Find Academic Supervisors

Maciej A MazurowskiView profile

Associate Professor

• Medical Imaging
• Machine Learning
• Computer Vision
• +3 more

Maciej A Mazurowski is an Associate Professor at Duke University School of Medicine, with dual appointments in the Department of Biostatistics & Bioinformatics and Radiology. He is also affiliated with the Department of Electrical and Computer Engineering and is a member of the Duke Cancer Institute. His research focuses on applying machine learning to medical imaging for improved diagnosis and treatment. Ph.D. in Computer Science from the University of Louisville (2008) Dr. Mazurowski's research emphasizes medical imaging , machine learning , and computer vision applications in radiology. His work includes automated segmentation , domain adaptation , prognostic modeling , and foundation models for MRI/CT analysis. His recent publications highlight trends in universal segmentation models (SegmentAnyBone, SegmentAnyMuscle), foundation models for MRI (MRI-CORE), and AI-driven diagnostic tools for breast cancer, glioblastoma, and thyroid nodules. Key challenges addressed include domain generalization , image harmonization , and ethical considerations in clinical AI. Incubation Award for innovative research commercialization Dr. Mazurowski has secured significant research funding from agencies including the National Institutes of Health , National Institute of Biomedical Imaging and Bioengineering , and American Roentgen Ray Society . His work spans CT segmentation , MRI analysis , and AI-based quality assessment across multiple imaging modalities.

Abbas SamaniView profile

Professor

• Biomedical Engineering
• Medical Imaging
• Computational Modeling
• +5 more

Prof. Abbas Samani is a Professor at the Department of Electrical and Computer Engineering and holds a joint appointment in the Department of Medical Biophysics at Western University . He is a core faculty member of the Biomedical Engineering Graduate Program and an Associate Scientist at Imaging Research Laboratories of Robarts Research Institute . His academic journey includes a Ph.D. from the University of Waterloo, an M.Sc. from the University of Tehran, and a B.Sc. from Amirkabir University of Technology. His research focuses on biological tissue computational modeling and its applications in medical imaging, intervention, and image analysis . He develops computer/image-assisted tools for minimally invasive disease diagnosis and therapy , targeting heart disease, cancer, and lung disease . Key projects include myocardium biomechanical modeling , handheld medical devices for breast cancer screening , and lung disease diagnostics via CT image segmentation . His recent publications emphasize ultrasound elastography , finite element modeling , and inverse problems in biomechanics , primarily in journals like IEEE Transactions on Computational Imaging and Translational Oncology . His work spans both computational modeling and medical device development . Selected Graduate Supervision : Ph.D. Candidates : Seyed Hassan Haddad, Elham Karami, Seyed Mohammad Hesabgar Graduated Ph.D. Students : Ali Sadeghi Naini, Seyed Reza Mousavi M.Sc. Students : Cristian Linte, Patrick Courtis, Joseph O'Hagan, Hatef Mehrabian, Hirad Karimi, Hosein Amooshahi, Seyed Mohammad Hesabgar, Nastaran Ghadarghadr, Shadi Shavakh, Ehsan Salamati, Ehsan Omidi Teaching Contributions : Graduate: BME9519B/CAMI9519B/ECE9202B/ECE9022B - Advanced Image Processing and Analysis , MBP9530A - Human Biomechanics and Biomedical Applications Undergraduate: ECE4438B - Advanced Image Processing and Analysis , ES1050 - Introductory Engineering Design and Innovation Studio , MBP3330F - Human Biomechanics and Biomedical Applications Research Affiliations : Robarts Research Institute - Associate Scientist at Imaging Research Laboratories Western University - Core Faculty, Biomedical Engineering Graduate Program

