Find Academic Supervisors

Mahmoud KeshavarziView profile

Researcher

• Developmental Psychology
• Cognitive Neuroscience
• Speech and Language Disorders
• +3 more

Mahmoud Keshavarzi is a Researcher in the Department of Psychology at the University of Cambridge. His work focuses on neurodevelopmental disorders, particularly developmental language disorder (DLD) and dyslexia, combining EEG analysis with machine learning techniques. His research examines speech processing deficits in children, neural entrainment mechanisms, and the impact of environmental factors like screen time on auditory responsiveness. He develops innovative methods for speech signal analysis and noise reduction using deep learning approaches. Key areas of investigation include: EEG-based neural markers for DLD and dyslexia Rhythmic speech processing in neurodevelopmental disorders Machine learning applications in clinical audiology Speech production and perception deficits in children Notable contributions include EEG classification frameworks for infant speech processing and noise reduction algorithms improving speech intelligibility for cochlear implant users. His interdisciplinary work bridges cognitive neuroscience, signal processing, and developmental psychology.

• Composition
• New Media Art
• Interactive Music Technology
• +4 more

David Kim-Boyle is a Casual Lecturer in Composition at the Sydney Conservatorium of Music, University of Sydney. He holds a BMus from Australian National University, MA (Hon) from University of Wollongong, and PhD from SUNY Buffalo. Specializes in real-time graphic scores and interactive music technology Active in VR/AR-based composition and live coding performance Collaborates with institutions like STEIM (Amsterdam) and Zentrum für Kunst und Medientechnologie (Karlsruhe) His research explores interactive systems that challenge traditional notation through extended open-forms, including work with immersive audiovisual environments and machine learning interfaces . Current projects involve reactive scores for ELISION ensemble and parametric piano etudes using k-d tree algorithms. Recent publications focus on VR collaboration , immersive notations , and 3D score design , presented at conferences like ICMC and NIME. He has received the Australian Arts Council Skills Development Grant (2010) and advises PhD candidate Deborah KIM. Selected Scientific Awards: Australian Arts Council Skills Development Grant (2010)

Angelo CangelosiView profile

Professor

• Cognitive Robotics
• Neural Networks
• Human-Robot Interaction
• +2 more

Angelo Cangelosi is Professor of Machine Learning and Robotics at the University of Manchester's School of Computer Science, where he co-directs the Manchester Centre for Robotics and AI. He holds affiliations as a Turing Fellow at the Alan Turing Institute and visiting positions at Università Cattolica Milano and Hohai University. Dr. Cangelosi leads the Cognitive Robotics Lab, focusing on cognitive and developmental robotics, neural networks, language grounding, human-robot trust, and healthcare robotics. His interdisciplinary work integrates computational modeling with psychological principles to advance socially intelligent systems. His research spans cognitive architectures for social perception, embodied language learning, and transparent human-robot interaction. Current projects explore theory of mind modeling, multimodal emotion recognition, and trustworthy AI systems for safety-critical applications. Cangelosi's publications demonstrate consistent innovation in human-robot collaboration frameworks, with recent work examining mental state modeling for trust, interpretable reinforcement learning, and ethical dimensions of creative AI systems. His scholarship bridges robotics, cognitive science, and AI ethics. Awards: IEEE ICDL-EPIROB Modelbot Award (2019) European Research Council Advanced Grant (2023) He directs multiple EU and UKRI grants including ERC Advanced project, TRAINCREASE, and PRIMI. Collaborations include Honda, BAE Systems, and Jacobs. As Editor-in-Chief of Interaction Studies and IET Cognitive Computation and Systems , he shapes discourse in cognitive robotics.

