Professor Hamid Laga leads research in computer vision and machine learning at Murdoch University's School of Information Technology, with cross-disciplinary applications in agriculture, biosecurity, and health. He holds a PhD in Computer Science from Tokyo Institute of Technology and serves as Professor of Information Technology. His work integrates the College of Science, Technology, Engineering and Mathematics and the Centre for Biosecurity and One Health. Laga's research spans fundamental algorithms in 3D shape analysis and cutting-edge applications of deep learning. Core technical innovations include geometric processing of botanical structures, neural field representations, and weakly-supervised 3D reconstruction methods. Applied research focuses on agricultural technology solutions for disease detection in crops, weed classification systems, and insect phenotyping tools. Recent publications demonstrate significant advancement in generative AI techniques for 4D shape representation, quantum neural networks, and transformer-based agricultural vision systems. His scholarly output consistently bridges theoretical computer science with practical applications in environmental and biological domains.
J. M. Howe is a Professor in the Department of Computer Science at City, University of London, with an active research career spanning over 25 years from 1997 to the present. With an ORCID identifier 0000-0001-8013-6941, Howe has established himself as a prominent researcher in formal methods, logic programming, and the intersection of symbolic and neural approaches to artificial intelligence. Howe's research interests center on abstract interpretation, program analysis, constraint solving, and more recently, neural-symbolic AI. His early work focused on logic programming and abstract domains, particularly systems of two variables per inequality (TVPI), which has remained a consistent thread throughout his career. In recent years, he has expanded into explainable AI, rule extraction from neural networks, and the development of the Neural Multi-Space (NeMuS) framework for integrating symbolic and neural approaches. His research has practical applications in program verification, security (particularly cross-site scripting detection), and agricultural technology. Analysis of Howe's publication trajectory reveals a consistent focus on foundational programming language theory that gradually evolved toward machine learning applications while maintaining strong theoretical underpinnings. His work shows increasing collaboration with researchers in neural networks while preserving his expertise in formal methods, creating a distinctive research niche at the intersection of symbolic and connectionist AI approaches. Howe has made significant contributions to the understanding of widening operators in abstract interpretation, constraint solving techniques, and methods for extracting interpretable rules from black-box neural networks. His research demonstrates both theoretical depth and practical applicability across multiple domains including software verification, security analysis, and computer vision applications. His collaborative work spans multiple institutions, with frequent collaborations with researchers like A. King, M. Brain, F. Mereani, and E. Robbins. This pattern of collaboration demonstrates his integration within both the formal methods and machine learning research communities.
Johannes Zimmer is a Professor in the Department of Mathematical Sciences at the University of Bath. His research lies at the intersection of applied analysis, mathematical physics, and materials science, with a focus on multiscale modeling and the derivation of macroscopic laws from microscopic dynamics. He is actively involved in research networks supported by the LMS, EPSRC, and EU, and has contributed to major workshops and collaborative projects in nonlinear PDEs and molecular dynamics. PhD, Technische Universität München (2000) Postdoctoral Fellow, California Institute of Technology Head of Emmy Noether Group, Max Planck Institute for Mathematics in the Sciences, Leipzig Faculty Member, University of Bath Zimmer's research centers on multi-scale methods for dynamic problems , particularly in the context of phase transitions, gradient flows, and molecular systems. He investigates the derivation of thermodynamic quantities such as entropy from microscopic models using tools like large deviation principles and Gamma-convergence . His work addresses rare events in particle systems, kinetic relations in phase boundaries, and the variational structure of irreversible processes. He has made significant contributions to the mathematical analysis of atomistic models , including the Fermi-Pasta-Ulam problem and Frenkel-Kontorova chains. His recent publications reveal a strong trend toward fluctuation theory , hydrodynamic limits , and regularized stochastic models such as the Dean-Kawasaki equation. He explores the geometry of dissipative evolution equations and the orthogonality of forces and fluxes in non-equilibrium systems. His work often combines probabilistic and analytical techniques to bridge scales in physical models. Scientific contributions include: Development of the MODOI and GeometricMD software packages for molecular dynamics simulations Co-organization of the One World Dynamics Seminar Participation in LMS Symposia and EPSRC-funded networks Editorship of Analysis and Stochastics of Growth Processes and Interface Models (Oxford University Press) Zimmer has advised multiple PhD students and postdoctoral researchers, including Daniel Sutton, Marios Stamatakis, and Marcus Kaiser. He has secured research funding from EPSRC, Leverhulme Trust, and EU networks. His teaching includes core courses such as Mathematics 2 , Numerical Solution of PDEs , and Advanced PDEs . He has also taught at Caltech and Technische Universität München. He leads research in variational methods for evolution and is part of the Bath Institute for Complex Systems, contributing to summer schools and interdisciplinary initiatives on complex networks and multiscale modeling.
