Find Academic Supervisors

Stuart McNicholasView profile

Researcher

• Molecular Graphics
• Crystallography
• Structural Biology
• +3 more

Stuart McNicholas is a Researcher at the University of York specializing in molecular graphics software development for structural biology. He serves as a Postdoctoral Research Associate within the CCP4 project ecosystem, primarily developing the CCP4MG molecular graphics program and contributing to the CCP4i2 graphical interface initiative for crystallographic analysis. His research centers on creating advanced tools for macromolecular visualization, including sophisticated atom-selection systems and dynamic scene generation wizards that integrate coordinate-derived graphics with abstract objects like text vectors and geometric shapes. Recent innovations include the "Simple Display Table" feature for high-throughput binding site analysis of homologous proteins and pioneering WebGL implementation for browser-based 3D molecular visualization without local software installation. McNicholas' publication record demonstrates consistent contributions to crystallographic software infrastructure, with key works focusing on intuitive molecular representation systems and integrated suites that streamline structural biology workflows through enhanced graphical interfaces and computational efficiency.

• Computer Graphics
• Computational Geometry
• Mesh Processing
• +3 more

Hans-Christian Ebke is a Researcher at the Department of Computer Science , RWTH Aachen University . He specializes in Computer Graphics and Computational Geometry , focusing on Quad Meshing , Surface Parametrization , and 3D Modeling . His work emphasizes interactive control, scale-aware algorithms, and robust extraction methods for high-resolution meshes. His notable contributions include the QEx method for reliable quad mesh extraction, the libQEx open-source implementation, and techniques for handling Scale-Invariant Directional Alignment and Level-of-Detail Quad Meshing . He has collaborated extensively with researchers like Marcel Campen, David Bommes, and Leif Kobbelt on geometric design and optimization problems. He has authored key publications at venues such as SIGGRAPH Asia , SIGGRAPH , and the Eurographics Symposium on Geometry Processing . These works address challenges in Integer-Grid Mapping , Cross Field Computation , and Vascular Structure Meshing , with applications in medical imaging and computer-aided design.

Craig H. JacksonView profile

Professor

• Representation Theory
• Braid Groups
• Quantum Algebras
• +6 more

Craig H. Jackson is a Professor and Interim Chair in the Department of Mathematics & Computer Science at Ohio Wesleyan University. He joined the faculty in 2009 after postdoctoral work at The Ohio State University. His teaching spans calculus, discrete math, algebra, and advanced topics like knot theory, representation theory, and topology, with a focus on interdisciplinary connections (e.g., algebraic topology, geometric representation theory). He co-teaches travel-learning courses to Alaska and the Caribbean, integrating fieldwork with mathematical modeling of climate and biological systems. B.S., University of Alaska, Anchorage M.S., The Ohio State University Ph.D., University of Chicago (Nilpotent Orbits in Semi-Simple Lie Algebras) His research bridges representation theory, braid groups, and quantum algebras with applications to mathematical biology, climate modeling, and cultural studies. Notably, he explores mathematical representations in non-mathematical texts like anime and film. Recent projects include a 2023 team-taught generative art course with Professor Jeff Nilan, featuring a digitally controlled Jacquard loom funded by the Thomas E. Wenzlau Grant (2022). He mentors undergraduate research, particularly in mathematical models of climate-glacier interactions, evolutionary game theory, and DNA abnormality detection. The articles reflect his interdisciplinary approach: 2020 work on ecological stability and weak interactions, 2013 climate-glacier models, 2012 analyses of identity in media through topology, and 2011 studies on braid group representations. His teaching emphasizes collaborative student seminars and projects, such as the 2015 Marine Biology and Math course in the USVI. His advising includes undergraduate honors projects on topics like DNA copy number variation, evolutionary game theory, and quantum computing. He actively engages in grants and projects, including the 2022 Wenzlau Grant for the digital loom, which integrates mathematics with fine arts.

