Find Academic Supervisors

Xiaojun QiView profile

Professor

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

Dr. Xiaojun Qi is a Professor and Department Head of Computer Science at Utah State University (USU), where he has been since 2002. He holds a Ph.D. in Computer Science from Louisiana State University. His research focuses on Image Processing, Machine Learning, Computer Vision, and Deep Learning, with notable contributions to medical imaging, object detection, and multimedia security. Qi leads the Computer Science department and oversees research projects funded by grants from organizations like UDOT and GRCO. He has advised numerous graduate and undergraduate students across projects such as fake face detection, facial expression recognition, and LiDAR-based traffic sign detection. His work integrates computational geometry and AI to address challenges in vision systems and biomedical applications. Education: Ph.D., Computer Science (Louisiana State University); M.S., Systems Science (Louisiana State University); M.S., Computer Science (Shenyang Institute of Automation, Chinese Academy of Sciences); B.S., Computer Science and Engineering (Donghua University). Research Interests: Image Processing, Machine Learning, Deep Learning, Computer Vision, Medical Imaging, Object Detection, Digital Watermarking, and Data Mining. He has organized numerous conferences and served on technical committees for VISAPP, CompIMAGE, and DEXA. Grants include UDOC (Traffic Sign Detection), GRCO (Medical Image Segmentation), and NSF REU (Computer Vision). His lab focuses on interdisciplinary projects such as vision-based navigation, steganalysis, and bio-inspired algorithms.

• Machine Learning
• Predictive Analytics
• Time Series Analysis
• +5 more

Nurilla Avazov is an Associate Professor of Data Science at the Inland School of Business and Social Sciences, Inland Norway University of Applied Sciences. He holds dual PhDs: a PhD in Computer Science (2021) from the University of Auckland and a PhD in Information and Communication Technology (2015) from the University of Agder. His research focuses on machine learning, predictive analytics, time series analysis, wireless communication modeling, IoT systems, e-healthcare solutions, and human activity recognition through advanced signal processing techniques. Dr. Avazov’s academic career includes significant contributions to high-impact refereed journals and conferences. His work spans theoretical developments in algorithm design, practical implementations in sensor networks, and interdisciplinary applications in healthcare and smart environments. He has developed novel trajectory-driven channel models for mm-wave systems and pioneered backscattering-based human activity recognition methods. His expertise combines data science with telecommunications engineering, addressing challenges in non-stationary channel analysis, radar systems for indoor localization, and cybersecurity through wireless signal inference. Current research trends emphasize multimodal sensor fusion, real-time activity tracking, and privacy-aware IoT architectures. Dr. Avazov has advised no formally recorded students in the provided data. His research grants and funded projects are not explicitly detailed here, though his publications indicate sustained external collaboration and funding support. He is affiliated with the Business Analytics research group at his institution.

Sanja FidlerView profile

Associate Professor

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +5 more

Sanja Fidler is an Associate Professor at the University of Toronto (Department of Computer Science) within the Mathematical and Computational Sciences School. She is also a co-founder and affiliated faculty at the Vector Institute and serves as VP of AI Research at NVIDIA. Her research focuses on the intersection of Computer Vision, Machine Learning, and Graphics, with emphasis on 3D reconstruction, generative models, and interactive methods. She previously held a Research Assistant Professor position at the Toyota Technological Institute at Chicago. Research Interests: Dr. Fidler specializes in Computer Vision and Machine Learning, particularly in areas such as 3D vision, 3D reconstruction/synthesis, object detection, and interactive scene understanding. Her work bridges graphics and vision, addressing challenges in geometric representations, generative models, and real-world applications like medical imaging and autonomous systems. Awards: She has received notable awards including the Canada CIFAR AI Chair (2018), Connaught Innovation Award (2020), NVIDIA Pioneer of AI Award (2016), and multiple outstanding reviewer distinctions at top conferences. Her teaching excellence was recognized with the Professor of the Year award by UofT's CS Student Union in 2014/2015. Collaborations & Labs: Leading the NVIDIA Toronto AI Research Lab and contributing to initiatives at the Vector Institute, her work integrates academic and industrial research. She co-organized workshops on topics like 'Geometry Meets Deep Learning' and '3D Indoor Scene Understanding' at major conferences.

