Find Academic Supervisors

• Optical Materials and Waveguides
• Optical Imaging
• Optical Security
• +6 more

Professor Radhakrishna Prabhu is a distinguished academic at Robert Gordon University's School of Computing, Engineering & Technology, where he leads the Instrumentation & Sensors Research Group and is affiliated with the Robotics & Autonomous Systems Research Group. With over 20 years of applied research experience, his work spans optical technologies, environmental sensing, and clean energy systems. His research expertise encompasses Optical Materials & Waveguides , Optical Imaging , Optical Security , Subsea Sensing , Optoelectronics , Microscopy , Spectroscopy , Lasers , Fluorescence Instrumentation , Lab-on-a-chips , and Renewable Energy Systems . Recent publications (2024-2025) reveal a strong focus on underwater optical communication systems, microplastic environmental impact studies, and building-integrated photovoltaic technologies. Professor Prabhu's scientific contributions are recognized through his professional affiliations: Fellow of Higher Education Academy (FHEA) Chartered Engineer (EC-IET) Member of Institute of Physics (IOP) Member of The Institution of Engineering & Technology (IET) Member of Institute of Electrical and Electronics Engineers (IEEE) He currently supervises multiple PhD students across diverse projects including underwater wireless networks, microplastic soil studies, and building-integrated photovoltaics. His active research portfolio includes the 'Portable Luminescent Chemical Compass' project (2025-2027) and 'Reusing Subsea Natural Gas Pipeline Infrastructure for Bulk Hydrogen Transportation' (2022-2023), demonstrating his commitment to addressing contemporary environmental and technological challenges through innovative research. The Instrumentation & Sensors Research Group under his leadership develops cutting-edge sensing technologies with applications ranging from environmental monitoring to security systems, contributing significantly to Robert Gordon University's research themes in Environment, Energy & Sustainability.

• Bayesian Networks
• Probabilistic Graphical Models
• Machine Learning
• +5 more

Rafael Rumí Rodríguez is a Professor in the Mathematics Department at the University of Almería, Spain, with extensive experience in probabilistic modeling and data analysis. He leads the 'Análisis de datos' research group and serves as Principal Investigator for multiple research projects, including the current 'Hacia una Inteligencia Artificial Probabilística Confiable (TOPAI-UAL)' (2023-2026). Dr. Rumí Rodríguez's research focuses on Bayesian networks and probabilistic graphical models , with applications spanning environmental science, ecology, agriculture, and education. His work includes developing methods for continuous distributions in hybrid Bayesian networks, flood risk assessment in Mediterranean regions, climate change impact modeling for Andalusian agriculture, and educational assessment analytics. He has created practical tools like the 'hcrBN' R package for hybrid Bayesian network applications. His publication record includes 43 articles , 2 books or chapters , 3 theses , and 31 conference papers , with an h-index of 16 (Scopus) and 15 (Web of Science). Many publications appear in Q1 journals, reflecting significant research impact across computer science, environmental science, and statistics. Scopus h-index: 16 Web of Science h-index: 15 Scopus i10-index: 27 Web of Science i10-index: 23 Dr. Rumí Rodríguez has secured substantial research funding throughout his career, including as Principal Investigator for the current TOPAI-UAL project (€120,100) and previous projects like the European AMIDST initiative (€323,490). His research group provides opportunities for students to engage in both theoretical advances and practical applications of probabilistic modeling, with strong connections to environmental and agricultural domains in the Mediterranean region.