Zhixiang ChenView profile

Lecturer

• Computer Vision
• Machine Learning
• Human Analysis
• +2 more

Dr. Zhixiang Chen is a Lecturer in Machine Learning at the Department of Computer Science, University of Sheffield, and an active member of both the Machine Learning and Computer Vision research groups. Previously, he held postdoctoral positions at Imperial College London and Tsinghua University. Education: PhD in Computer Science, Tsinghua University, China B.Eng. in Computer Engineering, Xi'an Jiaotong University, China His research focuses on advancing Computer Vision and Machine Learning through human analysis, object detection, and image retrieval systems. Recent work integrates deep learning for industrial applications like PCB inspection and autonomous systems, while earlier contributions pioneered video hashing techniques for efficient multimedia search. His methodology emphasizes robust feature representation and scalable algorithm design. Publication trends reveal a strategic shift from foundational hashing research (2016-2019) toward applied 3D vision and industrial automation (2020-2024), with increasing emphasis on uncertainty-aware models and sensor fusion for real-world deployment. Scientific Awards: China Society of Image and Graphics Doctoral Dissertation Award (2019) IEEE International Conference on Multimedia and Expo Best Paper Award (2018) China National Postdoctoral Program for Innovative Talents (2017) Dr. Chen currently leads the EPSRC-funded project 'VIM: towards a Vision based System for Inventory Management with Deep Learning' (2024-2026, £46,354) as Principal Investigator. He serves as a Grant Reviewer for UKRI-FLF and NSFC, Senior Program Committee Member for AAAI 2023, and regularly reviews for top-tier conferences (CVPR, ICCV, NeurIPS) and journals (IEEE T-PAMI, IEEE T-MM). As a core member of Sheffield's Machine Learning and Computer Vision research groups, he contributes to collaborative projects involving industrial partners and interdisciplinary teams focused on AI-driven solutions for healthcare, manufacturing, and autonomous systems.

Jacek BrodzkiView profile

Professor

• Topological Data Analysis
• Noncommutative Geometry
• K-Theory of Operator Algebras
• +7 more

Professor Jacek Brodzki, a faculty member at the University of Southampton since at least 2002, holds the title of Professor of Pure Mathematics and serves as Head of the School of Mathematical Sciences . He co-founded and directs the Centre for Geometry, Topology, and Applications (CGTA) , while leading the Southampton node of the £10M EPSRC-sponsored "Mathematical Foundations of Intelligence: An 'Erlangen Programme' for AI . His research spans topological data analysis , noncommutative geometry , and K-theory , with applications in medicine , physics , chemistry , and computer science . His work includes theoretical advancements in CAT(0) cubical spaces and applied projects like "Joining the Dots: From Data to Insight" , which integrates TDA, machine learning, and statistics for multidisciplinary challenges. Supported by grants from the EPSRC , Leverhulme Trust , and Alan Turing Institute , his research has produced groundbreaking applications in metasurface design , asthma biomarker discovery , and neural network analysis . Recent publications highlight his expertise in topological methods for disordered materials (2023), time-series analysis (2022), and stability of Mapper-type algorithms (2020). His theoretical work on the Baum-Connes conjecture and exactness of groups continues to influence geometric group theory. Scientific awards include Fellowship at the Alan Turing Institute (2019) . He actively supervises PhD students and collaborates across disciplines, spanning mathematics , physics , and precision medicine , and participates in the U-BIOPRED consortium for asthma research.

