Fabrizio Palumbo is an Associate Professor at the Department of Information and Communication Technology, University of Agder (UiA). His research spans artificial intelligence, machine learning, energy systems optimization, and computational neuroscience, with notable contributions to text classification, cybersecurity protocols, and zebrafish behavioral studies. He actively publishes in interdisciplinary venues including AI conferences and neuroscience journals. Key research areas include developing efficient neural network architectures for text analysis, exploring multilingual BERT models, optimizing energy storage systems via neural networks, and investigating neural mechanisms in zebrafish using behavioral protocols. His work bridges theoretical advancements with applied contexts, such as AI in investigative journalism and cybersecurity applications. Palumbo collaborates across disciplines, contributing to both technical and applied research. Notable recent publications address AI project methodologies in journalism and energy system optimization strategies.
Dhananjay Tomar is a Doctoral Research Fellow at the University of Oslo , affiliated with the Faculty of Mathematics and Natural Sciences . He is part of the Research Group for Digital Signal Processing and Image Analysis (DSB) , focusing on Deep Learning applications in histopathology , including domain generalization, giga-pixel image processing, and semi-supervised learning. His work also integrates software development for practical implementations. Research Interests: Domain Adaptation in Medical Imaging High-resolution Image Analysis Efficient Deep Learning Models Publications highlight contributions to synthetic data generation for training datasets and feature selection via autoencoders. No scientific awards are explicitly listed. His current role involves no formal advisees, and grants or funding details are not disclosed. He collaborates within the Digital Signal Processing and Image Analysis research group, contributing to interdisciplinary projects at the intersection of computer science and biomedical engineering.
Professor Tao Xiang holds the position of Professor of Computer Vision and Machine Learning at the University of Surrey, where he is also a Distinguished Chair. He leads the Body Behaviour Group at Samsung AI Centre, Cambridge. His research focuses on computer vision, including video surveillance, activity analysis, and sketch-based recognition, alongside machine learning domains like zero-shot learning and domain generalization. He has published over 160 papers (h-index 60) and secured £3.9M+ in grants. Key affiliations include the Faculty of Engineering and Physical Sciences and the Centre for Vision, Speech and Signal Processing (CVSSP). His work spans generative models for sketches, 3D shape retrieval from VR sketches, and virtual try-on systems. Notable contributions include the FS-COCO dataset for scene sketches and advancements in temporal action detection via vision-language prompting. Research interests emphasize cross-modal tasks (e.g., SBIR/FG-SBIR), domain adaptation, and transformer-based vision models. Collaborations with industry (Samsung AI) and foundational contributions to datasets (e.g., VR sketch collections) underscore his interdisciplinary impact. Future work includes creative object generation (PartCraft), explainable AI for sketches, and scalable generative models.
Isel Grau Garcia is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Information Systems group within the Department of Industrial Engineering and Innovation Sciences. Her research focuses on explainable AI, recurrent neural networks, time-series analysis, and bias mitigation in healthcare and business applications. She holds a Ph.D. in Computer Science from Vrije Universiteit Brussel (VUB) and has co-authored over 75 peer-reviewed publications. Key roles include co-promotor for three PhD students and involvement in the ENFIELD project on trustworthy AI. Education: Ph.D. in Artificial Intelligence, Vrije Universiteit Brussel (2020) M.Sc. in Artificial Intelligence, Central University of Las Villas (2014) B.Sc. in Computer Science, Central University of Las Villas (2011) Research Interests: Her work emphasizes interpretability of machine learning models, semi-supervised classification, and interdisciplinary applications in healthcare. Recent projects include developing Fuzzy Cognitive Map models without training data and quantifying trust in clinical decision support systems. Key Contributions: Her articles explore topics like bias detection via SHAP and FCMs, sparsity-optimized feature importance, and recurrent neural networks for temporal dynamics. These contributions align with UN SDG goals for innovation and health. Awards: Best Demo Award at BNAIC/BENELearn 2021 Industria Students Award – Best Talent (2024) Advising & Grants: Supervises three PhD students and collaborates on the ENFIELD project (€ funding). Active in organizing workshops and reviewing for top venues like ECAI and IEEE Transactions on Fuzzy Systems. Labs/Teams: Engaged in interdisciplinary collaborations with institutions like Warsaw University of Technology and the University of Lisbon, focusing on AI ethics and trustworthy decision systems.