• Bayesian Networks
• Probabilistic Graphical Models
• Machine Learning
• +5 more

Antonio Salmerón Cerdán is a Professor in the Mathematics Department at the University of Almería, where he has established himself as a leading researcher in probabilistic artificial intelligence and Bayesian networks. With over 25 years of academic experience, he leads the 'Análisis de datos' research group and serves as Principal Investigator for multiple nationally and internationally funded projects, including the current 'Hacia una Inteligencia Artificial Probabilística Confiable (TOPAI-UAL)' project (2023-2026). His research expertise spans theoretical and applied aspects of probabilistic graphical models, with particular focus on Bayesian networks, causal inference, and their applications across diverse domains. His work demonstrates a consistent trajectory from foundational theoretical contributions to practical implementations in software engineering, genomics, sports analytics, and trustworthy autonomous systems. Professor Salmerón's publication portfolio reveals a strong emphasis on methodological innovations in probabilistic reasoning, with recent work exploring divide-and-conquer approaches for causal computation, noise-robust classification methods, and the integration of observational and randomized data sources. His research shows increasing interdisciplinary reach, connecting computer science methodologies with applications in plant genomics, software maintenance, and healthcare. Journal Publications: 105 articles in high-impact venues including Ecological Informatics (Q1), International Journal of Approximate Reasoning (Q2), and ACM Transactions Research Funding: Principal Investigator for 9 major projects since 2001 totaling over €800,000 in funding Thesis Supervision: Director of 7 doctoral theses on probabilistic graphical models and their applications Metrics: h-index 22 (Web of Science), i10 index 59 His research program demonstrates a unique combination of theoretical rigor in probabilistic reasoning with practical applications across diverse scientific domains, positioning him at the forefront of reliable probabilistic AI development.

Marcin WardaszkoView profile

Assistant Professor

• Serious Game Design
• Simulation Games
• Gamification
• +5 more

Marcin Wardaszko is an Assistant Professor at Kozminski University where he heads the Department of Quantitative Methods and Information Technology and the MBA IT program. He also serves as a Visiting Professor at Vorarlberg University of Applied Sciences. His professional network extends internationally through his roles as a representative of Riva Solutions GmbH in Poland and East Asia, collaborator with HCD Learning Ltd in Shanghai, and Digital Game-Based Learning expert at Poland's NASK National Research Institute. Professor Wardaszko's research focuses on simulation games, serious game design, and gamification applications across educational and business contexts. His work explores how digital game-based learning enhances cognitive outcomes, how VR technologies can develop soft skills (particularly through his Cicero VR public speaking trainer), and how gamification improves workplace environments and organizational development. He has developed models for classifying simulation games based on their strategic application in organizational contexts. His recent publications demonstrate a strong trajectory in serious gaming research, with significant contributions in 2023 (INDCOR White Paper), 2022 (Urban Resilience and Serious Gaming), 2021 (Simulation Gaming Through Times), and 2018-2015 (multiple studies on organizational development, mobile learning effectiveness, and workplace gamification). His work spans interdisciplinary applications from warehouse management optimization to public speaking training in virtual environments. Scientific recognition includes: CEEMAN 2019 Champion in Teaching Award for VR-based learning program President of International Simulation and Gaming Association (2019-2020) Fellow in Association for Business Simulation and Experiential Learning Member of Swiss Austrian German Simulation and Gaming Association Polish representative in COST Action CA18230 on Interactive Narrative Design As head of the Center for Simulation Games and Gamification at Kozminski University, Professor Wardaszko leads development of innovative educational tools including HOTEL STARS business simulation, Cicero VR public speaking trainer, and dcView gamified data presentation system. His work bridges academic research with practical applications in education and business, demonstrating how games and gamification can transform learning and working environments. He has supervised numerous research projects and maintains active international collaborations across Europe and Asia.

Evgeny OsipovView profile

Professor

• Artificial Intelligence
• Vector Symbolic Architectures
• Hyperdimensional Computing
• +3 more

