Dr. Miaomiao Liu is a Research Fellow at the School of Computing, The Australian National University. Her research focuses on computer vision, 3D reconstruction, and neural rendering, with applications in robotics, autonomous systems, and renewable energy forecasting. She leads multiple projects including Next-Generation Aviation Safety Net Air Traffic Management, Machine Vision Techniques for Solar Power Forecasting, and 3D Vision Geometric Optimization in Deep Learning. Her work integrates cutting-edge techniques such as neural radiance fields, depth estimation, and self-supervised learning to address challenges in dynamic scene reconstruction, motion forecasting, and image deblurring. She has pioneered methods for mining supervision signals in dynamic regions and developing language-driven deblurring networks. Key Projects: Aviation safety systems, solar irradiance prediction, and geometric optimization in deep learning Research Themes: 3D scene understanding, human motion prediction, and neural rendering Technical Expertise: Neural networks, multi-view stereo, and physics-based modeling Dr. Liu's research has been published in top-tier venues like CVPR and IEEE conferences, with over 2,450 citations. While not explicitly listed as part of a lab, her work demonstrates strong collaboration with industry partners such as CSIRO and aerospace stakeholders. She actively supervises research students in areas like 3D vision and energy systems.
Prof. Arthur Seibel is a Professor for Product Development and Design at Leuphana University's Institute for Production Technology and Systems (IPTS). Previously, he served as a Group Leader and Department Head at Fraunhofer IAPT (2020–2023) and earned his habilitation (2021) in Design Theory from TU Hamburg. His expertise spans additive manufacturing, soft robotics, biomimetic design, and engineering design methodology. He leads projects like OPTUM-MAGNA (magnesium nanocomposites) and DigiMed (patient-specific implants), focusing on sustainable manufacturing and medical applications. Education: PhD (2015): TU Hamburg, 'On the Inclusion of Parameter Uncertainties into Engineering Design Computations' Dipl.-Ing. (2011): TU Hamburg, Mechanical Engineering with distinction Research Focus: Additive Manufacturing: Metal binder jetting, sintering processes, defect mitigation Soft Robotics: Silicone-based actuators, biomimetic grippers, recycling strategies Product Development: Methodology, creativity, team leadership in project management Materials Science: TPMS structures for heat sinks and implants Teaching: Courses include 'Methodical Product Development', 'Sustainability in Engineering', and 'Intellectual Property Management'. Labs/Teams: Heads the Product Development and Design group at IPTS, collaborating with Fraunhofer institutes on industrial and medical AM applications.
Mehmet Aktas is an Associate Professor in the School of Data Science and Analytics at Kennesaw State University, part of the College of Computing and Software Engineering. His research focuses on machine learning and data science, particularly applying topological data analysis (TDA) to network science challenges such as influence maximization and network matching in social, biological, and business networks. He holds a Ph.D. in Mathematics from Florida State University and an M.S. in Mathematics from Bilkent University. His educational background includes studies under Eriko Hironaka (Florida State) and Alex Degtyarev (Bilkent). Research interests emphasize TDA tools for addressing network problems, with applications across domains. Key work includes exploring deception effects in social networks, hypergraph classification, and graph compression techniques. His GitHub repositories (e.g., hypergraphPH, deception) reflect active engagement in algorithm development for network analysis. Publications span topics like influence maximization dynamics, bot detection via persistent homology, and topological approaches to graph classification. Despite no listed awards, his work demonstrates significant contributions to TDA applications in network science. Advising/grants details are not provided, but his research portfolio indicates active involvement in computational and topological method development. Research foci include sheaf Laplacian methods, diffusion processes on graphs, and higher-order interaction analysis in complex systems. He collaborates across disciplines, blending mathematical rigor with data-driven approaches to solve real-world network challenges.
Luca Cosmo is an Associate Professor at Ca’ Foscari University of Venice, specializing in Shape Analysis, Geometric Deep Learning, and Spectral Geometry. His research focuses on 3D shape analysis, spectral methods, and generative models for computer vision tasks. He has contributed to projects like the Average Mixing Kernel Signature (AMKS) and isospectralization techniques, advancing robust shape representation and correspondence. Previously, he held postdoctoral positions at the Geometric Deep Learning group under Prof. Michael Bronstein and collaborated with the spin-off DigitalViews Srl on industrial projects for quality control. He actively serves on program committees for top conferences (CVPR, ICCV, ECCV) and journals (IJCV, PR). His work bridges spectral geometry with machine learning, addressing challenges in partial shape matching, adversarial attacks on deformable shapes, and latent space preservation in generative models. Key trends in his publications include advancements in 3D shape generation (e.g., UniMoGen, 3D-WAG), spectral methods for subgraph learning, and quantum-inspired approaches to shape analysis. He has organized tutorials on shape analysis and contributed benchmark datasets like SHREC’16/17. His research emphasizes practical applications in computer graphics, robotics, and industrial automation. Luca’s advising and grants involve collaborations with institutions like Oxford University (Antonio Norelli) and industry partners. His lab, GLADIA, focuses on geometric deep learning and generative models. Current work includes universal motion generation, high-fidelity 3D reconstruction, and scalable generative models for large datasets.
