Find Academic Supervisors

Narada Dilp WarakagodaView profile

Associate Professor

• Artificial Intelligence
• Autonomous Systems
• Computer Vision
• +3 more

Narada Dilp Warakagoda is an Associate Professor at the University of Oslo, affiliated with the Section for Autonomous Systems and Sensor Technologies. His research focuses on Artificial Intelligence, Autonomous Systems, Computer Vision, Robotics, Machine Learning, and Deep Learning, with notable contributions to underwater acoustics, renewable energy forecasting, and unmanned vehicle control systems. He collaborates extensively with researchers across disciplines to advance applications in environmental monitoring, energy systems, and autonomous technologies. His work integrates advanced machine learning techniques with domain-specific challenges, such as synthetic aperture sonar analysis, spatio-temporal wind forecasting, and reinforcement learning for autonomous navigation. He has published in leading journals and conferences, addressing topics like probabilistic solar irradiance prediction, graph-based wind energy modeling, and deep learning for seabed munitions detection. Warakagoda’s research bridges theoretical advancements with practical implementations, emphasizing applications in robotics, environmental science, and defense technologies. His projects often involve interdisciplinary teams, reflecting his commitment to solving real-world problems through innovative AI-driven solutions.

Torsten BraunView profile

Professor

• Machine Learning
• Networking and Telecommunications
• Edge Computing
• +3 more

Prof. Torsten Braun is a Professor and Head of the Communication and Distributed Systems (CDS) research group at the Institute of Computer Science, University of Bern. His research focuses on advanced networking technologies, edge computing, and machine learning applications in telecommunications. He leads projects addressing challenges in 5G/6G networks, federated learning optimization, and intelligent systems for smart cities. His work integrates theoretical frameworks with practical implementations, emphasizing distributed systems, service-oriented architectures, and IoT security. Key research areas include edge caching strategies for VR/AR applications, trajectory prediction using reinforcement learning, and resilient network design against jamming attacks. Braun has contributed to innovations in vehicular networks (V2X), RAN intelligence, and decentralized machine learning frameworks. He holds leadership roles in developing adaptive resource management systems for edge-cloud environments and has pioneered solutions for energy-efficient federated learning in heterogeneous IoT ecosystems. Publications span topics like spatial-temporal point cloud sensing, mobility-aware service orchestration, and secure positioning systems. His team explores cross-disciplinary applications such as LoRaWAN-based urban heat monitoring and blockchain-inspired public key infrastructures for IoT (Veritaa-IoT). Braun actively engages in standardization and industry collaborations to advance next-generation network architectures.

Mengchu LiView profile

Assistant Professor

• Change Point Analysis
• High-Dimensional Statistics
• Robust Statistics
• +3 more

Roles & Affiliations: Dr. Mengchu Li is an Assistant Professor in the School of Mathematics at the University of Birmingham. Previously, he served as a Postdoctoral Research Fellow in the Department of Statistics at the University of Warwick (Feb 2023–Sept 2024), and completed his PhD in Statistics at Warwick under Prof. Yi Yu. Education: PhD in Statistics, University of Warwick (2023) MASt in Mathematical Statistics, University of Cambridge (2019) BSc in Mathematics and Economics, Durham University (2018) Research Interests: Focuses on statistical theory and methodology for heterogeneous data analysis, particularly in high-dimensional settings and under privacy constraints. Key areas include change point analysis, robust statistics, differential privacy, and transfer learning. His work emphasizes minimax optimality and explores fundamental trade-offs between data contamination, privacy, and statistical performance. Research Trends in Articles: Recent work spans federated learning with privacy guarantees, robust change point testing in high-dimensional settings, and privacy-preserving network analysis. His papers often bridge statistical theory with applications in machine learning and distributed systems. Awards & Honors: Harrison Award (2023) for highly commended PhD thesis NeurIPS 2022 Scholar Award Teaching & Service: Taught graduate courses on statistical inference and computation at Birmingham. Previously led tutorials in mathematical statistics and linear modeling at Warwick. Active reviewer for Annals of Statistics , Biometrika , NeurIPS , and other top venues. Contributed to the R package changepoints for change-point detection methods. Labs & Collaborations: Engaged in interdisciplinary research through workshops on heterogeneous data (2024) and change point analysis (2023). Collaborates with institutions like the Alan Turing Institute and Warwick’s Statistics department.

