جست‌وجوی استاد و پژوهشگر

Hopfield Hintonمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

John Hopfield and Geoffrey Hinton are distinguished academics who jointly won the 2024 Nobel Prize in Physics for their foundational contributions to machine learning and artificial neural networks. Their research established theoretical frameworks and scalable practical tools, bridging statistical mechanics and computational models inspired by biological neural networks. Research Interests: Their work integrates principles from theoretical physics (e.g., Ising model, Boltzmann equation) into artificial intelligence systems, enabling associative memory mechanisms and back-propagation algorithms critical to modern neural networks. They pioneered models like the Hopfield model and Boltzmann machine, which underpin advancements in AI and interdisciplinary applications such as protein structure prediction. Scientific Awards: Recipients of the 2024 Nobel Prize in Physics for discoveries enabling machine learning with artificial neural networks.

• Mathematical Modeling
• Sustainable Systems
• Computational Biology
• +۳ مورد دیگر

Elisa Benedetta Primavera Tiezzi serves as an Associate Professor in the Department of Information Engineering and Mathematical Sciences at the University of Siena, teaching Programming Fundamentals for Management Engineering and Mathematics undergraduate programs alongside Fuzzy and Real-Time Modeling for the Master's in Applied Mathematics. Her research integrates mathematical modeling with sustainability science and biological systems, emphasizing fuzzy logic applications in real-time environmental assessment and computational biology. Key contributions span emergy algebra for ecological accounting, hemostasis pathway modeling, and nutritional life cycle analysis in agro-food systems, demonstrating methodological innovation across theoretical and applied domains. Publication trends reveal consistent focus on sustainability metrics since 2011, evolving from emergy theory foundations toward contemporary work on food systems and biological modeling, with recent 2024 publications establishing computational frameworks for hemostasis and sustainable agriculture. Scientific awards: No awards were documented in source materials. Advising and grants: Student supervision details and research funding information were not provided in available texts. Labs and teams: No institutional affiliations with research laboratories or collaborative teams were specified.

Thomas Ihleمشاهده پروفایل

استاد

• Statistical physics of active matter
• Kinetic theory of swarms and flocks
• Soft condensed matter physics
• +۴ مورد دیگر

Prof. Thomas Ihle is a leading researcher at the University of Greifswald , heading the Soft Matter Theory group. His work bridges statistical physics , active matter , and computational modeling . Biology-inspired statistical and computational physics Statistical physics of active matter Kinetic theory of swarms and flocks Soft condensed matter physics Pattern formation far from equilibrium His research focuses on active matter systems , studying how energy uptake from the environment generates non-equilibrium phenomena like flocking, pattern formation, and memory effects. He uses kinetic theory and multi-particle collision dynamics to derive hydrodynamic equations and simulate complex liquids. Key trends in his recent publications include non-reciprocal interactions , exact collision operators , and hyperuniformity in active systems. Collaborations span institutions like Humboldt University Berlin , Northwestern University , and University of Gothenburg . Awards: Outstanding Referee 2017 (American Physical Society). Students: Mentored Jakob Mihatsch , Rüdiger Kürsten , and others in Master’s/Bachelor’s theses . Teaching includes a group seminar on active matter held every Friday.

Josefin Starkhammarمشاهده پروفایل

مدرس ارشد

• Marine Acoustics
• Biosonar
• Computer-Based Measurement Systems
• +۳ مورد دیگر

Josefin Starkhammar is a Senior Lecturer at the Division for Biomedical Engineering, Faculty of Engineering, Lund University. She serves as Principal Investigator for eSSENCE: The e-Science Collaboration, while also contributing to LU Profile Area: Natural and Artificial Cognition and LTH Profile Area: Engineering Health. Her research focuses on marine acoustics and biosonar with development of technological tools for bioacoustic studies. Acoustic finite element modeling Signal processing algorithms Underwater measurement systems Infrastructure non-destructive testing She teaches advanced computer-based measurement systems courses at LTH. Her research outputs span biological sciences, engineering, zoology, signal processing, and fluid mechanics. Current projects include 3D acoustic field propagation models for dolphin echolocation beam formation using in vivo CT imaging data. Josefin has received multiple awards including the Fred Fairfield Memorial Award (2007), Best Student Presentation (2008), and Outstanding Poster Presentation (2009). She actively participates in academic conferences like The COMSOL Conference 2023 and has served as external reviewer for PhD theses. Her work appears in journals such as the Journal of the Acoustical Society of America and JASA Express Letters.

