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Syed Muhammad AnwarView profile

Associate Professor

• Multimedia Communication and Signal Processing
• Image and Video Coding
• Multimedia Content Analysis
• +16 more

Syed Muhammad Anwar serves as an Associate Professor in Software Engineering at the University of Engineering and Technology (UET) Taxila, Pakistan. He maintains a significant dual affiliation with the Sheikh Zayed Institute at Children's National Hospital in Washington, DC, USA. Additionally, he holds leadership roles as Co-founder and CTO of Sense Digital PVT. Ltd. and Director of both the Virtual Reality and Machine Learning Lab and the Signal Image Multimedia Processing and Learning (SIMPLE) Group at UET Taxila. Dr. Anwar's research spans multiple cutting-edge domains at the intersection of signal processing, machine learning, and medical applications. His primary research interests include: Multimedia Communication and Signal Processing Image and Video Coding and Quality Assessment Biomedical Signal Processing and Brain-Computer Interfaces Medical Imaging including Segmentation, Detection, and Diagnosis Deep Learning applications in healthcare diagnostics Emotion Classification and Human Behavior Modeling His recent scholarly output demonstrates a strong emphasis on applying deep learning techniques to medical image analysis challenges, particularly in brain tumor segmentation, liver tumor detection, and Alzheimer's disease classification. There's also significant work in EEG-based applications including emotion recognition, stress quantification, and game expertise classification. His research effectively bridges theoretical machine learning advances with practical healthcare applications, showing particular strength in adapting deep learning architectures to medical imaging challenges across multiple organ systems. Dr. Anwar actively mentors the next generation of researchers through his leadership of the SIMPLE research group. His current advisees include: PhD Students: Sanay Muhammad Umar Saeed (Quantification of human stress), Romana Farhan (Security in body area networks), Nosheen Sohail (Medical Image Analysis), Amin Ullah (Knowledge extraction), and Saqib Mehboob (Structural health monitoring) MS Students: Haseeb Iftikhar (Doctor recommender system), Faizah Malik (Sentiment analysis), Samreena Aslam (Fashion image retrieval), Huma Shabbir (Fashion image tagging), Khola Rafiq (Ischemic stroke detection), and Saba Naseem (Blood vessel segmentation) As Director of the Virtual Reality and Machine Learning Lab and the SIMPLE research group, Dr. Anwar oversees a dynamic research environment focused on advancing signal processing, multimedia analysis, and machine learning applications, particularly in healthcare contexts. His lab maintains strong collaborations between UET Taxila and international institutions, including Children's National Hospital in Washington DC, facilitating technology transfer between academic research and clinical practice.

• Integrated Circuits
• Quantum Computing
• mm-Wave Transmission
• +3 more

Harald Pretl is a Professor at the Department of Integrated Circuits within the Institute for Integrated Circuits and Quantum Computing at Johannes Kepler University Linz (JKU). Holding the title Univ.-Prof., he maintains active research leadership with current projects extending through 2029 and 107 documented research outputs including patents, articles, and conference proceedings. His research spans integrated circuits, quantum computing, mm-wave transmission, biomedical sensing, and open-source EDA tools. He specializes in high-frequency circuit design, ultra-low-power biomedical sensors, and educational applications of open-source design methodologies for analog/mixed-signal IC development. Recent work demonstrates cross-disciplinary innovation bridging engineering, neuroscience, and artistic expression. Recent publications reveal strong emphasis on practical implementation challenges: THz transmitter efficiency, brain-computer interface applications, ADC performance metrics, and open-source layout automation. These works collectively advance wireless communication systems, biomedical instrumentation, and accessible semiconductor design education through open-toolchain development. Prof. Pretl has supervised 3 students and actively promotes open-source EDA adoption through educational initiatives. His funded projects include Sub-blocks generators for NSSAR ADC (2025-2029), Open Parasitic Extraction for KLayout, and United Micro Technology collaborations, demonstrating sustained industry engagement. He co-leads the JKU LIT - SAL Intelligent Wireless Systems Lab (IWS Lab) and contributes to the High-Performance Integrated Quantum Computing project. Current activities include 42 documented presentations such as 'Using Open-Source EDA Tools in Hands-On IC Design Education' (2025) and 'Recent Developments in Ultra-Low-Power Biomedical Sensing' (2025), reflecting his dual focus on research innovation and pedagogical advancement.

