Andrew Cumming is a Researcher at the School of Computing Engineering and the Built Environment , Edinburgh Napier University. His work spans database systems, information visualization, and computational biology, with a focus on SQL education and microarray data analysis. Research Interests Database technologies and SQL optimization Animated visualization for biological time-course data Interactive graphics in virtual reality environments Automated educational assessment systems Genetic algorithms for timetabling Publications (sorted by year) highlight his contributions to: 2014: Polygonal voxel rendering techniques 2007: SQL best practices and data digging methodologies 2005: Dynamic scatter-plot visualization for gene activity 2004: Automated SQL marking systems 2000: Constraint-based timetabling algorithms Contact: A.Cumming@napier.ac.uk | a.cumming@napier.ac.uk
Vajira Thambawita is a researcher at the University of Oslo's Department of Informatics, specializing in medical image analysis and AI-driven healthcare solutions. With over 90 publications since 2019, their work spans gastrointestinal endoscopy, reproductive medicine technology, and multimedia systems for clinical applications. Research focuses on medical image segmentation (polyp detection, sperm tracking), anomaly detection in time-series data, and multimodal analysis for clinical decision support. Key projects include the Medico Multimedia Task at MediaEval (sperm tracking), VISEM-Tracking dataset, and ImageCLEFmedical challenges for GI tract analysis. Their work bridges computer vision with clinical practice through collaborations with Oslo University Hospital and Simula Research Laboratory. Recent publications demonstrate strong trends in generative AI for medical data augmentation (SinGAN-Seg, PolypConnect), explainable AI for clinical validation , and multimodal integration (SoccerNet-Echoes). The research consistently targets real-world clinical problems with emphasis on transparency and robust evaluation. Thambawita actively contributes to major benchmark challenges including ImageCLEF, Medico, and Medical AI competitions, serving as organizer and participant in advancing evaluation standards for medical AI systems.
Jia Liang is a researcher at Henan Polytechnic University's School of Electrical Engineering and Automation, with a focus on Machine Learning , Compressed Sensing , and Privacy-Preserving Techniques . His work bridges Computer Science and Signal Processing , particularly in Radar Imaging and Medical Image Analysis . Key Collaborations: Di Xiao, Ying Luo, Qun Zhang, Hui Huang Technical Expertise: Federated Learning, SAR Imaging, Compressive Sensing, Adversarial Learning His research emphasizes secure data processing in IoT and cloud environments, with recent innovations in cross-disciplinary applications like biosignal analysis for cysticercosis diagnosis . Publications span top venues including IEEE Transactions on Aerospace Systems and Remote Sensing . Notable trends include privacy-preserving machine learning for federated systems and 3D radar imaging of rotating targets, alongside medical imaging solutions for chest radiographs and optical coherence tomography .
Xiao Zhang is a tenure-track faculty member at the CISPA Helmholtz Center for Information Security and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). He obtained his Ph.D. in Computer Science from the University of Virginia in 2022 under Prof. David Evans, following an M.S. in Statistics from the same institution and a B.S. in Mathematics from Tsinghua University. Research Focus Zhang's research spans foundational and applied aspects of trustworthy machine learning, with emphasis on: Security vulnerabilities in ML systems (adversarial attacks, data poisoning) Robustness guarantees for learning algorithms Optimization methods for deep learning Privacy-preserving techniques Multimodal and physical-world applications His recent work focuses on developing theoretically-grounded defenses against emerging threats to AI systems. Publication Trends Zhang's 15 most recent publications (2019-2025) demonstrate consistent focus on adversarial machine learning, with growing emphasis on: 1) Physical-world attacks/defenses, 2) Multimodal model security, and 3) Certified robustness guarantees. The work consistently appears at top-tier venues (NeurIPS, ICLR, CCS). Academic Activities He actively recruits students and collaborators for trustworthy ML research, with open positions for PhD candidates and research assistants. No awards or specific grant information is documented in the provided materials.