Mingzhe Jiang is an Adjunct Assistant Professor at the University of Waterloo, focusing on remote sensing and machine learning applications in environmental science. His work emphasizes sea ice classification, SAR imagery analysis, and deep learning models for geospatial data. He is affiliated with the Adjunct Faculty group at the university. Research interests include developing advanced algorithms for sea ice dynamics, uncertainty quantification in climate models, and automated environmental monitoring systems. His contributions span Bayesian neural networks, graph convolutional networks, and semi-supervised learning frameworks for polar and maritime applications. Key publications (2016–2025) highlight innovations in SAR data processing, including hierarchical pipelines for sea ice segmentation, dual-polarized imagery analysis, and unsupervised clustering techniques. His work often integrates satellite data from platforms like RADARSAT-2 with machine learning to improve environmental parameter estimation. No scientific awards or grants are explicitly listed in the provided information. He has not reported advisees or laboratory affiliations in the text.
Liam Burrows is a Researcher in the Department of Mechanical Engineering at the University of Bath. His work focuses on medical imaging and deep learning applications in pathology, particularly in tumor stroma analysis and meningioma segmentation. He holds a PhD in Mathematics from the University of Liverpool (2022), specializing in variational and deep learning methods for selective segmentation. His research addresses UN Sustainable Development Goals related to education and health. Education: Doctor of Philosophy (Mathematics) from University of Liverpool, specializing in 'Variational and Deep Learning Methods for Selective Segmentation' awarded June 2022. Research interests include combining mathematical modeling with deep learning to automate assessment of immunohistochemical stains and develop semi-automatic segmentation techniques for medical diagnoses. His recent work emphasizes histopathology image analysis and weakly supervised learning approaches. Publications (2022–2024) demonstrate expertise in variational models, hybrid pipelines, and automated lesion segmentation. No scientific awards explicitly listed but maintains active collaborations in medical imaging research. Engagement includes public talks like CMIT Summer Research Internship (June 2024) and NHSX Public Patient Information Group AI Talk (May-Nov 2024). No formal student advisement or grant details provided in available texts.
Yu Sun is a Professor in the Department of Computer Science and Engineering at the University of Central Arkansas (UCA). He holds a Ph.D. in Computer Science & Engineering from the University of Texas at Arlington. His research focuses on Multimedia Computing, Video Compression and Communication, Image Processing, and Wireless Videos. His work emphasizes optimizing video coding standards like HEVC, AVS2, and SHVC, with contributions to algorithms for rate control, intra prediction, and scalable video techniques. Research interests include developing efficient algorithms for 360-degree videos, generative adversarial networks (GANs) for image classification, and real-time video transmission over wireless systems. His publications span over two decades, addressing challenges in video compression efficiency, bufferless rate control, and neural network applications in multimedia systems. Dr. Sun’s articles highlight trends in hybrid coding strategies, probability-based optimization, and spatial/temporal scalability in video coding. His work bridges theoretical advancements and practical implementations for emerging technologies such as virtual reality video streaming and collaborative robotics. His academic webpage is available at https://faculty.uca.edu/yusun/ .
Angelo Sotgiu is an Assistant Professor at the University of Cagliari's Department of Electronic and Computer Engineering. His research focuses on machine learning security, adversarial attacks, and malware detection. He holds an MSc in Telecommunication Engineering and a PhD in Electronic and Computer Engineering from the University of Cagliari. During his PhD (2019–2023), he conducted a visiting research period at CISPA Helmholtz Center for Information Security (Saarbrücken, Germany). He has worked as a Research Collaborator for the National Interuniversity Consortium for Informatics (CINI) from 2022–2023. His work emphasizes adversarial robustness, AI security, and practical defenses against attacks on machine learning systems. Key contributions include the ImageNet-Patch dataset for adversarial patch benchmarking, the secml Python library for secure ML, and frameworks like Fader for fast adversarial example rejection. His research bridges theoretical advancements with real-world cybersecurity applications.
