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Timothy McMahanView profile

Assistant Professor

• Adaptive Virtual Environments
• Neurogaming
• Virtual Reality
• +7 more

Dr. Timothy McMahan is an Assistant Professor in the Department of Computer Science at the University of North Texas. His research focuses on adaptive virtual environments, neurogaming, and applying VR/AR technologies to neuropsychological assessment and training. He holds a Ph.D. in Computer Science (2016) and multiple advanced degrees from UNT. Education: Ph.D. Computer Science, University of North Texas (2014–2016) M.S. Computer Science, University of North Texas (2007–2013) B.S. Computer Science, University of North Texas (2003–2007) Postdoctorate in Computational Neuropsychology, University of North Texas (2016–2017) Research Interests: Adaptive VR environments for personalized user experiences Neurogaming applications in healthcare and education Machine learning for cognitive assessment and environmental adaptation EEG-based analysis of user engagement and cognitive workload VR tools for neuropsychological disorder diagnosis and treatment Recent work emphasizes VR applications in memory research, adaptive systems leveraging real-time ML predictions, and EEG-driven analyses of subjective experiences. Articles focus on bridging virtual and real-world interactions through computational neuroscience methods. No scientific awards or grants are explicitly listed in the provided materials. No advisees or lab affiliations are documented here.

• Distributed Systems
• IoT Protocols
• Big Data
• +4 more

Dr. Sotirios Kontogiannis is a Researcher and Director of the Distributed MicroComputer Systems Laboratory (MCSL) at the Department of Mathematics, University of Ioannina. He holds a PhD in Software Engineering from Democritus University of Thrace (2012), with a focus on load balancing algorithms for distributed systems. His career spans academic and private sector roles, including software development, research in FP7 projects, and technical leadership at TEKMON Geomatics. He currently teaches courses on Database Systems, Parallel Algorithms, and Educational Technologies at the University of Ioannina. Education: 5-year Engineering Diploma (DUTH, 2003), MSc in Microelectronics and Information Systems (DUTH, 2006), PhD in Load Balancing Algorithms (DUTH, 2012). Research Focus: Distributed systems, IoT protocols, sensor networks, big data, and smart agriculture technologies (e.g., beekeeping monitoring systems, wine fermentation frameworks). His work integrates IoT, AI, and edge computing to address challenges in agriculture, accessibility, and environmental monitoring. Notable inventions include patented smart beehive systems and tactile navigation tools for visually impaired individuals.

• Neuroscience
• Robotics
• Computational Biology
• +4 more

Prof. Pavan Ramdya, the DSM-Firmenich Next Generation Chair in Neuroscience at École Polytechnique Fédérale de Lausanne (EPFL), leads the Neuroengineering Laboratory. His research focuses on reverse-engineering biological intelligence in Drosophila melanogaster to inspire neuroprosthetics, robotics, and AI. He holds a PhD in Neurobiology from Harvard University and completed postdoctoral training in robotics (EPFL), neurogenetics (UNIL), and bioengineering (Caltech). University: École Polytechnique Fédérale de Lausanne (EPFL) School: School of Life Sciences Academic Rank: Professor His lab employs computational, engineering, genetic, and microscopy approaches to study neural population dynamics, biomechanics, and gene expression in limb-dependent behaviors. Key research trends from his publications include neuromechanical modeling of Drosophila , sensory-motor integration, and AI-robotics synergy for biological discovery. HFSP Career Development Award Swiss National Science Foundation Eccellenza Grant UNIL Young Investigator Award in Basic Science FENS-Kavli Network of Excellence member The lab mentors doctoral researchers such as Sibo Wang, Victor Stimpfling, and Femke Hurtak, alongside postdoctoral fellows like Jasper Phelps and alumni including Victor Lobato Rios. Collaborations span robotics (Auke Ijspeert), microrobotics (Sakar), and computational imaging (Fua).