Mathieu DelalandreView profile

Associate Professor

• Image Processing
• Computer Vision
• Document Analysis
• +7 more

Mathieu Delalandre is an Associate Professor at LIFAT Laboratory, University of Tours, France, specializing in image processing and document/video analysis since 2009. His research focuses on local approaches including local detectors, template matching, and transform domain processing, with applications in video copy detection, scene text detection, and document image networking. He received his PhD in Computer Science from Rouen University in 2005, followed by research fellowships at SCSIT (Nottingham, UK), L3i (La Rochelle, France), and CVC (Barcelona, Spain) from 2006-2009. His educational background established his foundation in document analysis and pattern recognition. Delalandre's research spans multiple domains of computer vision with emphasis on practical applications. His work bridges theoretical image processing techniques with real-world problems in document analysis and video content monitoring. Key contributions include robust symbol localization, performance evaluation frameworks for symbol recognition systems, and real-time text detection algorithms. His approach often combines structural and statistical methods for enhanced accuracy. His publication record demonstrates consistent focus on video and document analysis, with recent work emphasizing partial video copy detection (2021-2023), real-time text detection (2019-2021), and JPEG document processing (2015-2017). The trajectory shows evolution from fundamental symbol recognition research to applied multimedia analysis systems addressing contemporary challenges in copyright protection and content monitoring. As head of the StationTV project, Delalandre leads research on real-time processing of multi-channel television content. He has participated in numerous national and international research projects, contributing to advancements in document image analysis and video processing. His work has been supported through various research grants focusing on practical applications of computer vision. Delalandre is an active member of the RFAI research group at LIFAT Laboratory, where he contributes to the development of innovative image processing techniques. His team collaborates across European institutions, maintaining strong connections with previous research sites in the UK, Spain, and other French laboratories. Current work focuses on multimedia fact-checking systems and real-time video analysis solutions.

Donald SheehyView profile

Associate Professor

• Computational Geometry
• Topological Data Analysis
• Algorithms
• +3 more

Donald Sheehy is an Associate Professor in the Department of Computer Science at North Carolina State University's College of Engineering, where he conducts research at the intersection of computational geometry and topological data analysis. His work focuses on developing efficient algorithms for analyzing geometric and topological structures in data. B.S.E. in Computer Science from Princeton University (2005) Ph.D. in Computer Science from Carnegie Mellon University (2011) Postdoctoral researcher at Inria Saclay, France Former faculty member at University of Connecticut Sheehy's research spans computational geometry, topological data analysis, and algorithm design. He develops mathematical frameworks for understanding shape, structure, and complexity in data, with particular expertise in persistence diagrams, Voronoi diagrams, Delaunay triangulations, and metric space analysis. His work bridges theoretical computer science with practical applications in data analysis, often developing sparse representations of complex geometric structures. He has made significant contributions to the understanding of approximation algorithms in geometric settings and has applied his techniques to problems in scientific computing and simulation analysis. Analysis of his recent publications reveals a consistent focus on developing efficient algorithms for topological data analysis, particularly in creating sparse representations of complex structures like persistence diagrams and Delaunay filtrations. His work demonstrates a progression from fundamental geometric algorithms toward more sophisticated applications in data analysis, with increasing attention to practical computational efficiency while maintaining theoretical guarantees. A notable pattern is his development of approximation techniques that dramatically reduce computational complexity while preserving essential topological features. 2022 Fall Workshop on Computational Geometry ($14,700 NSF grant) CAREER: Algorithmic Challenges and Opportunities in Spatial Data Analysis ($277,465 NSF grant) Sheehy has secured significant research funding from the National Science Foundation, including a CAREER award focused on developing new data structures and algorithms for spatial data analysis. His work on the Fall Workshop on Computational Geometry demonstrates leadership in the computational geometry community. While specific student advising information isn't provided in the source material, his research program appears to involve substantial collaboration with graduate students and postdoctoral researchers, as evidenced by his numerous co-authored publications.