Lucian SulicaView profile

Professor

• Voice Disorders
• Laryngology
• Vocal Fold Injuries
• +7 more

Dr. Lucian Sulica is the Sean Parker Professor of Laryngology and Director of the Sean Parker Institute for the Voice at Weill Cornell Medical College. He is an attending Otolaryngologist at NewYork-Presbyterian Hospital with a clinical practice exclusively focused on voice disorders. Dr. Sulica served as the 2023 President of the American Laryngological Association and is a Fellow of the American Laryngologic, Rhinologic and Otologic Society (the Triologic Society). His educational background includes: A.B. from Dartmouth College (1989) M.D. from Georgetown University School of Medicine (1993) Residency at Georgetown University Medical Center Fellowship at New York Center for Voice & Swallowing Disorders, St. Luke's-Roosevelt Hospital Center Dr. Sulica's research focuses on evidence-driven principles for treating voice conditions, with particular emphasis on vocal fold injuries from voice use (especially in performers), neurologic voice disorders (including vocal fold paralysis, spasmodic dysphonia, and tremor), and in-office procedures for voice disorders that don't require general anesthesia. His work has addressed critical aspects such as gender differences in vocal fold injury, pathophysiology of laryngeal nerve injury, and diagnostic accuracy in voice disorders. Analysis of Dr. Sulica's recent publications reveals a strong focus on advancing diagnostic techniques through artificial intelligence, improving surgical outcomes for vocal fold lesions, understanding performers' unique voice care needs, and developing evidence-based protocols for vocal fold hemorrhage and other voice disorders. His research increasingly incorporates technology-driven approaches while maintaining a strong clinical focus on patient-centered outcomes. Dr. Sulica has received numerous prestigious awards including: Casselberry Award of the American Laryngological Association (2021) Felix Semon Lectureship in Laryngology, Royal Society of Medicine (2022) American Academy of Otolaryngology – Head & Neck Surgery Honor Award (2008) Consistent recognition as one of "Best Doctors in America" (2005-2024) Designation as one of "America's Top Physicians in Voice Disorders/Laryngology" (since 2006) Inclusion in "New York Magazine Best Doctors" list annually since 2013 As an educator, Dr. Sulica has authored more than 110 journal articles and 40 book chapters, and has edited three books including "Vocal Fold Paralysis," "Classics in Voice and Laryngology," and "Patologia Laringea y Fonocirugia" (in Spanish). He is passionate about teaching and has lectured extensively across the United States, Europe, Latin America, and Australia. His clinical work at the Sean Parker Institute for the Voice represents a comprehensive approach to voice care, incorporating innovative in-office procedures that have helped establish new standards in laryngology.

• Computational Pathology
• Medical Image Analysis
• Deep Learning
• +4 more

Nasir M. Rajpoot is a Professor in the Department of Computer Science at the University of Warwick, UK. His research focuses on computational pathology, medical image analysis, and deep learning applications in histology. He leads interdisciplinary projects integrating artificial intelligence with healthcare, particularly in cancer diagnostics and pathology workflows. Rajpoot’s work emphasizes developing robust algorithms for histology image analysis, including nuclear segmentation, tumor classification, and domain generalization in computational pathology. His contributions include the TIAToolbox, an open-source framework for tissue image analytics, and the CoNIC Challenge to advance nuclear detection and counting in histology images. He collaborates with clinicians and biologists to translate AI models into clinical practice, addressing challenges like tumor heterogeneity and staining variability. Rajpoot’s research spans colorectal, lung, and oral cancers, with a focus on predicting clinical outcomes via histological features and genomic data integration. Notable projects include the development of Handcrafted Histological Transformer (H2T) for unsupervised representations of whole slide images and the SAFRON framework for histology image synthesis. His work addresses domain adaptation, robustness evaluation, and explainability in AI-driven pathology systems.

Ping HeView profile

Professor

• Plant Immunity
• Molecular Biology
• Genetic and Biochemical Approaches
• +7 more

Ping He is a Professor in the Department of Molecular, Cellular, and Developmental Biology (MCDB) at the University of Michigan in Ann Arbor. His research focuses on plant immunity mechanisms, particularly using Arabidopsis as a model system to study pathogen defense activation, signaling pathways, and the interplay between immunity and environmental stress responses. He also leads the Molecular, Plant-Microbe Interaction Laboratory, applying interdisciplinary approaches (genetics, biochemistry, cellular biology) to enhance crop resilience through foundational plant science discoveries. His work bridges plant biology and computational biology, with recent contributions to AI-driven medical imaging applications such as bladder cancer treatment response assessment, lung cancer early detection, and breast tomosynthesis denoising. These efforts emphasize integrating machine learning into clinical workflows and establishing best practices for AI in healthcare. Research Highlights: Plant immunity signaling and environmental stress crosstalk Radiomics and deep learning for cancer diagnosis/prognosis AI model validation and multi-institutional clinical trials Medical imaging artifact correction (e.g., motion blur, noise) Publications emphasize AI applications in oncology imaging, radiologist decision support systems, and multimodal data fusion. He has contributed to AAPM task group guidelines for AI in computer-aided diagnosis and advocates for rigorous quality assurance frameworks in medical AI deployment.

