Find Academic Supervisors

• Embedded Systems
• System-on-Chip (SoC) Design
• Computer Architecture
• +5 more

Professor Klaus McDonald-Maier is a full Professor in the School of Computer Science and Electronic Engineering (CSEE) at the University of Essex , where he leads the Embedded and Intelligent Systems (EIS) Research Laboratory and heads the Intelligent Embedded Systems and Environments Research Group . He is also Director of Impact , Visiting Professor at the University of Kent, and Visiting Research Affiliate at NASA Jet Propulsion Laboratory, California Institute of Technology. Education PhD in High-Performance Parallel Neural Network Architectures, Friedrich-Schiller-University Jena (Germany, 1999) Electronic Engineering studies, University of Ulm (Germany) Electronic Engineering studies, Cardiff University (Wales) Electronic Engineering studies, École Supérieur de Chimie Physique Électronique de Lyon (CPE-Lyon) (France) Research Interests Professor McDonald-Maier’s research integrates embedded systems , System-on-Chip (SoC) architectures , and AI-driven robotics . He pioneers visual place recognition techniques that remain robust under severe appearance and viewpoint changes, develops cybersecurity frameworks based on ICMetrics for autonomous vehicles and IoT, and designs approximate real-time computing solutions for energy-constrained multicore and FPGA platforms. His work on radiation-tolerant systems supports space and nuclear applications, while his bio-inspired algorithms enable lightweight, neuromorphic perception on resource-limited robots. Publication Trends Between 2022 and 2025 his output converges on FPGA-accelerated AI , secure edge intelligence , visual navigation for autonomous systems , and healthcare analytics . He repeatedly couples rigorous algorithmic innovation with practical hardware deployment, yielding energy-efficient, real-time systems validated in domains ranging from autonomous driving to post-stroke rehabilitation. Scientific Awards & Recognition Best Paper Award – IEEE Transactions on Sustainable Computing (2024) Best Paper Award – IEEE/ACM DATE (2024) Best Paper Award – IEEE Systems Journal (2022) Best Paper Award – IEEE Sensors Journal (2021) Best Paper Award – IEEE Access (2020) Research Grants & Industrial Collaboration He has secured major funding from EPSRC , EU Horizon 2020 , Innovate UK , and industry partners. Current projects span trustworthy autonomy, radiation-hardened edge AI, and AI-enhanced rehabilitation technologies. He is Chief Scientist of UltraSoC Technologies Ltd and CEO of Metrarc Ltd , commercialising University research in semiconductor debug and cybersecurity respectively. Laboratory & Team Leadership As Director of the Embedded and Intelligent Systems Laboratory (EIS Lab) , he oversees a multidisciplinary team of researchers and PhD students, providing state-of-the-art FPGA, robotics, and embedded-systems facilities. The lab collaborates closely with NASA JPL, UK Atomic Energy Authority, and leading semiconductor firms to translate fundamental research into high-impact industrial solutions.

Sinan YıldırımView profile

Researcher

• Bayesian Statistics
• Monte Carlo Methods
• Data Privacy
• +3 more

Sinan Yıldırım is a Researcher in the Faculty of Engineering and Natural Sciences at Sabancı University, Turkey. His primary research focuses on Bayesian Statistics, Monte Carlo methods, and data privacy, with interdisciplinary applications in machine learning and signal processing. He holds a BSc and MSc in Electrical and Electronics Engineering from Boğaziçi University, followed by a PhD in Mathematical Statistics from the University of Cambridge. Postdoctoral research (2013-2015) at the University of Bristol’s School of Mathematics involved the EPSRC-funded project 'Intractable Likelihood: New Challenges from Modern Applications (i-like).' His work bridges theoretical statistics with practical problems in privacy, control systems, and energy optimization. Research interests emphasize Bayesian methodologies for privacy-preserving data analysis, dynamic modeling of complex systems, and stochastic optimization algorithms. Recent publications explore differential privacy in machine learning, Monte Carlo techniques for high-dimensional inference, and applications of Bayesian methods in robotics and energy systems. Advising and grants include contributions to multi-party resource sharing frameworks and privacy-aware algorithms. His work integrates computational methods with real-world challenges in engineering and policy modeling.