Conrad SandersonView profile

Researcher

• Artificial Intelligence
• Machine Learning
• AI Ethics
• +7 more

Dr. Conrad Sanderson is a Researcher and Team Leader at the Data61 division of CSIRO , focusing on artificial intelligence, machine learning, AI ethics, and high-performance numerical computing. He is also an Adjunct Professor at Griffith University . With over 150 publications and 11,000+ citations, he is renowned for developing influential open-source libraries like Armadillo and RcppArmadillo . Research Interests Artificial Intelligence & Deep Learning Responsible AI, Safe AI, and Ethical Trade-offs Numerical Linear Algebra and High-Performance Computing Recent Publications highlight advancements in: Dynamic graph anomaly detection via extreme value theory Fire propagation uncertainty estimation using neural emulators Resolving ethical tensions in AI implementation GPU-accelerated machine learning Scientific Awards Most cited paper award for thesis-based article Armadillo framework: 30+ million downloads Collaborations include researchers from Facebook, NASA, Boeing, and institutions like MIT and Stanford. His work bridges academia and industry through open-source contributions and interdisciplinary applications.

• Decentralized Machine Learning
• Federated Learning
• Industry 4.0
• +3 more

Alessio Mora is a postdoctoral researcher at the Department of Computer Science and Engineering (DISI), University of Bologna, specializing in decentralized learning techniques such as Federated Learning. His work focuses on communication efficiency, heterogeneous data handling, and distributed AI systems. PhD in Computer Science and Engineering, University of Bologna (2023) Master in Computer Engineering, University of Bologna (2019) Bachelor in Computer Engineering, University of Bologna (2016) His research spans decentralized learning, federated learning, and their applications in Industry 4.0, IoT, and edge computing. Key themes include model optimization, communication efficiency, and heterogeneous data distribution in cross-device settings. Recent publications highlight trends in federated learning infrastructure, including unlearning methods, knowledge distillation, and sparse compression techniques. His work integrates theoretical analysis with practical implementations in industrial and IoT contexts. Summer of Reproducibility Award – Flower Labs (2023) Awarded $2,000 USD for implementing and reproducing the FedMLB baseline in Flower, advancing reproducibility in federated learning. Mora has contributed to European projects like OntoTrans and SimDOME, focusing on ontology-based systems for materials modeling and industrial data translation. His technical expertise includes Docker, Stardog triplestore, FastAPI, and Python wrappers for chemical simulation tools.

William SmithView profile

Professor

• Computer Vision
• Computer Graphics
• Machine Learning
• +15 more

William Smith is a Professor in the Department of Computer Science at the University of York, where he leads the Vision, Graphics and Learning (VGL) research group. He holds a BSc and PhD in Computer Science from the University of York, completed in 2002 and 2007 respectively. He has served in key roles including Director of Admissions, PGT AI Programme Lead, and Research Group Lead. He is an Associate Editor for the journal Pattern Recognition and was a Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellow from 2019 to 2020. Education: BSc in Computer Science, University of York, 2002 PhD in Computer Vision, University of York, 2007 His research lies at the intersection of computer vision, computer graphics, and machine learning. He focuses on physics-based and 3D vision, shape and appearance modeling, and the application of statistical and machine learning methods. Key application areas include face and body analysis, surveying and mapping, object capture, and inverse rendering. His work leverages advanced mathematical tools such as convex and nonlinear optimization, manifold learning, computational geometry, and low-level vision techniques. His recent publications, spanning top venues like ECCV, NeurIPS, WACV, and IEEE T-PAMI, reflect a strong trend in neural scene representation, inverse rendering, and illumination modeling. Themes include relighting outdoor scenes, self-supervised pose estimation, spherical neural fields, and orientation-aware deep learning for omnidirectional inputs. These works demonstrate a consistent focus on integrating physical models with deep learning for robust and interpretable vision systems. Scientific Awards: Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellow (2019–2020) William Smith supervises a team of nine PhD students and has advised several alumni who have gone on to research and industry roles. He has served in significant service roles including Area Chair for ICCV, CVPR, and 3DV, Programme Chair for BMVC 2020, General Chair for CVMP 2019, and reviewer for major conferences since 2008. He is actively involved in the academic community and continues to contribute to cutting-edge research through his leadership in the VGL group at York. The Vision, Graphics and Learning (VGL) group, which he leads, conducts research in computer vision, graphics, and machine learning, with projects such as Branching Out (historic tree mapping), Senior Research Fellowship (model-based vision meets deep learning), Google Daydream (VR/AR head modeling), and LEAPP (polar archival photography). The group fosters a collaborative environment with both current and former members contributing to impactful research.