Alan W. ChiuView profile

Professor

• Neural Engineering
• Brain-Computer Interfaces
• Neuroprosthetics
• +1 more

Dr. Alan W. Chiu serves as Professor and Samuel F. Hulbert Endowed Chair in the Department of Biology & Biomedical Engineering at Rose-Hulman Institute of Technology, where he specializes in neural engineering applications for neurological rehabilitation and assistive technologies. His academic credentials include: PhD in Electrical and Computer Engineering from University of Toronto (2006) MASc in Electrical and Computer Engineering from University of Toronto (2002) BASc in Engineering Science from University of Toronto (2000) Dr. Chiu's research centers on developing neural models and brain-computer interfaces (BCI) to restore functionality for individuals with neurological disabilities. His work integrates oscillator-based neural modeling with real-time BCI systems, focusing on neuroprosthetics and error-correction mechanisms in cognitive tasks. Current projects explore mixed reality environments for prosthetic control and strategies to mitigate sensory distractors in BCI applications. Analysis of his 2021-2024 publications reveals a dual trajectory: advancing BCI robustness through machine learning-enhanced signal processing (particularly in SSVEP and ERP paradigms), while simultaneously innovating biomedical engineering education through interdisciplinary curriculum design. His research consistently bridges theoretical neural modeling with practical assistive device implementation. His primary honor is the Samuel F. Hulbert Endowed Chair. Additional distinctions may exist but are not documented in the source material. Dr. Chiu actively mentors undergraduate researchers as supervisor of year-round and summer projects, building on his prior role as co-director of Rose-Hulman's Independent Project/Research Opportunities Program (IPROP). He contributes to accreditation standards as BMES Commissioner for ABET's Engineering Accreditation Commission, though specific grant funding details remain undisclosed. His research group focuses on translating neural engineering concepts into functional prototypes, with particular emphasis on EEG-based control systems and cognitive error detection frameworks for communication assistance.

• Neuromorphic Electronics
• Neuroscience
• Biointerfaces
• +3 more

Dr. Hongwei Tan serves as Group Leader at the Department of Molecular Electronics, Max Planck Institute for Polymer Research (MPIP) in Mainz since November 2024. Previously, he was Research Fellow (2022-2024) and Postdoctoral Fellow (2017-2022) at Aalto University's Department of Applied Physics, following postdoctoral work at the University of Massachusetts Amherst on neurointerface devices. His academic credentials include: Bachelor of Physics, Nankai University (2011) Doctorate in Materials Physics and Chemistry, University of Chinese Academy of Sciences (2016), focusing on neuromorphic photomemristors Tan's research pioneers neuromorphic biointerfaces—integrating brain-inspired electronics with biological systems to enable energy-efficient computation and seamless biointeraction. His work targets transformative applications in brain-machine interfaces, prosthetics, and diagnostics through interdisciplinary convergence of neuroscience, materials science, and engineering. His publication portfolio (2015-2024) reveals a clear evolution from foundational optoelectronic memristor research toward complex neuromorphic systems for sensory processing. Key thematic trajectories include photomemristor-based vision systems, tactile coding with spiking nerves, and nanoionic human-machine interfaces, demonstrating consistent innovation in bioinspired hardware. As MPIP Group Leader, Tan directs research strategy and team mentorship within his neuromorphic electronics program, though specific advisees and grant details remain unlisted in available materials. His laboratory focuses on developing integrated neuromorphic systems—from novel materials and devices to algorithms—that facilitate bidirectional communication between electronic and biological neural networks for next-generation neurotechnologies.