Alina RoitbergView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Human Activity Recognition
• +7 more

Alina Roitberg is a Junior Professor (Assistant Professor) at the University of Stuttgart , affiliated with the Faculty of Computer Science, Electrical Engineering and Information Technology . Her research focuses on advancing computer vision, machine learning, and robotics applications, particularly in human activity recognition, domain adaptation, and synthetic data generation. She explores challenges in action understanding, cross-domain generalization, and real-world deployment of AI systems in fields like healthcare, autonomous vehicles, and industrial automation. Her work emphasizes robust learning under noisy conditions, multimodal data fusion, and ethical AI applications. Recent projects include foundational studies on large language models in construction (AEC), video-based muscle group estimation, and improving driver activity recognition for autonomous vehicles. She also investigates circular factory design through uncertainty-aware process optimization and human-robot interaction. Dr. Roitberg's contributions span academic publications and industrial collaborations, addressing both theoretical advancements and practical implementations. Her research bridges computer vision techniques with real-world problems, emphasizing scalability and ethical considerations in AI deployment.

Nicola DragoniView profile

Professor

• Cybersecurity
• Internet of Things (IoT)
• Machine Learning
• +7 more

Nicola Dragoni is a Professor in Cybersecurity Engineering at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). As Deputy Director and Head of Section, he leads research initiatives focused on securing emerging technologies. Key Research Areas : Internet of Things (IoT) security, machine learning for intrusion detection, cyber-deception techniques, fog computing, malware analysis, blockchain applications, and wireless sensor network security. Supervision : Actively supervising multiple PhD students in projects related to cyber-deception, moving target defense, and bio-inspired security mechanisms. Recent Publications : Contributions to IoT honeypots, drone identification via RF signals, passkey adoption challenges, and cyber range taxonomies.

• Algorithmic Fairness
• Responsible AI
• Deep Learning
• +5 more

Golnoosh Farnadi is an Assistant Professor at McGill University's School of Computer Science , an Adjunct Professor at Université de Montréal , and a Visiting Faculty Researcher at Google Research . She holds the Canada CIFAR AI Chair and is a core academic member of Mila – Quebec AI Institute . Her research focuses on algorithmic fairness , responsible AI , and optimization . She founded the EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms) to address bias and discrimination in AI systems. Key publications explore fairness in kidney exchange programs, generative model geometry, multilingual LLM de-biasing, and prototype-based recommender systems. Her work bridges causal inference, adversarial robustness, and ethical AI. Google Award for Inclusion Research (2023) Women in AI Awards North America Finalist (2023) Facebook Privacy Enhancing Technologies Award (2021) IVADO Postdoctoral Fellowship (2018–2021) She has supervised over 15 PhD and Master's students, including Prakhar Ganesh (McGill) and William St-Arnaud (Université de Montréal). Her teaching includes Responsible AI and Machine Learning courses at McGill and HEC Montréal.

• Computer Networks
• Unmanned Aerial Vehicles (UAVs)
• Semantic Communication
• +12 more

Gökhan Seçinti is an Assistant Professor in the Department of Computer Engineering at Istanbul Technical University, Faculty of Computer and Informatics. He currently serves as Vice Dean and has previously held the role of Vice Department Head. His research focuses on next-generation wireless networks, UAV communications, semantic communication, and AI-driven networking solutions. Research Interests: His work spans Unmanned Aerial Vehicles (UAVs) , Semantic and Task-Oriented Communication , Software-Defined and Cognitive Networks , 6G Communications , and AI in Networking . He develops practical testbeds for deep learning-based communication architectures and explores digital twin applications in aerial networks. Publication Trends: Recent publications emphasize decentralized UAV service deployment, beam alignment using UWB localization, TDMA scheduling for aerial swarms, and semantic flow control. These reflect a strong trend toward intelligent, adaptive, and context-aware communication systems for IoT and mobility. Best Paper Award, IEEE, 2022 Best Conference Paper, IEEE, 2016 Best Poster Paper Award, IEEE, 2015 Advising and Grants: He has supervised 4 academic works and leads multiple funded research projects, including TÜBİTAK and SRP grants on federated learning in flying networks, semantic VANETs, AI-based intrusion detection, and UAV-assisted IoT for crisis management. Labs and Teams: His work is supported by active research teams at ITU, focusing on testbed development using SDRs, digital twins, and real-world deployment of UAV networks. He collaborates internationally, including past affiliations with Northeastern University.