D. Keith WilliamsView profile

Professor

• Biostatistics
• Linear Models
• Statistical Graphics
• +5 more

D. Keith Williams serves as Professor and Vice-Chair of Education in the Department of Biostatistics at the University of Arkansas for Medical Sciences (UAMS), with dual appointments in the College of Medicine and College of Public Health. He additionally directs Biostatistics and Data Innovation at ACNC, demonstrating leadership in academic administration and research infrastructure. His academic foundation includes: B.S. in Mathematics Education from University of Central Oklahoma M.S. in Statistics from Oklahoma State University M.P.H. from University of Oklahoma Ph.D. in Biostatistics from University of Oklahoma Dr. Williams' research centers on advanced statistical methodologies including linear models, graphical techniques, and simulation approaches. His current focus targets efficient analysis of complex multivariate datasets from microbiome, metabolic, genetic, and whole-organism studies. This work addresses critical challenges in handling high-dimensional biological data where traditional methods fail, particularly in modeling multiple interdependent responses common in modern biomedical research. Analysis of his 2024-2025 publications reveals a strong interdisciplinary pattern spanning pediatric obesity, pregnancy complications, toxicology, and nutritional science. His biostatistical expertise consistently enables novel insights across diverse domains—from cannabis extract pharmacology to rheumatoid arthritis immunology—by developing specialized analytical frameworks for complex datasets. A notable trend involves translating methodological innovations into clinical applications, especially for maternal-child health and metabolic disorders. As Vice-Chair of Education, Dr. Williams oversees biostatistics curriculum development and training programs. His directorship at ACNC involves leading data science initiatives that support UAMS research enterprise. While specific grant details aren't provided, his extensive publication record indicates sustained extramural funding. He directs the Biostatistics and Data Innovation unit at ACNC, which integrates statistical expertise with cutting-edge computational resources to advance research across UAMS. This role positions him at the nexus of methodological development and translational science, fostering collaborations between statisticians and domain experts in medicine and public health.

Yi-Zhe SongView profile

Professor

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

Professor Yi-Zhe Song is a Professor of Computer Vision and Machine Learning at the University of Surrey's Centre for Vision, Speech and Signal Processing (CVSSP), one of Europe's largest AI research centers. He leads the SketchX Lab, focusing on understanding human sketching to advance computer vision and cognitive science. His roles include Co-Director of the Surrey Institute for People-Centred AI and Programme Lead of Surrey’s MSc in Artificial Intelligence. He holds a PhD (2008) and MSc (2004, Best Dissertation Award) from the University of Cambridge and Bath. Research interests span sketch-based systems, fine-grained visual classification, domain adaptation, and generative models. His lab has produced over 67 publications in top-tier conferences (CVPR, ICCV, ECCV) and journals, including a Best Paper Award at BMVC 2015. He is an Associate Editor for IEEE TPAMI and Frontiers in Computer Science, and has served as Programme Chair for BMVC 2021. Recent trends in his work emphasize explainable AI (SketchXAI), 3D reconstruction from sketches, and practical applications like virtual try-on and fashion-focused V+L models. His MSc AI programme integrates technical, ethical, and business aspects, preparing students for diverse AI roles. Awards: Best Paper Award (BMVC 2015), Senior Member of IEEE, Fellow of the Higher Education Academy. Lab Leadership: 3 academics, 2 postdocs, 14 PhD students in the SketchX Lab. Grants & Partnerships: Collaborations with industry and international funding bodies (e.g., EPSRC, São Paulo Research Foundation). Future Work: Expanding AI’s societal impact through inclusive education, human-centric AI, and democratizing generative AI tools.

Aleksandra PizuricaView profile

Professor

• Statistical modelling
• Probabilistic Graphical Models
• Bayesian inference
• +5 more

Aleksandra Pizurica is a Professor in statistical image modelling at Ghent University, Belgium, affiliated with the Group for Artificial Intelligence and Sparse Modelling (GAIM). She serves as Senior Area Editor for IEEE Transactions on Image Processing (2016–) and Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology (2016–), having previously held editorial roles at IEEE Transactions on Image Processing (2012–2016). Her educational background includes: Dipl. Ing. in Electrical Engineering, University of Novi Sad (1994) MSc in Telecommunications, University of Belgrade (1997) PhD in Engineering, Ghent University (2002) Prof. Pizurica's research centers on statistical modelling , probabilistic graphical models , and Bayesian inference , with significant contributions to sparse coding , signal/image processing , and machine learning . Her work bridges theoretical advances with applications in medical imaging, remote sensing, and cultural heritage preservation, particularly in image denoising, inpainting, and hyperspectral analysis. Analysis of her 15 most recent publications (2023–2025) reveals dominant themes in hyperspectral image processing (clustering/classification via model-aware deep learning), medical imaging (3D foot/ankle alignment, musculoskeletal segmentation), and cultural heritage (crack detection in paintings). Emerging trends include fairness in AI (skin color bias mitigation), scalable seabed mapping, and generative models for point cloud processing. Her notable recognition includes: Scientific Prize “de Boelpaepe” for 2013-2014 from the Royal Academy of Science, Letters and Fine Arts of Belgium While specific grant details and student advisement records aren't provided in available sources, her editorial leadership and research output indicate active supervision of graduate researchers. She leads initiatives in the Group for Artificial Intelligence and Sparse Modelling (GAIM), focusing on statistical image modeling and sparse representations for real-world applications. The GAIM research unit under her affiliation drives innovation in probabilistic modeling and machine learning, with projects spanning medical diagnostics, remote sensing, and digital art restoration, evidenced by recent publications in IEEE Transactions and high-impact journals.