Professor Evgeny Osipov is a full professor in Dependable Communication and Computation Systems at Luleå University of Technology, Department of Computer Science within the Department of Systems and Space Engineering. His research focuses on Communication and computing systems, with particular expertise in Artificial Intelligence frameworks. His educational background includes: PhD in Computer Science (Cum Laude) from University of Basel, Switzerland (2005) Licentiate of Technology in Telecommunications from KTH Royal Institute of Technology, Sweden (2003) Pre-doctoral school in Communication Systems from EPFL, Switzerland (1999) Engineer degree with Honors from Krasnoyarsk State Technical University, Russia (1998) Professor Osipov's research interests center around Vector Symbolic Architectures (also known as hyperdimensional computing), which serves as a bridge between symbolic and connectionist AI approaches. His work explores how mathematical properties of random hyperdimensional spaces can be leveraged for AI functionality, with potential applications in creating artificial general intelligence. His research is particularly relevant for low-resource machine learning tasks, such as those encountered in wearable Internet of Things devices. His recent publications (2024-2025) demonstrate a strong focus on improving classification performance using hyperdimensional computing techniques. He has explored confidence-driven training of centroids, implementations for spiking neural networks, and margin-based training approaches across numerous datasets to validate these techniques. Professor Osipov has received research funding from several notable organizations: Swedish Foundation for Strategic Research (grants UKR22-0024, UKR24-0014) Swedish Research Council (grants GU 2022/1963, 2022-04657) Luleå University of Technology Flemish Government Scholars at Risk (SAR) His active publication record across multiple high-impact journals indicates ongoing research activity and collaboration. His work on Vector Symbolic Architectures represents a significant contribution to the field of efficient AI computation, particularly for resource-constrained environments where traditional deep learning approaches would be impractical.

Althea DelwicheView profile

Professor

• Emerging digital media
• big data
• political propaganda
• +6 more

Althea Delwiche is a Professor in the Department of Communication at Trinity University in San Antonio, Texas. She holds a Ph.D. from the University of Washington and teaches courses on virtual world development, transmedia storytelling, mobile gaming, and related digital media topics. Her research focuses on emerging digital media, big data, political propaganda, participatory culture, and virtual worlds as tools for education and persuasion. She explores how our world is being transformed by participatory knowledge cultures where people collectively classify, organize, and create ideas in new ways. Her work examines the promises and pitfalls of participatory culture and redefines media literacy in an era of ubiquitous computing. Delwiche's recent scholarly contributions span virtual world ethnography, digital comics preservation, and the educational applications of gaming technologies. Her publications reveal a consistent trajectory examining how digital environments transform knowledge creation, storytelling, and social interaction across multiple platforms and contexts. Mellon Summer Undergraduate Research Fellowship (2015-2017) Trinity University Junior Faculty Award for Distinguished Teaching and Research (2007) MacArthur Foundation grant for State of Play V (2007) Professor Delwiche actively mentors undergraduate researchers, currently working with a student on using virtual worlds to promote empathy and capture endangered oral histories. She serves on multiple university committees including as Chair of the New Media Minor and the University Curriculum Council (2016-2017), and as a Board Member of KRTU-FM, Trinity University's Radio Station.

Chevaleyre YannView profile

Professor

• Artificial Intelligence
• Multi-Agent Systems
• Computational Social Choice
• +3 more

Chevaleyre Yann is a Professor of Computer Science at LAMSADE, Paris-Dauphine PSL University, where he has been working since 2017. Previously, he served as Professor of Computer Science at Paris-Nord University from 2009 to 2017 and as Director of the "data science" team at the LIPN laboratory. His academic journey includes a Lecturer position at LAMSADE, Paris-Dauphine University from 2002 to 2009, a Habilitation thesis at Paris-Dauphine University in 2009, and a Doctorate at Pierre and Marie Curie University under the supervision of Jean-Daniel Zucker from 1998 to 2001. Professor Chevaleyre's research spans multiple areas within artificial intelligence and computer science, with particular expertise in multi-agent systems, computational social choice, and machine learning. His work on preference modeling, voting theory, and resource allocation has significantly contributed to the field of computational social choice. More recently, his research has expanded into adversarial machine learning, robust classification, and generative models, reflecting the evolving landscape of AI research. His publication record demonstrates consistent contributions across multiple domains, with recent work focusing on precision-recall optimization in generative models, the role of randomization in adversarial robustness, and novel approaches to graphical bilinear bandits. His research shows a clear trajectory from foundational work in multi-agent systems toward more contemporary challenges in machine learning security and evaluation. Professor Chevaleyre has maintained active collaborations with researchers across France and internationally, evidenced by his extensive co-authorship network. His work bridges theoretical computer science with practical applications in areas ranging from robotics to bioinformatics.

