George Kesidis is a Professor in Computer Science and Engineering and Electrical Engineering at Penn State University. His research spans deep learning security, virtual reality optimization, and cloud computing. College of Engineering (Penn State University) Research Focus: Backdoor Attacks, DNN Robustness, Edge Caching Active in NSF and U.S. Navy-funded projects (2022-2026) His work addresses backdoor data poisoning , test-time evasion attacks , and DNN overfitting mitigation . He develops techniques like activation clipping, perturbation analysis, and statistical defense models. Recent projects include edge caching systems for VR and security-driven AI frameworks. Key article trends reveal expertise in adversarial deep learning, immersive media delivery, and cloud resource optimization. Current grants focus on multi-user VR, GPU scheduling, and serverless-cloud hybrid architectures. He collaborates extensively with researchers like David J. Miller and Xinyu Li, particularly on cloud-based adversarial defense mechanisms and VR streaming benchmarks.
Dr. Michael Stevens is a Senior Lecturer at University of New South Wales (UNSW) Canberra , where he focuses on advanced manufacturing and biomedical device control systems . His work bridges digital manufacturing for SMEs with smart artificial heart technologies , emphasizing industry collaboration and translational research. Specializes in physiological control systems for rotary blood pumps Develops unobtrusive fall detection systems for dementia patients Leads international projects on total artificial heart development Education : B.Eng (Medical - First Class Honours), Queensland University of Technology (2010) PhD in Physiological Control for Biventricular Assist Devices, University of Queensland (2014) Research Trends show consistent focus on: Machine learning for biomedical diagnostics (2018–2025) mmWave radar and thermal sensors in patient monitoring (2021–2024) Computational fluid dynamics in artificial heart modeling (2016–2024) Physiological control algorithms for rotary blood pumps (2011–2025) Scientific Awards : UNSW Scientia Education Award (2021) for contextual teaching Heart Foundation Runner-up for "Smart Artificial Hearts" pitch (2021) ARC PGC Supervisor Award (2017) for mentoring Grants & Supervision : Holds over $6 million in competitive funding including MRFF and ARC grants. Currently supervises 4 PhD students while maintaining industry partnerships with VitalCare and BiVACOR. Labs & Facilities : Works across UNSW Engineering labs and Graduate School of Biomedical Engineering platforms, including mock circulation loops and high-performance computing clusters for CFD simulations.
Ziming Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI) , with additional affiliations in Data Science and Robotics Engineering. He previously held research roles at Mitsubishi Electric Research Laboratories (MERL) and Boston University. PhD in Computing (2013) from Oxford Brookes University , UK MS in Computing Science (2010) from Simon Fraser University , CA BS in Computer Science and Technology (2005) from Northeastern University , China Research interests span computer vision , machine learning , and their applications in point cloud processing , medical imaging , autonomous driving , and IoT . He leads the Vision, Intelligence, and System Laboratory (VISLab) at WPI. Recent publications focus on 3D reconstruction , hyperbolic learning , and robust classifiers . Awards include the R&D100 Award 2018 and NSF funding for data-efficient deep learning. PhD Students: Yecheng Lyu (co-supervised), Guojun Wu (co-supervised), Hangrui Zhang, Xuechu Yu Master's Students: Yun Yue, Yuping Shao Visiting Scholars: Fangzhou Lin His lab partners with industry and academic institutions, focusing on autonomous systems , robotics , and scientific imaging projects.
Dr. Zhi Chen is a Lecturer in Computing at the School of Mathematics, Physics and Computing, University of Southern Queensland, specializing in Artificial Intelligence and Machine Learning with applications spanning digital agriculture and healthcare systems. Education: Master of Information Technology (MIT), University of Queensland, 2018 PhD, University of Queensland, 2023 Research Focus: His work centers on zero-shot learning, domain adaptation, and multimodal systems, addressing core challenges in computer vision and deep learning. Current projects integrate AI with agricultural risk modeling and medical diagnostics, emphasizing real-world deployment of robust algorithms under data-scarce conditions. Publication Trends: Recent output (2022-2025) shows concentrated expertise in source-free domain adaptation and generalized zero-shot learning, with significant contributions to plant disease recognition (via mobile multimodal systems) and diabetes subgroup analysis. His work consistently appears in premier venues including AAAI, CVPR, and ACM MM, demonstrating methodological innovation applied to critical domains like climate-resilient agriculture and precision medicine. Supervision: Currently serves as Associate Supervisor for a doctoral candidate developing parametric insurance models for oyster farms to mitigate climate-related risks from king tides and extreme weather events. Awards: No scientific awards were documented in the provided materials.
