جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۳ مورد دیگر

Dr. Varuna De Silva is a Reader (Associate Professor) in Machine Intelligence at Loughborough University London, where he serves as Programme Director for the Artificial Intelligence and Data Analytics MSc programme. He joined as a Lecturer in 2016 after industry experience at Apical Ltd, where his patented algorithms were deployed in over 500 million devices. His research focuses on scaling AI to real-world engineering systems through multi-agent reinforcement learning, multimodal computer vision, and simulation modeling. Education: B.Sc. (Hons) Engineering, University of Moratuwa, Sri Lanka (2007) Ph.D. Electronic Engineering, University of Surrey (2011) PG Cert in Academic Practice, Loughborough University (2018) His work spans reinforcement learning architectures, neuromorphic computing, and AI applications in healthcare, environmental systems, and autonomous vehicles. Recent publications demonstrate strong emphasis on interpretable AI, brain-computer interfaces, and causal reasoning in multi-agent environments, with consistent cross-disciplinary integration of machine learning techniques. Awards: IEEE Chester Sall Award, Consumer Electronics Society (2010) Vice Chancellor's Award for Best Post Graduate Research Student (2011) Overseas Research Scholarships Award (2008) He leads EPSRC-funded projects on next-generation machine intelligence and maintains industrial collaborations. As module leader for Bayesian Methods in Deep Learning and Statistical Methods in Finance, he drives curriculum development in AI. His team actively researches autonomous systems and human-AI interaction frameworks.

Catalin V Rusuمشاهده پروفایل

مدرس

• Modeling large-scale biologically inspired microcircuits
• Data mining and knowledge discovery
• Probabilistic planning and reinforcement learning
• +۳ مورد دیگر

Lecturer Catalin V Rusu is a faculty member at the Transylvanian Institute of Neuroscience (TINS) , where he directs the Applied Computational Intelligence Laboratory . His research spans biologically inspired neural systems , spiking neural networks , and machine learning applications in structural diagnostics . Rusu teaches courses like Advanced Programming Methods and Object-Oriented Programming . Research Interests Modeling cortical microcircuits and self-organizing neural systems Spiking neural networks for autonomous robotics (ROBBY framework) Damage detection in beam structures using frequency shifts and ML Predictive modeling for student attention and academic progress Probabilistic planning and reinforcement learning Teaching Requirements Students must bring laptops with Java/Python environments (JDK 8+/Python3) and IDEs (IntelliJ/PyCharm) Git setup required for collaborative coding sessions Hands-on sessions with extended durations (100 minutes with breaks) Contact Transylvanian Institute of Neuroscience (TINS) Str. Ploiești 33, Cluj-Napoca, Romania Email: rusu@bet-you-can-guess-it.much-obvious-very-wow

• Business Management
• Information Systems
• Fuzzy Cognitive Maps
• +۴ مورد دیگر

Jose Luis Salmeron Silvera is a University Professor at Universidad Pablo de Olavide (UPO) in Spain, affiliated with the Department of Business Organization and Marketing within the School of Business. His research focuses on the intersection of business management, information systems, and artificial intelligence, particularly through the lens of fuzzy cognitive maps and grey systems theory. Dr. Salmeron holds two PhD degrees: one from the University of Almería (2014) with thesis "Modeling of complex adaptive systems using fuzzy cognitive maps," supervised by Dr. José Antonio Torres Arriaza, and another from the University of Huelva (2000) with thesis "Information systems for management: a systemic approach," supervised by Dr. Paula Luna Huertas and Dr. Francisco José Martínez López. He supervises doctoral students in Engineering, Data Science and Bioinformatics, and Information Systems and Supply Chain Management programs. His research interests center around fuzzy cognitive maps , grey systems theory , and their applications in business decision-making, risk management, and complex system modeling. Dr. Salmeron has made significant contributions to extending traditional fuzzy cognitive maps with grey theory to handle uncertainty in environments with incomplete or imprecise data. His work bridges theoretical advancements in artificial intelligence with practical applications across healthcare, business management, and engineering domains. Dr. Salmeron's research has evolved from foundational work in fuzzy cognitive maps for business applications to more recent innovations incorporating federated learning, privacy-preserving techniques, and quantum-inspired approaches. His publications demonstrate a consistent focus on developing interpretable AI systems that can function effectively with limited or uncertain data, making his work particularly valuable for real-world applications where perfect information is rarely available. Among his notable achievements is the development of methods for learning fuzzy grey cognitive maps using nonlinear Hebbian-based approaches, which has found applications in medical decision support systems, particularly in radiotherapy treatment planning. His work has been cited in various fields including computer science, operations research, and biomedical engineering. Dr. Salmeron actively collaborates with researchers internationally, with notable co-authors including Elpiniki I. Papageorgiou and Salvador Bueno. His research group "STI Information Systems and Technologies" focuses on the application of advanced computational methods to solve complex business and engineering problems. Previously, he was also part of the "INFORMATION, ENVIRONMENT AND CHANGE" research group, reflecting the interdisciplinary nature of his work.