Matloob KhushiView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +7 more

Dr. Matloob Khushi serves as a Senior Lecturer in Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. With over 25 years of combined academic and industry experience, his work bridges theoretical AI advancements with practical applications in finance, healthcare, and public health domains. His research has established significant collaborations with international banks, healthcare institutions, and technology startups. Dr. Khushi earned his PhD in AI and Data Science from the University of Sydney, developing novel algorithms for genomic data analysis. His postdoctoral research at the Children's Medical Research Institute (2014-2017) pioneered AI-based diagnostic tools for medical condition detection. More recently, he developed bioinformatics tools for environmental assessment under a UKRI NEC grant. Research Focus FinTech Innovation : Creator of the SS Ratio (incorporating volatility and drawdown sensitivities), advanced portfolio optimization models, and synthetic data generation techniques for fraud detection and credit risk assessment Bioinformatics Leadership : Developer of AI tools for genomic analysis and early cancer detection, featured in SBS News and The Daily Telegraph Public Health NLP : Architect of systems for vaccine misinformation detection, mental health monitoring, and health surveillance on social media His publication portfolio shows consistent growth from foundational bioinformatics work to current multimodal AI applications, with increasing interdisciplinary collaboration across finance and healthcare sectors. Awards and Recognition Ranked among Stanford/Elsevier's top 2% of global AI scientists Recipient of Best Paper Awards from IEEE Transactions on Computational Social Systems and PeerJ Media recognition for cancer detection research by major news outlets Mentorship and Teaching Dr. Khushi has supervised six PhD candidates to completion and over 100 postgraduate dissertations. He teaches CS3002 Artificial Intelligence and mentors students in Final Year Projects. His supervision focuses on Deep Learning/NLP for FinTech prediction and Public Health Surveillance applications, emphasizing practical implementation of theoretical concepts.

Zhongliang JiangView profile

Researcher

• Medical Robotics
• Image Processing
• Robotic Learning
• +3 more

Dr. Zhongliang Jiang is a senior research scientist and leader of the Robotics and Ultrasound team (RobUSt) at the Chair of Computer Aided Medical Procedures (CAMP) at Technische Universität München. He holds a Ph.D. in computer sciences (summa cum laude) and has authored/co-authored over 40 top-tier publications in robotics and medical imaging. His research focuses on robotic ultrasound systems, medical image processing, and robotic learning. Education: Ph.D. in Computer Sciences, TUM (2022, summa cum laude) M.Eng. in Harbin Institute of Technology (2017) Research Assistant at SIAT (2017-2018) Research Interests: Medical Robotics: autonomous robotic ultrasound systems Image Processing: RGB-D/ultrasound segmentation, registration Robotic Learning: reinforcement/imitation learning Robotic Control: MPC, shared control, human-robot interaction Professional Contributions: Associate Editor for ICRA 2024/2025 Guest Editor for IEEE TRO special issue on Robot-Assisted Medical Imaging Main organizer of RAMI workshops at ICRA (2023-2025) Awards: MICCAI 2023 Best Paper Runner-up Gold Medal for Master's Thesis (2017) Lab & Teaching: Leading the RobUSt team developing advanced robotic ultrasound solutions Teaching courses like Computer Aided Medical Procedures and Medical Augmented Reality Supervised over 15 Master/PhD projects in robotic ultrasound and medical imaging

• Intelligent Robots
• Intelligent Information Processing
• Real-World Image Annotation and Retrieval
• +4 more

HARADA Tatsuya is a Professor at the Research Center for Advanced Science and Technology (RCAS), University of Tokyo. His research focuses on intelligent robotics , real-world image processing , and human-informatics AI systems . Degree: PhD Research Themes: Harada investigates real-world intelligent information processing, fast image recognition and retrieval, and AI applications in pathology diagnostics. His work spans explainable AI systems for cancer analysis, tactile sensor integration for humanoid robots, and knowledge acquisition via dialog systems. Research Categories: His projects have been funded through multiple Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) grants, including Grant-in-Aid for Scientific Research (A/B/C) and Innovative Areas programs. Specific projects include "Explainable AI diagnostic system for breast cancer" (2020-2021) and "Behavior Capture Suit with Motion Sensors" (2010-2013). Scientific Awards & Grants: Recipient of competitive JSPS grants for interdisciplinary research bridging robotics, computer vision, and medical diagnostics.