Prof. Jian Zhang is a Professor in the School of Electrical and Data Engineering at the University of Technology Sydney (UTS), specializing in computer vision, pattern recognition, and multimedia signal processing. He leads the Multimedia Data Analytics Lab at the Global Big Data Technologies Centre, focusing on agri-food sector applications such as livestock monitoring and AI-driven solutions for agricultural efficiency. Education : PhD, School of Information Technology and Electrical Engineering, University of New South Wales, 1999 MSc, The Flinders University of South Australia, 1994 BSc, East China Normal University, 1982 Research Interests : His work spans 2D/3D computer vision, large-scale image/video analytics, and cross-disciplinary projects in agriculture and remote sensing. He has pioneered AI systems for livestock counting, poultry welfare monitoring, and fish quality assessment, funded by organizations like Meat & Livestock Australia and Australian Eggs. Grants & Projects : Current projects include AI-based hen health monitoring ($5M+ funding since 2011) Collaborations with industry partners like Sydney Fish Market and Fremantle Port Students & Academic Leadership : Supervised 19 PhD graduates and 5 research fellows Recruiting new PhD candidates in computer vision and data analytics Labs & Teams : Director of the Multimedia Data Analytics Lab, collaborating with global experts through UTS's Distinguished Visiting Scholars program.
Sara Magliacane is an Assistant Professor at the University of Amsterdam , affiliated with the Amsterdam Machine Learning Lab (AMLab) and the Informatics Institute . She also holds a Research Scientist position at the MIT-IBM Watson AI Lab and has been an ELLIS Scholar since 2022. Education PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano BSc in Computer Engineering (2008), Università degli Studi di Trieste Research Focus : At the intersection of Causality and Machine Learning , her work addresses Causal Representation Learning from high-dimensional data (images, sequences) Causal Discovery in latent confounder scenarios Causality-inspired Reinforcement Learning for robustness and adaptability Neurosymbolic AI for theoretical guarantees Publication Trends : Her recent work explores Factored adaptation in non-stationary environments (NeurIPS 2022) Temporal causal identifiability (ICML 2022) Binary interaction-based causal discovery (UAI 2023) Safe exploration in visual RL (HSCC 2021) Structure learning lower bounds (NeurIPS 2020) Scientific Recognition : ELLIS Scholar (2022–present) Spotlight presentations at ICML 2022 and ICLR 2022 Advising & Collaborations : Currently supervising 6 PhD students at the University of Amsterdam and AUMC, with 12 alumni advisees. Collaborates with researchers at MIT-IBM Watson AI Lab, Simons Institute, and TUM.