Julien MayorView profile

Professor

• Language Acquisition
• Infant Speech Perception
• Cognitive Development
• +5 more

Julien Mayor is a Professor at the Department of Psychology, University of Oslo , specializing in Language Acquisition , Infant Speech Perception , and Computational Modeling in Psychology . His work bridges cognitive development with bilingualism and dialect exposure, focusing on how infants learn words through parental speech patterns. He leads the BabyLing project, developing tools like TLex for unbiased language screening.

Michele GelfandView profile

Professor

• Cultural Psychology
• Cross-Cultural Management
• Organizational Behavior
• +4 more

Michele Gelfand is a Professor of Organizational Behavior at Stanford Graduate School of Business and a Professor of Psychology (by courtesy) at Stanford University. She directs the Culture Lab, which investigates cultural norms, negotiation, conflict, revenge, forgiveness, and diversity through interdisciplinary collaborations with computer scientists, neuroscientists, and political scientists. Her research focuses on the concept of 'tightness-looseness' in cultures, exploring how rigid or permissive social norms shape societal responses to threats, innovation, and governance. She has received over $13 million in funding from the National Science Foundation, Department of Defense, and the FBI. Current Affiliation: Stanford Graduate School of Business Previous Affiliation: University of Maryland Gelfand's work spans cultural psychology, organizational behavior, and conflict management. Recent studies examine gene-culture interactions, gender inequality dynamics, and the role of cultural tightness in pandemic response. She has published in top journals like Science , PNAS , and The Lancet Planetary Health , often integrating neuroscience and computational methods. Her research on social norms during crises, particularly the early stages of the COVID-19 pandemic, highlights how cultural tightness-looseness influences compliance, identity, and policy effectiveness. Notable awards include election to the National Academy of Sciences (2021) and the American Academy of Arts and Sciences (2019), the Diener Award (2016), and the SIOP Distinguished Scientific Contributions Award (2022). She authored Rule Makers, Rule Breakers: How Tight and Loose Cultures Wire the World (Scribner, 2018) and co-edited multiple handbooks on conflict, negotiation, and cross-cultural psychology. Her lab's work extends to global cultural evolution, artificial intelligence's societal impact, and behavioral interventions in polarized environments. Key Themes: Cultural Tightness-Looseness, Social Norms, Conflict Resolution, Pandemic Behavioral Dynamics Grants: NSF, DoD, FBI Gelfand's interdisciplinary approach bridges psychology, business, and computational sciences, aiming to decode the primal templates of culture that govern human societies.

Gerd VandersteenView profile

Professor

• Nonlinear Systems Analysis
• RF Circuit Design
• System Identification
• +5 more

Gerd Vandersteen is a Professor at the Vrije Universiteit Brussel (VUB) in the Department of Electronics and Informatics within the Faculty of Engineering. He serves as part-time Director (30%) of the Doctoral School of Natural Science and (Bio)Engineering (NSE) at VUB. His career spans academic research and industry collaboration, with significant contributions to nonlinear systems analysis and RF circuit design. His research interests focus on Nonlinear Systems Analysis , RF Circuit Design , System Identification , and Microwave Engineering . Vandersteen combines theoretical expertise in modeling nonlinear systems with practical experience in analog and RF design. His work emphasizes simulation-based analysis techniques, particularly using the Best-Linear-Approximation method for nonlinear system characterization. Vandersteen's publication record shows consistent output with 363 research outputs, including 103 articles and 164 conference papers. His recent work spans diverse areas from Arabic language processing to advanced RF measurement techniques, demonstrating interdisciplinary reach while maintaining core expertise in electronic systems. Key trends include nonlinear distortion analysis, frequency response characterization, and system identification methodologies. Best Paper Award (2002) Best Paper Award for 'Synchronizing modulated NVNA measurements on a dense spectral grid' (2012) IEEE Senior Member (2007) IEEE Instrumentation and Measurement Society Outstanding Reviewer (2014) As an educator, Vandersteen teaches CAE tools for electronic design, microwave design, and telecommunication techniques at VUB. He mentors PhD students through the international spring school on system identification and modeling techniques. His research is supported by multiple active projects including SRP78 (Center for Model-Based Systems Improvement), HERC61 (110 GHz real-time oscilloscope development), and VLOV106 (Flemish AI Academy establishment).