Giorgio Mariani is a PostDoctoral Researcher at the University of Milano-Bicocca, having completed his Ph.D. in Computer Science at Sapienza, University of Rome under the supervision of Prof. Emanuele Rodolà. His work bridges theoretical machine learning with practical applications in audio processing and computer graphics. His primary research focuses on: Generative models, particularly autoregressive and diffusion-based approaches Audio and music synthesis technologies Computer graphics applications for animation and gaming Adversarial vulnerabilities in geometric data Mariani's publication trajectory shows significant progression from geometric deep learning toward advanced audio generation techniques. His recent work on music generation using diffusion models has been accepted at ICASSP 2024-2025, with 'Latent Autoregressive Source Separation' earning an oral presentation at ICLR 2024. His foundational work on 'Generating Adversarial Surfaces via Band-Limited Perturbations' (Computer Graphics Forum, 2020) established his expertise in 3D shape analysis security. Notable achievements include: Oral presentation at ICLR 2024 (top 5% acceptance rate) Multiple paper acceptances at ICASSP 2024 and upcoming 2025 conference Research bridging audio processing, computer graphics, and security domains As a postdoctoral researcher, Mariani continues to push boundaries in generative AI for audio applications while maintaining connections to his foundational work in 3D geometric data analysis, demonstrating exceptional interdisciplinary research capabilities.
Davide Murari is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge. His work focuses on the intersection of neural networks and dynamical systems, with an emphasis on structure preservation and numerical analysis. He holds a postdoctoral role where he explores theoretical and computational aspects of neural networks, particularly their connections to differential equations and physical systems. His research interests include approximation theory for neural networks, structure-preserving integrators, and applications in computational mechanics and inverse problems. Collaborations involve leading institutions such as NTNU (Norway) and the Alan Turing Institute. Murari actively presents at international conferences, including ICIAM, SIAM, and SciCADE, and publishes in top-tier journals like Computer Methods in Applied Mechanics and Engineering and Physica D . Key contributions include developing symplectic neural flows, enhancing Fourier neural operators with spatial features, and analyzing robustness in graph neural networks. His work bridges numerical mathematics and machine learning, addressing challenges in stability, accuracy, and scalability. Murari’s academic networks span computational mathematics and machine learning communities, with a focus on advancing theoretical foundations while solving practical engineering and scientific problems.
Dr. Adam Kortylewski leads an Emmy Noether research group at the University of Freiburg's Department of Computer Science. His work focuses on developing robust computer vision systems that reliably understand images under challenging conditions including occlusion, novel viewpoints, and adverse weather. The research combines deep learning with computer graphics to create 3D-aware neural network architectures. Research interests include: Robustness to out-of-distribution scenarios 3D object understanding from 2D images Neural analysis-by-synthesis approaches Occlusion handling in visual recognition Compositional network architectures Adversarial robustness Recent publications demonstrate advancements in self-supervised 3D learning, semantic correspondence, and neural rendering. Articles show consistent focus on improving model robustness through 3D-aware architectures and compositionality. Dr. Kortylewski received prestigious Emmy Noether funding from the German Research Foundation (DFG) to support his work on reliable computer vision. His research has applications in autonomous vehicles, industrial robotics, and safety-critical vision systems.
Jack Koplowitz is an Associate Professor in the Department of Electrical & Computer Engineering at Clarkson University's Coulter School of Engineering & Applied Sciences. His research focuses on pattern recognition, information theory, image/signal processing, computer vision, and communication theory. He holds a Ph.D. in Electrical Engineering from the University of Colorado (1973), an M.E. from Stanford University (1968), and a B.E. from City College of New York (1967). His teaching spans pattern recognition, coding theory, signals and systems, and communications. Research highlights include contributions to digital geometry (e.g., contour reconstruction, digital convex polygons) and applications of neural networks in power systems. He has published extensively since the 1970s, with notable work in subpixel accuracy, photovoltaic modeling, and hierarchical image representations. No scientific awards are explicitly listed. His academic contributions emphasize foundational work in digital image processing, signal theory, and algorithmic approaches to geometric problems.
