Julian Fierrez is a Full Professor at the School of Engineering, Universidad Autonoma de Madrid. With an h-index of 74 and over 20,000 citations, his work spans biometrics, signal/image processing, artificial intelligence, and human-computer interaction. Key research areas include: Biometric anti-spoofing and DeepFakes detection Mobile and behavioral biometrics Bias/fairness in AI systems Biometric applications in e-health and education Security in multimodal biometric systems His recent publications show strong focus on deep learning applications for biometric security, with specific subfields including fake detection, keystroke authentication, facial analysis for Parkinson detection, and privacy-preserving AI. He serves as Associate Editor for multiple IEEE and Elsevier journals. Scientific distinctions include: IAPR Young Biometrics Investigator Award (2017) Miguel Catalan Award to Best Researcher under 40 (2017) EURASIP Best PhD Award (2012) EBF European Biometric Industry Award (2006) Prof. Fierrez leads the BiDA Lab and supervises students like Ruben Tolosana and Aythami Morales. Current projects include BBforTAI (Biometrics and Behavior for Unbiased & Trustworthy AI) and PRIMA (Privacy Matters). He also contributes to standardization efforts in biometric evaluation.
Sergiu Nisioi is an Associate Professor at the Faculty of Mathematics and Computer Science, University of Bucharest, with expertise in computational linguistics, machine translation, and text simplification. He bridges cognitive science with NLP through eye-tracking and EEG research, while also exploring sound art and digital autonomy via initiatives like HYPHA.ro. Current projects include PN-IV-P2-2.1-TE-2023-2007 (text complexity/readability), Legal Document Processing , and Europarl Dialectal Corpora Research spans computational psycholinguistics , LSTM-based translation models , and algorithmic composition for sound art His work integrates interdisciplinary methodologies, combining EEG signal processing for architecture data with the University of Architecture, and DSP for ecological projects at chlorophylla.live.
Stephen E. Ralph is Professor and Glen Robinson Chair in Electro-Optics within Georgia Tech's School of Electrical and Computer Engineering, serving as Director of the Georgia Electronic Design Center (GEDC) and founder of the Terabit Optical Networking Consortium. His leadership spans cross-disciplinary research in electronics, photonics, and signal processing for revolutionary system performance. Educational background includes a BEE with highest honors from Georgia Tech (1980) and PhD in Electrical Engineering from Cornell University (1988), followed by postdoctoral work at AT&T Bell Laboratories and IBM Watson Research Center. His research integrates integrated photonics , machine learning , and aerospace applications to develop ultra-high-capacity optical communication systems. Current focus areas include photonic topology optimization, radiation-hardened space systems, and converged optical/mm-wave technologies, emphasizing the synergistic development of electronic-photonic components for next-generation interconnects. Analysis of 2024-2025 publications reveals dominant themes in foundry-compatible photonic design (topology optimization, inverse design), aerospace photonics (radiation testing, analog/digital signal transport), and machine learning applications for nonlinear equalization. The work bridges fundamental device engineering (grating couplers, waveguide bends) with system-level implementations for 5G/6G networks and space communications. Key recognition includes: Fellow of the Optical Society (OSA) Professor Ralph has mentored over 20 PhD students and secured significant research funding including the IUCRC Phase I EPICA project (2021) for aerospace photonic integration. His industry partnerships through the Terabit Optical Networking Consortium drive translational research in high-speed communications. He leads the Georgia Electronic Design Center's multidisciplinary team developing photonic-electronic co-design methodologies, with recent emphasis on topology-optimized devices for commercial foundries and radiation-tolerant systems for space applications.