Lars LinsenView profile

Professor

• Interactive Visual Analysis
• Medical Visualization
• Scientific Visualization
• +4 more

Prof. Dr.-Ing. Lars Linsen is a full Professor of Computer Science at the Westfälische Wilhelms-Universität (WWU) Münster, leading the VISualization & graphIX (VISIX) group. His primary affiliation is the Institute of Computer Science within the Faculty of Mathematics and Computer Science. He holds adjunct professorships at Jacobs University, Bremen, and has held previous academic roles including Full Professor at Jacobs University (2012–2017) and Associate/Assistant Professor roles in Germany and the U.S. His research focuses on interactive visual analysis, medical visualization, and scientific visualization, with applications in life sciences and engineering. Education: PhD (Dr.-Ing.) in Computer Science from Universität Karlsruhe (2001), M.Sc. (Diplom) in Computer Science (1997), B.Sc. (Vordiplom) in Computer Science (1994). Awards: IEEE Visualization Design Contest Winner (2008, 2022, 2018), Preis des Fördervereins des Forschungszentrum Informatik (2002). Research Highlights: Develops visualization tools for medical imaging (e.g., mass spectrometry imaging, MRI data analysis) and physical simulations (e.g., wildfire spread analysis, asteroid impact modeling). Active in EU-funded projects like Pig-Pro-QuO (surface coatings) and cells-in-motion initiatives. Supervised over 20 PhD/MS advisees, including notable graduates in medical visualization and simulation ensemble analysis. Publications: Over 100 peer-reviewed articles in top venues like IEEE Transactions on Visualization and Computer Graphics, Computers & Graphics, and EuroVis. Key works include SciVis contest-winning wildfire analysis frameworks and medical visualization tools for stenosis detection. Teaching: Offers courses on visualization, computer graphics, and computational science. Actively involved in thesis supervision and curriculum development at both WWU Münster and Jacobs University. Grants & Collaborations: Principal investigator on DFG-funded projects (e.g., hemodynamics simulations, ensemble visualization) and industry collaborations (e.g., Tascon GmbH for coating quality analysis). Member of the Cells-in-Motion Interfaculty Centre and CDH board at WWU.

Karen PanettaView profile

Professor

• Signal Processing
• Image Processing
• Simulation Modeling
• +6 more

Karen Panetta is a Professor at Tufts University School of Engineering with appointments in Electrical and Computer Engineering, Computer Science, Mechanical Engineering, and Academic Services. She currently serves as Dean of Graduate Education for the School of Engineering and holds the title of Distinguished Professor. Ph.D. in Electrical Engineering, Northeastern University M.S. in Electrical Engineering, Northeastern University B.S. in Computer Engineering, Boston University Dr. Panetta's research focuses on developing efficient algorithms for simulation, modeling, and signal and image processing for security and biomedical applications. Her work brings together artificial intelligence, machine learning, and visual sensing systems to create solutions for robot vision and biomedical imaging. She develops algorithms inspired by the human visual system to enable machines to 'see' like humans, with applications in homeland security, biomedicine, facial recognition, and search and rescue operations. Her research has significant humanitarian applications, addressing global challenges facing women and children. Dr. Panetta has received numerous prestigious awards including induction into the National Academy of Engineering (2023), the Presidential Award for Science and Engineering Education and Mentoring (2011), and the IEEE Award for Distinguished Ethical Practices (2013). She is a fellow of multiple prestigious academies including the National Academy of Inventors, European Academy of Sciences and the Arts, and IEEE. Member, National Academy of Engineering (2023) Presidential Award for Science and Engineering Education and Mentoring (2011) IEEE Award for Distinguished Ethical Practices (2013) Fellow, National Academy of Inventors Fellow, European Academy of Sciences and the Arts Fellow, Asia-Pacific Artificial Intelligence Association As an educator and mentor, Dr. Panetta founded the nationally acclaimed Nerd Girls program to promote engineering to young students, particularly women. She previously served as worldwide director for IEEE Women in Engineering and editor-in-chief of the IEEE Women in Engineering magazine. Her approach to graduate education emphasizes the importance of building strong collaborative relationships between faculty and students, with a focus on proactive communication and documentation of research progress. Dr. Panetta's humanitarian research applies engineering solutions to global challenges, including developing technology to help doctors find cancerous tumors, security screeners find concealed weapons, and law enforcement agencies find criminals and missing children. Her work demonstrates a commitment to 'Doing The Right Thing' by addressing issues affecting populations with limited resources or 'voice' in society.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +7 more