• Neuroergonomics
• Human-Machine Interaction
• Human Factors
• +3 more

Dr. Julian Elias Reiser is a Research Fellow in the Experimental Ergonomics and Stress & Work Design group at the Leibniz Research Centre for Working Environment and Human Factors (IfADo), affiliated with TU Dortmund University. His work focuses on applying mobile neuroimaging techniques, particularly EEG, to assess cognitive workload and human performance in real-world settings such as driving and walking. His educational background includes: PhD in Psychology (2020) from TU Dortmund University, with a thesis titled "Neuroergonomics in the wild - Workload assessment using mobile EEG" M.Sc. Psychology from the University of Ulm (2016) B.Sc. Business Psychology from Leuphana University Lüneburg (2014) Dr. Reiser's research interests lie at the intersection of neuroscience and human factors. He investigates how cognitive processes unfold during complex tasks in natural environments, using mobile EEG and eye-tracking to measure workload, attention, and operator state. His work aims to improve human-machine interaction and workplace design by providing objective neurophysiological measures of mental effort. His recent publications (2023-2025) highlight a strong emphasis on real-world applications of neuroergonomics. Key themes include the use of eye blink-related potentials as indicators of cognitive load, the impact of automation on driver attention, and the development of signal processing methods for decoding brain activity in dynamic settings. His research spans transportation, occupational health, and human-computer interaction. No scientific awards are listed in the available information. There is no information provided regarding students advised or research grants obtained. Dr. Reiser is part of the Experimental Ergonomics team led by Prof. Dr. Edmund Wascher within the Ergonomics department at IfADo. The team conducts cutting-edge research on human factors in various work contexts, with a focus on developing and applying neuroergonomic methods to assess and enhance human performance in real-world environments.

Monty EscabiView profile

Professor

• Auditory Neuroscience
• Computational Neuroscience
• Speech Recognition
• +3 more

Monty Escabi is a Professor in the Department of Biomedical Engineering at the University of Connecticut (Storrs), where he leads the Physiological Acoustics Laboratory. His research bridges neurophysiology and computational modeling to understand auditory processing, focusing on neural coding of complex sounds in the inferior colliculus, thalamus, and cortex. He develops biologically inspired algorithms for noise-robust speech recognition systems. Education: B.S., Electrical Engineering, Florida International University (1993) M.S., Electrical Engineering: Signal Processing and Stochastic Modeling, Columbia University (1995) Ph.D., Bioengineering, University of California at Berkeley and San Francisco (2000) Research Interests: Dr. Escabi investigates how the central nervous system processes speech and natural sounds in noisy environments. His work combines neurophysiological recordings, computational modeling, and psychoacoustics to study: (1) spectrotemporal encoding in auditory pathways, (2) neural mechanisms for sound-source segregation, and (3) biologically inspired hearing technologies. Key areas include neural ensemble coding, auditory hierarchy optimization, and translating neurobiological principles into machine learning models for speech recognition. Publication Trends: Recent articles focus on neural mechanisms of auditory perception, computational models of speech recognition, and sound-statistics-based approaches to hearing science. Predominant themes include noise-robust auditory processing, hierarchical neural coding, and applications in neuroprosthetics, reflecting interdisciplinary integration of neuroscience, engineering, and machine learning. Laboratory: The Physiological Acoustics Laboratory employs large-scale neural recordings and computational techniques to study natural hearing principles and develop advanced auditory technologies.

Pan WangView profile

Assistant Professor

• Generative AI
• Human-machine Co-creativity
• Brain Computer Interface
• +3 more

Pan Wang is an Assistant Professor at the Faculty of Industrial Design Engineering (IDE) at TU Delft. Her research focuses on AI for design, particularly Human-machine (AI) co-creativity, integrating human-computer interaction, brain-computer interfaces, and AI-driven creative processes. She developed the 'symbiotic creativity' framework, an interactive system enabling human-AI collaboration throughout creative workflows. Education: PhD from the Dyson School of Design Engineering and Data Science Institute at Imperial College London. Research emphases include generative AI, brain-computer interfaces, and AI-generated artworks. She explores innovative methods for human-AI interaction in design contexts. Publications (2024–2025) highlight advancements in AI-co-creation tools, generative models for typography, and evaluation frameworks for AI art. Current work addresses interdisciplinary applications of AI in creative industries. No secondary employment declared (2023–2026). Teaches research modules (3 & 5 ECTS) at IDE, contributing to both academic and applied design education.