• Women in Academia
• Remote Work
• Research Methodologies
• +3 more

Eleni Tzouramani is a Senior Lecturer in the School of Business and Creative Industries, focusing on transformative educational practices and organizational dynamics. Her work bridges academic research with practical policy implications, particularly in crisis management among women academics and remote work frameworks. She co-led the Digital Maieutic Project, integrating AI and Universal Design for Learning principles, for which her team received the UKAIS Teaching Innovation Grant (2024). Her research interests span spirituality's role in organizational cultures, leadership development, and multimodal learning strategies. She actively contributes to parliamentary policy discussions, submitting evidence on home-based working for the UK House of Lords. Tzouramani has presented at international forums on topics like AI ethics in education and leadership navigation during transitional phases. She currently oversees a Knowledge Transfer Partnership in leadership development and collaborates with institutions to design inclusive educational frameworks.

• Spiking Neural Networks
• Brain-Computer Interfaces
• EEG Analysis
• +4 more

Gang Pan is a Professor at Zhejiang University's College of Computer Science and Technology, where he leads research in neural networks, brain-computer interfaces, and neuromorphic computing. His work bridges computer science, neuroscience, and biomedical engineering, focusing on developing novel AI approaches inspired by biological neural systems. He maintains extensive collaborations with researchers including Shijian Li, Qian Zheng, and Huajin Tang. Professor Pan's research centers on spiking neural networks (SNNs) and their applications in brain-computer interfaces, medical diagnostics, and efficient neuromorphic computing. His work explores how SNNs can model biological neural processes while offering energy-efficient alternatives to traditional deep learning. Recent projects include EEG-based mental health diagnostics, neural decoding of visual perception, and battery-free neural recording systems. His approach integrates computational neuroscience with practical AI applications, particularly in healthcare contexts. Analysis of his 15 most recent publications reveals strong trends in neuromorphic computing, with particular emphasis on spiking neural networks for medical applications. His work spans from theoretical advances in SNN architectures to practical implementations in EEG analysis, mental health diagnostics, and neural interface hardware. The interdisciplinary nature of his research connects computer science, neuroscience, and biomedical engineering, with increasing focus on clinical applications of neural decoding technologies. Professor Pan actively mentors students and researchers, as evidenced by his numerous collaborative publications across multiple labs. His research is supported by significant grants enabling work on neuromorphic hardware, brain-computer interfaces, and medical AI applications. The consistent high-impact output demonstrates sustained funding support for his innovative research directions. His laboratory focuses on neuromorphic computing systems, brain-computer interface development, and neural signal processing. The research environment integrates theoretical AI development with practical hardware implementation, creating a pipeline from algorithm design to clinical application. The lab maintains strong connections with neuroscience researchers and medical professionals to ensure clinical relevance of their technological innovations.

• Aerothermodynamics
• Mechanical Engineering
• Process Engineering
• +3 more

Dr. Ndaona Chokani is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich. Their research focuses on aerothermodynamics, mechanical engineering, and process engineering, with a recent emphasis on interdisciplinary applications in computer vision and image processing. Affiliated with the Professur f. Aerothermodynamik, their work spans theoretical and applied domains. While explicit educational background details are not provided, their academic career includes contributions to fields such as fluid dynamics and process systems engineering. Research interests are inferred from departmental affiliation and recent publications, emphasizing computational methods in mechanical systems and image quality assessment. Recent publications highlight work in perceptual quality metrics for images and videos, immersive systems (VR/360 content), and machine learning applications in 3D pose estimation and generative models. Key areas include diffusion models for low-light imaging, spatial-temporal geometric networks, and benchmarking frameworks for image harmonization. No awards or grants are explicitly listed in the provided text. Their research often involves collaborations on datasets like Salient360! and tools for gaze data analysis in 3D environments. Future work appears to focus on improving perceptual metrics and expanding applications in healthcare and immersive technologies.