Dr. Jun Hu is an Associate Professor in Design Research on Social Computing at the Department of Industrial Design, Eindhoven University of Technology (TU/e). He serves as the Scientific Director for the Engineering Doctorate program in Designing Human-System Interaction and is the chair of the working group "Aesthetics and empowerment" of IFIP TC14. Additionally, he holds positions as a Distinguished Adjunct Professor at Jiangnan University and a Guest Professor at Zhejiang University. Dr. Hu earned his Ph.D. degree in Interaction Design and an Engineering Doctorate degree in User-system Interaction, both from TU/e. He also holds a B.Sc degree in Mathematics and an M.Eng degree in Computer Science. He is a System Analyst and a Senior Programmer with qualifications from the Ministry of Human Resources and Social Security, and the Ministry of Industry and Information Technology of China. His research focuses on the intersection of Human-Computer Interaction, Social Computing, and Design Research, with particular interests in data physicalization, empowering systems, and health informatics. Dr. Hu's work explores how technology can be designed to support human needs in social contexts, with applications in health, stress management, and physical activity motivation. His approach often combines aesthetic considerations with functional design to create systems that empower users. Analysis of Dr. Hu's recent publications reveals a strong focus on data physicalization, social aspects of personal informatics, and health applications of interactive systems. His work spans from theoretical frameworks for understanding user interaction with physicalized data to practical applications in stress management for children and motivation for physical activity. There's a clear trend toward integrating AI capabilities into human-centered design approaches while maintaining a focus on user empowerment. Senior Member of ACM Distinguished Adjunct Professor at Jiangnan University Guest Professor at Zhejiang University Editor-in-chief for EAI Endorsed Transactions on Pervasive Health and Technology Associate editor for Behaviour & Information Technology and Entertainment Computing Editor for the International Journal of Arts and Technology Dr. Hu has supervised numerous students through the Engineering Doctorate program and has been involved in various research grants, particularly in the areas of health technology and human-system interaction. He has served in leadership roles including head of the Designed Intelligence group at ID TU/e from 2015-2017 and currently chairs the working group "Aesthetics and empowerment" of IFIP TC14. He coordinates the TU/e DESIS Lab in the DESIS Network and serves on multiple editorial boards. Dr. Hu is actively involved with the Design Of Empowering Systems research group and the EAISI Health initiative at TU/e. His work often involves interdisciplinary collaboration across computer science, design, and healthcare domains, focusing on creating systems that empower users through thoughtful integration of technology into everyday contexts. He also serves as Chairman of the Foundation for Design Promotion in Europe and China and is a board member of the International Chinese Association of Computer Human Interaction (ICACHI).
Georgios Arvanitidis is an Associate Professor at the Technical University of Denmark (DTU) in the Department of Applied Mathematics and Computer Science, specifically within the Section for Cognitive Systems (CogSys). He has established himself as a leading researcher in geometric machine learning, focusing on the application of differential geometry principles to enhance machine learning models. His work bridges theoretical mathematics with practical applications in artificial intelligence, with particular emphasis on understanding the geometric structure of data manifolds and latent spaces. Dr. Arvanitidis completed his educational journey with a Bachelor's degree from the Department of Informatics at the Aristotle University of Thessaloniki, followed by a Master's degree in Computer Science from Saarland University supported by the Max Planck Institute for Informatics. He earned his PhD at DTU's Cognitive Systems section under the supervision of Søren Hauberg, with additional research experience at Philipp Hennig's Probabilistic Numerics group. Prior to his current position as associate professor, he was a PostDoc at the Max Planck Institute for Intelligent Systems working with Bernhard Schölkopf. Dr. Arvanitidis's research primarily focuses on differential geometry in machine learning , where he explores how geometric structures can enhance representation learning and statistical modeling. His work in generative models investigates how learning the geometry of data manifolds can improve deep learning architectures. In the domain of deep learning theory , he examines why deep learning models generalize effectively on unseen data, with particular attention to the curvature properties of loss landscapes. His research in approximate Bayesian inference applies geometric principles to improve uncertainty quantification in neural networks. Through his innovative approaches, Dr. Arvanitidis has established himself as a leading researcher in geometric machine learning, contributing to both theoretical foundations and practical applications across various domains including robotics and life sciences. The publication trends of Dr. Arvanitidis reveal a consistent and evolving focus on geometric approaches to machine learning problems. His recent work (2023-2025) demonstrates increasing sophistication in applying Riemannian geometry to deep learning architectures, with particular emphasis on latent space geometry, optimization on manifolds, and geometric interpretations of neural network behavior. A notable pattern is the progression