Joseph MakinView profile

Assistant Professor

• Mathematical Neuroscience
• Neural Computation
• Brain-Machine Interfaces
• +7 more

Joseph Makin is an Assistant Professor of Electrical and Computer Engineering at Purdue University, affiliated with the Elmore Family School of Electrical and Computer Engineering. His research focuses on mathematical neuroscience, neural computation, and brain-machine interfaces (BMIs), with a strong emphasis on machine learning applications in neuroscience. He leads the Makin Lab, which develops algorithms for BMI/BCI systems, investigates theoretical aspects of neural computation, and explores pure machine learning principles inspired by biological systems. Education: Not explicitly listed in provided texts. His work bridges computational neuroscience and engineering, particularly in decoding speech and movement from neural activity using ECoG arrays. Notable projects include real-time speech decoding in paralyzed individuals and developing encoder-decoder frameworks for cortical activity translation. The lab also focuses on advancing recurrent neural network architectures and probabilistic graphical models, as evidenced by their textbook An Introduction to Modern Statistical Learning . Recent research trends emphasize self-supervised learning for speech decoding, hybrid system modeling of biological processes, and translational applications in neuroprosthetics. His team collaborates with neurosurgeons and biomedical engineers to translate theoretical insights into clinical tools. Scientific Awards: No explicit awards listed, though his BCI project reached finalist status in a major competition. Makin advises graduate students in machine learning and BMI projects, prioritizing strong backgrounds in neural networks and statistical methods. The Makin Lab is part of Purdue's broader efforts in computational neuroscience and biomedical engineering.

Quentin GallotView profile

Researcher

• Neuroinformatics
• Computational Neuroscience
• Neural Signal Processing
• +3 more

Quentin Gallot is affiliated with the Neuroinformatics Professorship at ETH Zürich (Eidgenössische Technische Hochschule Zürich), located at Y55 G72 Winterthurerstrasse 190 in Zurich, Switzerland. His role involves supporting research activities within the Neuroinformatics domain. While specific research interests are not explicitly detailed in the provided text, his department's focus suggests engagement with computational models of neural systems, neuroimaging analysis, and interdisciplinary approaches to understanding brain function. Professional activities likely include laboratory support, data management, and technical collaboration within the neuroscientific community. No formal academic awards or student advisement records are mentioned in the text. His institutional contact information reflects active participation in the institute's operational structure.

Shayne LoftView profile

Professor

• Cognitive Psychology
• Applied Cognitive Psychology
• Human Factors
• +4 more

Shayne Loft is a Professor at the School of Psychological Science, University of Western Australia (UWA), affiliated with UWA Data Institute and UWA Defence and Security Institute. He holds an ORCID identifier (0000-0002-5434-0348) and has extensive expertise in cognitive psychology, applied cognitive psychology, and human factors. His research focuses on automation transparency, decision-making, situation awareness, and prospective memory in safety-critical environments like aviation, maritime operations, and air traffic control. Education: PhD from The University of Queensland (funded by Australian Research Council and industry grants exceeding $6 million). Teaching includes Masters-level courses in Industrial/Organisational Psychology and Fourth-Year Statistics. Research interests emphasize cognitive processes in complex systems, particularly how humans interact with automated systems. Key themes include automation reliability perception, multitasking under pressure, and team performance in control room environments. His work addresses challenges in human-automation teaming, transparency design, and error detection in safety-critical tasks. Publications (135+ articles) explore topics like automation failure detection, decision support systems, and neurocognitive aspects of memory. Recent trends show focus on submarine control systems, air traffic management, and adaptive automation interfaces. Scientific Awards: School of Psychological Science Senior Research Award (2023) Grants: Over 37 projects including ARC grants (>$3M) and US Office of Naval Research collaborations Advising involves supervising 16 graduate students. Current lab focuses include cognitive modeling of automation interactions and developing human-centered interfaces for defense applications.

Abe KarnikView profile

Senior Lecturer

• Human-Computer Interaction (HCI)
• Display Technologies
• Prototyping
• +4 more

Dr. Abe Karnik is a Senior Lecturer in Human-Computer Interaction (HCI) at the School of Computing and Communications (SCC), Lancaster University. He has been at Lancaster since 2013 and previously worked as an Assistant Systems Engineer at Tata Consultancy Services (2004-2009). His research focuses on hardware-centric prototyping of novel display technologies, including multi-view, organic, and stereoscopic systems. He integrates HCI principles into cross-disciplinary projects to enhance scientific research through digital technology. Education : Ph.D. in Computer Science, University of Bristol, UK B.E. in Instrumentation and Control Engineering, Nirma Institute of Technology, Gujarat University, India PG Cert. in Academic Practice, Lancaster University, UK Research Interests : Deformable displays, tangible interactibles, haptics, brain-computer interfaces, and volumetric displays. Current projects include 3D volumetric displays (EPSRC-IAA) and Sci-Seedlets (plant science education). Teaching : Module convener for undergraduate and postgraduate courses, co-teaching roles, and supervision of undergraduate and MSci dissertations. Active in international partnerships at Lancaster University Ghana, Sunway Malaysia, and BJTU China. Grants & Projects : MRC-P2D (MobiESense) and EPSRC-IAA (Volumetrics) AI:Lab (AI in Low Carbon Building, 2024-2025) Sci-Seedlets (plant science education initiatives, 2021-2023) Labs/Teams : Based at InfoLab21, involved in prototyping labs for interactive systems and display technologies.