Sadra Sadehمشاهده پروفایل

پژوهشگر ارشد

• Computational Neuroscience
• Neural Network Modeling
• Brain Connectivity
• +۵ مورد دیگر

Dr. Sadra Sadeh is a Senior Research Fellow at King's College London, working within the Centre for Developmental Neurobiology at the Institute of Psychiatry, Psychology & Neuroscience. His research focuses on building computational models of brain networks to understand how brain structure relates to its dynamics and function. He leads the Sadeh Lab, which approaches neuroscience problems through data-constrained computational modeling and theoretical analysis of large-scale neuronal networks. Dr. Sadeh's research interests span computational neuroscience, neural network modeling, brain connectivity, and the intersection of neuroscience with artificial intelligence (Neuro-AI). His work aims to reveal fundamental mechanisms underlying brain operation in both healthy and disease conditions, with potential applications for understanding neurological disorders such as epilepsy and Parkinson's disease. His research has significant implications for developing brain-computer interfaces and advancing artificial intelligence models. His recent publications demonstrate expertise in hippocampal networks, memory consolidation, neural dynamics, and computational modeling approaches. His work frequently appears in high-impact journals including Nature Neuroscience, Nature Reviews Neuroscience, and PNAS. Wellcome Trust Career Development Award recipient Dr. Sadeh maintains active collaborations with leading neuroscientists including Professor Oscar Marín and Professor Laura Andreae at King's College London, and researchers at the Francis Crick Institute. His current research is supported by the Wellcome Trust project 'Perturbative analysis of brain networks' where he serves as Co-Investigator.

Tianlong Chenمشاهده پروفایل

استادیار

• Machine Learning
• Sparsity
• Robustness
• +۵ مورد دیگر

Tianlong Chen is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill, where he established the UNITES Lab (University of North Carolina, AI Trustworthiness, Efficiency, and for Science Group). He joined UNC in Fall 2024 after completing his Ph.D. in Electrical and Computer Engineering at the University of Texas at Austin (2023) and serving as a postdoctoral researcher at MIT (CSAIL), Harvard University (BMI), and the Broad Institute of MIT & Harvard. His research focuses on building accurate, trustworthy, and efficient machine learning systems , with emphasis on: Sparsity and model compression techniques Robustness against adversarial challenges Learning-to-optimize methodologies Graph representation learning Diffusion models and generative AI Quantum computing applications Bioengineering and healthcare AI solutions His publications demonstrate strong trends in large language model optimization , efficient multimodal systems , and trustworthy AI frameworks , with frequent appearances at top venues (NeurIPS, ICML, CVPR, ICLR). Significant scientific honors include: IBM and Adobe Ph.D. Fellowships Best Paper Award at Learning on Graphs Conference 2022 AdvML Rising Star (2023) Amazon Research Award (2024) Two UNC Accelerating AI Awards (2025) NAIRR Pilot Award (2025) He leads 19+ advisees in the UNITES Lab and has secured research funding from Cisco, Amazon, and institutional grants. Current projects explore sparsity-aware architectures, LLM alignment, and efficient multimodal learning systems.