Anthony TUNG Kum Hoe is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he has established himself as a leading researcher in database systems and data mining. He is also affiliated with the NUS Graduate School for Integrative Sciences and Engineering and serves as a SINGA supervisor. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (2001), an M.Sc. in Information Systems & Computer Science from NUS (1998), and a B.Sc. with 2nd Class Upper Honours in Information Systems & Computer Science from NUS (1997). Professor Tung's research spans several interconnected areas within database systems and data mining. His primary focus is on developing efficient methods for indexing and searching complex data structures including time series, trajectories, trees, graphs, and high-dimensional objects. He has pioneered work in visual query processing, keyword search, and ranking systems. His GENIE (Generic Inverted Index) and LAMP (semi-Lazy Mining Paradigm) projects represent significant contributions to big data analytics, particularly in handling the 'variety' aspect of big data by providing unified frameworks for processing diverse data structures while preserving semantic meaning. His research bridges theoretical database concepts with practical applications in visual data mining, collaborative analytics, and just-in-time model construction. His recent publications reveal a clear evolution from traditional database research toward more complex analytics on diverse data types. While maintaining his core expertise in database indexing and query processing, his work has expanded to incorporate machine learning techniques, particularly in areas like nearest neighbor search, anomaly detection, and predictive analytics. There's a noticeable trend toward interdisciplinary applications, with publications spanning computer vision, natural language processing, transportation systems, and social computing. His research group consistently publishes in top-tier venues including SIGMOD, VLDB, ICDE, and KDD, demonstrating both theoretical rigor and practical relevance. 2005 Best Paper Award for 'Indexing DNA Sequences Using q-grams' 2007 Invited panel speaker on 'Advice for a successful database researcher career in Asia' at SIGMOD 2010 Guest Lecturer for VLDB Database School 2012 VLDB 2012 Research PC Co-chairs 2015 10 Years Best Paper Award, DASFAA 2015 Invited to SIGMOD 2008 and SIGKDD 2008 Program Committees Professor Tung has supervised numerous PhD students and research associates throughout his career, including notable researchers like Zhang Zhenjie (recipient of the 2007 President Graduate Fellowship) and Wang Nan (published in SIGMOD'08). His research group has been consistently productive, with students publishing in top conferences including SIGMOD, ICDE, and VLDB. His professional service is extensive, having served as PC Chair for COMAD'06, Research PC Co-chair for VLDB 2012, and on program committees for virtually all major database and data mining conferences over the past two decades. His research has been supported by various grants that have enabled significant contributions to database technology. His GENIE and LAMP projects represent a cohesive research direction focused on developing systematic approaches to big data analytics. GENIE provides a unified platform for storage and retrieval of big data with various structures, while LAMP introduces a novel paradigm for predictive analytics that combines the strengths of lazy and eager learning approaches. These projects have evolved to incorporate GPU acceleration and parallel processing capabilities, reflecting his commitment to addressing real-world scalability challenges in data-intensive applications.
Maozhen Li is a Professor in the Department of Electronic and Electrical Engineering at Brunel University of London , within the College of Engineering, Design and Physical Sciences . He serves as the Vice-Dean of the NCUT Transnational Education (TNE) programme, overseeing a joint school with North China University of Technology. He has been at Brunel since 2002, progressing from Lecturer to Professor in 2013. Education: PhD, Institute of Software, Chinese Academy of Sciences (1997) Postdoctoral Research, School of Computer Science and Informatics, Cardiff University (1999–2002) His primary research interests lie in high performance computing, big data analytics, and artificial intelligence, with applications in smart grids, smart manufacturing, and cybersecurity. He focuses on developing interpretable, robust, and lightweight AI models, including work in causal AI, parallel machine learning, and edge computing. His research integrates advanced techniques such as deep learning, reinforcement learning, and blockchain for real-world system optimization. An analysis of his recent publications reveals a strong and consistent research trajectory in AI-driven solutions for environmental monitoring (e.g., PM2.5 prediction), industrial defect detection, IoT security, and intelligent transportation. His work frequently combines deep learning with graph-based modeling and federated or reinforcement learning, emphasizing scalability, efficiency, and robustness in distributed and edge environments. Scientific Awards and Recognition: Fellow of the Institution of Engineering and Technology (IET) Fellow of the British Computer Society (BCS) Shortlisted for the Computing UK BIG DATA EXCELLENCE AWARDS 2018 in the category of Most Innovative Big Data Solution Maozhen Li has successfully supervised 25 PhD students and examined over 30 PhD theses externally. He has secured significant research funding from EPSRC, the European Union (Horizon 2020), Innovate UK, and the Royal Society , with projects including Z-BRE4K, IoRL, and TDX-ASSIST. He serves as an Associate Editor for journals such as the Journal of Cloud Computing and the International Journal of Grid and High Performance Computing . Research Groups and Teams: He is affiliated with the Intelligent Engineering Frameworks (IEF) research group at Brunel, contributing to collaborative efforts in AI, IoT, and smart systems. His leadership in transnational education also fosters international research collaboration between Brunel and Chinese institutions.