Sandjai BhulaiView profile

Researcher

• Machine Learning
• Artificial Intelligence
• Optimization
• +4 more

Dr. Sandjai Bhulai is a Researcher at Centrum Wiskunde & Informatica (CWI) in Amsterdam, The Netherlands, and affiliated with Vrije Universiteit Amsterdam. He works in the Stochastics department within the Faculty of Science, focusing on applying machine learning and optimization techniques to solve complex real-world problems across diverse domains including telecommunications, mental health crisis intervention, and operations research. His research spans multiple critical areas of applied mathematics and computer science: Development of novel machine learning evaluation frameworks (Dutch Draw, Dutch Scaler) Optimization algorithms for facility location and supply chain problems Telecommunications signal processing using deep learning Mental health applications, particularly suicide prevention helpline analysis Natural language processing for conversation analysis in crisis intervention Dr. Bhulai's recent work demonstrates exceptional interdisciplinary impact. His Dutch Draw framework provides a universal baseline for binary classification problems that has been adopted across multiple research communities. His mental health research has directly influenced suicide prevention practices through machine learning analysis of helpline conversations, identifying specific counselor interventions that improve outcomes. In telecommunications, he's developed neural network approaches that simultaneously handle multiple modulation schemes, improving efficiency in wireless communications. His scientific contributions appear in leading venues including: Computers and Operations Research Journal of Classification IEEE Open Journal of the Communications Society JMIR Mental Health Journal of Applied Probability Dr. Bhulai maintains strong collaborative relationships with researchers across institutions, particularly with Rob van der Mei at CWI. He has secured funding for impactful projects including the collaboration between Stichting 113 Suicide Prevention and CWI, which applies machine learning to improve crisis intervention services. His work exemplifies how theoretical advances in machine learning can address critical societal challenges.

• Biomedical Research
• Cystitis, Interstitial
• Drug Delivery Systems
• +7 more

Darryl T. Martin is an Assistant Professor in the Department of Urology at the Yale School of Medicine and a member of the Yale Cancer Center. His research focuses on understanding the biological factors and molecular determinants that drive cancer progression and metastasis, particularly in urologic cancers. Dr. Martin is affiliated with the Cancer Signaling Networks program and the Janeway Society at Yale, demonstrating his integration into multiple research communities within the institution. Dr. Martin received his PhD from Memorial University of Newfoundland, where he trained as a cancer molecular biologist studying tumor growth and differentiation under the supervision of Drs. Robert Gendron and Hélène Paradis. He then completed postdoctoral training with Dr. Robert M. Weiss at Yale University School of Medicine, focusing on drug delivery and cancer progression, which laid the foundation for his current research program. Dr. Martin's research program investigates three main areas: defining distant metastasis by elucidating biologic factors and molecular determinants; modulating the tumor microenvironment to transform immunosuppressive conditions into immune-activated states; and enhancing tumor targeting using surface-modified nanoparticles. His work combines molecular biology, translational science, and nanomedicine to develop novel approaches for cancer treatment, with particular emphasis on prostate and bladder cancers. Analysis of Dr. Martin's recent publication record reveals a strong translational focus, bridging basic molecular mechanisms with clinical applications. His work spans genomic testing for prostate cancer, treatment optimization for bladder cancer variants, and nanoparticle-based drug delivery systems, demonstrating both breadth and depth in urologic oncology research. Dr. Martin is an active member of several professional organizations: American Association for Cancer Research (AACR) Society for Basic Urologic Research (SBUR) American Society for Biochemistry and Molecular Biology (ASBMB) The Martin Lab is currently funded by Department of Defense (DoD) and foundation grants, supporting research on cancer progression and metastasis. Dr. Martin collaborates extensively with clinical colleagues at Yale, reflecting the interdisciplinary nature of modern cancer research and his commitment to translating laboratory discoveries into clinical applications. Dr. Martin's laboratory focuses on developing practical applications for cancer treatment, particularly through nanoparticle systems that can deliver drugs to malignant tissues with high specificity. His research represents an important bridge between basic cancer biology and clinical oncology, with potential to improve outcomes for patients with urologic malignancies.