Marco FumeroView profile

Research Fellow

• Geometric Deep Learning
• Geometry Processing
• Representation Learning
• +2 more

Marco Fumero is a PostDoctoral Researcher at the Institute of Science and Technology Austria (ISTA), where he conducts foundational research at the intersection of geometry and artificial intelligence. Previously, he completed his Ph.D. in Computer Science at Sapienza University of Rome as a core member of the GLADIA research group under Professor Emanuele Rodolà's supervision, establishing a trajectory bridging theoretical geometry with practical deep learning applications. Ph.D. in Computer Science, Sapienza University of Rome Dr. Fumero's research program centers on exploiting geometric structures to revolutionize artificial intelligence systems, with primary focus on geometric deep learning, geometry processing, and representation learning. He pioneers methodologies for analyzing neural network latent spaces through spectral geometry and dynamical systems theory, developing frameworks that enable cross-model communication and zero-shot transfer. His work systematically addresses challenges in representation alignment, latent space dynamics, and disentangled feature extraction, with direct applications in 3D shape analysis, multimodal learning, and quantum-inspired computing. This research demonstrates exceptional theoretical rigor while maintaining strong connections to real-world problems in computer vision and scientific computing. His publication record reveals a dominant trend toward unifying geometric principles with deep learning architectures, particularly through spectral methods and functional map theory. The 2024-2025 publications showcase a coherent evolution from foundational latent space analysis (e.g., attractor dynamics in autoencoders) to practical frameworks for cross-model communication (e.g., cycle-consistent merging and semantic alignment). Key thematic threads include zero-shot capability development, invariance exploitation, and the translation of classical geometry processing techniques into neural network contexts. These contributions have established new paradigms for latent space manipulation across computer vision, graphics, and multimodal AI. Spotlight presentation at ICLR 2024 for "From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication" Multiple papers accepted at NeurIPS 2024 including "Latent Functional Maps" and "C2M3" During his doctoral training at Sapienza, Dr. Fumero actively mentored junior researchers within the GLADIA group, contributing to the development of next-generation geometric AI specialists through collaborative projects and technical guidance. His research has been supported by institutional funding from Sapienza University and ISTA, with potential backing from European research initiatives targeting foundational AI advances. Current work focuses on scaling geometric deep learning frameworks to complex multimodal scenarios while maintaining theoretical guarantees. Dr. Fumero maintains strong ties to the GLADIA research group at Sapienza University of Rome, which specializes in geometric learning and data analysis. At ISTA, he operates within a highly collaborative interdisciplinary environment that emphasizes theoretical computer science and its applications, contributing to the institute's mission of advancing frontier research through mathematical rigor and computational innovation.

Margaret Allotey-PappoeView profile

Professor

• Graphic Design
• Visual Communications
• Art Education
• +3 more

Professor Margaret Allotey-Pappoe serves as the Department Chair and Professor of Graphic Design at the School of Arts and Humanities, Mount Vernon Nazarene University (MVNU), Ohio. She has held progressive roles since 2011, including Assistant Professor (2011–2016) and Associate Professor (2016–2021). Her academic journey includes an MFA and MA in Visual Communications from Bradley University and a B.A. in Communication Design from Kwame Nkrumah University of Science and Technology, Ghana. Her professional interests span graphic design, cross-cultural artistic collaborations, and Christian art. Notable recognitions include the MVNU Innovation in Education Grant (2017), the Hospitality Award (2016), and Advisor of the Year (2014–2015). She has organized and participated in exhibitions such as Reflections of Nana Ekow Maison and Between the Shadow and the Light , emphasizing intercultural and faith-based themes. Her presentations include talks at Still Point Gallery (GA), Belmont University (TN), and Samford University, focusing on art’s role in cultural and religious discourse. She actively engages in professional organizations like AIGA, College Art Association (CAA), and Christians in the Visual Arts (CIVA).