Sherif Hashem is a Full Professor of Information Sciences and Technology at George Mason University , affiliated with the Science and Technology campus in Manassas, VA. His research spans cybersecurity, artificial intelligence, cyber policies, and information security management , with over 60 publications and 2600+ citations. PhD in Industrial Engineering from Purdue University (1993) MSc in Engineering Mathematics from Cairo University BSc in Communication and Electronics Engineering from Cairo University Completed Senior Executive Program at Harvard Business School Research Interests focus on cybersecurity governance, AI-driven data analysis, and digital policy frameworks. His work bridges technical and strategic aspects of information security, particularly in developing nations. Publication Trends reveal expertise in neural network ensembles, hyperspherical classification models, and digital forensics . Early work emphasized financial forecasting and pattern recognition, evolving into national cybersecurity strategies. Labs & Teams : No specific affiliations mentioned in the text.
Dr. Zhidong Xiao serves as Principal Academic (Associate Professor) at Bournemouth University's National Centre for Computer Animation within the Faculty of Media and Communication. With over ten years of leadership experience including roles as Programme Leader, Head of Education, and Deputy Head of Department, he drives academic strategy and research innovation in computer animation and digital media. His work bridges technical excellence with creative industry applications through extensive collaborations across the UK and China. Dr. Xiao's educational foundation includes a PhD in Computer Graphics (2010) and postgraduate certificates in Education Practice (2010) and Research Degree Supervision (2011) from Bournemouth University, complemented by a BEng (Hons) in Thermodynamics from Taiyuan University of Technology, China (1994). PhD in Computer Graphics, Bournemouth University (2010) PGCE in Education Practice, Bournemouth University (2010) PGCE in Research Degree Supervision, Bournemouth University (2011) BEng (Hons) in Thermodynamics, Taiyuan University of Technology (1994) His research spans Computer Graphics, Motion Capture, Artificial Intelligence, and Virtual Reality with focus on physics-based simulation, sign language recognition, and motion synthesis. Recent work integrates partial differential equations with machine learning to solve animation challenges in facial realism, deformation simulation, and 3D reconstruction. His interdisciplinary approach connects computer science with creative industries, healthcare applications, and educational technology while advancing core techniques in neural rendering and motion analysis. Analysis of his 15 most recent publications reveals consistent innovation in physics-based animation techniques (40%), motion capture processing (25%), and neural approaches to 3D reconstruction (35%). Key trends include the fusion of analytical physics models with deep learning architectures, development of efficient real-time simulation methods, and expansion into accessibility applications through sign language recognition systems. Scientific recognitions include: Fellow of British Computer Society (2023) Fellow of Higher Education Academy (2011) Best Poster Award at Pacific Graphics 2014 He maintains active peer review roles for EPSRC, ESRC, IEEE Transactions on Multimedia, and ACM SIGGRAPH conferences. Dr. Xiao has supervised seven PhD students to completion while currently guiding Alexandra Sergeeva Alexdottir's research on Phantom Touch phenomena. His grant portfolio demonstrates strong industry-academia collaboration: Principal Investigator Capturing and representing sign language (British Council, 2025) VE Communication Programme (Erasmus+, 2020) Co-Investigator Rehabilitation Enhancement via Motion Capture (BU Fusion Fund, 2013) Cross-Channel Film Lab (Interreg, 2012) Digital Beijing Opera Project (2010) As a core member of Bournemouth's Computer Graphics and Visualisation Research Group and Centre for Digital Entertainment, he leads initiatives in motion capture technology through AccessMocap Studio. His international outreach includes invited lectures across China on computer animation education and visual effects techniques, strengthening global partnerships in creative technology development.
Dr. Sarah Bentley is an Assistant Professor at Northumbria University, part of the Faculty of Engineering and Environment. She holds a PhD in Mathematics from the University of Reading (2019) and a MMath from Durham University (2013). Her research focuses on space physics, space weather forecasting, and the application of machine learning to understand magnetospheric dynamics. She investigates ultra-low frequency (ULF) waves and their role in energizing Earth’s radiation belts, with a particular interest in developing predictive models for space weather impacts. Joined Northumbria as a Vice-Chancellor's Fellow in 2020. Current projects include STFC-funded research on solar and space physics, emphasizing radial diffusion and wave-particle interactions. Her work bridges computational methods and physical phenomena, leveraging AI to analyze large datasets from spacecraft observations. She supervises PhD students in topics like graph neural networks for magnetic field characterization and machine learning-driven space weather forecasting. Key contributions include probabilistic models for ULF wave prediction, radial diffusion benchmarking, and causal network analysis for space weather variables. She actively engages in EDI initiatives, advocating for neurodivergent inclusivity in academic environments.