• Machine Learning
• Operations Research
• Supply Chain Management
• +7 more

Hany Osman is an Associate Professor in the Master of Data Analytics program at the University of Niagara Falls Canada, holding a PhD in Industrial Engineering from Concordia University and a Professional Engineer (PEng) license in Ontario. His academic-industrial career bridges theoretical research with practical applications across multiple sectors. Dr. Osman's research spans three interconnected domains: Machine Learning & Data Analytics : Specializing in logical analysis of data, cost-sensitive learning, and ensemble techniques for industrial applications Operations Research : Developing nature-inspired metaheuristics (cuckoo search, ant colony optimization) for NP-hard problems in manufacturing and logistics Supply Chain Management : Focusing on sustainable optimization of lot sizing, production planning, and inventory control under stochastic conditions His recent publications (2023-2024) reveal a strategic pivot toward AI-integrated manufacturing systems, notably the CAPP-GPT framework for generative AI in process planning and emission-aware lot sizing models. This work demonstrates consistent translation of theoretical advances into industrial solutions for rail, oil, and smart manufacturing sectors. Professional credentials include: IBM Mastery Certificate in Predictive Data Analytics Professional Engineer (PEng) license from Ontario Dr. Osman leverages extensive industrial experience in supply chain logistics, oil industry optimization, and education technology to inform both research and teaching. His supervision in the Master of Data Analytics program emphasizes hands-on application of machine learning to real-world operational challenges, with students contributing to publications in Manufacturing Letters and related journals. While no formal lab is specified, his research group operates at the intersection of data science and industrial engineering, maintaining strong industry partnerships that drive applied projects.

• Astrophysics
• Cosmology
• Earth Physics
• +3 more

Dr. D'Angelo Giulia is a Fixed-term University Researcher (Ricercatrice universitaria a tempo determinato) at the University of L'Aquila , affiliated with the Department of Physical and Chemical Sciences . Her academic discipline is classified as PHYS-05/B - Physics of the Earth System, Planets, Space and Climate, with a recruitment field of 02/PHYS-05 covering Astrophysics and cosmology, earth-space-and climate physics. Her research interests span multiple areas of physical sciences including Astrophysics , Cosmology , Earth Physics , Space Physics , Climate Physics , and Planetary Science . These interconnected fields suggest a research program focused on understanding physical processes across different planetary environments, including Earth's climate system and broader cosmic phenomena. Dr. D'Angelo Giulia is based at the Renato Ricamo Building located at via Vetoio, Coppito - 67100 L'Aquila, Italy. As a fixed-term researcher, she is likely involved in both research activities and teaching responsibilities within the Department of Physical and Chemical Sciences, though specific details about her teaching assignments or research projects are not provided in the available information.