• Deep Learning
• Machine Learning for Biological and Medical Data
• Artificial Intelligence
• +4 more

Dr Benjamin Mashford is a Research Fellow at the John Curtin School of Medical Research, Australian National University (ANU), specializing in interdisciplinary applications of deep learning, machine learning, and nanoelectronics to biomedical challenges. His work bridges computational methods with immunology, infectious diseases, and advanced diagnostics. Affiliation: Division of Immunology and Infectious Diseases, ANU Research Group: The Enders Group - Models of Human Primary Immunodeficiencies Research Focus: Mashford's work spans: Deep learning for biological data analysis (flow cytometry, EEG, genomic sequences) Neuromorphic and low-power computing systems Quantum dot technology for optoelectronics Energy-efficient health-monitoring devices Medical signal processing and predictive analytics Publication Trends: Recent work emphasizes hyper-dimensional cytometry representations, automated phenotyping systems, and cross-domain applications of machine learning from immunology to mineral processing. He also explores hardware implementations for biomedical diagnostics, including neurostimulation and fall detection systems. Supervision: Registered as a supervisor for graduate research students.

• Perception-Action Systems
• Robotics
• Sports Science
• +4 more

Michael McBeath is a Professor in the Department of Psychology at Arizona State University (ASU), with additional roles as adjunct faculty in Kinesiology, Neuroscience, Life Sciences, Animal Behavior, Electrical Engineering, the School of Arts, Media, and Engineering, and ASU’s Global Sport Institute. His research focuses on computational modeling of perception-action systems in dynamic environments, blending psychology, engineering, and robotics. He holds a B.A. from Brown University (Psychology & Electrical Engineering), an M.S. from UC Santa Barbara (Electronic Instrumentation), and a Ph.D. from Stanford University (Psychology with Electrical Engineering minor). His work explores interception strategies in sports, robotics, and animal behavior, alongside auditory perception and navigation. Notable projects include the linear optical trajectory theory for baseball catching, robotic ball-interception algorithms, and studies on human perception biases. McBeath has secured grants from NSF, ONR, and Interval Corporation. His research has been published widely, including in Science , and popularized through media like Saturday Night Live. Beyond academia, he plays harmonica and contributes to outreach initiatives like the ASU Brain Fair. His teaching spans perception, human factors, and research methods. McBeath’s interdisciplinary collaborations span robotics, music, and neuroscience, reflecting his commitment to bridging theoretical and applied research.

• Human-Robot Interaction
• Artificial Intelligence
• Augmented Reality
• +4 more

John McCormick is a Professor and Director of the Centre for Transformative Media Technologies at Swinburne University of Technology. He holds a Lecturer position in Interactive Media within the School of Social Sciences, Media, Film and Education. His research focuses on human-robot interaction, artificial intelligence, and extended realities (AR/VR/MR), with a particular emphasis on collaborative artistic environments and intelligent systems. McCormick has led over 28 grants, including ARC-funded projects exploring cobot learning frameworks and human-robot collaboration. He has collaborated globally with institutions like Ars Electronica and the Venice Biennale, producing installations such as Real Robot Dance Challenge and Aurora Australis Ultimo Choro VR . His teaching spans previsualization and mixed reality, with HDR supervision in topics like AI-generated content and ergonomics of surgical robotics. McCormick’s work bridges art and technology, emphasizing accessibility through projects like haptic interfaces for dance audiences and volumetric interaction systems. Key grants include the ARC Training Centre for Collaborative Robotics and the Holobody project advancing mixed reality technologies. McCormick’s interdisciplinary approach is evident in projects like Hyperrelia (State Library of Victoria) and Antarctic Dataspheres , integrating movement data with environmental storytelling. His research also addresses healthcare ergonomics through studies on surgeon discomfort in laparoscopic procedures, advocating for inclusive tool design.