• Computer Vision
• 3D Mesh Quality Assessment
• Deep Learning
• +3 more

Ilyass Abouelaziz is a Research Fellow at CESI Reims Campus, where he is affiliated with the LINEACT research team. His academic affiliation is with CESI, an engineering institution in France, where he focuses on computer science and engineering disciplines. Dr. Abouelaziz completed his PhD at the Faculty of Sciences of Rabat (2014-2020) with research on reference-free evaluation of 3D mesh quality using deep learning and visual saliency. He also earned a Research Master's Degree in Computer Science and Telecommunications from the same institution (2012-2014), with focus on Computer Vision and Visual Saliency, and a Fundamental License in Computer Science, Electronics, and Automation (2009-2012). His research interests span multiple areas at the intersection of computer vision and artificial intelligence, with particular expertise in 3D mesh quality assessment, point cloud processing, deep learning applications, and computer vision. His work combines theoretical foundations with practical applications in digital modeling and visual quality evaluation. His publication record demonstrates consistent output in top-tier journals and conferences, with a clear research trajectory focused on blind and no-reference quality assessment methods for 3D meshes. His work increasingly incorporates deep learning approaches, particularly convolutional neural networks, while maintaining connections to traditional computer vision techniques and geometric features. Dr. Abouelaziz serves as a reviewer for prestigious publications including Optics Express and IEEE International Conference on Image Processing, and has participated in various scientific committees for international conferences in signal processing and telecommunications. He currently supervises doctoral student Hafsa Benallal on research related to point cloud segmentation and cleaning for digital model creation. His teaching activities encompass algorithms, web development, programming (C, JAVA, Python), Artificial Intelligence, and Image processing for engineering students at levels ranging from 2nd to 5th year.

• Machine Learning
• Robotics
• Computer Vision
• +12 more

Alberto Tellaeche Iglesias is a Lecturer at the University of Deusto's College of Engineering, Department of Computing, Electronics and Communication Technologies. He holds a Ph.D. in Systems Engineering and Automation (UNED, 2008) and an M.Sc. in Automation and Electronics Engineering (University of Deusto, 2001). With over 17 years in industrial R&D, he has worked at Tekniker Foundation (2007–2019) and led research projects under European FP7/H2020 and national programs. Ph.D., Systems Engineering and Automation, UNED (2008) M.Sc., Automation and Electronics Engineering, University of Deusto (2001) His research focuses on Machine Learning , Robotics , and Computer Vision , particularly for industrial automation and precision agriculture. He has published 15 recent papers on topics like 3D vision systems, federated learning for defect detection, and hybrid classifiers for agricultural monitoring. His work bridges Pattern Recognition and Industrial Automation , with applications in quality control, human-robot interaction, and smart cities. Grants and projects include EU-funded AI-Driven Cognitive Robotic Platforms (2021–2024), HAZITEK (2019–2022), and national initiatives like Edge Technologies for Industrial Distributed AI (2021–2024). He has supervised the thesis 'Progress in Industrial Photogrammetry' (2019) and serves on doctoral committees. He previously worked at Tekniker Foundation's Smart and Autonomous Systems unit and is part of interdisciplinary teams integrating 3D Vision and Deep Learning for industrial applications.

• Image processing
• Statistical modeling
• Bayesian estimation
• +3 more

Mourad ZRIBI is a Maître de Conférences (Associate Professor) with Habilitation à Diriger des Recherches (HDR) qualification. He is affiliated with the IMAP research team, focusing on statistical methods and image processing. His academic work spans multiple disciplines including statistics, computer vision, and medical imaging. Dr. ZRIBI's research interests primarily center around statistical modeling for image processing applications. His work encompasses Bayesian estimation techniques, particle filtering methods, and advanced image restoration algorithms. He has made significant contributions to medical image analysis, particularly in brain MR image segmentation, and has developed innovative approaches for content-based image retrieval systems using deep learning techniques. His research bridges theoretical statistics with practical applications in computer vision and medical diagnostics. His publication record shows a consistent focus on applying statistical methods to image processing challenges. Recent work emphasizes Bayesian approaches, particle filters, and deep learning techniques for image restoration, segmentation, and retrieval. His research demonstrates a progression from fundamental statistical modeling to increasingly sophisticated applications in medical imaging and computer vision. Dr. ZRIBI collaborates extensively with researchers across multiple institutions, as evidenced by his co-authored publications. His work has appeared in reputable journals including Hacettepe Journal of Mathematics and Statistics, Communications in Statistics, and Computer Methods in Biomechanics and Biomedical Engineering. He is an active member of the IMAP research team, which appears to focus on image processing and analysis methodologies. His research contributes to advancing statistical techniques for practical applications in medical imaging and computer vision.