• Machine Learning
• Natural Language Processing
• Small Language Models
• +۴ مورد دیگر

Richard Diehl Martinez is a Research Fellow in Computer Science at the University of Cambridge, where he conducts cutting-edge research at the intersection of machine learning, linguistics, and neuroscience. His academic journey includes completing a PhD at Cambridge as a Gates Scholar, following earlier studies at Stanford University where he earned both an M.S. in Computer Science and a B.S. in Management Science. Prior to his doctoral work, he gained industry experience as an Applied Research Scientist at Amazon Alexa, focusing on language modeling research. His research interests center on improving the performance and efficiency of small language models through innovative pre-training techniques and analytical methods. Martinez has made significant contributions to understanding convergence challenges in smaller architectures, frequency bias mitigation, and curriculum learning approaches inspired by infant language acquisition. He is the creator of Pico, an open-source framework for hypothesis-driven small language model research and development. Martinez's publication record demonstrates consistent output in top-tier venues including EMNLP, ACL, and CoNLL, with his paper 'CLIMB: Curriculum Learning for Infant-inspired Model Building' earning the Best Paper award at CoNLL 2023. His work spans theoretical investigations of learning dynamics and practical framework development, addressing fundamental challenges in small language model training and efficiency. Gates Scholar Best Paper Award at CoNLL 2023 for CLIMB research As a Research Fellow, Martinez likely supervises graduate students and contributes to the research community through collaborations with colleagues at Cambridge and beyond. His work on the BabyLM Challenge connects him with an international community focused on advancing small-scale language model capabilities. Martinez's research bridges cognitive science theories with practical model development, offering insights that benefit both theoretical understanding and real-world applications of language models.

Michael Teichmannمشاهده پروفایل

پژوهشگر

• Modeling intelligent systems
• Adaptive systems
• Learning mechanisms
• +۲ مورد دیگر

Michael Teichmann is a Researcher at the Professorship of Artificial Intelligence, Department of Computer Science, Chemnitz University of Technology. He has held this position since April 2015, following his role as a PhD student (2012–2015) in the interdisciplinary Research Training Group 'Crossworlds,' which explored connections between virtual and real social environments in communication, emotions, sensomotorics, and learning. Education: Dr. rer. nat. in Artificial Intelligence (2018), Chemnitz University of Technology. Dissertation: 'A plastic multilayer network of the early visual system inspired by the neocortical circuit.' Major in Computer Science with minors in Artificial Intelligence and Operation Research (2004–2010), Chemnitz University of Technology. Research Focus: His work centers on biologically inspired computational models, including: Adaptive learning mechanisms in neural networks Visual perception and invariance learning Emotion-driven perceptual processing Robustness in intelligent systems under information loss Publication Trends: His 15 most recent articles demonstrate a consistent focus on neural plasticity, bio-inspired vision systems, and computational neuroscience. Key thematic clusters include Hebbian learning stabilization, spiking neural networks for image processing, emotion-computer vision intersections, and competitive learning for enhanced robustness in feature detection. Awards: University award for best computer science thesis (2010) Affiliations: He operates within the Professorship of Artificial Intelligence lab, contributing to projects on neural modeling and vision systems. His prior involvement in the 'Crossworlds' research group emphasized interdisciplinary collaboration between computer science and social sciences.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۴ مورد دیگر

Luís Nunes is an Associate Professor at Iscte – Instituto Universitário de Lisboa, Department of Information Science and Technology (ISTA), since 1997. He is an integrated researcher at ISTAR-Iscte, focusing on Computational Modeling of Systems . His academic journey includes a PhD in Computer Engineering (2006) from the University of Porto, a Master’s in Electrical and Computer Engineering (1997) from the Technical University of Lisbon, and a Computer Science degree (1993) from the University of Lisbon. Luís Nunes’ research spans Artificial Intelligence, Machine Learning, Data Science , and their applications in public administration, hotel revenue management, mobile security , and human activity recognition . His work addresses predictive modeling for booking cancellations, automated security testing of Android apps, and AI-driven public policy analysis. His recent publications emphasize predictive analytics in tourism , malware detection , and human behavior modeling . Projects like ISDAPPP (Principal Investigator) and AIH (Researcher) highlight his focus on AI for public policy and healthcare data standards. He mentors numerous PhD and Master’s students, including Clara Nunes Barrancos and Miguel Lopes Valadares. Luís Nunes actively contributes to teaching, offering courses such as Introduction to Machine Learning , Autonomous Agents , and Big Data in Public Policies . He has coordinated courses like Applied Artificial Intelligence Project at the Master’s level.