Cristian Axenieمشاهده پروفایل

استاد

• Neural Networks
• Cognitive Computing
• Artificial Intelligence
• +۳ مورد دیگر

Prof. Dr.-Ing. Cristian Axenie serves as a Professor within the Faculty of Informatics at Nuremberg Institute of Technology, where he leads the Cognitive Neurocomputing research group. His institutional contact includes office room SP.488 and direct email correspondence. His research centers on cognitive neurocomputing, spanning neural networks, cognitive computing, artificial intelligence, machine learning, computational neuroscience, and neuroinformatics. This interdisciplinary focus aims to develop brain-inspired computing architectures that bridge biological cognition with artificial systems, emphasizing efficient neural modeling and adaptive learning frameworks. As an active faculty member, he contributes to both theoretical and applied research in neurocomputing, with his work influencing the development of next-generation intelligent systems through the integration of neuroscience principles and computational engineering.

Andrea Soltoggioمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Lifelong Learning
• +۱۱ مورد دیگر

Dr. Andrea Soltoggio is a Reader in Artificial Intelligence (Associate Professor) and Research Coordinator in the Computer Science Department at Loughborough University's School of Science. He holds a combined BSc and MSc in Computer Science from the Norwegian University of Science and Technology and Politecnico di Milano, and a PhD from the University of Birmingham. His academic journey includes research positions at EPFL, the University of Central Florida, and Bielefeld University where he served as Technical Coordinator for the FP7 European project AMARSi. His research focuses on creating AI systems that learn continuously like biological brains, with particular emphasis on lifelong learning, collaborative knowledge sharing, and energy-efficient AI. Dr. Soltoggio has made significant contributions to the development of algorithms that enable AI systems to share knowledge without centralized control, as demonstrated in his MOSAIC framework published in 2025 and his Nature Machine Intelligence paper on collective AI systems. His recent publications reveal a strong trend toward sustainable AI development, with multiple projects focused on reducing the energy footprint of machine learning systems. His work bridges neuroscience and AI, exploring how biological learning mechanisms can inform more adaptive artificial systems. The research spans from theoretical frameworks to practical applications in robotics, healthcare diagnostics, and edge computing. Fellow of the Higher Education Academy (FHEA) Published in Nature Machine Intelligence (2024) Lead researcher on multiple funded projects including FP7 AMARSi Regular media contributor on AI topics (BBC, The Conversation) Dr. Soltoggio actively supervises PhD students and postdoctoral researchers in the Computer Science Department, with current projects focusing on lifelong learning, neuromorphic computing, and sustainable AI. His research group collaborates with Loughborough Business School on digital decarbonization initiatives and maintains connections with international AI laboratories. The team has access to advanced computational resources including Nvidia A100 GPUs and various robotic platforms for experimental validation of their theories.

• Artificial Intelligence
• Computer Science
• Cellular Automata
• +۳ مورد دیگر

Tom Glover is an Assistant Professor in the Department of Computer Science within the Faculty of Technology, Art and Design at Oslo Metropolitan University. His office is located at Stensberggata 29, 0170 Oslo (Office number: SG204) with contact information including mobile: +47 975 38 894 and office: +47 672 37 712. Dr. Glover's research focuses on the intersection of cellular automata, artificial intelligence, and complex systems. His work spans theoretical computer science, artificial life, and practical applications in reservoir computing and control systems. His research group is part of the Innovation, Digital Transformation and Sustainability Research groups within the Department of Computer Science. Analysis of his publications reveals a consistent focus on cellular automata as computational models, with particular emphasis on their application in reservoir computing, network dynamics, and self-organizing systems. His work demonstrates how simple computational rules can generate complex behaviors with applications across multiple domains including control systems and biologically inspired computing. As an active researcher, Glover has published in reputable venues including the International Journal of Parallel, Emergent and Distributed Systems, Complex Systems, and proceedings from the Artificial Life Conference series. His recent work (2023-2024) shows continued development in sensitivity analysis of cellular automata and network topologies.