Professor Arcot Sowmya is a distinguished academic at the University of New South Wales, serving as Professor in the School of Computer Science and Engineering. With a strong background in both computer science and mathematics, she has established herself as a leading researcher in machine learning and computer vision applications, particularly in medical imaging and diagnostics. Dr. Sowmya earned her PhD in Computer Science from the Indian Institute of Technology, Bombay, along with an MTech in Computer Science, MSc in Mathematics, and BSc in Mathematics from the same institution. Her academic journey has positioned her at the intersection of theoretical computer science and practical medical applications. Her research interests span multiple domains with a primary focus on Machine Learning for Computer Vision . She has made significant contributions to learning object models, feature extraction, segmentation, and recognition techniques. Her work extends into medical image analysis, computer-aided diagnostics, high-resolution remote sensing, and biomedical informatics. More recently, she has applied similar techniques to social sciences domains, developing improved forecasting models for genocide and politicide. Her earlier work also includes contributions to real-time, concurrent, and embedded systems. Analyzing her recent publications reveals a strong trend toward medical applications of computer vision and deep learning. Her work spans from OCT-based glaucoma diagnosis to tumor segmentation, lung disease detection, and breast cancer prognosis. She has successfully bridged computer science with clinical medicine, developing practical tools for disease diagnosis and prediction that incorporate explainable AI approaches. Professor Sowmya's collaborative approach is evident in her extensive publication record across multiple journals and conferences. She has worked with researchers from diverse fields including ophthalmology, oncology, neurology, and public health, demonstrating the interdisciplinary nature of her research. Her laboratory work focuses on developing robust deep learning architectures for medical image analysis, with particular attention to segmentation networks, transformer models, and multimodal data fusion techniques. Her team has developed specialized networks for lung segmentation, tumor detection, and disease classification that address specific challenges in medical imaging.

• Face Recognition
• Developmental Prosopagnosia
• Neurodiversity in Women
• +3 more

Dr. Anna Bobak is a Senior Lecturer in Psychology at the University of Stirling, UK. She holds a PhD from Bournemouth University (2016) and joined Stirling as a Research Assistant on an EPSRC project under Peter Hancock before transitioning to her current role. Her primary research focuses on individual differences in unfamiliar face recognition, particularly developmental prosopagnosia, and the reliability of face-processing assessments. She also investigates neurodiversity in women, emphasizing lived experiences of autism and ADHD, including camouflaging behaviors and societal awareness. Research Interests: Face Recognition: Examines perceptual strategies, diagnostic criteria (e.g., Balanced Integration Score), and technological applications in forensic contexts. Neurodiversity: Explores gender-specific manifestations of autism and ADHD, societal perceptions, and support mechanisms. Cognitive Methodology: Advances psychometric rigor in face-processing studies and critiques measurement validity. Her work bridges theoretical research and real-world applications, such as evaluating automated face recognition technology’s biases and collaborating on initiatives like #ScienceForUkraine to aid displaced academics. She is affiliated with the Cognition in Complex Environments research group and contributes to global security and resilience themes at Stirling.