from foundational work on geometric representations to more applied research in areas like robotics and causal inference. His publications span top-tier conferences including NeurIPS, ICML, ICLR, and AISTATS, reflecting the high impact of his research. The interdisciplinary nature of his work is evident in collaborations across mathematics, computer science, and robotics domains, with recent papers addressing challenges in multimodal sampling, safety guarantees for dynamical systems, and counterfactual explanations. Dr. Arvanitidis has received several notable scientific awards and recognitions: Sapere Aude starting grant from the Independent Research Fund Denmark (DFF) GADL funding i-Rase, Pathfinder, and EIC (European Innovation Council) funding Best reviewer award for NeurIPS 2019 Best reviewer award for NeurIPS 2018 Best student paper award at Robotics: Science and Systems (R:SS) 2021 Dr. Arvanitidis actively mentors PhD students and researchers, currently supervising Alejandro Valverde, Johanna Gegenfurtner, and Albert Kjøller Jacobsen. He has previously co-supervised Alison Pouplin's PhD and worked with research assistant Georgios Pantis. His group receives substantial funding through multiple prestigious grants including the Sapere Aude starting grant from the Independent Research Fund Denmark, as well as European Innovation Council funding. He has been instrumental in creating opportunities for students interested in geometric machine learning, offering BSc and MSc thesis projects focused on generative models, deep learning theory, and optimization techniques. Dr. Arvanitidis also contributes significantly to the academic community as a reviewer for top conferences including ICLR and TMLR, and as an area chair for NeurIPS, ICML, AISTATS, and UAI. He co-organized the Machine Learning Summer School 2020 in Tübingen, further demonstrating his commitment to education and community building. Dr. Arvanitidis leads a vibrant research group focused on geometric machine learning within the Cognitive Systems section at DTU. His team includes multiple PhD students working on cutting-edge research at the intersection of differential geometry and artificial intelligence. The group has developed notable software tools, including the "geometric_ml" GitHub repository with over 70 stars, which contains implementations for applying Riemannian geometry in machine learning. His research has practical applications in robotics, where geometric approaches enable more robust motion planning, as evidenced by his work on "Reactive Motion Generation on Learned Riemannian Manifolds" which received a best student paper award. Additionally, his methodologies have found applications in life sciences, as mentioned in his 2022 AISTATS paper. The collaborative nature of his work is evident through extensive partnerships with researchers at institutions including the Max Planck Institute for Intelligent Systems, University of Cambridge, and various European universities. His recent news items indicate active engagement with the academic community through talks, conference presentations, and ongoing supervision of new PhD students joining his group.
Dr. Gloria Roberts is a Research Fellow at the Black Dog Institute, affiliated with the University of New South Wales' Faculty of Medicine, School of Psychiatry. Her research focuses on identifying predictors of bipolar disorder development in high-risk populations, with particular emphasis on neural mechanisms of executive functioning and emotional processing. Location: Black Dog Institute, Hospital Road, Prince of Wales Hospital, Randwick NSW 2031 Contact: +61 2 9382 8324 | ORCID: https://orcid.org/0000-0002-1966-5120 Education Background: B.Sc in Applied Psychology (University College Cork, Ireland, 2002) M.Sc in Neuropharmacology (National University of Ireland Galway, Ireland, 2003) Diploma in Statistics (Trinity College Dublin, Ireland, 2006) PhD in Neuroscience (Trinity College Dublin, Ireland, 2008) Dr. Roberts' research program centers on the neural basis of emotional dysregulation characteristic of mood disorders, employing structural and functional Magnetic Resonance Imaging as her primary research tool. Her work integrates advanced neuroimaging analysis techniques including diffusion tensor imaging tractography, dynamic causal modeling, graph theory, and machine learning approaches. She maintains active collaborations with Queensland Institute of Medical Research (Brisbane), Neuroscience Research Australia (Sydney), and the Centre for Healthy Brain Ageing (Sydney). Analysis of Dr. Roberts' publication record (94 journal articles, 2 book chapters, 25 conference papers) reveals a consistent research trajectory focused on neurocognitive patterns in bipolar disorder. Her recent work increasingly incorporates machine learning techniques to identify predictive biomarkers, with a growing emphasis on longitudinal studies tracking high-risk populations. The interdisciplinary nature of her research bridges neuroscience, psychiatry, and computational methods to address fundamental questions about mood disorder development. Scientific Contributions: Extensive publication record across multiple formats (journal articles, book chapters, conference presentations) Development of innovative neuroimaging analysis techniques for bipolar disorder research Establishment of multi-institutional collaborations across Australia Integration of machine learning approaches with traditional neuroimaging methods Dr. Roberts actively mentors junior researchers and contributes to the broader scientific community through peer review activities and participation in research networks focused on mood disorders. Her work has significant implications for early intervention strategies and the development of novel therapeutic approaches for bipolar disorder.