Otar Akanyetiمشاهده پروفایل

مدرس ارشد

• Bio-inspired Robotics
• Health Informatics
• Underwater Robotics
• +۳ مورد دیگر

Dr. Otar Akanyeti is a Senior Lecturer in the Department of Computer Science at Aberystwyth University. His research integrates engineering, physics, computer science, and biology to advance underwater robotics and health informatics. He holds a BSc in Electronic Engineering, an MSc in Embedded Systems and Robotics, and a PhD in robot learning from the University of Essex. He co-founded the Aberystwyth Stroke Research Group and leads projects on AI-driven healthcare interventions. His research focuses on two primary strands: 1) Bio-inspired Robotics , developing sensors and control systems for underwater autonomy using biological principles; and 2) Health Informatics , employing wearable technology and AI to improve stroke rehabilitation and chronic disease management. Current investigations include hydrodynamic imaging, collective intelligence for data mining, and predictive tools for neurorecovery. Akanyeti's recent publications emphasize interdisciplinary approaches, with articles spanning gait analysis using smartphone sensors, clinical outcome prediction in stroke, and biomimetic robotics. His work consistently bridges biomechanics, machine learning, and clinical applications. He leads multiple funded projects including 'Miscanthus AI' (plant phenotyping for Net Zero), 'Intelligent Exercise Practitioner for Stroke Survivors', and 'iNavigate' (brain-inspired navigation technologies). His lab utilizes advanced equipment like instrumented treadmills and eye-tracking systems.

• Cyber Security
• Critical Infrastructure Protection
• Machine Learning
• +۷ مورد دیگر

Konstantinos Demertzis is a Postdoctoral Researcher in Cyber Security of Critical Infrastructures at the School of Civil Engineering, Democritus University of Thrace. He also serves as Chief Information Security Officer at the Research & Informatics Directorate of the Greek Army and maintains research affiliations with National and Kapodistrian University of Athens. His academic journey includes PhD studies in Advanced Machine Learning and multiple Master's degrees spanning Mathematics, Cyber Security, Big Data, and Networking Technologies. PhD in Advanced Machine Learning (2017) - Democritus University of Thrace MSc in Mathematics (2020) - University of Crete MSc in Cyber Security and Big Data (2020) - University of Thessaly MSc in Networking Technologies (2012) - University of the Aegean BSc in Military Science (1996) - Academy of Non-Commissioned Officers Demertzis's research spans the intersection of machine learning, cybersecurity, and critical infrastructure protection. His work focuses particularly on developing intelligent systems for cyber defense of urban critical infrastructure, with applications in smart grids, environmental monitoring, and disaster response. His methodology often combines biologically inspired computational intelligence approaches with blockchain technology and federated learning frameworks. He has published extensively in high-impact journals with particular emphasis on anomaly detection, adaptive learning systems, and privacy-preserving architectures. His recent publications demonstrate a clear trend toward increasingly sophisticated integration of machine learning with critical infrastructure security. The 15 most recent papers show progression from foundational machine learning applications to complex, multi-layered security architectures incorporating blockchain, explainable AI, and cross-modal neural networks. His work consistently addresses real-world security challenges in industrial control systems, smart cities, and military applications. Windows Azure for Research Award Winner (2015-2016) Multiple Postgraduate Academic Excellence Scholarships Google Scholarship (2017-2018) Cisco CCNA Cyber Ops Scholarship (2017-2018) Best Research Paper Award at ISCRAM-med 2017 IBM Cloud Award (2017) Demertzis has advised multiple research projects including the Operational Programme 'Digital Convergence' and the Electoral Observatory Internet Research Programme. His technical expertise spans a wide range of cybersecurity domains including network security, blockchain applications, and AI-powered threat detection systems. He maintains active collaborations across academic and military institutions, with particular focus on protecting critical national infrastructure. His laboratory work primarily centers on the Lab of Forest-Environmental Informatics and Computational Intelligence, where he has developed intelligent models for environmental risk assessment and critical infrastructure protection. Current research directions include blockchained adaptive federated auto meta-learning architectures for cybersecurity and privacy in Industry 4.0 environments.