Diego MisseroniView profile

Professor

• Mechanics of Solids and Structures
• Architected materials
• Mechanical metamaterials
• +7 more

Diego Misseroni is a full Professor of Aerospace Structures at the Department of Civil, Environmental and Mechanical Engineering of the University of Trento, Italy. His academic career has progressed from Assistant Professor (2016-2022) to Associate Professor (2022-2023) and finally to Full Professor (2024-present). He has held prestigious visiting positions including a Fulbright Visiting Scientist at Princeton University (2023) and will serve as a Westlake Distinguished Fellow at Westlake University, China (2025-2026). Dr. Misseroni earned his PhD in Engineering of Civil and Mechanical Structural Systems from the University of Trento in 2013, following a Master's Degree in Civil Engineering with Full Honors from the same institution in 2010. His early career included a Marie Curie Experienced Researcher position at the University of Liverpool (2014-2015). His research focuses on the Mechanics of Solids and Structures, with particular emphasis on architected materials, metamaterials and origami engineering, buckling and instabilities of structures undergoing large deformations, and fracture mechanics. His innovative experimental approaches have led to significant theoretical advancements in understanding mechanical metamaterials and origami structures. Design and testing of mechanical metamaterials with extreme static and dynamic performance Development of novel experimental setups for studying Poisson effects in origami tessellations Creation of three-phase microstructured materials with tunable properties Investigation of flutter and divergence instability in viscoelastic rods Design of microstructured media with negative thermal expansion and negative Poisson's ratio His publication record shows a strong focus on mechanical metamaterials and origami engineering, with significant contributions appearing in high-impact journals including Nature Communications, PNAS, Advanced Materials, and Extreme Mechanics Letters. His work demonstrates expertise in bridging theoretical modeling with advanced experimental validation, particularly in the areas of reconfigurable structures and mechanical metamaterials. Dr. Misseroni has received numerous prestigious awards recognizing his contributions to the field: 2025: Member of the European Academy of Sciences and Arts (youngest ever admitted to Technical & Environmental Sciences class) 2025: SES Huajian Gao Young Investigator Medal 2025: Westlake Distinguished Fellowship 2024: ASME Young Investigator Award 2023: ERC Consolidator Grant for project 'Self-Foldable Origami-Architected Metamaterials' 12 journal covers featuring his research results He leads significant research funding including an ERC Consolidator Grant (€1.93M), HORIZON-CL4 project as Research Unit Coordinator (€513K), and Fulbright Research Fellowship. His laboratory at University of Trento is equipped with advanced testing systems including universal testing machines, vibration test systems, digital image correlation, and multiple 3D printers for prototyping mechanical metamaterials. His research group designs, realizes, and tests mechanical metamaterials, origami systems, flexible structures, and architected materials with extreme static and dynamic performance by combining mechanical modeling, computational analysis, and advanced experimental methods to create advanced, adaptive mechanical systems for a wide range of applications.

Osman ErvanView profile

Researcher

• Autonomous Systems
• Robotics
• Control Theory and Applications
• +2 more

Osman Ervan is a Research Assistant at the Department of Control and Automation Engineering, Faculty of Electrical and Electronics Engineering, Istanbul Technical University. He has been actively contributing to academic research since 2017, with expertise in robotics, autonomous systems, and control theory. His research focuses on robotics and sensor data processing, particularly involving LiDAR systems, point cloud registration, and tensor voting methods. Recent projects include advanced sampling techniques for point cloud data, autonomous robotic imaging systems, and fuzzy logic-based obstacle avoidance algorithms. Key publication trends show a strong emphasis on 3D point cloud processing (2019-2023), sensor configuration optimization (2015-2016), and autonomous navigation systems (2021-2023). His work spans both theoretical and applied aspects of robotics. Dr. Ervan is based at ITU Ayazaga Campus, Istanbul, Turkey. He has published 8 research outputs since 2015, with active collaborations in multi-robot systems development and sensor integration research.