• Computer Vision
• Medical Imaging
• Deep Learning
• +4 more

Zhifeng Wang is a Professor at the National University of Defense Technology's School of Computer Science, specializing in computer vision, medical imaging, and artificial intelligence. His research spans multiple application domains with a recent focus on medical image synthesis, 3D reconstruction, and fluid dynamics simulation. Research interests include developing advanced AI techniques for medical imaging applications, particularly in vascular structure analysis and synthesis. His work bridges computer vision with healthcare applications, focusing on creating anatomically accurate 3D representations without invasive procedures. He has made significant contributions to diffusion models, neural radiance fields (NeRFs), and meta-learning frameworks for cross-domain applications. His publication record shows a clear progression from foundational work in graph algorithms and communications systems to cutting-edge medical AI applications. Recent work demonstrates expertise in combining state space models with diffusion techniques for medical image generation, showing particular strength in maintaining anatomical continuity in 3D vascular structures. While specific awards aren't documented in the provided information, his publications in top venues including CVPR 2025 indicate significant recognition in the computer vision community. His collaborative work spans multiple institutions and research domains, suggesting an active research group with diverse funding sources. His laboratory appears to focus on medical imaging AI, with particular emphasis on non-invasive diagnostic techniques. Current projects involve developing sophisticated diffusion models that can generate high-fidelity angiographic images from non-contrast inputs, potentially reducing patient exposure to harmful contrast agents.

Jean-Michel DISCHLERView profile

Professor

• Computer Graphics
• Texture Synthesis
• Procedural Modeling
• +4 more

Jean-Michel Dischler is a full Professor of Computer Science at the University of Strasbourg, affiliated with the ICUBE Lab (UMR CNRS-UDS 7357). He leads rendering and visualization research activities within the Department of Computer Science at the Faculty for Mathematics and Computer Science. His academic roles include former leadership of the Computer Science department, vice-director of LSIIT Lab (until 2008), and joint-director of the 3D Computer Graphics Group in ICUBE (until 2018). He has organized major conferences like Eurographics 2014 and EGSR/HPG 2019, and chaired the Eurographics association since 2021. Research focuses on texture synthesis, procedural modeling, 3D acquisition, and high-performance graphics. Key projects include the ASTex C++ library for texture generation. His work bridges procedural and data-driven methods, addressing challenges like non-stationary textures and real-time rendering of complex materials. He has contributed to Eurographics committees, journals, and professional governance, including a decade on the EG professional board. Scientific Awards: Fellow of the Eurographics Association Grants & Projects: INRIA CALVI project (2010), ASTex library development Labs/Teams: ICUBE Lab (3D Graphics Group), former LSIIT Lab Future Work: Continued innovation in procedural texture synthesis and real-time graphics

• High-performance computing
• Decimal arithmetic
• Parallel programming
• +4 more

Alvaro Vazquez Alvarez is an Assistant Professor at the Higher Technical School of Engineering, University of Santiago de Compostela. He holds a PhD in Computer Engineering (2009) from the same institution, focusing on High-performance decimal floating point units under the supervision of Dr. Elisardo Antelo Suárez. His research interests span high-performance computing, decimal arithmetic hardware design, parallel programming models, and LiDAR data processing. His work emphasizes FPGA implementation of arithmetic units, numerical algorithms optimization, and compiler/runtime systems for parallel environments. He leads the CograDe Research Group (Computer Graphics and Data Engineering), exploring advanced topics in parallel computing and data engineering applications. Key contributions include universal MPI binding frameworks for new languages and efficient LiDAR point cloud processing algorithms. Notable technical expertise includes: digit-by-digit algorithms, redundant number systems, CORDIC-based computations, and multi-operand decimal arithmetic implementations. His publications bridge theoretical numerical methods with practical hardware/software co-design solutions. Current research focuses on extending parallel programming interfaces, improving decimal arithmetic precision in modern architectures, and applying advanced geometric processing techniques to 3D data challenges.

David Lopez VilariñoView profile

Professor

• Computer Vision
• LiDAR Data Processing
• FPGA Acceleration
• +4 more

David Lopez Vilariño is a Professor at the Department of Electronics and Computing, Faculty of Physics, University of Santiago de Compostela. He holds a PhD in Electronics from the same university (2001), specializing in active contour algorithms and FPGA implementations. His research focuses on LiDAR data processing, reconfigurable computing architectures, and embedded vision systems. He is a member of the ARQCOMP (Computer Architecture) and Artificial Vision research groups. Education: PhD in Electronics (2001), thesis on 'Active contours at the pixel level: design and implementation on cellular network architectures' under Dr. Diego Cabello Ferrer. Research interests include: LiDAR-based 3D modeling for urban and environmental applications FPGA acceleration for high-performance computing Reconfigurable hardware architectures for embedded vision systems Real-time video surveillance and motion estimation His work bridges hardware-software co-design with advanced sensing technologies, particularly in geomatics and medical imaging applications.