Dr. Weihao Li is a Research Fellow at The Australian National University's School of Computing, specializing in computer vision and machine learning. His research focuses on object detection, image segmentation, open-set recognition, and point cloud segmentation. He holds a Dr. rer. nat. (PhD equivalent) and is registered to supervise research students. His research interests revolve around advancing techniques for dynamic instance segmentation, open-set learning, and 3D point cloud analysis. Notable projects include the ANU bushfire smoke dataset and contributions to generalized semantic segmentation and anomaly recognition. His work emphasizes data augmentation strategies and weakly-supervised learning methods. Key technical areas include synthetic dynamic instance copy-paste for video segmentation, curved geometric networks for anomaly detection, and cross-modal fusion in building facade analysis. He collaborates on computing-for-social-good initiatives, such as environmental monitoring via hyperspectral imaging. Dr. Li's publications span 2016–2024, with a focus on advancing computer vision through innovative architectures and methodologies. His recent work explores open-set recognition, few-shot learning with reinforced attention, and geometric prior-based segmentation techniques.
Dr. Patrick W. C. Ho is a Lecturer in the Department of Electrical & Computer Systems Engineering (ECSE) at Monash University Malaysia School of Engineering. He holds a PhD in Electronics Engineering from the University of Nottingham Malaysia Campus (2016), with research focusing on non-volatile FPGA architectures using memristors. His academic journey includes roles as a Scholarly Teaching Fellow and unit coordinator for courses like ECE2131 Electrical Circuits and ECE4063 Large Scale Digital Design. He has industry experience with Intel Microelectronics and Altera Corporation, alongside teaching A-level Physics at Methodist College Kuala Lumpur. Education: BEng (First Class Honours) in Engineering (2009) MSc in Science (2012) PhD in Electronics Engineering (2016) Research Interests: Dr. Ho specializes in memristor-based non-volatile memory systems, VLSI design, and FPGA architectures. His work bridges hardware design with emerging materials, as seen in his Q1 journal article on memristive LUTs. Collaborations with CAD-IT expand his focus into AI, image processing, and object recognition. Recent projects include studies on memristor substrate performance (2023–2026) and UAV communication reliability (2021–2024). Teaching and Industry Engagement: As ECSE’s Industrial Training Advisor and IAP representative, he actively connects academic curricula with industry needs. His teaching spans foundational engineering courses and advanced digital design modules. Labs and Collaborations: Active in CAD-IT partnerships for student FYP co-sponsorship. Research groups focus on nanotechnology, machine learning integration in UAV systems, and memristor material analysis.
Rynson W.H. Lau is a Professor of Computer Science at City University of Hong Kong (CityU), leading research in Computer Graphics, Computer Vision, and Deep Learning. He holds an Honorary Professorship at Swansea University. Previously, he served on faculties at Durham University and The Hong Kong Polytechnic University. His work focuses on advancing graphics and vision techniques, including deep learning applications for graphics/vision problems, with publications in top venues like SIGGRAPH, CVPR, and NeurIPS. He has received the Adobe Research Gift (2023) and the Springer Nature Editorial Contribution Award (2025) for his editorial contributions to the International Journal of Computer Vision . Education: B.Sc. (First-class Honors) in Computer Systems Engineering from University of Kent Ph.D. in Computer Science from University of Cambridge Research Interests: Computer Graphics: Focused on 3D reconstruction, rendering, and real-time performance capture. Computer Vision: Specializing in saliency detection, object recognition, and low-light scene enhancement. Deep Learning: Developing generative models and diffusion-based frameworks for graphics and vision tasks. Editorial Roles: Editorial Board Member, International Journal of Computer Vision and IET Computer Vision . Guest Editor for special issues in journals like ACM Transactions on Internet Technology and IEEE Transactions on Multimedia. Teaching: 2024/25 Academic Year: CS4185: Multimedia Technologies and Applications CS4188/CS5188: Virtual Reality Technologies and Applications Research Team: Advises over 20+ students and collaborates internationally. Recent projects include AI-driven VR systems for healthcare and advanced 3D content generation using diffusion models.