• Smart Cities
• Cyber-Physical Systems (CPS)
• Internet of Things (IoT)
• +10 more

Dr. Hung Cao is an Assistant Professor of Computer Science at the University of New Brunswick, where he directs the Analytics Everywhere Lab. His work focuses on interdisciplinary research in Cyber-Physical Systems (CPS), IoT, Edge/Fog/Cloud Computing, and Explainable AI, addressing societal challenges through data-driven solutions. Prior roles include PostDoc Fellow and Data Scientist at the People in Motion Lab, UNB, and Lecturer/Researcher at Vietnam National University. He holds a Ph.D. in Geomatics Engineering (specializing in Data Science) from UNB (2020), an M.Sc. in Computer Science from University College Dublin (2015), and a B.Eng. from Vietnam National University (2011). Research interests span Smart Cities, Embedded AI, TinyML, Federated Learning, and Real-time Systems. He has led projects with Cisco, NB Power, and other industry partners to develop scalable analytics frameworks for IoT applications. Dr. Cao actively contributes to technical communities (IEEE Smart City, Edge Computing, etc.), serving as a reviewer for journals and conferences, and a Topic Editor for Electronics Journal . His innovations include the Analytics Everywhere framework for spatio-temporal data analysis, MACeIP platform for smart cities, and energy-efficient IoT systems for environmental monitoring. Current work emphasizes human-centered AI for healthcare diagnostics and industrial inspection systems.

Tianhao WangView profile

Assistant Professor

• Differential Privacy
• Machine Learning Privacy
• Data Security
• +3 more

Tianhao Wang is an Assistant Professor in the Department of Computer Science at the University of Virginia School of Engineering and Applied Science. His work focuses on advancing differential privacy and machine learning privacy, with particular expertise in privacy-preserving technologies for data synthesis, adversarial machine learning, and secure AI systems. His research interests span differential privacy mechanisms, secure data sharing, and mitigating privacy risks in modern AI systems. He explores how to protect sensitive information in machine learning models, synthetic data generation, and network analysis while maintaining utility. Recent work highlights include developing benchmarks for private image synthesis (DPImageBench), safeguarding text data from misuse (ExpShield), and analyzing privacy threats in pre-trained language models. His publications reflect a strong emphasis on both theoretical foundations and practical applications of privacy-preserving techniques. Dr. Wang's contributions address cutting-edge challenges in AI ethics, secure machine learning, and privacy engineering, with implications for healthcare, cybersecurity, and data-driven decision-making systems.

Helen CaiView profile

Senior Lecturer

• International Business
• Circular Economy
• Sustainable Supply Chain Management
• +11 more

Dr. Helen Cai is a Senior Lecturer in International Business and Circular Economy at Middlesex University Business School, where she serves as Programme Leader for the BA International Business Administration and BA Business Management (Top-Up). She also leads the Doctor of Business Administration (DBA) programme delivered in China through a partnership with United Business Institutions (UBI). Additionally, she holds a Full Visiting Professorship at Jiaxing University, China. Her academic leadership extends to editorial roles as Senior Editor of Cogent Business and Management and member of the editorial board of the Journal of World Business . She is Vice President of Marketing for the International Case Study Research Association (ICRA). Dr. Cai’s research focuses on international business, sustainability, green innovation, and corporate environmental governance. She employs advanced methodologies such as fuzzy-set qualitative comparative analysis (fsQCA), econometrics, and scientometric analysis. Her work addresses critical issues including green supply chains, carbon emissions, foreign direct investment, and institutional influences on innovation. The 15 most recent publications highlight a strong trend toward environmental sustainability, circular economy, and data-driven policy analysis. Her recent work spans blockchain in green supply chains, AI in rehabilitation, carbon governance, land use, and migration policy, demonstrating interdisciplinary breadth and policy relevance. Bronze award, The First National Collegiate Olympiad Mathematical Mirror Competition (2022) Honourable Mention, Interdisciplinary Contest in Modelling (2022) First Prize, Asia and Pacific Mathematical Contest in Modelling (2021) Third Prize, THE International Case Competition (2021) Dr. Cai has extensive supervisory experience, currently guiding multiple PhD candidates and having chaired over 30 DBA viva panels. She has supervised over 60 BA, 150 MSc, and 75 MBA dissertations. She has participated in 72 PhD/DBA committees in roles including external examiner, internal examiner, and panel chair. Her grants and funding are not explicitly mentioned, but her research output and editorial roles suggest sustained scholarly engagement. She leads research teams focused on circular economy and international business, and her future work is likely to expand into AI-driven sustainability analytics, cross-border green innovation, and climate governance. Her prior industry experience as a Senior Economist at China’s Central Bank enriches her applied research perspective.