Dr. Jan Hamann is a Senior Lecturer at the School of Physics , University of New South Wales, specializing in theoretical cosmology. His research focuses on analyzing high-precision astrophysical observations to study the universe's history, composition, and inflationary models. Education: PhD in Cosmology from Hamburg University (2007) Research Interests: Cosmic Microwave Background (CMB) analysis, inflationary features, dark matter (sterile neutrinos), cosmological parameter estimation, and machine learning applications to astrophysical data. Supervision: Primary supervisor for PhD students Yuqi Kang, Julius Wons, and Nathan Cohen; secondary supervisor for Kai Yi; and Honours supervisor for Jahanvi Maheshwari. Teaching: Courses include PHYS1241 Higher Physics 1B (Special) , PHYS4143 General Relativity , and PHYS3115 Particle Physics and the Early Universe . Recent publications (2024–2017) address CMB lensing, inflationary model optimization, sterile neutrino constraints, and machine learning techniques for cosmological data. He has contributed extensively to Planck mission analyses, particularly in CMB power spectra, isotropy tests, and inflationary parameter constraints.
Miguel Ángel Cazorla Quevedo is a Professor in the Department of Computer Science and Artificial Intelligence at the University of Alicante's Higher Polytechnic School. He has served as a professor since 1995, progressing through all academic ranks to become a full professor in 2017. His administrative roles include being Director of the University Institute of Computer Research, Coordinator of the Robotics Engineering degree program, and Director of the Artificial Intelligence Master's program. Dr. Cazorla holds a PhD in Computer Engineering (2000) and a Computer Engineering degree (1995), both from the University of Alicante. His research has consistently focused on computer vision with applications to robotics. Over his career, he has expanded into 3D data processing, deep learning applications across multiple domains including medical imaging and traffic analysis, and more recently, Large Language Models. His current research emphasizes social robotics, applying these technologies to assist dependent individuals. His publication record includes over 70 JCR-indexed articles (more than 30 in Q1 journals) and over 100 conference papers. Recent publications show a strong focus on holographic classroom integration, depth estimation, adversarial attacks in neural networks, medical image analysis, and educational applications of AI. His work bridges computer vision, robotics, deep learning, and practical applications in healthcare, education, and transportation. Senior Member of IEEE Dr. Cazorla has supervised 22 doctoral theses and 198 undergraduate/master's theses in the last five years. He serves as Principal Investigator on multiple national research projects including 'Asistente para Personas con TEA y fobias mediante Realidad Virtual y Aumentada' (2023-2026) and 'MEEBAI: A Methodology for Emotion-Aware Education Based on Artificial Intelligence' (2022-2025). He also leads several technology transfer projects with companies like CYPE SOFT and EMBENTION. He leads the Robotics, Vision and Intelligent Technologies (RoViT) research group within the University Institute of Computer Research, which focuses on applying computer vision and AI to real-world problems in social robotics, healthcare, education, and industry.