Nadja AlthausView profile

Researcher

• Object Categorization in Infants
• Language Acquisition
• Visual Processing
• +3 more

Nadja Althaus is a Postdoctoral Researcher at the University of Oxford's Department of Experimental Psychology, affiliated with the Oxford Babylab and the Oxford Centre for Developmental Science. Previously, she held postdoctoral positions at Oxford Brookes University Babylab and the Centre for Brain and Cognitive Development, Birkbeck College, London. She completed her PhD at Birkbeck College and holds an MA in Linguistics, Psychology, and Computer Science from the University of Tübingen, with a visiting period at the University of Toronto. Education includes a PhD from the Centre for Brain and Cognitive Development, Birkbeck College, University of London (2006–2010), and an MA in Linguistics, Psychology, and Computer Science from the University of Tübingen (2000–2006). She also spent time as a visiting student at the University of Toronto's Department of Computer Science (2003–2004). Her research interests focus on infant object categorization, exploring how language and perceptual cues influence cognitive development. She employs eye-tracking and computational modeling to study visual and cross-modal processing, language acquisition, and machine learning applications in cognitive science. Recent publications highlight her focus on multi-sensory integration in development, the application of saliency maps in visual cognition studies, and foundational work on connectionism in neuroscience. These contributions underscore her interdisciplinary approach, merging empirical research with theoretical modeling. Nadja has advised no listed students but has contributed to grants through her research roles. She is a key member of the Oxford Babylab and collaborates within the broader developmental science community. Her work is centered at the Oxford Babylab, where she investigates infant cognition through innovative methodologies.

• Virtual Reality
• Computer Graphics
• Human-Computer Interaction
• +3 more

Michitaka Hirose is a prominent researcher in Virtual Reality (VR) and Human-Computer Interaction (HCI), with a focus on immersive technologies, haptics, and perceptual systems. His work spans over three decades, contributing to advancements in VR navigation, multisensory feedback, and applications in education, healthcare, and entertainment. Collaborations with institutions like the University of Tokyo (inferred via publication patterns) are evident through frequent co-authorships with researchers such as Tomohiro Tanikawa and Takuji Narumi. His research emphasizes practical implementations of VR, including redirected walking systems, shape illusion controllers, and systems for motor skill learning through virtual co-embodiment. Key Areas: Virtual Reality, Haptics, Perception Models, Collaborative Training, and Immersive Displays Notable Contributions: Development of VR controllers for shape rendering (e.g., Transcalibur), studies on avatar appearance effects, and systems for remote spatial understanding Publications highlight a blend of technical innovation and user-centric design, addressing challenges in VR navigation, sensory feedback, and social interaction within virtual environments. His work often bridges theoretical research with real-world applications, such as training systems and healthcare tools.

Neil DundonView profile

Researcher

• Neuroscience
• Cognitive Neuroscience
• Decision-Making
• +6 more

Neil Dundon is a Project Scientist and postdoctoral researcher at the University of California, Santa Barbara (UCSB), within the College of Letters & Science. His research focuses on the neural foundations of expertise, decision-making under stress, and the interplay between behavioral and neural variability in psychiatric conditions. He holds a BA in Psychology from University College Dublin (2007), an MSc in Applied Psychology from Trinity College Dublin (2009), and a PhD in Cognitive Neuroscience from a joint program between the University of Bologna and Bangor University (2015). His doctoral thesis explored treatment plasticity in homonymous visual field defects. Prior to his current role, he conducted postdoctoral research at the University of Freiburg (until 2017), investigating neural mechanisms in child and adolescent psychiatric conditions. Neil's research interests span cognitive neuroscience, neuroplasticity, and multisensory integration, with a focus on stress physiology, decision-making frameworks, and autonomic nervous system dynamics. His work bridges clinical and experimental approaches, addressing topics such as visual rehabilitation, ADHD-Autism comorbidity, and emotional processing deficits in neurodevelopmental disorders. His recent articles highlight advancements in understanding neural encoding during motor tasks, cross-modal interactions between cardiac physiology and neural activity, and the role of hormones in brain structure-function relationships. Neil collaborates with Prof. Scott Grafton and contributes to studies on action selection, stress multiplexes, and non-invasive cardiac monitoring technologies. Though no scientific awards are explicitly cited, his contributions to visual field rehabilitation and multisensory integration research are notable. Neil's academic trajectory reflects a strong emphasis on translational neuroscience, integrating computational models, neuroimaging techniques, and clinical insights to advance understanding of human cognition and behavior.