Professor Adrian Hilton is a distinguished faculty member at the University of Surrey, serving as Director of the Centre for Vision, Speech and Signal Processing (CVSSP) and Director of the Surrey Institute for People-Centred AI. He is affiliated with the School of Computer Science and Electronic Engineering and leads the Visual Media Research Lab (V-Lab). His research focuses on pioneering next-generation 4D computer vision technologies that enable machines to understand and model dynamic real-world scenes. Key areas include 3D/4D shape capture, computer vision, machine learning, graphics, and animation for applications in sports analysis, film/TV production, virtual reality, and medical imaging. His work bridges the gap between real and computer-generated imagery, with notable contributions in volumetric capture, motion capture, and free-viewpoint video. Hilton's recent publications demonstrate a strong trend toward multimodal integration, particularly combining audio and visual processing for spatial audio applications, while advancing 4D reconstruction techniques for human performance capture. His work increasingly incorporates transformer architectures and neural rendering techniques for improved illumination estimation, shadow modeling, and multi-view consistency. Scientific Awards and Recognition Two EU IST Innovation Prizes Manufacturing Industry Achievement Award Royal Society Industry Fellowship (2008-2011) Royal Society Wolfson Research Merit Award in 4D Vision (2013-2018) Fellow of the Royal Academy of Engineering (FREng) Fellow of the International Association for Pattern Recognition (FIAPR) Fellow of the Institution of Engineering and Technology (FIET) Hilton actively mentors PhD and post-doctoral researchers through his leadership of CVSSP, which has a grant portfolio exceeding £31M and comprises 170 researchers. He has successfully commercialized several technologies, including systems used by the BBC for sports commentary visualization. His research collaborations span major industry partners including BBC, BT, Sony, Framestore, and The Foundry. He co-founded the G3 Games forum and the CVMP Conference on Visual Media Production, demonstrating strong engagement with the creative industries. Current research projects include the S3A Programme Grant in Future Spatial Audio and InnovateUK's ALIVE project for 360 video reconstruction.
Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.
Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.
Jost-Diedrich Graf Von Hardenberg is a Full Professor in the Department of Environmental, Land and Infrastructure Engineering (DIATI) at the Polytechnic University of Turin. He serves as the Energy and Climate Change Area Coordinator and is a Scientific Advisor of the HPC-AI Advisory Council. His research focuses on climate science and geophysical fluid dynamics, with significant contributions to understanding climate change impacts and Earth system modeling. Professor Von Hardenberg's research interests span multiple areas of climate science, including climate dynamics, geophysical fluid mechanics, hydrological cycle analysis, numerical climate modeling, and precipitation downscaling. His work particularly emphasizes climate tipping points and extremes, Rayleigh-Bénard convection in geophysical contexts, high-resolution Earth-system climate modeling, and stochastic approaches to precipitation downscaling. His research integrates theoretical, computational, and observational approaches to address fundamental questions about climate system behavior and change. His recent publications demonstrate a strong focus on Atlantic Meridional Overturning Circulation (AMOC) dynamics, climate extremes in Alpine regions, urban climate effects, and interdisciplinary applications of climate science to ecological and conservation challenges. His work bridges fundamental climate dynamics with practical applications for climate adaptation and environmental management. Among his notable recognitions are the Research in Paris award from the Maire de Paris (2009) and Fellowships at the London School of Economics and Political Science (2001-2002, 2004-2006). He has participated in numerous research networks including the European Geosciences Union section 'Nonlinear Processes in Geophysics' (2004-2012), the EC-Earth Consortium (2012-present), and COST Action ES0805 TERRABITES 'The terrestrial biosphere in the earth system' (2010-2014). Professor Von Hardenberg actively supervises PhD students including Marianna Albanese, Maria Clara Corda, Sara Filippini, and Jacopo Grassi, among others. He leads multiple significant research projects such as ROTurb (Resolving Ocean Macroscale Turbulence), LocClima (Impact of LOCal conditions on Italian microCLIMAtes), CRAWL (Carbon Release in A Warming cLimate), and CliMOC (Climate Impacts of the Atlantic Meridional Overturning Circulation), with funding from EU, national programs, and commercial contracts. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, where he contributes expertise in climate data analysis and modeling. His collaborative work extends to multiple institutions through non-commercial agreements with the National Research Council and various international research consortia focused on climate system understanding.