• Algorithms and Computational Theory
• Information Systems
• Big Data
• +4 more

Dr. Emrullah DEMİRAL is a Lecturer in the Software Engineering Department at the Faculty of Engineering, Karabuk University, Turkey. He has been serving as a full-time academic staff member since 2024, holding additional administrative roles as the Director of Safranbolu Vocational School and Department Head at Karabuk University. Dr. DEMİRAL completed his PhD in Computer Engineering from Karabuk University Graduate School of Education in 2024. He earned his Master's degree in Computer Engineering (Thesis) from Karabuk University Institute of Science in 2015, and holds two bachelor's degrees: one in Political Science and Public Administration from Anadolu University (2015), and another in Mathematics from Ege University (2011). Dr. DEMİRAL's research focuses on several key areas within computer science and engineering. His primary interests include Algorithms and Computational Theory , Information Systems , Big Data , and Artificial Intelligence . Within these broad fields, he has made significant contributions to indoor navigation systems using RFID technology , positioning systems , and geographic information systems . His work often combines theoretical computer science with practical applications in emergency evacuation systems, traffic management, and spatial data analysis. He has developed innovative approaches for 3D indoor environments, including robot navigation prototypes and smart fire evacuation systems that leverage artificial neural networks. Analysis of Dr. DEMİRAL's publication record reveals a consistent focus on spatial information systems and navigation technologies. His most recent work (2024-2025) explores GPS trace data clustering and optimization, indoor robot prototypes guided by RFID positioning systems, and mobile-web-based geographic information applications. His research trajectory shows a progression from foundational work on RFID-based positioning systems (2013-2015) to more sophisticated applications integrating artificial intelligence and big data analytics. The interdisciplinary nature of his work bridges computer engineering, geographic information science, and emergency management systems. Dr. DEMİRAL has been actively involved in several research projects, including TÜBİTAK 4004 project on 'Science and Technology in Nature Walking: Awareness of Environment and Climate Change!' and various projects on RFID-based positioning and navigation systems. His research has been supported by both TÜBİTAK and university funding sources. He teaches a diverse range of courses at both undergraduate and graduate levels, including Principles of Artificial Intelligence, Introduction to Software Engineering, Programming II, Web Design, Data Science, and various specialized courses in computer technology and mobile programming.

• Electrical and Computer Engineering
• Deep Learning
• Image Processing
• +5 more

Fatema Albalooshi is an Assistant Professor in the Computer Engineering department at the University of Bahrain since 2015. She holds a Ph.D. in Electrical and Computer Engineering from the University of Dayton (2015) and an MSc in Electrical Engineering from the University of Nottingham (2009). Education: PhD, Electrical and Computer Engineering, University of Dayton (2015) MSc, Electrical Engineering, University of Nottingham (2009) BSc, Electrical Engineering, University of Nottingham (2009) Research Interests: Deep Learning Image Processing Computer Vision Biometrics Machine Olfaction Environmental Remote Sensing Peer Review Contributions: Applied Intelligence (5 reviews) Signal, Image and Video Processing (16 reviews) Electronics (1 review) Computers in Biology and Medicine (1 review) Email: falbalooshi@uob.edu.bh

Anders LoggView profile

Professor

• Digital Twin Cities
• Finite Element Methods
• Computational Fluid Dynamics (CFD)
• +5 more

Anders Logg is a Professor of Computational Mathematics at Chalmers University of Technology , Sweden. He serves as Director of the Digital Twin Cities Centre and leads the interdisciplinary VirtualCity@Chalmers project, aiming to build digital twins of urban environments for simulating traffic, wind flow, pollution dispersion, and flooding. Expertise in finite element methods and FEniCS software development Pioneer in mesh generation with tools like mshr Active in scientific computing and domain-specific languages His research team includes experts in mathematical modeling, Unreal Engine development, and building information modeling . Recent work focuses on integrating IoT with digital twins for energy management and developing scalable simulation frameworks for urban planning. Key research trends across publications include: Digital twin technologies for urban environments Mesh generation in complex geometries Multiphysics simulations (fluid dynamics, heat transfer) Automated computing through domain-specific languages Machine learning integration with urban modeling