Dr. Wei Zhang is a Researcher at the Complex Systems and Data Science group within The University of Sydney. Her work focuses on network science, evolutionary dynamics, and complex systems, with an emphasis on modeling social systems and analyzing meso-scale structures in social networks. She combines theoretical approaches, data-driven modeling, and web-based experimental methods to study emergent collective phenomena. Dr. Zhang holds a PhD in Network Science from ETH Zurich. Her research spans interdisciplinary topics including data science applications to complex systems and the detection of structural patterns in social networks. Recent projects include exploring domain adaptation techniques in machine learning and developing methods for 3D object detection and model compression. Her publications reflect expertise in computer vision, machine learning, and environmental science. Key themes include domain adaptation networks, 3D reconstruction, and LiDAR-based forestry analysis. While no specific awards are mentioned, her work demonstrates significant contributions to theoretical and applied data science. Dr. Zhang collaborates on projects involving interdisciplinary teams, though specific grants or lab affiliations are not detailed in the provided text. Her current research continues to bridge complex systems theory with modern data-driven methodologies.
Christopher Saunders is a Professor of Statistics and affiliate faculty in Natural Resource Management at South Dakota State University (SDSU). He holds a Ph.D. in Statistics from the University of Kentucky, an M.S. in Statistics from the same institution, and a B.S. in Mathematics from California State University, Chico. His professional experience spans roles at The MITRE Corporation, George Mason University, and as a visiting professor at the University of Salzburg. Saunders specializes in statistical learning theory, forensic identification of source problems, and biometric analysis, with applications in forensic science, pattern recognition, and large-scale simulations. His work emphasizes quantitative methods for evidence interpretation, algorithmic bias mitigation, and forensic data validation. Research interests include statistical methods for forensic source attribution, hierarchical data modeling, and the application of machine learning to forensic challenges. Saunders has contributed to foundational studies on error rate assessment in forensic evidence, probabilistic evidence evaluation, and the development of automated systems for handwriting and biometric analysis. His interdisciplinary work bridges statistics, computer science, and forensic science, addressing practical problems in criminal investigations and national security. Educations: Ph.D. in Statistics, University of Kentucky, 2008 M.S. in Statistics, University of Kentucky, 2006 B.S. in Mathematics, California State University, Chico, 2002 Visiting Scientist, FBI Labs Forensic Science Research, 2013 Notable awards include the 2020 Outstanding Researcher Award and 2016 Young Investigator Award from the Jerome J. Lohr College of Engineering. His grants include leadership on NIH-funded projects and collaborations with MITRE Corporation, National Institute of Justice, and other federal agencies. Saunders has advised graduate students in forensic statistics and led teams developing statistical frameworks for evidence interpretation and forensic validation. His research has advanced methodologies for explosive material analysis, handwriting examination, and forensic database design. Current projects focus on algorithmic fairness in forensic systems and improving the accuracy of biometric and trace evidence evaluation through advanced statistical modeling.
Alejandro F. Frangi is the Bicentenary Turing Chair in Computational Medicine and Royal Academy of Engineering Chair in Emerging Technologies at the University of Manchester, with joint appointments in the School of Computer Science and School of Health Sciences. He serves as Director of the Christabel Pankhurst Institute and leads the NIHR Manchester Biomedical Research Centre's Digital Infrastructure theme. His education includes a PhD in Medicine from Utrecht University (2001) and an undergraduate degree in Telecommunications Engineering from Universitat Politècnica de Catalunya (1996). He holds honorary positions at KU Leuven and is an Alan Turing Institute Fellow. Professor Frangi's research bridges medical image analysis and computational modeling, with emphases on: Machine learning for population imaging In silico clinical trials for medical devices Computational physiology in cardiovascular and neurosciences Statistical methods for image-based biomarkers His recent publications demonstrate strong focus on AI-driven medical image reconstruction, computational modeling of vascular diseases, and virtual clinical trial methodologies. Research trends show consistent innovation in deep learning architectures for 3D medical image processing. Awards and honors include: IEEE Engineering in Medicine and Biology Technical Achievement Award (2021) Fellow of Royal Academy of Engineering (2023) ERC Advanced Grant recipient President's International Initiative Award from Chinese Academy of Science He has supervised 28+ PhD students to completion and currently leads multiple major grants including a £2.7m Royal Academy of Engineering Chair award. His laboratory develops open-source platforms (GIMIAS, MULTI-X) and has spun off three companies (GalgoMedical, adSilico). As Director of the Christabel Pankhurst Institute, he oversees interdisciplinary teams working on health technology innovation. He founded the InSilicoUK Innovation Network to advance regulatory science for in silico methods.