Renjie Liao is an Assistant Professor (tenure-track) in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), with an associated appointment in the Department of Computer Science. He is also a Faculty Member at the Vector Institute and a Canada CIFAR AI Chair. Prior to UBC, Dr. Liao was a Visiting Faculty Researcher at Google Brain and held a Senior Research Scientist position at Uber Advanced Technologies Group during his PhD. He earned his B.Eng. (Automation) from Beihang University, M.Phil. (CS) from the Chinese University of Hong Kong, and PhD (CS) from the University of Toronto. His research focuses on probabilistic and geometric deep learning , with key contributions in deep generative models, geometric deep learning, neural algorithmic reasoning, and generalization bounds. Notable areas include 3D point cloud analysis, self-driving systems, and healthcare applications using graph neural networks. His work bridges theoretical foundations (e.g., PAC-Bayes bounds) with practical applications like motion forecasting and medical imaging. Education: B.Eng. in Automation, Beihang University (2011) M.Phil. in Computer Science, CUHK (2015) PhD in Computer Science, UofT (2021) Dr. Liao has received awards such as the RBC Graduate Fellowship and Connaught International Scholarship. His lab (Deep Structured Learning Lab) emphasizes principled mathematical approaches to solving complex problems. He advises students in machine learning, computer vision, and robotics, encouraging applications from those with strong coding/mathematical backgrounds. Labs/Teams: Deep Structured Learning Lab (UBC) Vector Institute Collaboration
Jerry Talton is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois at Urbana-Champaign (UIUC). He holds a PhD in Computer Science from Stanford University (2011), and BS and MS degrees from UIUC (2003 and 2006, respectively). His research focuses on computer graphics, machine learning, and human-computer interaction, with notable work on emoji-first communication, rating systems analysis, and data-driven design tools. Key contributions include the Opico study on emoji communication in social apps and the Bricolage algorithm for web design transfer. He received the Siebel School Young Alumni Achievement Award (2022). His work spans academic collaborations with institutions like Stanford and industry applications in social media and design. Research interests emphasize user interaction, algorithmic ranking, and leveraging big data for design automation. His publications address challenges in social networks, fluid simulation, and educational outreach through initiatives like Scavenger Hunt, enhancing student retention in computer science.
Jennifer Listgarten is a Professor in the Electrical Engineering and Computer Science (EECS) Department, the Center for Computational Biology, and the Bioengineering program at the University of California, Berkeley. She serves as a member of the steering committee for the Berkeley AI Research (BAIR) Lab and holds the Jeffrey Huber and Angel Vossough Chancellor's Chair in Computational Biomedicine. Her multidisciplinary appointments reflect her work at the intersection of computer science, statistics, and biological sciences. Professor Listgarten completed her Ph.D. in Computer Science from the University of Toronto in 2007, following undergraduate degrees in Physics and Computer Science from Queen's University in Canada. Prior to joining UC Berkeley, she spent a decade (2007-2017) at Microsoft Research with positions in Cambridge, MA, Los Angeles, and Redmond, WA. Her research focuses on developing and applying machine learning methods to solve problems in biology and medicine, with current emphasis on protein design, optimization, and engineering for properties such as expression, fluorescence, binding, and stability. She also works on computational chemistry methods, drug repositioning, and machine learning methodology at the intersection of graphical models, neural networks, and variational inference. Her earlier work addressed statistical genetics methods for correcting confounding factors in GWAS, epigenome-WAS, and eQTL studies, as well as immunoinformatics problems including HLA class I epitope prediction. Her recent publications demonstrate a strong trajectory in applying machine learning to protein engineering, with numerous high-impact papers in Nature Biotechnology, Science Advances, and top machine learning conferences. Her work shows increasing integration of computational methods with experimental validation, particularly in CRISPR technology and protein design applications. Bakar Fellows Spark Award (2024) Professor Listgarten actively mentors PhD students through EECS, the Center for Computational Biology, and Bioengineering programs. She maintains scientific advisory roles with several biotechnology companies including Dayzero Diagnostics, Deep Apple Therapeutics, Inscripta, and Fable Therapeutics (where she serves as Academic co-founder). Her research group collaborates with prominent scientists including Chris Garcia (Stanford), Phil Romero (U Wisconsin), David Savage (UC Berkeley), and David Schaffer (UC Berkeley). The Listgarten lab operates within the Berkeley Artificial Intelligence Research Lab (BAIR) and the Center for Computational Biology (CCB), focusing on developing computational methods that enable new biological insights and therapeutic applications, particularly in the areas of protein engineering and CRISPR technology.