Sam Nallaperuma-HerzbergView profile

Academic

• Machine Learning
• Artificial Intelligence
• Optical Communication Systems
• +5 more

Dr. Sam Nallaperuma-Herzberg is an Assistant Research Professor at the University of Cambridge's Department of Computer Science and Technology. He holds prestigious fellowships including the Borysiewicz Interdisciplinary Fellowship, Bye Fellowship at Murray Edwards College, and the Schmidt/Accelerate Fellowship in AI for Science and Society. His research focuses on machine learning applications in optical communication systems, neural networks for medical diagnostics, and reinforcement learning for network optimization. Affiliations: Department of Computer Science and Technology, Murray Edwards College Key Research Themes: Machine Learning, AI for Healthcare, Optical Fiber Networks, Reinforcement Learning Algorithms His work bridges computational methods with real-world challenges in telecommunications and healthcare. Notable achievements include developing graph neural networks for insomnia modeling and optimizing ultra-wideband fiber-optic systems using deep learning. He has been recognized with multiple interdisciplinary research fellowships, reflecting his cross-cutting contributions. Research outputs span over 30 peer-reviewed articles since 2017, with recent emphasis on AI-driven solutions for optical network optimization and circRNA-RBP interaction prediction. His projects include the BrainTwin initiative applying AI to sleep disorder analysis. Awards: Borysiewicz Interdisciplinary Fellow Bye Fellow, Murray Edwards College Schmidt/Accelerate Fellow in AI for Science and Society Current research explores hybrid neural architectures for medical diagnostics and Gaussian process modeling for optical signal optimization. He collaborates extensively on EU-funded initiatives and industry partnerships in AI and telecommunications.

• Bioinformatics
• Health Informatics
• Affective Computing
• +3 more

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University. His research focuses on bioinformatics, health informatics, affective computing, and GPU computing. He holds a BSc, MSc, and PhD in Computer Science from the University of Athens and the Athens University of Economics and Business. Before Durham, he was a Postdoctoral Research Fellow and Lecturer at the University of the West of Scotland (2016–2020). His work spans biometric identification via EEG signals, medical image analysis, and deep learning applications in healthcare. Education: BSc (Hons.) in Informatics and Telecommunications from National and Kapodistrian University of Athens MSc in Computer Science from Athens University of Economics and Business PhD in Computer Science from National and Kapodistrian University of Athens Research interests include bioinformatics (e.g., microarray analysis), health informatics (e.g., ECG-based emotion modeling), and GPU computing for real-time applications. He has pioneered studies on EEG-based biometrics and affect recognition in human-horse interaction. Recent articles explore chest X-ray classification using deep learning , AI-generated content detection , and trajectory prediction with graph networks . His research emphasizes interdisciplinary applications of machine learning in healthcare and education. He advises six postgraduate students and collaborates on UK parliamentary AI governance reports. Notable contributions include the BED EEG dataset and the DREAMER emotion recognition database.

• Bioinformatics
• Health Informatics
• Affective Computing
• +4 more

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University. His research focuses on bioinformatics, health informatics, affective computing, machine learning, and GPU computing applications. He holds a PhD in Computer Science from the National and Kapodistrian University of Athens (Greece), with prior roles including Postdoctoral Research Fellow and Lecturer at the University of the West of Scotland (2016-2020). Education: BSc (Hons) Informatics and Telecommunications, National and Kapodistrian University of Athens (Greece) MSc Computer Science, Athens University of Economics and Business (Greece) PhD Computer Science, National and Kapodistrian University of Athens (Greece) Research Interests: Bioinformatics, health informatics, affective computing (e.g., emotion recognition using EEG/ECG signals), machine learning applications in medical imaging and video quality, GPU-accelerated algorithms for biomedical data analysis, and biometric identification systems. His work bridges computational methods with healthcare, education technologies, and human-computer interaction. Advising & Students: Supervising postgraduate students in AI-driven healthcare, computer vision, and affective computing. Recent collaborations include projects on AI-generated content detection (De-Factify 4.0), chest X-ray image analysis (CLN network), and trajectory prediction using graph neural networks. Labs/Teams: Active in Durham's AI research groups, contributing to interdisciplinary projects in medical imaging, cybersecurity, and educational technology. Collaborates internationally in EU-funded initiatives and UK parliamentary AI governance consultations (AGENCY project).