Hongsheng HuView profile

Lecturer

• Data Privacy
• Machine Unlearning
• Trustworthy Machine Learning
• +5 more

Dr. Hongsheng Hu is currently a Lecturer in the School of Information and Physical Sciences at the University of Newcastle, Australia, specializing in the Data Science and Statistics focus area. Prior to this position, he served as a Postdoc Research Fellow at CSIRO's Data61 from October 2022 to August 2024. His academic journey includes a Doctor of Philosophy in Computer Systems Engineering from the University of Auckland in New Zealand, establishing his foundation in advanced computing systems. Dr. Hu's research centers on enhancing the trustworthiness of machine learning systems, with particular emphasis on identifying critical privacy vulnerabilities within machine learning models and developing robust defensive strategies. His work spans several key domains including adversarial machine learning (30% focus), statistical data science (30% focus), and data and information privacy (40% focus). He investigates membership inference attacks, machine unlearning techniques, and privacy-preserving mechanisms in federated learning environments. His research addresses fundamental challenges in AI security, exploring how machine learning models can be compromised through sophisticated privacy attacks and developing methods to mitigate these vulnerabilities while maintaining model utility. Analysis of Dr. Hu's publication record reveals a strong research trajectory focused on machine learning security and privacy. His work consistently addresses vulnerabilities in machine learning systems, particularly examining membership inference attacks, machine unlearning mechanisms, and privacy-preserving techniques in federated learning. The research spans top-tier venues including IEEE Security & Privacy, USENIX Security, NDSS, NeurIPS, IJCAI, AAAI, and WWW, demonstrating both technical depth and recognition by the research community. His publications show an evolving focus from foundational privacy attacks to developing more sophisticated unlearning techniques and robust defense mechanisms, with increasing citation counts indicating growing impact in the field. Active Program Committee member for USENIX Security, NDSS, ICLR, IJCAI, WWW, ICDM, ECML, and PKDD Invited reviewer for IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Pattern Analysis and Machine Intelligence (IPAMI), and ACM Computing Surveys (CSUR) Dr. Hu currently serves as Course Coordinator for STAT6020 and STAT2020 Predictive Analytics at the University of Newcastle. As an academic supervisor, he co-supervises one PhD student working on 'Identifying and Mitigating Vulnerability in Recommender Systems' at Macquarie University. His research collaborations span multiple countries, with significant publication counts in Australia (18), China (12), New Zealand (10), and the United States (7), reflecting an active international research network focused on AI security challenges.

Hao ZhangView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Computer Vision
• +6 more

Hao Zhang is an Associate Professor in the Department of Computer Science at the Manning College of Information and Computer Sciences (CICS), University of Massachusetts Amherst. He directs the Human-Centered Robotics Laboratory (HCRLab), focusing on lifelong collaborative autonomy, robot adaptation, and human-robot teaming. His research integrates robotics, AI, and machine learning to develop algorithms for real-world applications like manufacturing, autonomous driving, and environmental monitoring. He holds an NSF CAREER Award and DARPA Young Faculty Award, among other recognitions. Dr. Zhang earned a PhD from the University of Tennessee, Knoxville (2014) and an MS from the Chinese Academy of Sciences (2009). His work addresses challenges in unstructured environments through innovations like self-reflective terrain adaptation and graph-based perception systems. He actively promotes equity in robotics through his PROGRESS outreach program. His research sponsors include NSF, DARPA, and industry partners such as Toyota. Publications span conferences like RSS, ICRA, and IROS, with best paper awards. He serves on editorial and program committees for top-tier journals/conferences including RA-L, NeurIPS, and AAAI.

• Internet of Things (IoT)
• Cybersecurity
• Sensor Networks
• +3 more

Shiwei Fang is an Assistant Professor in the Department of Computer & Cyber Sciences within the School of Computer and Cyber Sciences at the University of North Georgia. He holds a Ph.D. in Computer Science from the University of North Carolina (2021) and a B.E. in Computer Science from the State University of New York (2015). His research focuses on IoT systems, cybersecurity, sensor networks, and edge computing, with notable contributions to multimodal analytics, privacy visualization tools, and geospatial tracking datasets. He advises the Graduate Student Organization and serves on the SCCS Academic Web Oversight Committee. Education: Ph.D., Computer Science, University of North Carolina, 2021 B.E., Computer Science, State University of New York, 2015 Research interests include IoT security, context-aware systems, and sensor fusion. His work on IoBT-MAX and GDTM datasets highlights expertise in experimentation frameworks and geospatial tracking. Recent publications explore privacy risks in IoT, AR-based privacy visualization, and efficient inference models for edge computing. He has contributed to over 25 peer-reviewed articles since 2015, with a focus on real-world IoT applications and hardware-software co-design. Service roles include faculty advising and committee participation. He teaches courses like CSCI 3170/5170 on Computer Organization, bridging theoretical computer science with practical hardware concepts.