Auke Ijspeertمشاهده پروفایل

استاد

• Robotics
• Computational Neuroscience
• Machine Learning
• +۲ مورد دیگر

Auke Ijspeert is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), leading the Biorobotics Laboratory (BioRob). He earned a B.Sc./M.Sc. in physics from EPFL (1995) and a Ph.D. in artificial intelligence from the University of Edinburgh (1999). After postdoctoral work at IDSIA, EPFL, and the University of Southern California (USC), he returned to EPFL as an SNF assistant professor, becoming associate professor in 2009 and full professor in 2016. He holds primary affiliation with EPFL's Institute of Bioengineering and secondary affiliation with the Institute of Mechanical Engineering. 1995: B.Sc./M.Sc. in Physics, EPFL 1999: Ph.D. in Artificial Intelligence, University of Edinburgh 2002: SNF Assistant Professor, EPFL 2009: Associate Professor, EPFL 2016: Full Professor, EPFL His research explores the intersection between robotics, computational neuroscience, nonlinear dynamical systems, and machine learning. He develops robotic models to study animal locomotion principles and creates bio-inspired adaptive controllers. His work extends to assistive technologies like exoskeletons and smart furniture for mobility assistance. Notable publications include Science 2007 and 2014, and Nature 2019 with Nyakatura et al. He has received numerous awards, including: 2020: IEEE Fellow 2006: SNSF Professorship 1997: Marie Curie Scholarship 2024: IEEE ICRA Most Influential Paper Award 2019: CLAWAR Best Paper Prize 2018: SAB Best Conference Paper Award 2014: IEEE RO-MAN Best Paper Award 2007: IEEE Humanoids Best Paper Award 2002: ICRA Overall Best Paper Award Professor Ijspeert teaches courses in autonomous robotics, computational motor control, and legged robots at EPFL. He supervises numerous PhD students and contributes to editorial boards of leading journals in robotics and neuroscience. His laboratory investigates locomotion principles through robotic models and simulations, bridging biological understanding with technological innovation.

Ioannis Mastorakosمشاهده پروفایل

دانشیار

• Nanomaterials Science
• Computational Materials Science
• Mechanical Properties of Metallic Materials
• +۴ مورد دیگر

Ioannis Mastorakos is an Associate Professor in the Mechanical & Aerospace Engineering Department at the Coulter School of Engineering & Applied Sciences , Clarkson University. He is also affiliated with the Center for Advanced Materials Processing (CAMP). His academic journey includes a B.Sc. in Physics (1994) and a Ph.D. in Engineering (2004) from Aristotle University of Thessaloniki, Greece. Prior to his current position, he held roles such as Visiting Assistant Professor and Clinical Assistant Professor at Washington State University’s School of Mechanical and Materials Engineering before joining Clarkson in 2014 as an Assistant Professor. His research focuses on nanomaterials science , particularly the mechanical properties and applications of nanoscale composite nanofoams and high entropy alloys. He employs molecular dynamics, Monte Carlo simulations, dislocation dynamics, and finite element analysis to study deformation mechanisms across length scales. Recent work includes multiscale modeling of nanofoams, dislocation patterning in aluminum, and the mechanical behavior of bivalve shells under predation. Key research trends in his articles emphasize multiscale modeling of metallic systems, high entropy alloys , nanofoam mechanics , and bio-inspired materials . His studies bridge atomic-scale phenomena with macroscopic material behavior, addressing challenges in energy absorption, structural resilience, and alloy design. Awards/Grants: Co-PI on NSF-CMMI-MEP 1634640 (2016-2019) for strengthening metallic nanofoams via ligament-scale design. Mastorakos advises on graduate research in materials science and collaborates on grants focusing on irradiation effects, nanocomposites, and additive manufacturing. His lab, part of Clarkson’s CAMP, integrates computational and experimental approaches to advance next-generation materials.

• Cloud Computing
• Edge Computing
• Distributed Systems
• +۹ مورد دیگر

Amjad Ullah is a Research Fellow at the School of Computing, Engineering and the Built Environment , Edinburgh Napier University. His expertise spans cloud computing, edge computing, and bio-inspired models for resource management. MSc in Distributed Systems (University of Leicester, 2011) PhD in Cloud Elasticity (University of Stirling, 2017) Research interests include: Orchestration and runtime management in cloud-to-edge environments AI-driven resource optimization and network cost analysis Privacy-preserving encryption techniques Serverless computing frameworks and microservices deployment Current projects: Swarmchestrate (2024-2026): Decentralized orchestration across cloud-to-things continuum GREET (2025-2029): Explainability in cyber-physical systems Long-range Perceptive AVs (2024-2026): Autonomous vehicle situational awareness