• Computer Vision
• Deep Learning
• Point Cloud Processing
• +3 more

ABOUELAZIZ Ilyass is a Researcher-Lecturer at CESI, specializing in Computer Sciences. He holds a PhD from Mohammed V University (2014-2020) and a Master's in Computer Sciences and Telecommunications (2012-2014). His research focuses on Point Cloud Processing, Mesh Quality Assessment, Deep Learning, Building Information Modeling, and Computer Vision. He currently supervises PhD candidate Hafsa Benallal on Point Cloud Segmentation and Denoising for BIM. PhD Topic: No-reference mesh visual quality assessment using deep learning and visual saliency Master's Topic: Computer Sciences and Telecommunications Teaching activities include courses on Algorithms, Web Development, Programming (C, JAVA, Python), Artificial Intelligence, and Image Processing at 2nd to 5th-year engineering levels. He employs active pedagogy, lectures, and practical sessions (TP/TD). Scientific contributions include 20+ peer-reviewed publications in venues like IEEE Access, Pattern Recognition, and conferences such as ICIP and SITIS. He serves as a reviewer for Optics Express and IEEE ICIP, and has organized sessions at ATSIP (2017), SETIT (2016-2017), and URAC Day (2015). Research highlights include pioneering work on graph-based deep learning for mesh quality assessment and developing outlier-removal techniques for point clouds. His work bridges computer vision with sustainable energy solutions, such as photovoltaic prediction and building decarbonization. Future projects emphasize BIM integration with deep learning for smart building applications and enhancing 3D mesh quality metrics using novel neural architectures.

Jaspar Joseph-LesterView profile

Professor

• Art & Curating
• Urban Studies
• Human Geography
• +4 more

Jaspar Joseph-Lester is a London-based artist, researcher, and academic affiliated with the Royal College of Art’s School of Arts & Humanities. His work bridges art practice, curating, and interdisciplinary research, focusing on urban studies, spatial theory, and the ideological underpinnings of modernity. He holds a PhD in Fine Art from Goldsmiths College (1994 BA, 1998 MA from Slade School of Fine Art). Currently supervising 4 full-time and 3 part-time MPhil/PhD students, he has led research initiatives like the Horizon 2020 SPACEX project and co-edited influential works such as Walking Cities: London (2020). Key roles include former Reader in Fine Art at Sheffield Hallam University (2002–2013), visiting faculty at CalArts, Goldsmiths, and Wimbledon College of Art, and organizer of international conferences like the Oxford Future of Cities conference (2016). His research explores urban renewal, image-architecture interplay, and critical spatial narratives through projects like Modern Interiors and TEGEL: Propositions and Speculations . He has authored/co-authored over 20 books, articles, and curated exhibitions, including works addressing casino architecture and post-war urban landscapes. Recent collaborations include the Trigger Point network with institutions in Lisbon, Berlin, and Vienna, investigating modern architecture’s cultural impact. His work emphasizes art’s role in rethinking urban futures through walking methodologies, speculative mappings, and interdisciplinary dialogue.

Horst PosseggerView profile

Researcher

• Computer Vision
• Autonomous Systems
• Multiple Object Tracking
• +3 more

Horst Possegger is a Computer Vision researcher at the Institute of Computer Graphics and Vision at Graz University of Technology, Austria. He holds a BSc and MSc in Software Development and Business Management (2011, 2013) and a PhD in Computer Science (2018), all from Graz University of Technology. His research focuses on multiple object tracking/detection, human behavior analysis, and video analysis with applications in autonomous systems, LiDAR data processing, and robotics. Key projects include trajectory prediction, 3D object detection, and domain adaptation techniques for real-world scenarios. Recent work emphasizes cross-modal learning (e.g., vision-language models for LiDAR data), robust localization in challenging environments, and lightweight motion prediction models. He contributes to the Learning, Recognition & Surveillance (LRS) group and has published extensively in top-tier venues like IEEE/CVF CVPR and IROS. Publications span autonomous driving benchmarks, UWB-based localization, and warehouse automation. His work often bridges theoretical advancements with practical implementations in robotics and surveillance systems.