Philipp Koehn is a Professor in the Department of Computer Science at Johns Hopkins University, with additional affiliation at the University of Edinburgh. His primary research focuses on statistical and neural machine translation, specifically developing methods to leverage large-scale digital information for cross-lingual communication. He leads the Machine Translation Research Group and maintains key resources like the Moses toolkit and Europarl corpus. His research interests span: Core machine translation techniques (statistical/neural approaches) Low-resource and unsupervised translation methods Cross-lingual representation learning Speech-to-speech translation systems Large-scale parallel data mining and alignment Evaluation methodologies for generated text Koehn's recent publications demonstrate strong focus on improving translation efficiency (dynamic compression, streaming models), robustness (noise handling, error correction), and accessibility (low-resource languages, radio speech processing). Key trends include multilingual generalization, document-level coherence, and human-centered evaluation. Significant scientific recognition includes: ACL Fellow (2024) IAMT Award of Honor (2015) European Inventor Award Finalist (2013) He currently advises PhD students Rachel Wicks, Elina Baral, Bismarck Odoom, and Weiting Tan. His Machine Translation Group develops widely-used open-source tools and organizes major conferences including WMT and MT Marathon.
David Atienza is a Professor in the Department of Electrical Engineering at the School of Engineering, Swiss Federal Institute of Technology in Lausanne (EPFL), renowned for pioneering embedded systems education and research in ultra-low power computing. His innovative teaching methods, including using Nintendo DS consoles and smartphones to teach embedded systems, earned him the 2015 EPFL Teaching Award in Electrical Engineering. His research focuses on Embedded Systems , Edge AI , and Wearable Healthcare , with breakthroughs in energy-efficient hardware-software co-design for biomedical applications. Key contributions include open-source platforms like X-HEEP and HEEPocrates for ultra-low power edge computing, and frameworks like SzCORE for seizure detection benchmarking. His work bridges computer architecture with real-world healthcare challenges, emphasizing privacy-preserving algorithms and sustainable computing. Recent publications (2023-2025) reveal a dominant trend toward biomedical edge AI and sustainable computing , with 70% of articles targeting healthcare wearables (seizure detection, cough monitoring) and 30% addressing energy efficiency in data centers and edge devices. His research consistently integrates open-hardware principles (RISC-V) with novel algorithm-hardware co-design. Awards include: 2015 EPFL Teaching Award in Electrical Engineering section While specific advising details are unreported, his extensive publication record and leadership in multi-partner projects like Sustainable Textile Electronics (STELEC) indicate active graduate supervision and significant research funding. His group develops open-source hardware frameworks used globally in academia and industry. He leads the Embedded Systems Laboratory at EPFL, driving projects in ultra-low power RISC-V architectures, biomedical wearables, and sustainable computing. Current initiatives include carbon-aware data center frameworks and multi-modal health monitoring systems deployable on commercial wearables.
Jürgen Sauer is a Full Professor at the University of Fribourg , affiliated with the Department of Psychology under the Faculty of Letters and Human Sciences . With over 120 publications, his research focuses on Human-Machine Interaction , Usability Testing , User Experience (UX) , and Automation Design , particularly in high-stakes environments like X-ray baggage screening and spaceflight simulations . Email: juergen.sauer@unifr.ch Phone: +41 26 300 7622 Address: RM 01 bu. C-1.117, Rue PA de Faucigny 2, 1700 Fribourg Orcid: 0000-0003-2105-1694 His research projects, funded by the Swiss National Science Foundation (FNS), include: Improving work design for airport security officers (2019-2024): Developed pictorial scales for measuring psychological constructs in security environments. Social stress and support in hybrid teams (2018-2023): Investigated machine-induced social stressors and mitigation through social support. Automation in visual inspection tasks (2014-2018): Examined adaptable automation for baggage screening and system reliability effects. Usability testing effectiveness (2012-2016): Analyzed cultural background impacts and non-usability product features influencing test outcomes. Key contributions include the Luggage Inspection Simulation (LIS) environment for modeling work environments and the development of pictorial usability scales for multilingual applicability. His work bridges ergonomics , human factors , and applied psychology , with notable collaborations with researchers like Adrian Schwaninger and Andreas Sonderegger . His recent publications (2025-2014) analyze: Human-machine performance under false alarms and miscues Social stressor dynamics in hybrid teams Usability scale animation effects Phubbing behavior in professional contexts Accessible website design for non-disabled users
Baosheng Yu serves as an Assistant Professor of Digital Health at the Lee Kong Chian School of Medicine, Nanyang Technological University (NTU), Singapore, with prior experience as a Research Fellow at the University of Sydney, Australia. His academic credentials include: Bachelor of Engineering (B.E.) from University of Science and Technology of China (USTC), 2014 Ph.D. from University of Sydney (USYD), 2019 Dr. Yu's research integrates cutting-edge artificial intelligence with multimodal medical data—spanning imaging, clinical text, and physiological signals—to revolutionize diagnostic precision and therapeutic efficacy. His work bridges Artificial and Augmented Intelligence , Biomedical Informatics , and Data Science , with specialized focus on medical image segmentation, clinical NLP for electronic health records, and real-time signal analysis for patient monitoring systems. He actively recruits PhD candidates and Research Associates/Fellows for digital health initiatives, indicating robust research momentum. While specific grant portfolios remain unspecified, his methodology suggests strong alignment with Singapore's national priorities in AI-driven healthcare transformation and precision medicine.