• Neural Engineering
• Control Theory
• Dynamical Systems
• +3 more

Jeff Moehlis is a Professor in the Department of Mechanical Engineering at the University of California, Santa Barbara (UCSB), affiliated with the College of Engineering. His research focuses on control theory, dynamical systems, and their applications to neuroscience and biomedical engineering. He explores topics such as neural oscillator synchronization, optimal control strategies for biological systems, and nonlinear dynamics in energy harvesting systems. Moehlis's work integrates mathematical modeling with computational methods, addressing challenges in phase reduction techniques, stochastic systems, and collective behavior in populations. His contributions span theoretical advancements (e.g., Hamilton-Jacobi equation solutions) and practical applications (e.g., deep brain stimulation optimization and robotic control systems). He maintains an active research group and collaborates across disciplines to advance understanding of complex systems. His publications emphasize interdisciplinary approaches, leveraging machine learning, symbolic regression, and data-driven methods to analyze and control systems ranging from neural networks to energy harvesting devices. Moehlis's office is located in ENGR II RM. 2350, and he can be reached via moehlis@engineering.ucsb.edu.

Heath DemareeView profile

Professor

• Emotional Processing
• Cognitive Processing
• Decision-Making
• +3 more

Heath Demaree is a Professor of Psychology at Case Western Reserve University, specializing in the intersection of emotional and cognitive processing. His research focuses on decision-making mechanisms, particularly exploration-exploitation dynamics, and how personality traits and emotions influence these processes. He leads the Demaree Lab, exploring topics like risk-taking behavior, affect regulation, and cognitive neuroscience methods. Education details are not explicitly provided, but his academic career spans over two decades with a strong focus on empirical research. He advises graduate students Amanda Merner, Rock Lim, and Kyle LaFollette. Research interests include: Emotion-cognition interactions Risk learning and decision-making Neurophysiological correlates of affect Computational modeling (e.g., diffusion decision models) Individual differences in behavioral outcomes His work bridges psychology and neuroscience, employing methods like EEG, psychophysiological measurements, and behavioral experiments. Over 50 publications demonstrate contributions to understanding emotional processing in decision contexts. He collaborates on tools like the FlexDDM Python package for diffusion decision modeling. Notable projects include: Impact of incidental emotions on reinforcement learning Physiological correlates of emotion regulation Cognitive load in medical decision-making

Elisabetta VersaceView profile

Senior Lecturer

• Animal Cognition
• Experimental Evolution
• Early Cognition
• +3 more

Dr. Elisabetta Versace is a Senior Lecturer (Associate Professor) at Queen Mary University of London, affiliated with the School of Biological and Behavioural Sciences. She is part of the Centre for Brain and Behaviour and the Centre for Biodiversity and Sustainability. Her research focuses on animal cognition, predispositions, experimental evolution, and early development. Key interests include crossmodality, dance science, and positive psychology. Her work examines innate and learned behaviors in animals, particularly in chicks, tortoises, and insects, exploring topics like social preferences, nociception, and tactile perception. Dr. Versace has secured significant funding from institutions such as the Leverhulme Trust, Royal Society, and BBSRC. Notable grants include a £269,334 Leverhulme Trust grant (2021–2026) on generalization using sparse data and a Royal Society fellowship (2021–2024) for studying induction problems in animals and artificial systems. Her research outputs span robotics applications in agriculture, crossmodal perception in tortoises, and foundational questions in cognitive development. She leads the Prepared Minds Lab (preparedmindslab.org), advancing interdisciplinary studies in animal behavior and artificial intelligence.