Julia M. StephenView profile

Adjunct Professor

• Neuroscience
• Developmental Neuroscience
• Cognitive Neuroscience
• +4 more

Julia M. Stephen serves as an Adjunct Professor in the Department of Physics and Astronomy at the University of New Mexico and holds dual appointments as Director of the MEG/EEG Core and Professor of Translational Neuroscience at the Mind Research Network (MRN). Her primary research基地 focuses on advanced neuroimaging techniques to investigate brain development across the human lifespan, with particular emphasis on translational applications for neurological and psychiatric disorders. Dr. Stephen earned her PhD from the University of Minnesota, establishing the foundation for her specialized work in biophysics and neuroimaging. Her educational background directly supports her current methodological expertise in magnetoencephalography and multimodal brain imaging approaches. Her research program centers on developmental neuroscience, specifically examining typical and atypical brain maturation from infancy through aging. Key investigation areas include fetal alcohol spectrum disorders (FASD), autism spectrum disorders, schizophrenia, and Alzheimer's disease, with a focus on identifying neural biomarkers through MEG, EEG, and fMRI. She employs rigorous multimodal approaches to study sensory processing, cognitive development, and neural network dynamics, particularly investigating how prenatal exposures and aging processes alter brain function. Analysis of Dr. Stephen's publication record reveals consistent focus on developmental trajectories of brain function, with strong emphasis on FASD (7/15 articles), schizophrenia (4/15), and neuroimaging methodology development (3/15). Her work demonstrates increasing specialization in infant and child neurodevelopment since 2018, with recent publications highlighting MEG applications for early biomarker identification in prenatal exposure conditions and multisensory integration deficits. Dr. Stephen leads multiple NIH-funded research initiatives including the DevMind Study (tracking childhood brain development), investigations into prenatal alcohol exposure markers, and schizophrenia-related multisensory processing projects. Her laboratory infrastructure includes the MEG/EEG Core facility at MRN, which provides advanced neuroimaging capabilities for developmental and clinical neuroscience research. As a Principal Investigator, she directs interdisciplinary teams comprising neuroscientists, physicists, and clinicians to advance understanding of brain-behavior relationships across the lifespan.

• Materials Science
• Photovoltaics
• Semiconductor Physics
• +3 more

Bruno Ehrler is a Professor at the University of Groningen (honorary) and Group Leader of the Hybrid Solar Cells Group at AMOLF in Amsterdam since 2014. His research focuses on perovskite materials science, including fundamental studies and device applications like solar cells. He holds significant grants (ERC Starting, NWO Vidi) and is a WIN Rising Star award recipient. He previously worked at the University of Cambridge, where he earned his PhD in Physics under Prof. Neil Greenham. His expertise spans optoelectronics, quantum dots, and singlet fission photovoltaics. Ehrler contributes to national energy initiatives and serves on advisory boards for the Dutch Chemistry Council and nanoGe conferences. Education: PhD in Physics, University of Cambridge (2009–2012) MSci in Physics, University of London (Queen Mary College, 2005–2009) Studies at RWTH Aachen and University of London Research Interests: Perovskite materials, solar cell efficiency, ion migration dynamics, photonic materials, and sustainable energy technologies. His work bridges fundamental material science with applied device engineering, addressing challenges in scalability, stability, and energy conversion efficiency. Grants & Awards: ERC Starting Grant (2020): Artificial synapses from halide perovskites NWO Vidi Grant (2017): Metal halide perovskites WIN Rising Star Award (2018) Advising & Labs: Leads the Hybrid Solar Cells Group at AMOLF, focusing on perovskite innovation. Collaborates on national energy projects like the Netherlands Energy Research Alliance (NERA). Active in mentoring early-career researchers through advisory roles and grant programs.