• Artificial Intelligence
• Robotics
• Evolutionary Computing
• +۴ مورد دیگر

Kyrre Glette is a Professor at the University of Oslo's Department for Informatics, affiliated with the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion and the Robotics and Intelligent Systems (ROBIN) research group. His work focuses on co-designing robot bodies and behaviors using AI methods, particularly through the open-source robotic platform DyRET. Research interests include evolutionary robotics, bio-inspired computing, and embodied AI. Research Groups: Robotics and Intelligent Systems (ROBIN) RITMO Centre of Excellence fourMs Lab Projects: COCOMO: Co-evolution of Control and Morphologies Multimodal Elderly Care Systems (MECS) Predictive and Intuitive Robot Companion (PIRC) His publications explore topics like evolutionary algorithms for morphological adaptation, embodied music interfaces, and neural mechanisms of auditory prediction. Advising focuses on MSc projects in evolutionary robotics and modular robot control.

• Graph Neural Networks
• Geometric Deep Learning
• Non-Euclidean Foundation Models
• +۳ مورد دیگر

Rex Ying is an Assistant Professor in the Department of Computer Science at Yale University's School of Engineering & Applied Science. His research focuses on developing expressive, scalable, and explainable algorithms for graph-structured data through graph neural networks and geometric learning. His work spans graph learning applications in recommender systems, anomaly detection, social network analysis, protein networks, drug discovery, and physical simulations. Key research thrusts include Non-Euclidean Foundation Models , Geometric Deep Learning , and Explainable AI for graph representations, with recent emphasis on hyperbolic geometry for hierarchical data modeling. Recent publications reveal strong trends in foundation models for scientific discovery (particularly spatial biology), hyperbolic adaptations of LLMs , and multimodal integration for biomedical applications. His lab actively bridges theoretical geometric learning with real-world applications in biology and physics. Baidu Scholarship 2019 Area Chair for LoG 2022 Conference Blue Sky Best Paper Award at ACM KDD 2025 NSF core program award on foundation models for scientific discovery Ying leads the Graph and Geometric Learning Lab, which develops open-source tools like PyTorch Geometric. He actively recruits PhD students for research on GNN frontiers, theoretical studies of graph learning, and applications in social/natural sciences. Recent grants include NSF funding for scientific foundation models and industry collaborations with Amazon/Ericsson. The lab maintains strong industry partnerships with telecom (Ericsson), healthcare (spatial omics), and social platforms, while organizing key workshops like Non-Euclidean Foundation Models at WebConf 2025 and Graph Signal Processing at GSP 2025.

Tjeerd V. olde Scheperمشاهده پروفایل

استاد

• Dynamic Systems
• Neurocomputation
• Biological Systems Modeling
• +۳ مورد دیگر

Dr. Tjeerd V. olde Scheper is a Professor and Research Lead in Computing at the School of Engineering, Computing, and Mathematics, Oxford Brookes University. He specializes in Dynamic Systems and Neurocomputation, focusing on biological problem-solving mechanisms and their applications in engineering, health, and medical fields. His notable contributions include the Rate Control of Chaos (patented) for stabilizing complex systems and Criticality Analysis for machine learning and biological data analysis. He leads the UG Computing Programmes and supervises PhD/MSc students in biomodelling and AI. Education: Holds MSc and PhD degrees in relevant disciplines (details unspecified). Active in research grants including DASA (£90k, 2020), Newton Fund (£97k, 2018), and multiple HEIF grants. Collaborates with institutions globally through projects like CARDIGAN (diabetic gait analysis) and [SENTIENT] (intelligent decision support). Research interests span bio-inspired control systems, nonlinear dynamics, and AI applications in healthcare. His work bridges computational models with real-world challenges in robotics, biomedical engineering, and environmental control (e.g., wind turbines, combustion engines). He contributes to interdisciplinary groups like CAVEPET, VAIL, and the Machine Learning & Robotics Group. Grants/Awards: Secured £90k+ from DASA and £97k+ from the Newton Fund. HEIF grants for wind turbine and engine control projects (2011–2012). Member of AAAS, IEEE, and neuroscientific societies. Consultancy roles include workshops on AI for diabetes and neural networks. Advising & Labs: Supervised Salman Khan (2023). Affiliated with CAICS, MOReS, DASOBU, and AIDAS. Leads projects like Map-to-Human Research Roadmap (NASA-funded, 2024–2026), emphasizing applied AI and movement science.