Enyue Annie LuView profile

Professor

• High Performance Computing
• Computer and Communication Networks
• Algorithm Design and Analysis
• +5 more

Dr. Enyue (Annie) Lu is a Professor of Computer Science at Salisbury University, affiliated with the Department of Computer Science within the Richard A. Henson School of Science & Technology. She holds a Ph.D. from the University of Texas at Dallas. Her research focuses on High Performance Computing, including parallel and distributed processing, cloud computing, and GPU computing. She also explores Computer and Communication Networks, such as network security and wireless sensor networks, alongside Algorithm Design and Graph Theory applications in bioinformatics and medical imaging. Dr. Lu has led and contributed to numerous research projects, including the REU (Research Experience for Undergraduates) program, which emphasizes emerging computing in science and engineering. Her work spans interdisciplinary areas like medical imaging algorithms and stable matching problems, leveraging GPU acceleration and parallel computing techniques. She has advised over 20 students in these areas and collaborates with institutions like the University of Maryland Eastern Shore and Johns Hopkins University. Her publications emphasize practical applications of theoretical computer science, such as improving tomographic imaging and network intrusion detection through machine learning and distributed systems. Despite not listing explicit awards, her extensive contributions to undergraduate research and algorithm development highlight her scholarly impact.

• Multimedia Computing
• Computer Vision
• Computer Graphics
• +7 more

Professor Zhiyong Wang is a faculty member at the School of Computer Science, University of Sydney, serving as Deputy Director of Sydney Informatics Hub and Director of the Multimedia Computing Laboratory. His research focuses on multimedia computing, including information retrieval, computer vision, AI, and applications in agriculture, healthcare, and environmental monitoring. He holds a B.Eng., M.Eng. from South China University of Technology, and a Ph.D. from The Hong Kong Polytechnic University. His work bridges theoretical advancements and real-world applications, with over 200 peer-reviewed publications. He is a member of IEEE and former President of Australia Pattern Recognition Society (APRS). Research Interests: Enabling computers to understand and create multimedia content, with emphasis on human-centric computing, remote sensing, and interdisciplinary applications. Notable areas include action recognition, medical image analysis, and environmental monitoring. Current Roles: Leads the Multimedia Computing Lab, collaborates with Sydney Institute of Agriculture and Brain and Mind Centre. Teaches COMP5216 (Mobile Computing), COMP5405 (Digital Media Computing), and COMP5425 (Multimedia Retrieval). Students: Supervises 9 Ph.D. students focusing on topics like precision agriculture, biomedical engineering, and AI-driven video analytics. Key projects include soil moisture forecasting, biomechanical risk analysis for knee osteoarthritis, and explainable machine learning.

Guillaume DUCOURNAUView profile

Professor

• Terahertz (THz) Communications
• Photonics
• Semiconductor Devices
• +3 more

Guillaume DUCOURNAU is a **Professor** at the **Institute of Electronics, Microelectronics and Nanotechnologies (IEMN)**, part of the **University of Lille**, France. His research focuses on **Terahertz (THz) wireless communications**, including the development of THz sources, detectors (e.g., photomixers, Schottky diodes), and instrumentation for millimeter-wave and THz characterization. He leads projects such as **GRAPH-X**, **TIMES (6G SNS)**, and coordinates ANR-funded programs like **FUNTERA** (THz converters) and **SYSTERA** (systems beyond 90 GHz for future networks). His work spans European collaborations (e.g., **ThoR H2020**) and national initiatives, including the COM'TONIQ project for 300 GHz THz communications. **Research Interests**: THz communication systems, photonic integrated circuits, semiconductor device characterization (e.g., GaN Schottky diodes), on-wafer measurement techniques up to 1.1 THz, and hybrid packaging strategies for silicon ICs. His contributions include advancements in frequency stability, high-data-rate links (e.g., 327 Gbps), and topological photonics for 6G applications. **Key Projects**: STREP ROOTHz (2010-2013), TERASONIC (ANR/DFG), France 2030 PEPR programs. **Grants**: ANR INFRA 2013, ANR PEPR, Marie-Curie TERAOPTICS network. **Labs & Groups**: Active in the **PHOTONIQUE THz** and **EPIPHY** groups at IEMN, focusing on device characterization, silicon technologies, and collaborative research across 12+ labs in SYSTERA.