Tim GerritsView profile

Researcher

• Scientific Visualization
• High-Performance Computing
• In-Situ and In-Transit Visualization
• +7 more

Dr. Tim Gerrits is a researcher at the Institute for Visualization (VIS) at RWTH Aachen University, where he leads the Visualization Team. His work bridges scientific visualization, high-performance computing, and immersive technologies, with a strong focus on in-situ and in-transit analysis for large-scale simulations. University: RWTH Aachen University Institute: Institute for Visualization (VIS) Role: Lead of the Visualization Team Tim Gerrits' research centers on developing tools and frameworks for efficient and interactive visualization of complex scientific data. His interests include ensemble data analysis, uncertainty visualization, virtual reality interaction techniques, and leveraging game engines like Unreal Engine for scientific applications. He is particularly active in the domain of neuronal network simulations and oceanographic modeling. His recent publications highlight a strong trend toward accessible, real-time, and hybrid visualization workflows. He has contributed to the development of DaVE, a curated database of visualization examples to support HPC users, and Insite, a lightweight pipeline for in-transit processing in neuroscience simulations. His work emphasizes usability, performance, and integration with existing scientific workflows. Scientific Awards: Best Paper Award at IEEE Uncertainty Visualization Workshop, 2024 Honorable Mention Award at Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM), 2022 Dr. Gerrits actively mentors and collaborates on interdisciplinary projects involving computational neuroscience and climate modeling. He has led the curation of datasets for the IEEE SciVis Contest and promotes open science through Zenodo-hosted resources. His lab focuses on building scalable, user-centered visualization systems that empower domain scientists to gain early insights from massive simulations.

• Rehabilitation Robotics
• Neural Technologies
• Brain-Computer Interfaces
• +10 more

Dingguo Zhang is a Reader in Robotics Engineering at the Department of Electronic & Electrical Engineering, Faculty of Engineering & Design, University of Bath. He is affiliated with several interdisciplinary research centers including the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI, the Centre for Bioengineering & Biomedical Technologies (CBio), the Bath Institute for the Augmented Human, and IAAPS. His work bridges robotics, neuroscience, and biomedical engineering to develop advanced assistive and rehabilitative technologies. His research focuses on Rehabilitation Robotics, Neural Technologies, Brain-Computer Interfaces (BCIs), and Robotic Exoskeletons . He investigates how neural signals such as EEG and EMG can be used to control prosthetic devices, exoskeletons, and communication systems, particularly for individuals with neurological impairments. His work also involves signal processing, machine learning, and adaptive control strategies to improve human-machine interaction. Key themes include decoding motor and speech intentions, fatigue assessment, and closed-loop neuromodulation. The recent publications reflect a strong trend toward invasive and non-invasive neural interface development , especially in decoding speech from intracranial EEG and enhancing BCI performance through advanced preprocessing and deep learning. There is also significant focus on wearable robotics for stroke and spinal cord injury rehabilitation , with integration of soft actuators, hybrid control, and real-time adaptation. Multimodal sensing (e.g., EEG-fNIRS, EMG-force) and patient-centered design are recurring elements across his work. Dr. Zhang has received recognition through his status as an IEEE Senior Member (EMBS, RAS, SMC) and IFESS Lifetime Member . He serves on the editorial boards of five journals including IEEE Transactions on Neural Systems & Rehabilitation Engineering and Frontiers in Neurorobotics , and has held leadership roles such as co-chair of IFESS 2024. He is actively involved in research funding and supervision. He is Principal Investigator (PI) on the EPSRC-funded dSPEECH project, which aims to decode speech using invasive BCIs, and Co-Investigator on an NIHR-funded project developing an ear-switch-controlled exoskeleton for stroke rehabilitation . He welcomes PhD students and is committed to training the next generation of researchers in neurotechnology and robotics. Dr. Zhang leads or contributes to research within collaborative labs and teams focused on bioengineering, augmented human systems, and accountable AI . These include the Centre for Bioengineering & Biomedical Technologies and the Bath Institute for the Augmented Human, where interdisciplinary teams develop next-generation assistive technologies grounded in ethical and transparent AI principles.