جست‌وجوی استاد و پژوهشگر

• Cognitive Neuroscience
• Neuroscience
• Memory Studies
• +۳ مورد دیگر

Professor Maria Wimber is a faculty member in the School of Psychology & Neuroscience at the University of Glasgow. Her research focuses on understanding how the human brain reconstructs memories, with a particular emphasis on neural oscillations (e.g., theta rhythms) and the adaptive nature of memory. She employs techniques such as EEG, MEG, fMRI, and direct hippocampal recordings from epilepsy patients to study memory dynamics, interference, and reconsolidation. Key achievements include receiving an ERC Starting Grant and a British Academy Fellowship. Her work is funded by grants from the ESRC, BBSRC, and Stiftelsen Olle Enkvist. She supervises postgraduate students like Stratos Koukouvinis and Christopher Postzich, and collaborates with researchers on projects involving neuroimaging tools like the Brain Time Toolbox. Her research highlights how memory retrieval can both stabilize and disrupt competing memories, with findings published in journals like Nature Neuroscience and Current Biology. She explores mechanisms like theta phase separation, cortical pattern suppression, and the hippocampus's role as a 'switchboard' between perception and memory. Recent studies include reconstructing visual memory trajectories and investigating how theta oscillations coordinate memory reactivation. Her lab's work bridges cognitive psychology and neuroscience, with implications for understanding memory disorders and cognitive aging.

Andrew Maidenمشاهده پروفایل

استاد

• Computational Optics
• Ptychography
• Coherent Diffractive Imaging
• +۵ مورد دیگر

Professor Andrew Maiden is a faculty member at the University of Sheffield's School of Electrical and Electronic Engineering, specializing in Computational Imaging. He leads the Semiconductor Materials and Devices Research Group. His research focuses on advancing optical systems through computational methods like ptychography, which enhances microscopy and imaging precision. With a PhD from Durham University (2005) and an MEng from the University of Birmingham (2001), Maiden's career includes pioneering work with Professor John Rodenburg on ptychography and a brief industry stint commercializing microscopy technologies. He teaches Digital Signal Processing (DSP) to third-year students and advises researchers such as Cao S (PhD graduate). Research interests include Coherent Diffractive Imaging (CDI), electron microscopy phase imaging, and inverse problem solutions. His work bridges computational algorithms with practical applications in optics and materials science. Maiden has contributed to over 50 peer-reviewed publications and holds patents on ptychography-related imaging techniques. His lab focuses on developing high-resolution imaging tools without traditional lenses, emphasizing low-dose radiation and high-throughput bio-imaging. Teaching and mentorship play key roles in his academic contributions, shaping future engineers in signal processing and computational methods. Collaborations span academia and industry, reflecting his dual focus on innovation and real-world application.

• Condensed Matter Physics
• Materials Science
• X-ray Scattering Techniques
• +۴ مورد دیگر

Uwe Klemradt is a Professor in the Department of Physics at RWTH Aachen University. His research focuses on experimental condensed matter physics, with a strong emphasis on advanced characterization techniques such as X-ray scattering and in situ studies of thin film growth. Key areas include ferroelectric materials, phase transitions, and nanotechnology. His work often involves the exploration of material properties at surfaces and interfaces, leveraging synchrotron radiation and laboratory-based light sources. Recent studies include investigations into the crystallization dynamics of BaTiO3 thin films, resistive switching in oxide films, and the structural evolution of heterostructures. Notable contributions span thin film deposition methods, such as RF sputtering, and the analysis of material behavior under external fields (e.g., magnetic field-induced ferroelectricity). His research bridges fundamental physics with applications in electronics and nanotechnology. No scientific awards are explicitly listed. His advising and grant activities are not detailed in the provided texts. The Department of Physics at RWTH Aachen University serves as his primary affiliation, with access to advanced experimental facilities.

• Cognitive Neuroscience
• Neural Plasticity
• Language Processing
• +۵ مورد دیگر