Michele Cooke is an Associate Professor at the University of Massachusetts Amherst , affiliated with the School of Earth and Sustainability. Her research focuses on mechanical modeling of fault systems, particularly in Southern California, and she leads initiatives for disability equity in geosciences. Contact: cooke@umass.edu | (413) 577-3142 | Morrill 3 230, 611 N Pleasant St, Amherst, MA Research Interests: Active faulting and work minimization in fault evolution Integration of analog experiments (sandbox/claybox) with numerical models Disability equity in geosciences (e.g., The Mind Hears mentoring forum) Subduction zone hazards and energy budgets Earthquake early warning accessibility for deaf communities Education: Ph.D., Stanford University
Jon Andoni Duñabeitia is a Full Professor at the School of Languages and Education of Universidad Nebrija in Madrid. He serves as Director of the Centro de Investigación Nebrija en Cognición (CINC) and the International Chair in Cognitive Health . With an h-index of 43 (Scopus), he has published 170+ articles across psycholinguistics, multilingualism, cognitive training, and virtual reality applications in education. His research examines how language processing interacts with cognitive load, emotional modulation, and technological innovation. Principal Investigator for 8+ projects funded by Spanish Government, Basque Government, BBVA Foundation Associate Editor and Editorial Board Member of high-impact journals Recognized among Spain's top 3% scientists across all disciplines Recent publications span topics including: Second-language reading dynamics in VR environments Multilingual cognitive interactions in neurological conditions Emoji/typographic effects on word processing Computerized cognitive assessment and training systems He actively contributes to scientific meetings as invited speaker across Europe, Asia, and Americas. His work bridges basic research in psycholinguistics with applied technologies for cognitive health.
Leonora Kaldaras is an Assistant Professor in the Department of Curriculum & Instruction at Texas Tech University College of Education. Her research focuses on equitable personalized learning, AI-driven assessment systems, and cognitive development in STEM education. She holds a dual Ph.D. in Curriculum, Instruction and Teacher Education and Measurement and Quantitative Methods from Michigan State University (2020) and has worked with Nobel laureate Carl Wieman on AI-guided feedback tools. Education: Dual Ph.D. (2020), Michigan State University Science Education Certificate, BGSU B.S. in Chemistry (2009), BGSU Research Interests: Personalizing learning through technology, equity in blended/personalized learning, and fostering knowledge transfer via self-guided strategies. She specializes in NGSS-aligned assessments and AI-enhanced feedback systems for STEM education. Article Trends: Her recent work (2023-2025) emphasizes AI-driven assessment design, NGSS-aligned learning progressions, and cognitive frameworks for math-science integration. Earlier publications (2012-2016) focus on biophysics but transitioned to education post-2020. Scientific Awards: New and Noteworthy Invited Symposium by American Chemical Society Top Downloaded Article (JRST, 2019) Top Cited Article (JRST, 2021-2022) Grants: NSF DrK-12 Co-PI (2022-2026) for AI feedback systems in NGSS classrooms. Labs & Collaborations: Formerly at Stanford University Graduate School of Education and University of Colorado Boulder PhET Interactive Simulations Project, working closely with Nobel laureate Dr. Carl Wieman.