Agnes FlöelView profile

Professor

• Cognitive Neuroscience
• Sleep Physiology
• Brain Stimulation
• +7 more

Prof Dr Agnes Flöel is a Professor at the University of Greifswald Medical School, where she leads research at the intersection of cognitive neuroscience, sleep physiology, and brain stimulation. Her work focuses on understanding neural mechanisms underlying memory formation and cognitive decline in aging, with particular emphasis on sleep-related processes and non-invasive brain stimulation techniques. Her research interests center on sleep physiology , cognitive neuroscience , and neurostimulation . Dr. Flöel investigates how slow oscillations, spindles, and delta waves interact during sleep to support memory consolidation, particularly in older adults and those with cognitive impairment. Her work extends to developing interventions combining cognitive training with transcranial direct current stimulation (tDCS) to enhance cognitive function in aging and early Alzheimer's disease. She also contributes to methodological advancements in sleep staging through deep learning approaches and addresses technical challenges in neuroimaging. Dr. Flöel's publication record demonstrates consistent productivity with multiple high-impact papers annually, primarily focused on sleep neuroscience, brain stimulation, and cognitive aging. Her work shows strong collaborative patterns with researchers across Germany, particularly within the SFB 1315 research consortium. Recent publications (2022-2024) increasingly incorporate computational approaches, deep learning, and multimodal neuroimaging to address fundamental questions about neural plasticity and memory. Her research has been supported through the Collaborative Research Center 1315 (SFB 1315) funded by the German Research Foundation, focusing on neuronal circuits in aging. Dr. Flöel serves as principal investigator for multiple clinical trials examining brain stimulation approaches for cognitive enhancement in older adults and those with mild cognitive impairment. Dr. Flöel leads a research group investigating sleep-brain interactions in aging, collaborating closely with computational neuroscientists, neurologists, and psychologists. Her team utilizes multimodal approaches including EEG, MRI, and behavioral assessments to understand neural mechanisms of cognitive decline and potential interventions.

José R. CarriónView profile

Associate Professor

• Operator Algebras
• C*-algebras
• Classification Theory
• +3 more

Dr. José R. Carrión is an Associate Professor in the Department of Mathematics at Texas Christian University (TCU), where he has been teaching since 2015. His research focuses on operator algebras, particularly the structure and classification theory of amenable C*-algebras and their interactions with groups and dynamical systems. Dr. Carrión received his Ph.D. from Purdue University in 2013, followed by a postdoctoral position at Penn State University before joining TCU. Dr. Carrión's research centers on operator algebras, with a particular emphasis on the structure and classification theory of amenable C*-algebras. His work explores the interaction between operator algebras and groups, dynamical systems, and K-theory. He investigates topics such as nuclear maps, quasidiagonality, crossed products, and the classification of C*-algebras through various invariants and approximation techniques. His recent work has focused on tracial properties, homomorphisms, and topological aspects of E-theory, contributing to the ongoing classification program for nuclear C*-algebras. Analysis of Dr. Carrión's publications reveals a consistent focus on classification problems in operator algebras, particularly concerning C*-algebras. His research spans both theoretical foundations and specific classification results, often in collaboration with leading researchers in the field including Christopher Schafhauser, Marius Dadarlat, and Stuart White. Recent work shows increasing attention to tracial properties and homomorphisms in nuclear C*-algebras, reflecting current trends in the classification program for operator algebras. Dr. Carrión has secured significant research funding through the National Science Foundation, including grants DMS-2430050 for the 2025 CBMS conference, DMS-2400111 for the Brazos Analysis Seminar, and DMS-1837227 for the East Coast Operator Algebras Symposium. He serves as principal investigator for these projects and collaborates with researchers across multiple institutions. Dr. Carrión teaches a comprehensive range of mathematics courses at TCU from undergraduate calculus through advanced graduate courses including Operator Theory and C*-algebras, Functional Analysis, and Algebraic Topology. He has taught Math 10283 (Applied Calculus), Math 10524 (Calculus I), Math 20524 (Calculus II), Math 30053 (Introduction to Mathematical Proof), Math 30524 (Calculus III), Math 30224 (Linear Algebra), Math 40553 (Fourier Analysis), and numerous graduate-level courses. Dr. Carrión has organized and co-organized multiple major conferences including the 2025 CBMS conference on recent developments in C*-algebra classification, the Brazos Analysis Seminar series, the Texas Geometry and Topology Conference (TGTC), the East Coast Operator Algebras Symposium (ECOAS), and the Great Plains Operator Theory Symposium (GPOTS), establishing himself as a key figure in the operator algebras research community.