Raffaello Camoriano is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Polytechnic University of Turin. He is also an Affiliated Researcher at the Istituto Italiano di Tecnologia. His academic work spans multiple roles including serving as an Editorial Board Member for MACHINE LEARNING journal (since 2024) and Associate Editor for IEEE ROBOTICS AND AUTOMATION LETTERS (since 2022). He is an active member of IEEE Robotics and Automation Society (since 2024) and ELLIS - European Laboratory for Learning and Intelligent Systems (since 2022). Dr. Camoriano's research focuses on the intersection of artificial intelligence, machine learning, and robotics. His work particularly emphasizes robot learning, incremental/lifelong learning, structured learning, and kernel methods. His research has significant applications in medical robotics, particularly in laser-based skincare procedures and prosthetic control systems. He has developed innovative approaches for federated learning, point cloud processing for robotics, and trajectory generation for humanoid robots. His work bridges theoretical machine learning with practical robotics applications, addressing challenges in resource efficiency, adaptation to changing environments, and human-robot interaction. His recent publications demonstrate a strong trajectory in advancing federated learning techniques, with particular emphasis on resource-efficient personalization and handling heterogeneous data. He has made significant contributions to robotics applications including upper-limb prosthetics using high-density sEMG, autonomous navigation using point clouds, and medical robotics for laser procedures. His work consistently combines theoretical machine learning advances with concrete robotics applications, showing expertise in both domains. Best PhD Thesis Award of the Computational Intelligence Society Italy Chapter 2017 Dr. Camoriano actively supervises PhD students in the Intelligenza Artificiale program, including Stephany Ortuno Chanelo, Andrea Protopapa, and Leonardo Iurada. He has led research projects including the ECML-PKDD 2023 International Workshop 'Adapting to Change: Reliable Learning Across Domains' and the International Workshop 'Learning Meets Model-based Methods for Manipulation and Grasping' (2023). His teaching portfolio includes Fundamentals of Artificial Intelligence, Machine and Deep Learning, and Algorithms and Programming across multiple engineering programs at Polytechnic University of Turin.
Artem Sokolov serves as an Honorary Professor in the Department of Computational Linguistics at Heidelberg University and as a Research Scientist at Google Berlin. His primary research focuses on machine translation and structured prediction within natural language processing. Previously, he held positions at Amazon, the Statistical NLP Group at Heidelberg University led by Prof. Stefan Riezler, LIMSI, and Orange Labs in France, contributing to advancements in statistical and neural machine translation systems. He earned his PhD in Computer Science and Artificial Intelligence from the IRTCITS research center in Kyiv. His doctoral thesis investigated randomized algorithms for locality-sensitive embeddings of the Levenstein edit distance, establishing foundational work for efficient string similarity search in computational linguistics and intrusion detection systems. Dr. Sokolov's research expertise spans machine translation, imitation learning, bandit algorithms, and weakly supervised learning. He has pioneered methods for learning from partial feedback in structured prediction tasks, particularly addressing exposure bias in sequence generation and multi-facet evaluation of translation systems. His work bridges theoretical machine learning with practical NLP applications, emphasizing robustness against noisy data and scalable optimization techniques for real-world deployment. Analysis of his recent publications reveals trends toward scalable influence functions for model interpretability, multi-attribute control in machine translation, and rigorous auditing of multilingual datasets. His research consistently intersects natural language processing, machine learning optimization, and data quality assessment, with increasing emphasis on ethical AI considerations and efficient learning from weak supervision signals. Scientific awards include: 1st place at ECML/PKDD Discovery Challenge 2010 (English quality task) 2nd place at ECML/PKDD Discovery Challenge 2010 (general task) 2nd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 3rd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 As co-Principal Investigator for the 2015-2017 grant "Weakly Supervised Learning of Cross-Lingual Systems", Dr. Sokolov developed techniques for learning cross-lingual rankings from weakly supervised data sources like patent citations and Wikipedia hyperlinks. He has mentored students through teaching advanced courses including Imitation Learning, Stochastic Learning, and Statistical Machine Translation at Heidelberg University, supervising seminar projects on structured prediction and optimization algorithms. Dr. Sokolov is an active member of the Statistical NLP Group at Heidelberg University and collaborates with research teams at Google Berlin. His current work focuses on advancing production-scale machine translation systems through scalable inverse reinforcement learning and robust training methodologies, building on his extensive background in both academic research and industrial applications.