Mahmoud El-SakkaView profile

Associate Professor

• Image Compression
• Medical Imaging
• Image Segmentation
• +5 more

Mahmoud El-Sakka is an Associate Professor at the Department of Computer Science, University of Western Ontario since 1999. Previously, he was a faculty member at the University of Waterloo (1997–1999). He holds a B.Sc. and M.Sc. from Alexandria University (Egypt) and a Ph.D. in Systems Design Engineering from the University of Waterloo. His research focuses on medical imaging, image processing, and computer-aided diagnostics. He has served as Chair of the graduate program (2002–2007) and undergraduate program (2017–present) in Computer Science at Western Ontario. El-Sakka is a Senior Member of the IEEE and a licensed Professional Engineer in Ontario. His work spans grants from NSERC, internal university funding, and industry collaborations. Major research areas include image compression, segmentation, and medical applications like vascular analysis and echocardiography. He has led over 20 funded projects since 1999, emphasizing interdisciplinary approaches in healthcare technology. Academic contributions include advisory roles in summer programs, thesis evaluations, and conference participation. His service includes roles as Pro-Chancellor at convocations and involvement in equipment purchasing committees. Collaborations include consulting with NCR Canada and VRP Web Technology.

Pingfu FuView profile

Professor

• Survival analysis
• Clinical trials
• Cancer research
• +4 more

Pingfu Fu, PhD, is a Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University's School of Medicine. He is also a member of the Developmental Therapeutics Program at the Case Comprehensive Cancer Center. His expertise spans biostatistics, mathematics, and computer science, with a focus on cancer research and HIV/AIDS. Dr. Fu advises researchers on study design and statistical methodology for clinical and pre-clinical studies. He teaches courses in survival data analysis and clinical trials, and was recognized as 'Professor of the Year' in 2010 by the Department of Epidemiology and Biostatistics. Education: PhD in Biostatistics (Case Western Reserve University, 2001), MS in Statistics (Case Western Reserve University, 1996), MS in Mathematics (Xiangtan University, 1988), and BS in Mathematics (Jiangxi Normal University, 1984). His research interests include survival analysis, tree-based methods, clinical trials, and statistical applications in medical research. He has co-authored numerous peer-reviewed articles, focusing on cancer disparities, radiomics, and computational pathology. Professional memberships include the American Statistical Association, American Mathematical Society, and American Cancer Society. Dr. Fu holds editorial roles at Reviews on Recent Clinical Trials , Journal of Clinical Oncology , and Journal of the National Cancer Center . His work has addressed mathematical challenges in stochastic processes and resolved statistical issues in study design and tree-based models. Notable contributions include developing risk prediction models for cancer outcomes and advancing interdisciplinary collaborations across oncology, biostatistics, and computer science. His lab focuses on integrating computational methods with clinical data to improve patient outcomes.

Dorte HammershøiView profile

Professor

• Acoustics
• Hearing Science
• Audio Engineering
• +5 more

Dorte Hammershøi is a Professor in the Department of Electronic Systems at The Technical Faculty of IT and Design, Aalborg University, Denmark. Her research focuses on acoustics, sound engineering, and hearing science with significant contributions to human hearing, ear canal acoustics, and audio technology applications. Her research interests include: Temporary Threshold Shift and frequency resolution in human hearing Ear canal acoustics and sound pressure level measurement Distortion Product Otoacoustic Emission (DPOAE) analysis Impulse response and acoustic impedance studies Hearing aid technology and rehabilitation methodologies Virtual reality audio interfaces and accessibility applications Professor Hammershøi's recent publications demonstrate a strong clinical-engineering interdisciplinary approach, bridging theoretical acoustics with practical hearing rehabilitation applications. Her work on hearing aid fitting methodologies, occupational noise exposure effects, and virtual reality audio interfaces shows consistent innovation in translating engineering principles to clinical practice. The research shows particular attention to individualized hearing solutions and accessibility technologies. Her scientific contributions have been recognized with: Dansk Lydpris 2020 (awarded November 17, 2021) Ambassadør for Aalborg (awarded September 15, 2004) Professor Hammershøi has supervised 5 PhD students and led numerous research projects including the ongoing "Audio Only VR for Blind Gamers" project (2024-2028) funded by the Independent Research Foundation of Denmark, and the completed "BEAR: Better Hearing Rehabilitation" project (2016-2022). Her research has attracted significant media attention with 110 press/media appearances discussing hearing damage prevention, tinnitus, and public health implications of noise exposure. She maintains active professional engagement through committee memberships (46 documented activities), international collaborations, and contributions to clinical practice guidelines. Her work continues to influence both academic research and practical applications in hearing science and audio engineering.