Santiago Manuel López GarcíaView profile

Associate Professor

• Economic History
• Science and Technology Studies
• Technological Change
• +4 more

Santiago Manuel López García is an Associate Professor at the University of Salamanca, affiliated with the Institute of Science and Technology Studies (IeCyT) and the Department of Economics and Economic History. He has served as Head of the Master and PhD program in Social Studies of Science and Technology at Salamanca University since 2010 and was Vice-rector of Prospective and Development at the university from 2004 to 2009. His educational background includes a BA in Contemporary History from the Autonomous University of Madrid (1984) and a PhD in Economics (Technological Change) from the University Complutense of Madrid (1996). His doctoral thesis focused on 'El saber tecnológico en la política industrial del primer régimen de Franco' (Technological knowledge in the industrial policy of the first Franco regime). López García's research spans economic history, science and technology studies, and innovation policy, with particular focus on technological change, industrial clusters, and renewable energy development. His recent work has examined Spain's position in wind power development, technological innovation through patent analysis, and the historical evolution of science policy in Spain. His publications demonstrate consistent engagement with both historical analysis of technological development and contemporary issues in science policy, showing a trajectory that connects Spain's industrial past with its current innovation challenges. He has published extensively in journals related to economic history, science policy, and technology studies. As Editor of Investigations of Economic History (Journal of the Spanish Association of Economic History) from 2009 to 2011, he contributed significantly to the field of economic history scholarship. His research connects historical patterns of technological development with contemporary innovation policy challenges. He is involved with multiple research groups including CaUSAL (Academic Culture, Heritage and Social Memory) and HISCYT (Unit for the Study of the History of Industry and Science and Technology Systems), demonstrating his interdisciplinary approach to the study of science, technology, and society.

Jon StammersView profile

Research Fellow

• Artificial Intelligence
• Data Science
• Smart Manufacturing
• +3 more

Dr Jon Stammers serves as the Senior Theme Lead for Data, Connectivity and AI within the Integrated Manufacturing Group at the Advanced Manufacturing Research Centre (AMRC), University of Sheffield. He joined the AMRC in 2013, initially working in the Machining Group's Process Monitoring and Control team, and has since taken on leadership of the Data, Connectivity and AI theme. Additionally, he has lectured at the AMRC Training Centre and is an active member of the Centre for Machine Intelligence and the IET Manufacturing Technical Network. Stammers holds an MEng and PhD in Electronic Engineering. His doctoral research investigated automated identification of urban and natural audio signals using time-domain feature extraction and ensemble neural network classifiers. His primary research interests focus on leveraging data from connected manufacturing processes to enable Smart Factories. This includes developing open-source data architectures, data visualization techniques, computer vision applications, AI and machine learning algorithms, data security measures, and data science methodologies. He is particularly interested in how AI can serve as a practical tool to enhance daily manufacturing operations and the broader societal impacts of technology adoption in industrial settings. Analysis of his recent publications reveals a consistent emphasis on machine tool health monitoring, anomaly detection in machining processes, and the integration of AI for predictive maintenance. His work bridges theoretical advancements in signal processing and machine learning with practical industrial applications, contributing to more efficient and reliable manufacturing systems. No scientific awards, prizes, or fellowships were mentioned in the provided information. Stammers has previously lectured at the AMRC Training Centre, contributing to workforce development in advanced manufacturing. While no formal PhD or Master's advisees are listed in the provided information, his role in training is evident through his educational contributions. He has secured significant research funding as Principal Investigator and Co-Investigator on multiple projects, including the ATI-funded "Securing Aerospace Manufacture in the UK" (£974,000), Innovate UK's "Data-driven manufacturing" (£111,000), EPSRC's "Autonomous Method for Detecting Cutting Tool and Machine Tool Anomalies" (£1.02M), and several others totaling over £2.5 million. His current projects span AI for machining design, hydrogen storage, and geospatial AI for housing layouts. Stammers leads the Data, Connectivity and AI theme at the AMRC, which focuses on enabling Smart Factories through innovative data and AI solutions. He is part of the Integrated Manufacturing Group, a key research team within the AMRC dedicated to advancing manufacturing technologies.