Philippe Guyenne is a Professor in the Department of Mathematical Sciences at the University of Delaware, affiliated with the College of Arts & Sciences. His research focuses on nonlinear wave phenomena in fluids, with applications to oceanography and coastal engineering. Nonlinear water waves Wave breaking dynamics Complex fluid-structure interactions Hamiltonian systems Wave turbulence Biomechanics applications Guyenne employs advanced numerical methods and mathematical modeling to study wave behavior, including validation against laboratory experiments and field measurements. Recent work explores connections between surface wave dynamics and quantum mechanics analogies. His publications highlight interdisciplinary impacts in computer graphics, climate modeling, and acoustics. Research themes include: Fluid-structure interactions in bone acoustics Stochastic modeling of random topography effects Coherent wave superposition mechanisms Hamiltonian formulations for physical oceanography Optical flow estimation using fluid dynamics Weak turbulence kinetic theory
Stephen Wilson is a Research Professor in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science, where he conducts advanced research in fluid mechanics and mathematical modelling. He is actively involved in leading research projects, supervising PhD students, and contributing to the academic community through publications and professional activities. Education: Doctor of Philosophy (PhD), The Mathematics of Ship Slamming, University of Oxford (1987–1989) Master in Science (MSc), Mathematical Modelling and Numerical Analysis, University of Oxford (1986–1987) Bachelor of Arts (BA), University of Oxford (1983–1986) Stephen Wilson's research focuses on the mathematical modelling of fluid mechanics problems using asymptotic, analytical, and numerical methods. His work spans thin-film flows, evaporating droplets, non-Newtonian fluids, liquid crystals, fluid-structure interaction, and biomechanics. He applies these models to real-world challenges in industrial coating, microfluidics, and biological systems such as the human knee. His research integrates theoretical analysis with practical applications, often involving complex multiphysics phenomena. His recent publications (2024–2025) reveal a strong emphasis on evaporation dynamics, droplet behavior, and liquid crystal flows, primarily published in high-impact journals like Physical Review E and Journal of Fluid Mechanics . These works frequently involve collaborations with researchers such as Alexander W. Wray and Brian R. Duffy, and they demonstrate consistent application of asymptotic techniques to interfacial flows and thin-film systems. Scientific Awards and Honors: Leader of the winning team at Physics with Industry 2016 Recipient of the IOP Printing and Graphics Science Group Prize (2009) Elected Fellow of the Institute of Mathematics and its Applications (IMA) (2008) Wilson has served as Principal Investigator on multiple EPSRC-funded Doctoral Training Partnership (DTP) projects since 2018, indicating active PhD supervision and research leadership. He previously held the position of Joint Editor-in-Chief of the Journal of Engineering Mathematics (2011–2015), contributing significantly to academic publishing. His research is supported by major funding bodies and involves extensive collaboration across institutions and disciplines. He is involved in several research labs and teams focused on fluid dynamics, mathematical modelling, and industrial applications. His group works on projects involving free-surface flows, multiphysics models, and nematic liquid crystals, often in collaboration with experimentalists and engineers. These teams contribute to both fundamental science and applied technological development in areas like coating processes and biomedical systems.
Roger Beecham is Associate Professor in Visual Data Science at the School of Geography, University of Leeds, and Director of Research & Innovation at the Leeds Institute for Data Analytics (LIDA). He co-leads LIDA's Visualization and Science of Data Science programmes and serves as Programme Leader for GISc Distance Learning. His academic home resides within the Faculty of Environment, where he bridges geographical analysis with cutting-edge data science methodologies. His research spans Data Visualization, Spatial Statistics, and Applied Data Science across transport, health, crime science, and political geography domains. Beecham develops visualization techniques for analyzing large social science datasets, with particular focus on uncertainty quantification and methodological rigor. His work addresses the 'Forking Paths' problem in data analysis and promotes transparent scientific practices through visual analytics. Current projects include INFUZE (zero-carbon mobility) and SaferActive, funded by EPSRC, ESRC, ERC, NIHR, and Wellcome Trust. Beecham's scholarly contributions manifest in top-tier journals like IEEE TVCG, Accident Analysis & Prevention, and Transport Research Part C. His upcoming 2025 CRC Press book Visualization for Social Data Science synthesizes his methodological innovations. Research outputs demonstrate consistent focus on visual inference frameworks, spatial pattern analysis, and open-source implementation. EPSRC-funded transport safety research Alan Turing Institute Methods Challenge leadership Wellcome Trust health geography projects ERC spatial data science collaborations He supervises doctoral researchers including Juan P. Fonseca-Zamora, Juliana Novaes, and Seán Ó Héir through the SENSE CDT program. Teaching responsibilities include GEOG5009 Visualization for Social Data Science and GISc Distance Learning MSc coordination. Beecham maintains active GitHub repositories demonstrating reproducible research practices and collaborates extensively through the Institute for Spatial Data Science.
Lukas Luft is a postdoctoral researcher at the Autonomous Intelligent Systems group within the Department of Computer Science at the University of Freiburg. His work spans robotics and quantum physics, focusing on probabilistic methods for robot localization, multi-robot systems, and causal inference. Post Doc (2020–present) PhD in Computer Science, University of Freiburg (2020) Master and Bachelor in Physics, RWTH Aachen and University of Freiburg His research in Robot Localization and Mapping includes advanced probabilistic techniques like Bayes filters, decentralized algorithms for multi-robot systems, and change detection in environments using full posterior distributions. He also explores Causality and Foundations of Quantum Physics , applying entropic inequalities and information theory to causal discovery and non-locality. The articles highlight his contributions to robotics, particularly in sensor modeling for Lidar, simultaneous localization and mapping (SLAM), and efficient probabilistic methods. In quantum physics, his work addresses causal structures and entropic information, bridging AI with foundational physics. Luft has collaborated with leading researchers, including Prof. Wolfram Burgard and Bernhard Schölkopf, and contributed to key conferences like Robotics: Science and Systems (RSS) and IEEE IROS.
Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.
Murilo da Silva Baptista is a Reader at the Institute for Complex Systems and Mathematical Biology , within the School of Natural and Computing Sciences at the University of Aberdeen . He has been with the university since 2009, initially as a Senior Lecturer and promoted to Reader in 2014. He is actively accepting PhD students in Physics, Mathematics, and Engineering, and his research is internationally recognized in the fields of complex systems and chaos theory. His research focuses on understanding the relationship between function—such as information processing, collective behavior, and synchronization—and structure in large networked complex systems. He applies analytical methods, data science, nonlinear time series analysis, and machine learning to model systems in neuroscience, smart engineering, and Earth sustainability. He is a leading scientist in chaos-based communication, demonstrating how chaotic signals can enable smart and secure wireless and underwater communication systems. His work includes theoretical developments in phase definition in chaotic oscillators, chaos-based cryptography using Poincaré return times, and the discovery of phenomena like Collective Almost Synchronization, which enhances machine learning for EEG signal prediction. His recent publications (2023–2025) span a wide range of applications, including chaotic image and 3D model encryption, UAV surveillance using chaotic paths, causal feature selection in health systems, modeling neurological disorders, and socio-environmental analysis in Brazil. These works reflect a strong trend toward applying nonlinear dynamics and network science to real-world engineering, biomedical, and societal challenges. Scientific Contributions and Recognition: Proved a conjecture on the analytical calculation of Poincaré first return times using unstable periodic orbits. Contributed foundational work to chaos-based cryptography. Proposed a formula linking mutual information to Lyapunov exponents, supporting the Infomax theory of brain evolution. Discovered the phenomenon of Collective Almost Synchronization in complex networks. Demonstrated that causality is a space-time phenomenon, not purely temporal. Advising and Research Support: He is currently supervising PhD students in Physics, Maths, and Engineering, indicating active mentorship. His research is supported by analytical developments and data-driven modeling. He leads work on optimal wireless chaos communication, synapse modeling, brain network changes post-surgery, and socio-economic causality in Brazil. His collaborations span institutions in the USA, Brazil, Germany, and Portugal. Labs and Research Groups: He is affiliated with the Institute for Complex Systems and Mathematical Biology at Aberdeen, a hub for interdisciplinary research in nonlinear dynamics, network theory, and their applications across physical, biological, and social systems.
Glenn Van Wallendael is an Associate Professor at Ghent University's Faculty of Engineering and Architecture , affiliated with the Department of Electronics and Information Systems . He leads research in video coding, digital watermarking, and immersive media technologies. Academic Focus: Video compression standards (HEVC, H.266), AI for multimedia, virtual reality Key Collaborations: iMinds, imec, European research consortia Research Interests include: Video compression algorithms (HEVC, SVC, MV-HEVC) Digital watermarking for copyright protection Machine learning applications in image/video analysis Quality of Experience (QoE) in immersive environments Recent Publications (2024-2025) show expertise in: Deepfake detection using vision transformers Medical image landmarking tools Lightweight geometric approximation methods AI-driven video quality assessment Doctoral Mentorship includes supervising: 2021: Hannes Mareen (video forensics) 2020: Vasileios Avramelos (light field coding) 2017: Johan De Praeter (adaptive video encoding)
Alfredo Pina Calafi is a Professor at the Public University of Navarra's Department of Statistics, Computer Science and Mathematics. His work focuses on educational technology, computer graphics, and multimedia systems, with significant contributions to e-learning and technology integration in education. He is actively involved in curriculum adaptation to the European Higher Education Area (EHEA) and has pioneered projects like the Virtual Classroom initiative recognized with the 3rd Prize in National Awards for Educational Innovation (2005). His research spans augmented reality applications for linguistic heritage preservation, educational robotics in primary education, and interactive digital solutions for science learning. Education: PhD in Computer Science (University of Zaragoza, 2001) Key Projects: ALFA T_GAME (teaching materials exchange for computer graphics), VideoPlanet (planetarium visualization systems), and mobile emergency decision-making systems for Pamplona's bull run Research interests emphasize bridging technology and education through innovative tools. Over 25 years of academic contributions include directing three doctoral theses and co-authoring books on educational robotics and digital narratives. He has coordinated major educational adaptation projects and led teams developing virtual environments and intelligent systems for diverse applications. Notable collaborations include work with institutions like the European Center for Soft Computing and participation in international congresses on artificial intelligence and educational informatics. His work consistently integrates pedagogical theory with cutting-edge technological solutions.