Jesus Fontechaمشاهده پروفایل

استاد

• Ubiquitous Computing
• mHealth
• Sensors
• +۲ مورد دیگر

Jesus Fontecha is a Professor at the University of Castilla-La Mancha (UCLM) in the Department of Technologies and Information Systems. He is a member of the MAmI Research Group since 2008, focusing on Ambient Intelligence and Ubiquitous Computing. Secretary of the Asociación para la Inteligencia Ambiental (AIAm) Manager of Wellbeing and Healthcare Living Lab (WeCareLab) at ITSI-UCLM His research spans Ubiquitous Computing , mHealth , and Smart Monitoring , with over 40 publications and leadership in projects like a 1-year Mapfre Foundation initiative and a 3-year regional health tech endeavor. Key contributions include: Context-aware assistive systems Mobile frailty assessment tools Energy-efficient wearable data storage Recent publications analyze: 3D skeletal motion synthesis via LSTM networks (2024) Smart plug-based abnormal behavior detection (2023) Diabetes self-management technology trends (2022) Scientific contributions recognized through: Best Paper Award Guest editor for 3 JCR-indexed special issues Editorial board member of Wireless Communications and Mobile Computing and Journal of Healthcare Engineering

• Biomimetic Robotics
• Neurorobotics
• Spiking Neural Networks
• +۵ مورد دیگر

Benedikt Feldotto is a Researcher at the Technical University of Munich, affiliated with the Department of Computer Science, Chair of Robotics, Artificial Intelligence and Real-Time Systems led by Prof. Knoll. He works on the Neurorobotics Platform as part of the European Human Brain Project and serves as lead developer of the NRP Robot Designer since 2017. His educational background includes a Bachelor of Engineering in Mechatronics from Baden-Wuerttemberg Cooperative State University (DHBW) with industry experience in automation pre-development, including a development stay in the USA. He further specialized with a Master of Science in "Robotics, Cognition, Intelligence" at Technical University of Munich. Feldotto's research focuses on biomimetic learning in neurorobotic systems, with particular emphasis on spiking neural networks for embodied cognition. His work bridges cognitive neuroscience and robotics, exploring how robots can interact naturally with humans and environments through biologically-inspired learning mechanisms. Key areas include musculoskeletal modeling, human-robot interaction, and the ethical implications of learning robots in society. He actively develops simulation tools to enable large-scale neurorobotic experiments and has contributed significantly to the Human Brain Project's Neurorobotics Platform infrastructure. His publication record shows a clear progression from foundational platform development toward increasingly sophisticated embodied simulations using high-performance computing. Recent work emphasizes scaling spiking neural networks for robotic control, analyzing neural network architectures for specific motor tasks, and validating biomechanical models against human movement data. The research consistently connects theoretical neuroscience with practical robotics applications. His scientific recognition includes: Best Poster Award from the Graduate School of Bioengineering (2018) As an educator, Feldotto has taught Cognitive Systems course exercises from Spring Semester 2018 through Spring Semester 2022. He regularly offers thesis and project opportunities in biomimetic robotics and neural network learning, though no current openings are listed. His teaching and research are supported through the European Human Brain Project funding framework. Feldotto leads development of the NRP Robot Designer, a critical tool within the Neurorobotics Platform ecosystem. He has presented this work internationally through workshops at major conferences including the European Robotics Forum and Japanese Neural Network Society meetings. His outreach extends to public exhibitions at the Deutsches Museum and participation in ethics discussions on robot stereotypes, demonstrating commitment to both technical advancement and societal implications of neurorobotics research.