Jochen BaierView profile

Professor

• Language Didactics
• Literature Didactics
• Cultural Studies
• +7 more

Jochen Baier is a Professor at the Pedagogical University of Schwäbisch Gmünd , serving as deputy head of the Institute for Language & Literature and head of the English Department . His academic work focuses on language and literature didactics , media education , interactive drama , and body language in virtual language learning . He has led research projects on dramatic methods in digital contexts, new media integration, and cross-cultural coaching, emphasizing bilingual teaching and intercultural communication. Current research includes Body Language in Virtual Language Learning Scenarios and practical implementation of EBU Unteaching Prejudices through student projects. He explores subfields like Neural Language Processing , Interactive Learning , and Cultural Awareness in digital environments. Baier’s publications span books on nonverbal communication, teaching literature with digital tools, and bilingual curricula (e.g., Abitur NRW preparation materials). His work also addresses Shakespeare pedagogy , digital edutainment , and intercultural studies .

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +7 more

Prof. Dr. Michael Guckert is a Professor of Business Informatics - Artificial Intelligence at the Department of MND (Management, Natural Sciences, Design) at Technische Hochschule Mittelhessen (THM). He serves as Spokesperson of the KITE Competence Center, Board member at hessian.AI, and Director at StudiumPlus. His research spans multiple domains with a strong focus on AI applications in medical contexts and multi-agent systems. Dr. Guckert's research interests center on applying artificial intelligence to solve complex problems across multiple domains. His primary focus is on medical applications, particularly using AI for electrocardiogram (ECG) analysis, cardiac pathology detection, and clinical decision support systems. He also maintains significant research in multi-agent systems for traffic simulation, model-driven development approaches, and trustworthy/explainable AI systems. His work bridges theoretical AI development with practical applications in healthcare, manufacturing, and transportation. Analysis of Dr. Guckert's recent publications reveals a clear trend toward medical AI applications, particularly in cardiology. His work on the RisKa project demonstrates how AI can interpret ECG data to detect heart conditions, with significant emphasis on explainability and trustworthiness. Parallel to this medical focus, he maintains active research in multi-agent traffic simulation systems (AGADE), acoustic emission analysis, and model-driven development approaches. The publications show increasing collaboration with medical researchers, indicating a strategic focus on interdisciplinary AI applications in healthcare. Dr. Guckert leads the RisKa project (2022-2024), a €736,682 initiative funded by the Ministry for Digital Strategy and Development, which develops an AI-based clinical decision support system for ECG analysis. His research group at THM KITE Competence Center collaborates with Justus Liebig University in Giessen on biomarker research. He has secured multiple research grants for projects involving AI in medical diagnostics, traffic simulation, and manufacturing quality control. His work demonstrates strong industry-academia collaboration, particularly in applying AI to solve real-world problems in healthcare and transportation. Dr. Guckert directs the KITE Competence Center at THM, which focuses on information technology research and applications. His team includes researchers specializing in medical AI, multi-agent systems, and model-driven development. The RisKa project team specifically brings together computer scientists and cardiologists to develop clinically useful AI tools. His work with hessian.AI positions him at the forefront of AI development in the Hesse region of Germany, facilitating collaboration between academic and industry partners.

Martin Paul NawrotView profile

Professor

• Neural Information Processing
• Reinforcement Learning
• Synaptic Plasticity
• +7 more

Prof. Dr. Martin Paul Nawrot is a faculty member at the Institute of Zoology , University of Cologne . His research focuses on neural information processing , reinforcement learning , and synaptic plasticity in biological and artificial systems. He also develops large-scale brain simulations to study attractor dynamics for sensory-motor integration, motor control, and decision-making in primates and insects. Current research areas include neuromorphic computing , spiking neural networks , and computational neuroscience Key projects involve modeling insect behavior , neural coding , and cross-species AI applications His recent publications (2023-2025) explore Drosophila larva locomotion , synaptic plasticity mechanisms , and neuromorphic hardware for real-time simulations. These works span neural circuits , behavioral modeling , and computational tools like GeNN and NEST. Despite his academic prominence, no scientific awards are mentioned in the available texts.