• Medical Image Analysis
• Distributed Systems
• Acoustic Location
• +2 more

Kuo-Ming Chao is a Researcher at the Centre for Research in Arts and Creative Exchange within the School of Arts, Humanities, and Social Sciences. His work bridges computational methods and applied research, with focus areas spanning medical imaging systems, distributed computing architectures, acoustic positioning technologies, social network analysis, and multimodal misinformation detection. Chao maintains active involvement in the academic community through sustained editorial contributions and conference participation. Research Focus: His interdisciplinary research integrates computer science with domain-specific applications. Key themes include: Development of orchestrated workflows for medical image analysis systems Multimodal fusion techniques for information verification Acoustic signal processing for location tracking Graph-based algorithms for social network prediction Professional Activities: Peer reviewer for Service-Oriented Computing and Applications journal since 2006 Invited keynote speaker for the 2025 IEEE Conference on Artificial Intelligence

• Optimization
• IoT
• Sensors
• +3 more

ZGHAL Mourad is a Researcher-Lecturer at CESI LINEACT, holding an HDR (2008) from Sup’Com, Carthage University and a PhD in Electrical Engineering (2000) from University Tunis Manar. He specializes in Optimization, IoT, Sensors, and Smart Healthy Cities , with a strong focus on Photonic Crystal Fibers and Nonlinear Optics . Education: HDR in Engineering (2008), Sup’Com, Carthage University PhD in Electrical Engineering (2000), University Tunis Manar Engineering Degree in Telecommunications (1995), Sup’Com, Carthage University Research Interests: Mourad’s work bridges IoT sensor networks with optical communication systems . He pioneers mid-infrared supercontinuum generation in chalcogenide fibers and explores optical mode multiplexing for high-speed communications. His recent work integrates federated learning for intrusion detection in smart grids and optimizes photovoltaic energy systems for building decarbonization . Publications Trends: His 2023–2025 work emphasizes AI-driven energy management , cybersecurity for IoT , and federated learning frameworks . Earlier contributions (2016–2019) focused on nonlinear optical effects in photonic fibers and high-bit-rate networks . Awards: Elected Vice-Präsident of the International Commission for Optics Fellow Optica (ex OSA) and SPIE Associate scientist at ICTP (UNESCO Category 1 Institute) Advising & Grants: Supervised 9 PhD students (e.g., Z. MONLA’s work on BIM/VR in building maintenance). Active member of the LINEACT Scientific Council and CTI Commission des Titres d’Ingénieurs. Labs & Teams: Leads the Engineering and Numerical Tools research team at CESI LINEACT. Collaborates with IMT Télécom SudParis as an Adjunct Professor.

• Medical Imaging
• Pediatric Cardiology
• MRI Reconstruction
• +3 more

Dr. Qing Zou is an Assistant Professor in the Division of Pediatric Cardiology, Department of Pediatrics at the University of Texas Southwestern Medical Center (UTSW). He holds secondary appointments in the Department of Radiology and the Advanced Imaging Research Center (AIRC). His research focuses on medical imaging techniques, particularly magnetic resonance imaging (MRI), with an emphasis on pediatric cardiology applications. Dr. Zou’s work integrates deep learning and advanced reconstruction algorithms to address challenges in dynamic imaging, motion compensation, and undersampling problems. Education: Ph.D. in Mathematics/Computer Science (University of Iowa) Postdoctoral Training in Cardiac MRI (University of Iowa) Research Interests: Dr. Zou explores cutting-edge methodologies in MRI reconstruction, including deep learning-driven approaches for free-breathing and non-contrast-enhanced imaging. His projects address pediatric-specific needs, such as congenital heart disease diagnostics and rapid whole-heart angiography. He also investigates mathematical foundations of sampling theory and high-dimensional data analysis. Recent Research Trends: His publications highlight advancements in motion-resolved imaging, Gaussian representation-based reconstruction, and generative models for dynamic MRI. Key themes include improving temporal resolution, reducing imaging time, and enhancing vascular visualization without contrast agents. Awards: No honors or awards explicitly listed in the provided text. Advising & Grants: No formal advisees/students or grant details mentioned. His research is likely funded through institutional resources or collaborative grants. Labs/Teams: Dr. Zou is affiliated with the Advanced Imaging Research Center (AIRC) at UTSW, a hub for innovative imaging technologies and interdisciplinary research.