Elie Bou-Zeidمشاهده پروفایل

استاد

• Atmospheric Science
• Urban Environmental Modeling
• Air Flow Dynamics
• +۵ مورد دیگر

Elie Bou-Zeid is a Professor of Civil and Environmental Engineering at Princeton University and serves as the Director of the Program in Environmental Engineering and Water Resources . He previously directed the Metropolis Initiative for urban technology until 2022. His research integrates theory, numerical simulations, and field observations to study atmospheric flows, heat/pollutant transport, and sustainability in cities . Bachelor's : American University of Beirut Master's : American University of Beirut and Johns Hopkins University PhD : Johns Hopkins University He leads the Thermofluids of Urban and Natural Environments (TUNE) Lab , focusing on urban meteorology, renewable energy integration, and climate resilience . His work addresses inequitable urban environmental conditions and develops technologies for sustainable cities , including the CityReader Project . Recent publications explore urban heat islands, blue infrastructure, machine learning for climate, and boundary layer dynamics . Awards include the 2024 STAC Award , Beyond Bauhaus Prototyping the Future Award , and Fondation Latsis Internationale Prize . Awards : STAC 2024, Beyond Bauhaus 2024, EPFL Latsis Prize 2022 Editorial Roles : Editor of Journal of the Atmospheric Sciences (2020–2025) He advises five Ph.D. students and collaborates with postdocs like Einara Zahn and Young Paul Yi . His lab investigates kirigami-inspired ventilation, agrivoltaic farms, and downscaling climatic variables using Bayesian deep learning .

• Sustainable Manufacturing
• Industry 4.0
• Industry 5.0
• +۳ مورد دیگر

Tim van Erp is a Senior Lecturer in Advanced Manufacturing and Design at Flinders University’s College of Science and Engineering. With over 12 years of experience in academia and industry across Europe, he focuses on designing and managing complex manufacturing systems, particularly addressing sustainability and digitalization trends. Education: PhD in Engineering (Technische Universität Berlin, 2018), Diploma in Industrial Engineering (Otto-von-Guericke-Universität Magdeburg, 2012), Lecturer Certificate (University of Southern Denmark, 2023) Research: His work bridges Industry 4.0/5.0 with sustainability, emphasizing digital twins, green hydrogen value chains, and resilient manufacturing systems. He collaborates internationally, including the Danish-Australian Green Hydrogen Value Chains partnership. Teaching: Coordinates topics like ICT Management and Professional Standards, Engineering Management, and Complex Project Management, while lecturing on Engineering Materials and Systems. Grants: Led projects funded by Innovation Fund Denmark (DKK 783,000 and DKK 7.5 million) and the Fabrikant Mads Clausen Fond (DKK 120,000). Leadership: Member of Flinders’ Factory of the Future Leadership Committee and Chief Investigator for a mobile Industry 4.0 learning factory at Tonsley campus.

• Artificial Intelligence
• Quantum Machine Learning
• Deep Learning
• +۶ مورد دیگر

Prayag Tiwari is a Senior Lecturer at Halmstad University's School of Information Technology, where he conducts cutting-edge research at the intersection of artificial intelligence, quantum computing, and healthcare applications. With an SMIEEE designation and an active research profile, Dr. Tiwari contributes significantly to both academic and practical advancements in intelligent information systems. His research interests span a diverse spectrum including Artificial Intelligence, Quantum Machine Learning, Deep Learning, Healthcare, Bioinformatics, NLP/LLM, Computer Vision, Multimodal Learning, and Intelligent Systems. This interdisciplinary focus enables him to tackle complex problems across multiple domains, particularly in developing quantum-inspired algorithms for healthcare applications and advancing multimodal learning techniques for real-world challenges. Dr. Tiwari's publication record demonstrates consistent high-impact contributions across multiple domains, with recent work showing a clear trend toward quantum machine learning applications, particularly in healthcare and bioinformatics. His research frequently combines traditional deep learning approaches with quantum computing principles to develop novel solutions for drug interaction prediction, medical data processing, and multimodal analysis. The interdisciplinary nature of his work is evident in publications spanning computer vision, natural language processing, and blockchain applications for healthcare security. 2022 Best Paper Award for a publication in Neural Networks Multiple Highly Cited Papers in various journals Editor for Neural Networks and IEEE Transactions on Fuzzy Systems As an academic leader, Dr. Tiwari serves as an editor for prestigious journals including Neural Networks and IEEE Transactions on Fuzzy Systems, contributing to the advancement of knowledge in his fields of expertise. His extensive publication record in top-tier venues such as TMLR, IJCV, TNNLS, AAAI, ACL, CIKM, ICASSP, ECIR, and ECML demonstrates significant scholarly impact. While specific grant information isn't detailed in the provided materials, his research output suggests active funding for projects in quantum machine learning and healthcare applications. Dr. Tiwari's work appears to be conducted within collaborative research environments at Halmstad University, particularly within the School of Information Technology's research groups focused on artificial intelligence and quantum computing applications. His publications frequently involve multi-institutional collaborations, suggesting active participation in both national and international research networks focused on advancing machine learning techniques for practical applications.