• Cybersecurity
• Artificial Intelligence
• Automata Theory
• +4 more

Eva Maia is a cybersecurity researcher at Instituto Superior de Engenharia do Porto (ISEP), affiliated with the Research Group in Engineering and Intelligent Computing for Innovation and Development . She holds a PhD in Computer Science from the University of Porto and has contributed extensively to AI-driven cybersecurity solutions for cyber-physical systems in healthcare, airports, and industrial sectors. Education: PhD in Computer Science (2016), University of Porto MSc in Computer Science (2010), University of Porto BSc in Computer Science (2008), University of Porto Her research integrates Artificial Intelligence with Cybersecurity , focusing on adversarial machine learning, network intrusion detection, and data privacy. Recent projects include SAFE , VESTA , and GENIUS , addressing security in 6G networks, ransomware defense, and generative AI for software development. Key trends in her 15 most recent articles (2022–2025) include: AI robustness , privacy-preserving neural networks , and holistic security frameworks for healthcare and industrial systems. Notable awards: Best Application Paper Award 2023 , Young Engineer Innovation Award , and Industry 5.0 Award . Scientific Contributions: Coordinated student supervision in MSc theses on topics like Adversarial ML and Secure Chatbots Evaluated HORIZON Europe proposals and served as EC expert for AI/cybersecurity projects Developed tools like HERA (network traffic analysis) and TestLab (automated software testing) Leadership Roles: Co-Investigator for EU/National Projects (CYDERCO, AIDA, PHRESH) Programme Committee member for ESORICS and EUROSIS conferences Organizing Committee member for DCAI and CANOPY workshops

Andrzej WicherView profile

Associate Professor

• Psychoacoustics
• Audiology
• Tinnitus Research
• +5 more

Andrzej Wicher serves as Associate Professor and Head of the Department of Acoustics within the Faculty of Physics and Astronomy at Adam Mickiewicz University (UAM) in Poznań, Poland. His academic leadership extends to membership on the Council of Disciplines of Physical Sciences and Astronomy since 2016, and previous service as Deputy Director of the Institute of Acoustics (2016-2019). Internationally, he holds an honorary Adjunct Professor position at La Trobe University in Melbourne, Australia. Wicher's research spans psychoacoustics, audiology, and auditory neuroscience, with particular focus on tinnitus mechanisms, otoacoustic emissions (especially DPOAE), binaural hearing phenomena, and speech intelligibility assessment. His work integrates clinical applications with fundamental auditory system modeling, recently expanding into environmental noise impacts—particularly wind turbine acoustics—on cognitive functions. This research portfolio reflects through 15+ recent publications showing consistent interdisciplinary connections between auditory diagnostics, neurological assessment, and real-world sound environments. His scientific contributions are recognized through multiple Rector's Awards for both organizational and scientific activities at UAM (2015-2024). As an active member of the Polish Academy of Sciences' Committee on Acoustics, European Acoustical Society, and Polish Acoustical Society, he contributes to the global acoustics community while co-organizing major conferences including the Open Seminar in Acoustics and Fechner Day. Wicher's teaching program includes specialized courses in psychoacoustics, hearing diagnostics, and auditory system analysis. His research infrastructure benefits from international collaborations with institutions including University of Cambridge, Tohoku University, University of Manchester, and University of Minnesota, supported through national (NCN) and international (EU FP6, NOR/POLNOR) grants.

Raif BAYIRView profile

Professor

• Robotics
• Hybrid and Electric Vehicles
• Artificial Intelligence
• +3 more

Prof. Dr. Raif Bayır is a Turkish academic at Karabük University's College of Engineering, Department of Mechatronics Engineering. With a career spanning 2000-2024, he has held continuous full-time faculty positions from Assistant Professor to Professor. His research focuses on Robotics, Hybrid/Electric Vehicles, and Artificial Intelligence. Doctorate: Gazi University (2005) - Electronics & Computer Education Postgraduate: Gazi University (1998) - Electronics & Computer Education Undergraduate: Gazi University (1995) - Electronics & Computer Education His work integrates Artificial Intelligence techniques into Electric Vehicle systems, Robotics, and Agricultural Engineering applications. Recent publications emphasize Deep Learning for Mask Detection, Real-Time Battery Monitoring, and Autonomous Navigation Systems. Scientific awards include: 2017 METU Line-Following Robot 1st Prize 2016 TÜBİTAK Domestic Product Award 2015 TÜBİTAK Electromobil Best Design Prize He has advised over 20 graduate theses on topics spanning Electric Vehicle Components, Beehive Monitoring Systems, and Intelligent Control Applications. His research teams have developed multiple TÜBİTAK-supported projects including Automotive Test Stands and Battery Management Systems.