Apurva NarayanView profile

Adjunct Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +6 more

Dr. Apurva Narayan is an Affiliate Professor in Computer Science and Data Science, and an Associate Member in Mechanical Engineering at the University of British Columbia's Irving K. Barber Faculty of Science. His research focuses on artificial intelligence, machine learning (particularly explainable AI and quantum ML), data mining, cybersecurity of cyber-physical systems, and decision-making under uncertainty. He holds a PhD from the University of Waterloo (Systems Design Engineering) and a Bachelor's in Electrical Engineering from Dayalbagh Educational Institute. Education: PhD in Systems Design Engineering, University of Waterloo (2015) Bachelor of Engineering in Electrical Engineering, Dayalbagh Educational Institute (2008) Research Interests: Explainable AI/ML and quantum machine learning Data analytics and mining Safety/security of cyber-physical systems Graph-theoretic analysis of complex systems Decision-making under uncertainty Reverse engineering complex software systems Awards: Systems Society of India Young Scientist Award (under 40) MITACS Accelerate Award (2013) Dr. T.E. Unny Memorial Award (2011) Multiple teaching nominations at University of Waterloo Advising & Grants: Supervises graduate students in AI/ML and cybersecurity. Secured grants including MITACS Accelerate and Waterloo scholarships. Active in patent filings related to timed regular expressions. Labs/Teams: Engaged in collaborations within the UBC Department of Computer Science and cross-disciplinary projects with Mechanical Engineering on system reliability and quantum computing applications.

Yangming LiView profile

Research Fellow

• Image Analysis
• Machine Learning
• Mathematical Modeling
• +7 more

Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.

Emanuele PrincipiView profile

Associate Professor

• Electrical Engineering
• Signal Processing
• Machine Learning
• +4 more

Emanuele Principi serves as an Associate Professor in the Department of Information Engineering at the College of Engineering, Università Politecnica delle Marche (UNIVPM). His academic appointment falls under the scientific sector IIET-01/A - Elettrotecnica (Electrical Engineering). Based at Via Brecce Bianche 12 in Ancona, Italy, Professor Principi maintains regular office hours on Fridays from 15:00-18:00 via Microsoft Teams, and can be contacted at e.principi@univpm.it or by phone at 0712204626. Professor Principi's research spans electrical engineering with a strong focus on signal processing, machine learning applications, and energy systems. His work particularly emphasizes non-intrusive load monitoring (NILM), acoustic signal analysis, and renewable energy integration. He has developed innovative approaches combining deep learning with electrical engineering principles to address challenges in energy disaggregation, sound event classification, and smart grid technologies. His research bridges theoretical advancements with practical applications in sustainable energy systems and intelligent monitoring solutions. Analysis of Professor Principi's publication trends reveals a consistent research trajectory with increasing focus on machine learning applications for energy systems. His recent work (2023-2025) demonstrates growing sophistication in knowledge distillation techniques for non-intrusive load monitoring, with increasing attention to interpretability and edge computing implementations. He has also expanded his research into photovoltaic-thermal systems with thermoelectric generators, showing diversification into renewable energy conversion while maintaining his core expertise in electrical signal processing. Professor Principi has made significant contributions to the fields of non-intrusive load monitoring and acoustic signal processing through his extensive publication record. His work on knowledge distillation frameworks, weakly supervised learning approaches, and real-time signal processing systems has advanced methodologies in energy monitoring and sound event classification. Professor Principi actively advises students and researchers in electrical engineering, signal processing, and machine learning applications. His research projects likely involve collaborations with industry partners in energy technology and smart grid development, though specific grant details are not provided in the available information. He appears to lead or participate in research teams focused on developing practical implementations of his theoretical work, particularly in edge computing applications for energy monitoring. Professor Principi's laboratory or research group likely focuses on signal processing for energy systems and acoustic analysis, with equipment supporting electrical measurements, audio signal acquisition, and machine learning model development. His work suggests integration of hardware and software systems for real-world implementation of monitoring technologies.