Tyler Maunu is an Assistant Professor of Mathematics at Brandeis University, affiliated with the Department of Mathematics and the Benjamin and Mae Volen National Center for Complex Systems. His research focuses on statistical methodologies, optimization algorithms, machine learning, and their applications to computer vision and data science. He holds a Ph.D. in Mathematics from the University of Minnesota-Twin Cities, alongside multiple advanced degrees from the same institution. Maunu's research advances robust subspace recovery and optimal transport, emphasizing scalable and privacy-aware techniques. His work bridges theoretical foundations (e.g., non-convex optimization landscapes) with practical applications in data recovery and generative modeling. Notable areas include Bures-Wasserstein geometry, stochastic gradient methods, and adversarial robustness in high-dimensional data analysis. His articles span topics like preconditioned Langevin Monte Carlo, optimal transport barycenters, and scalable graph matching algorithms. While no explicit awards are cited, his contributions reflect ongoing innovation in computational statistics and optimization. His affiliation with the Volen Center underscores interdisciplinary engagements in complex systems research.
Jan Eric Lenssen is a Senior Researcher at the Max Planck Institute for Informatics leading the Geometric Representation Learning Group , and a Founding Engineer at Kumo.ai . He holds a Dr.-Ing. in Computer Science from TU Dortmund University (2022) and has conducted research internships at Facebook Reality Labs and Nnaisense. Affiliations: ELLIS Unit Faculty Member and Saarland Informatics Campus Fellow Education: PhD under Prof. Heinrich Müller, TU Dortmund (2022) His research focuses on differentiable algorithms , neural fields , and graph-based methods for 3D perception and generative modeling. Key contributions include frameworks like SplineCNN , PyTorch Geometric , and methods for scalable 3D reconstruction, human-object interaction modeling, and relational deep learning. Publications span top venues ( CVPR, ECCV, ICCV, NeurIPS ) with highlights including Best Paper Honorable Mention (ECCV 2022) and Best Paper Award (BIOSIGNALS 2018) . He co-founded the RelBench benchmark for relational database learning and leads development of geometric deep learning tools. Key Labs/Teams: Geometric Representation Learning Group (MPI), Kumo.ai Research Team, and the Computer Vision and Machine Learning department at MPI.
Christopher Young is a researcher with a diverse academic portfolio spanning Cognitive Science, Computer Science, and Biomedical Engineering. His work primarily focuses on Human-Computer Interaction , Virtual Reality applications , and Child Development in educational contexts. While his university affiliation is not explicitly stated in the provided texts, he earned his PhD from the University of Illinois Urbana-Champaign in 1992, specializing in digital filters. Young's interdisciplinary research bridges education , technology , and biomedical rehabilitation . Research Trends : His publications from 2024 to 1992 reveal a trajectory in Virtual Reality for workforce and management training Embedded Systems and energy-efficient object detection Cognitive Development in children's numerical and spatial abilities Biomedical Applications including surgical video summarization and motor rehabilitation Architectural Design with augmented reality interfaces Awards & Collaborations : No explicit awards are listed in the texts. His collaborations include prominent figures like Susan C. Levine (Cognitive Science), Boris Murmann (Electrical Engineering), and Barbara Cutler (Computer Science).
Dr. Scott Timpany is an Associate Professor and Programme Leader for Undergraduate Archaeology at the University of the Highlands and Islands' Orkney Archaeology Institute. His research focuses on palaeoecology, submerged landscapes, and the interaction between past communities and their environments. He leads projects such as the Kirkwall Townscape Heritage Initiative Archaeology Programme (2015–2017) and has supervised multiple PhD projects, including studies on prehistoric and Norse communities in the Northern Isles. His expertise includes investigating submerged forests (e.g., Severn Estuary, Sussex), burnt mound sites in Ireland and Northern Ireland, and the impact of environmental changes on ancient communities. Recent projects explore former lochs' sediment records to understand historical pollution and water quality shifts. Key research interests span palaeoecological techniques, woodland management in prehistoric contexts, and the cultural significance of trees. His work contributes to UN Sustainable Development Goals related to environmental sustainability and heritage conservation. Publications highlight studies on burnt mounds, submerged forests, and Bronze Age cremation practices. He frequently engages in outreach, including lectures on submerged forests and community archaeology projects. His research has been featured in media outlets, including articles on sunken forests and ancient relics in Orkney.
