadic ), fault-tolerant federated learning systems, and privacy-preserving image analysis methods. Önen collaborates on EU-funded initiatives like TREDISEC (trustworthy cloud security) and PAPAYA , emphasizing practical implementations. She actively contributes to open-source tools like Fed-BioMed for federated healthcare AI. Her work bridges theoretical cryptography with real-world deployment challenges.

Ferdi DOĞANView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +7 more

Ferdi Doğan is an Assistant Professor in the Department of Computer Engineering at Adıyaman University. He holds a PhD in Software Engineering (2021, Fırat University), an MSc in Computer Systems (2012, Sakarya University), and a BSc in Computer Systems (2002, Selçuk University). His research focuses on artificial intelligence, machine learning, deep learning, image processing, and cybersecurity. He has led TÜBİTAK projects on AI-driven solar panel defect detection, SCADA system security, and post-earthquake psychological analysis using AI. He is a member of IEEE and ACM, and has presented at over 10 international conferences. Professional Experience: Joined Adıyaman University as a Lecturer in 2007, became Department Head (2011-2019), and is currently Director of the Research and Application Center. He advises graduate theses and consults on TÜBİTAK projects. His applied research includes AI in agriculture, solar energy, and SCADA security algorithms. Teaching: Courses include Artificial Neural Networks, Internet Programming, and Software Engineering. He has taught at both undergraduate and graduate levels since 2002, with over 50 recorded courses in programming, algorithms, and web design. Awards: No specific awards listed but has secured TÜBİTAK grants and participated in international projects. His work has been cited over 12 times in peer-reviewed journals.

Shruti TopleView profile

Researcher

• Systems Security
• Privacy
• Machine Learning
• +3 more

Shruti Tople is a Principal Researcher at Microsoft Research in the Azure Research – Security and Privacy group. She holds a Ph.D. from the School of Computing at the National University of Singapore (NUS) , where she received the Dean's Graduate Research Excellence Award . Her work focuses on quantifying and mitigating information leakage in machine learning models while preserving their utility. Key Research Areas include: Systems Security Privacy in Machine Learning Differential Privacy Causal Learning Transfer Learning for Vision & Language Models Recent Publications address challenges such as membership inference attacks, federated backdoor defense, and privacy-enhanced deep learning. Her open-source projects like Analyzing PII Leakage and RobustDG provide practical tools for differential privacy and domain generalization. She collaborates with interns from institutions like NUS , University of Waterloo , and Imperial College London . Scientific Awards : Dean's Graduate Research Excellence Award (NUS)

• Heterogeneous Architectures
• High-Performance Computing
• Computer Systems Technologies
• +4 more

Leonel Augusto Pires Seabra de Sousa is a Full Professor at the University of Lisbon's Instituto Superior Técnico and a principal researcher at INESC-ID. His primary research focuses on heterogeneous architectures for high-performance computing, computer systems technologies, and cryptographic hardware acceleration. He holds a Fellow status with the IET and has been recognized as a Senior Member of both ACM and IEEE. Roles: Full Professor, Researcher (INESC-ID) Education: Details not explicitly provided in text His work spans parallel computing, embedded systems, and bioinformatics applications. Key contributions include energy-efficient architectures, RNS-based cryptographic accelerators, and GPU-driven phylogenetic analysis. Over 150 publications highlight his impact in these areas. Recent projects include COPMA (post-quantum cryptography hardware) and low-power 5G positioning systems. Awards include the HiPEAC Paper Award and multiple academic recognitions. Advising focuses on PhD/MSc students in HPC and embedded systems. Collaborates with industry partners on FPGA and GPU-based solutions.