Dr. Mahuya Bandyopadhyay is an Associate Lecturer in the School of Mathematical and Physical Sciences at the University of Technology Sydney (UTS), holding this position since January 2024 after serving as a Teaching Associate from March 2018 to January 2024. With over ten years of university-level teaching experience in Mathematics and Statistics—including three years as a lecturer in India prior to UTS—she is a dedicated Pure Mathematician specializing in advanced geometric structures. Her academic credentials include: PhD in Differential Geometry, University of Calcutta, Kolkata, India M.Sc. in Pure Mathematics, University of Calcutta, Kolkata, India Dr. Bandyopadhyay's research is anchored in Differential Geometry , with foundational work on Riemannian and semi-Riemannian manifolds applied to general relativity and cosmology. Her current interests extend to wave mechanics in solids and Lie representation theory. Her eight publications (2000–2025) demonstrate deep engagement with geometric frameworks, particularly quasi-Einstein manifolds, Ricci solitons, and Sasakian manifolds, reflecting consistent theoretical innovation in curvature analysis and manifold classification. Her publication timeline reveals an evolution from classical geometry (e.g., triangle properties in 2000) toward specialized investigations of soliton structures and recurrence properties in contemporary works, underscoring sustained contributions to geometric topology. No scientific awards or fellowships are documented in available sources. In teaching, she coordinates Discrete Mathematics and Calculus 1, and previously led Forensic Statistics. Her instructional portfolio spans Mathematics 1/2, Calculus, Statistics, Regression Analysis, and Design Data and Decisions. While instrumental in course design and delivery, no graduate student supervision or research grant involvement is indicated. She maintains active membership in MERGA, the Australian Mathematical Society, and the Calcutta Mathematical Society, India, and is proficient in English, Hindi, and Bengali.
Marco Fumero is a PostDoctoral Researcher at the Institute of Science and Technology Austria (ISTA), where he conducts foundational research at the intersection of geometry and artificial intelligence. Previously, he completed his Ph.D. in Computer Science at Sapienza University of Rome as a core member of the GLADIA research group under Professor Emanuele Rodolà's supervision, establishing a trajectory bridging theoretical geometry with practical deep learning applications. Ph.D. in Computer Science, Sapienza University of Rome Dr. Fumero's research program centers on exploiting geometric structures to revolutionize artificial intelligence systems, with primary focus on geometric deep learning, geometry processing, and representation learning. He pioneers methodologies for analyzing neural network latent spaces through spectral geometry and dynamical systems theory, developing frameworks that enable cross-model communication and zero-shot transfer. His work systematically addresses challenges in representation alignment, latent space dynamics, and disentangled feature extraction, with direct applications in 3D shape analysis, multimodal learning, and quantum-inspired computing. This research demonstrates exceptional theoretical rigor while maintaining strong connections to real-world problems in computer vision and scientific computing. His publication record reveals a dominant trend toward unifying geometric principles with deep learning architectures, particularly through spectral methods and functional map theory. The 2024-2025 publications showcase a coherent evolution from foundational latent space analysis (e.g., attractor dynamics in autoencoders) to practical frameworks for cross-model communication (e.g., cycle-consistent merging and semantic alignment). Key thematic threads include zero-shot capability development, invariance exploitation, and the translation of classical geometry processing techniques into neural network contexts. These contributions have established new paradigms for latent space manipulation across computer vision, graphics, and multimodal AI. Spotlight presentation at ICLR 2024 for "From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication" Multiple papers accepted at NeurIPS 2024 including "Latent Functional Maps" and "C2M3" During his doctoral training at Sapienza, Dr. Fumero actively mentored junior researchers within the GLADIA group, contributing to the development of next-generation geometric AI specialists through collaborative projects and technical guidance. His research has been supported by institutional funding from Sapienza University and ISTA, with potential backing from European research initiatives targeting foundational AI advances. Current work focuses on scaling geometric deep learning frameworks to complex multimodal scenarios while maintaining theoretical guarantees. Dr. Fumero maintains strong ties to the GLADIA research group at Sapienza University of Rome, which specializes in geometric learning and data analysis. At ISTA, he operates within a highly collaborative interdisciplinary environment that emphasizes theoretical computer science and its applications, contributing to the institute's mission of advancing frontier research through mathematical rigor and computational innovation.