Leo Mršićمشاهده پروفایل

استاد

• Computer Science
• Information and Communication Technology
• Graph Theory
• +۵ مورد دیگر

Prof. Dr. Leo Mršić serves as a Full Professor and Vice-Rector for Science and Research at University of Algebra, Croatia, while concurrently holding the position of Vice-President for Technological Development in the National Council for Higher Education, Science and Technological Development of the Republic of Croatia. His leadership spans academic administration and national science policy formulation. His academic foundation includes: Degree in Insurance from Department of Foreign Trade, Faculty of Economics, University of Zagreb Master's in Business Statistics from Department of Informatics, Faculty of Economics, University of Zagreb PhD in Information Science (Graph Theory and Statistical Models in Business) from Department of Information Sciences, Faculty of Humanities and Social Sciences, University of Zagreb Mršić's research bridges theoretical computer science with practical applications, specializing in quantum-inspired algorithms, fuzzy graph theory, and statistical modeling. His work demonstrates exceptional interdisciplinary reach across cybersecurity, healthcare diagnostics, transportation systems, and sustainable supply chains, consistently translating complex mathematical frameworks into real-world solutions for industry and government sectors. Analysis of his recent publications reveals dominant trends in quantum-fuzzy hybrid systems, with 78% of 2024-2026 works integrating quantum computing principles with fuzzy logic for optimization problems. Key application domains include secure communications (22% of publications), healthcare analytics (18%), and traffic/logistics systems (15%), demonstrating strategic alignment with Croatia's national technological development priorities.

Naomi Feldmanمشاهده پروفایل

استاد

• Computational Linguistics
• Cognitive Science
• Machine Learning
• +۵ مورد دیگر

Naomi Feldman is a Professor at the University of Maryland with dual affiliations in the Department of Linguistics and the University of Maryland Institute for Advanced Computer Studies (UMIACS) . She is also affiliated with the Department of Computer Science , the Program in Neuroscience and Cognitive Science , and the Computational Linguistics and Information Processing (CLIP) Lab . Her research focuses on computational psycholinguistics , applying methods from statistics, machine learning, and automatic speech recognition to model cognitive processes underlying speech perception, phonetic learning, and language acquisition . Key areas include understanding how humans learn language structure in complex environments and developing computational strategies to assist language learners and clinicians. Research combines infant phonetic learning with neural network modeling Investigates cross-linguistic speech recognition and selective attention mechanisms Explores language discrimination , developmental language disorders , and computational models of aphasia Her recent publications (2022-2025) cover topics such as reward-based language learning, rhythm analysis in speech, self-supervised speech representations, and argument role sensitivity in language models. These works have received recognition like the Computational Modeling Prize in Perception & Action and Best Paper Award Honorable Mention . Naomi actively advises students across three graduate programs: Linguistics , Neuroscience and Cognitive Science (NACS) , and Computer Science . Her research team includes postdocs, PhD candidates, and undergraduate collaborators. She has received grants including an NIH Pilot Grant to assist children with specific language impairment. She is a core member of the university's interdisciplinary Language Science community and participates in projects crossing multiple departments. Her lab maintains open-source code repositories for research reproducibility.