Nikitas Karanikolas serves as Professor in the Department of Informatics and Computer Engineering at the University of West Attica since March 2018, following a distinguished career progression from Assistant Professor (2004) to Associate Professor (2010) and Professor (2014) at the Technological Educational Institute of Athens. His professional trajectory includes significant roles as Systems Head of TEI Athens Library (1996-1997) and Chief of Informatics at Aretaieio University Hospital (1997-2004), alongside leadership positions in the Greek Computer Society as Board Member (2004-2006) and Secretary General (2006-2008). His academic foundation includes: Bachelor's in Statistics and Informatics from Athens University of Economics and Business (1988) PhD in Applied Informatics from Athens University of Economics and Business (1994) with thesis "Technological and Linguistic approaches in Natural Language Understanding" Dr. Karanikolas maintains an exceptionally broad research portfolio spanning Natural Language Processing , Computational Linguistics , Medical Informatics , and Green Energy systems. His work consistently bridges theoretical computational frameworks with practical healthcare applications, particularly evident in recent dementia care technologies and Greek language processing systems. The interdisciplinary nature of his research connects computational phonology with medical diagnostics and e-government applications. Analysis of his 15 most recent publications reveals a pronounced shift toward AI-driven healthcare solutions (particularly dementia patient monitoring), multilingual NLP systems (Greek and Polish), and urban safety applications . His work demonstrates consistent methodology development in ontological representations and multimodal fusion techniques, with increasing emphasis on real-world clinical and governmental implementations since 2023. No scientific awards were documented in the source materials. With 16 journal papers, 68 conference publications, and six authoritative Greek university textbooks, Dr. Karanikolas maintains an active research trajectory. His advising capacity is evidenced through extensive publication mentorship, particularly in medical informatics and NLP projects. While specific grant details are unavailable, his hospital information system implementations and textbook authorship suggest successful research funding acquisition. Current research activities focus on multimodal aggression prediction systems for dementia care, Greek language ontological frameworks, and urban navigation safety applications, primarily conducted through the University of West Attica's informatics infrastructure.
Geoffrey Handsfield is an Assistant Professor at the University of North Carolina at Chapel Hill, holding appointments in the Department of Orthopaedics and the Lampe Joint Department of Biomedical Engineering. His work integrates advanced medical imaging, computational modeling, and mechanical experimentation to improve clinical orthopaedic medicine and understand musculoskeletal form and function. Educated at East Carolina University (B.S. Physics, Summa Cum Laude with Mathematics Minor), the University of Virginia (Ph.D., Biomedical Engineering), and as a Whitaker Postdoctoral Scholar at the University of Auckland, his research focuses on musculoskeletal MRI, computational modeling of muscle-tendon interactions, and pediatric cerebral palsy rehabilitation. Education: Ph.D., Biomedical Engineering, University of Virginia, 2014 Postdoctoral Fellowship, Auckland Bioengineering Institute, 2014–2016 (Whitaker Scholar) B.S., Physics (Summa Cum Laude), Mathematics Minor, East Carolina University, 2008 Research Interests: Dr. Handsfield’s lab pioneers techniques like ultra-high contrast MRI and 3D ultrasound to study muscle-fascia interactions, tendon mechanics, and pediatric cerebral palsy. Key areas include: Image-based computational models for personalized musculoskeletal analysis Muscle architecture and regeneration in children and athletes Biomechanical interventions for cerebral palsy rehabilitation Non-invasive muscle profiling for clinical decision-making Awards: Aotearoa Early Career Research Fellow, Robertson Foundation Early Career Research Award, Australia-New Zealand Orthopaedic Research Society Whitaker Postdoctoral Scholar Academic All-American in Swimming & Diving (2007–2008) Advising & Grants: While specific grant details are not listed, his lab’s focus on pediatric cerebral palsy and musculoskeletal modeling suggests involvement in NIH-funded or foundation-sponsored research. No formal advisees are listed in the provided data. Labs & Teams: His interdisciplinary lab collaborates with clinicians and engineers to translate imaging and modeling innovations into clinical practice, focusing on tools like the dSIR MRI sequence and high-dimensional muscle clustering for athlete and pediatric populations.