Khaled Rasheed is a Professor at the School of Computing, University of Georgia, where he has served in various academic capacities since 2000. His current appointment as Professor in the Franklin College of Arts & Sciences - Division of Physical & Mathematical Sciences, School of Computing began in August 2017. Previously, he served as Associate Professor (2006-2017) and Assistant Professor (2000-2006) at the same institution. Dr. Rasheed also serves as Graduate Program Faculty in the School of Computing since 2003. Dr. Rasheed earned his Doctor of Philosophy in Computer Science from Rutgers State University of New Jersey in 1998, following a Master of Science in Computer Science from the same institution in 1995. His undergraduate education includes a Bachelor of Science in Computer Science from Alexandria University, Egypt, completed in 1990. Dr. Rasheed's research focuses on Artificial Intelligence, with particular expertise in Genetic Algorithms, Evolutionary Computation, Data Mining, and Machine Learning. His work bridges theoretical AI development with practical applications across diverse domains. His research spans Bioinformatics and Health Informatics, Computational Intelligence, Engineering Design Optimization, and specialized applications in Poultry Science and Agriculture. His interdisciplinary approach has led to significant contributions in applying AI techniques to solve real-world problems in agriculture, healthcare, and engineering. Recent work demonstrates his focus on deep learning applications for animal behavior monitoring, crop yield prediction, and protein structure analysis, showing both technical innovation and practical impact. Dr. Rasheed's scholarly output shows a clear progression from foundational AI research toward domain-specific applications. His recent publications (2023-2025) reveal a strong emphasis on agricultural applications of AI, particularly in poultry science and crop management, while maintaining contributions to core AI methodology development. His work demonstrates consistent citation impact across multiple domains, with particular influence in agricultural technology applications of computer vision and deep learning. Student Career Success Influencer Award 2023 Student Career Success Influencer Award 2022 Outstanding Faculty Service Award Second Best Paper Dr. Rasheed has secured multiple significant research grants, including a current project with COBB-VANTRESS INCORPORATED (2025-2027) for developing tracking systems for poultry, and a major USDA NIFA grant (2022-2028) for forest sustainability research. His funded projects demonstrate his ability to translate theoretical AI research into practical applications with economic and environmental impact. His grant portfolio spans multiple funding agencies including NIH, USDA, and industry partners, reflecting the interdisciplinary nature of his work. Dr. Rasheed maintains an active research laboratory focused on evolutionary computation and machine learning applications. His work often involves interdisciplinary collaborations across computer science, agriculture, biology, and engineering. His recent publications and grants indicate a strong emphasis on applying AI to agricultural challenges, particularly in poultry science and crop management, while maintaining a foundation in core AI methodology development.
Carlos Ramisch is an Assistant Professor in Computer Science at Aix Marseille University, France, affiliated with the TALEP research group at LIS (Laboratoire d'Informatique et Systèmes). His work centers on computational linguistics and natural language processing, with a primary focus on multiword expressions (MWEs), language models, and semantic analysis. He actively contributes to the PARSEME community, organizing shared tasks on verbal MWE identification. His research includes developing the mwetoolkit for MWE discovery and SLICE for interpretable contextual embeddings. He has led funded projects such as SELEXINI (2022-2026) and PARSEME-FR (2016-2021). His methodological innovations span cross-lingual dependency parsing via typological features (NAACL 2019), compositionality prediction for nominal compounds (Computational Linguistics 2022), and lexical substitution datasets (IWCS 2017). He supervises students in NLP internships and co-authored the educational comic strip La grande aventure du TAL . His editorial roles include the LSP series on Phraseology and MWEs, and he has chaired multiple MWE workshops (2010-2022).
Huijuan Xu is an Assistant Professor in the Department of Computer Science and Engineering. Her research spans artificial intelligence, computer vision, and knowledge representation, with a focus on temporal modeling, semantic reasoning, and multimodal learning. She has contributed to advancements in virtual reality streaming, knowledge graph completion, and weakly-supervised video analysis. Research output: 32 publications (2015-2025), including 15 peer-reviewed articles and conference contributions Core research areas: Representation Learning (100% match), Knowledge Graph (100% match), Temporal Action Detection (86% match), and Motion Feature Learning (73% match) Her recent work explores: 2025 : Bandwidth-optimized VR streaming for edge devices 2024 : Neural concept reasoning for image retrieval and avatar generation from sparse data 2023 : Zero-shot scene graph generation and bias mitigation in visual QA