Martin AumüllerView profile

Lecturer

• Algorithms
• Algorithm engineering
• Algorithmic privacy
• +6 more

Martin Aumüller is a Lecturer in Theoretical Computer Science Algorithms at the IT University of Copenhagen . He serves as Head of Education and Master of Software Design , focusing on algorithm engineering, differential privacy, and similarity search. Research interests include: Algorithm engineering for high-dimensional data Locality-sensitive hashing and nearest neighbor search Privacy-preserving machine learning Fairness in approximate search algorithms Benchmarking and evaluation of similarity search tools Publications trends highlight his work on approximate nearest neighbor search , privacy-preserving techniques , clustering algorithms , and scalable outlier detection in high-dimensional spaces. His recent projects (2024-2025) focus on fairness, differential privacy, and efficient indexing. Grants and projects : DIREC (2020-2025): Digital Research Centre Denmark (Innovation Fund Denmark) DIREC: Bias and Benefit of Approximate Nearest Neighbor Search (2022-2025): Principal Investigator (Innovation Fund Denmark) BARC (2017-2024): Basic Algorithms Research Copenhagen (Villum Fonden) SSS (2014-2019): Scalable Similarity Search (European Commission)

Tauhidul AlamView profile

Assistant Professor

• Artificial Intelligence
• Robotics
• Motion Planning
• +1 more

Tauhidul Alam serves as Assistant Professor in the Department of Computer Science within the College of Arts and Sciences at Louisiana State University Shreveport (LSUS), where he has taught since 2019. His research focuses on advancing autonomous robotic systems through innovations in artificial intelligence and cyber-physical applications. Dr. Alam holds a Ph.D. in Computer Science awarded in 2018. His scholarly work centers on robotics challenges including motion planning for underwater vehicles, multi-robot coordination under resource constraints, and energy-aware autonomous navigation. Key research domains span artificial intelligence, cyber-physical security using blockchain, and persistent monitoring in constrained environments. Analysis of his 14 recent publications reveals a strong emphasis on solving real-world robotics problems in marine and aquatic settings. His work consistently addresses uncertainty handling, multi-agent coordination, and security vulnerabilities, with increasing integration of data-driven methodologies across autonomous systems research. Scientific recognition includes: Best Student Paper Finalist at MTS/IEEE OCEANS Conference (2018) Dr. Alam teaches undergraduate courses including Computer Architecture (CSC 242), Database Systems (CSC 315), and Artificial Intelligence (CSC 465), alongside graduate-level instruction in Programming Languages (CSC 620) and Cloud Computing (CSC 690). Information regarding advised students, research grants, or laboratory affiliations is not specified in available materials.

Brandon LuciaView profile

Professor

• Intermittent Computing
• Energy-harvesting Systems
• Concurrency
• +7 more

Brandon Lucia is a Full Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. He leads the abstract research group focusing on the intersection of computer architecture, systems, and programming languages. His work bridges theoretical foundations with practical implementations in energy-constrained environments. Lucia's research centers on intermittent computing systems and edge computing in extreme environments. His work on energy-harvesting systems has established fundamental principles for batteryless computing, while his orbital edge computing research pioneers computational intelligence for nanosatellite constellations. These research thrusts address critical challenges in reliability, efficiency, and programmability for systems operating under severe power constraints. His publication record shows a clear evolution from foundational work on intermittent computing models to sophisticated applications in space computing and edge intelligence. Recent publications demonstrate increasing integration of dataflow architectures with energy-harvesting constraints, particularly in satellite constellations where computational resources must be managed across distributed, power-constrained platforms operating in extreme environments. NSF CAREER Award (2017) IEEE TCCA Young Computer Architect Award (2019) Sloan Foundation Fellowship (2021) ASPLOS Best Paper Awards (2018, 2020) OOPSLA Distinguished Paper and Artifact Awards (2015) Lucia actively mentors numerous PhD students including Brad Denby, Zhuo Cheng, and Emily Ruppel, many of whom contribute significantly to his research program. His abstract research group maintains strong industry connections while pursuing fundamental advances in computing systems. The group has developed multiple open-source tools including Legerdemain for program analysis and MultiCacheSim for cache coherence simulation. His laboratory work spans from theoretical foundations of intermittent computing to practical implementations in space systems. Current projects include computational nanosatellite constellations, energy-minimal dataflow architectures, and secure edge computing systems that operate reliably despite frequent power failures.