Ramanjit K. Sahi is a Professor and Graduate Coordinator for Mathematical Finance at Austin Peay State University (APSU), where she has been since 2007. She holds a Ph.D. in Mathematical Sciences from the University of Texas at Dallas and an M.S. in Mathematics from San Jose State University. Her research focuses on Mathematical Finance, Mathematical Modeling, Risk Analysis, and Knot Theory. She has published extensively in journals like Journal of Knot Theory and Ramifications , International Journal of Pure and Applied Mathematics , and Cogent Economics & Finance , with notable contributions to numerical methods for financial derivatives and topological knot invariants. Dr. Sahi has supervised over 20 student projects, including works on portfolio risk analysis, molecular symmetry applications, and sports statistics. She has secured grants totaling over $35,000, including a Google Community Grant ($10,000) and multiple Student Academic Success Initiative (SASI) grants for math outreach events. Her academic contributions also include revitalizing calculus and pre-calculus curricula through APSU Title III grants, emphasizing student-centered pedagogy. She actively engages in interdisciplinary research, bridging knot theory with molecular symmetry studies and financial modeling.
Dr. Tashnim Chowdhury is a Professor in the Department of Computer Science at Capitol Technology University. He holds a Ph.D. in Information Systems (AI and Machine Learning) from the University of Maryland Baltimore County, an M.S. in Electrical Engineering from the University of Toledo, and a B.S. in Electrical and Electronics Engineering from Chittagong University of Engineering and Technology. His research focuses on Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing, with applications in disaster damage assessment and large language models. He has published extensively on topics like UAV-based semantic segmentation for natural disaster analysis and high-resolution aerial imagery datasets for post-flood scene understanding. Dr. Chowdhury has over 3 years of industry experience as a Machine Learning Engineer and Software Engineer at companies such as Comcast, Intel, and Fluence Automation. His work bridges academic research with practical applications in AI, generative models, and environmental monitoring. He has advised students in academic settings and contributes to dataset development for critical infrastructure analysis.
Giovanni Paolini is an Associate Professor of Mathematics at the University of Bologna (Italy) since 2023. Previously, he worked as an Applied Scientist/Senior Applied Scientist at Amazon Web Services and Caltech (2019–2023), and held a postdoctoral position at the University of Fribourg (2019). He earned his PhD in Mathematics from Scuola Normale Superiore (Pisa, Italy) in 2019, under Prof. Mario Salvetti, with a thesis on topology and combinatorics of affine reflection arrangements. His research spans combinatorics, topology, group theory , and machine learning (deep learning and NLP). Notable contributions include resolving the K(π,1) conjecture for affine Artin groups and developing AI for strategic games like 7 Wonders Duel. He organizes events such as MATH-MIND: Mathematics-Industry Networking Days (2025) and has participated in workshops like Artin Groups and Arrangements at MSRI (2024). Teaching roles include courses on geometry, combinatorial topology, and machine learning at the University of Bologna. His work bridges pure mathematics and applied AI, with publications in journals like Inventiones Mathematicae and conferences like ICLR and ICML. Collaborations include projects with Stefano Soatto (UCLA), Mario Salvetti, and interdisciplinary teams at Amazon.
HINDAWI Mohammed is a Researcher-Lecturer at CESI (France) specializing in Computer Science with a focus on Data Mining, Big Data, and Machine Learning. He holds a PhD in Computer Science from INSA Lyon (2013) and teaches at the engineering level. His current research emphasizes Dimensionality Reduction in semi-supervised learning and frugal/embedded AI applications. Education: PhD in Computer Science (2013), INSA Lyon MSc in Knowledge and Decision (2008), INSA Lyon BSc in Software Engineering (2005), Aleppo University Research interests include semi-supervised learning, feature selection, ensemble methods, and clustering techniques. He leads the Engineering and Numerical Tools research team and contributes to the Advanced Automation: AI and Data Science group. His work addresses practical applications like patient monitoring via machine learning and battery state estimation. Publications span over 15 peer-reviewed articles in journals like Knowledge and Information Systems (KAIS) and conferences such as IEEE MMSP and ACM CIKM. He supervises PhD students like Trésor Yao Koffi and collaborates on projects involving embedded systems and healthcare tech.