Professor Gesa Hartwigsen is a leading cognitive neuroscientist holding dual appointments as Professor for Cognitive and Biological Psychology at Leipzig University and Lise Meitner Research Group Leader at the Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS). She leads the Research Group Cognition and Plasticity, focusing on neural networks for higher cognitive functions, adaptive plasticity in cognition, and language network reorganization after stroke. Her educational background includes: Studies of Psychology (Dipl.-Psych., 2001-2006) at Kiel University PhD in Neurology (Dr. phil., 2007-2010) at Kiel University Habilitation in Psychology (2018) at University of Potsdam Hartwigsen's research spans cognitive neuroscience with particular emphasis on language processing, neural plasticity, and brain stimulation techniques. Her work investigates how brain networks support semantic cognition, how they adapt following neurological damage, and how they change across the lifespan. She employs advanced methodologies including transcranial magnetic stimulation (TMS), functional MRI, and concurrent TMS-fMRI to establish causal relationships in neural processing. Analysis of her recent publications reveals a strong focus on language networks, executive control mechanisms, and neural plasticity. Her work increasingly integrates advanced computational approaches with neurostimulation to understand semantic processing, speech comprehension in challenging environments, and recovery from language disorders. A significant trend is her development of methodological frameworks for concurrent TMS-fMRI, establishing new standards in the field. Her notable scientific recognition includes: ERC Consolidator Grant (2023-2027) for "The Flexible Brain: (Re-)shaping Adaptation in Semantic Cognition" NVIDIA GPU grant (Titan Xp Donation) Hartwigsen has secured substantial research funding, including multiple DFG projects as Principal Investigator. Her current DFG Research Unit (2023-2026) focuses on "Modulation of brain networks for memory and learning by transcranial electrical stimulation," where she serves as PI. She has successfully led multiple interdisciplinary projects examining language networks, neural plasticity, and brain stimulation effects. At the MPI CBS, she leads the Cognition and Plasticity research group, which investigates how brain networks support cognitive functions and adapt to challenges. Her team employs a multimodal approach combining neurostimulation, neuroimaging, and behavioral methods to understand the causal architecture of cognitive networks, particularly in language processing and semantic cognition.

Lei Tianمشاهده پروفایل

دانشیار

• Computational imaging and sensing
• Computational microscopy
• Imaging in scattering media
• +۵ مورد دیگر

Lei Tian is an Associate Professor in the Department of Electrical and Computer Engineering and the Department of Biomedical Engineering at Boston University's College of Engineering. He leads the Computational Imaging Systems Lab and maintains affiliations with the Neurophotonics Center, Photonics Center, Center for Information & System Engineering, Rafik B. Hariri Institute for Computing, and Nanotechnology Innovation Center. His educational background includes: PhD, Massachusetts Institute of Technology, 2013 MS, Massachusetts Institute of Technology, 2010 Professor Tian's research integrates optics and computation to overcome physical limitations in imaging systems. His work spans computational imaging and sensing, computational microscopy, imaging in scattering media, phase retrieval, and neurophotonics. He develops next-generation imaging systems with applications in biomedical microscopy, neuroscience, semiconductor metrology, and advanced vision applications, emphasizing the joint design of optical components and computational algorithms. His publication record shows a strong progression from fundamental computational imaging techniques to practical applications, with increasing integration of deep learning approaches to solve challenging imaging problems in scattering media and neural environments. His work consistently bridges theoretical advances with real-world applications. Professor Tian has received numerous prestigious awards: Boston University Provost's Scholar-Teacher of the Year Award (2025) Optica Fellow (2025) Early Career Excellence in Research, BU College of Engineering (2021) NSF CAREER Award (2019) Dean's Catalyst Award (2018) The Fumio Okano Best 3D Paper Prize (2018) As an advisor, he has successfully mentored at least 10 PhD students to completion as of mid-2025, with recent graduates including Jeffrey Alido, Jiabei Zhu, Chang Liu, Hao Wang, and Joseph Greene. His research is supported by substantial funding including a $2 million NIH grant for the Computational Miniature Mesoscope (CM2), a $1.75M grant from NIBIB for cancer cell metabolism research, and funding from the Chan Zuckerberg Initiative. His Computational Imaging Systems Lab pioneers innovative imaging techniques that synergistically combine optical hardware with computational algorithms, making significant contributions to computational microscopy, intensity diffraction tomography, neural imaging systems, and deep learning applications in optical imaging for both biomedical and industrial applications.

• Biomedical Engineering
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Aggelos K. Katsaggelos is a Professor in the Department of Electrical Engineering and Computer Science at Northwestern University's McCormick School of Engineering. His research focuses on biomedical imaging, machine learning, and computer vision applications in healthcare. He has collaborated extensively with interdisciplinary teams, including clinicians and engineers, to develop advanced algorithms for medical diagnosis and image analysis. Key research interests include medical image processing, deep learning for diagnostics, and computational methods in cardiology. His work spans applications such as MRI and ultrasound analysis, automated pathology detection, and multimodal sensing for health monitoring. Recent articles highlight contributions to myocardial scar quantification, lung ultrasound scoring, and AI-driven cough detection. His methodologies often combine domain-specific physics with modern machine learning techniques to solve real-world clinical challenges. Notable collaborations include projects with institutions like the University of Chicago and international teams in astrophysics and cognitive science. His work emphasizes translating algorithmic advancements into practical clinical tools.

Thomas Fennelمشاهده پروفایل

استاد

• Strong-Field Physics
• Ultrafast Processes
• Nanoplasma Dynamics
• +۳ مورد دیگر

Prof. Thomas Fennel is a faculty member at the Max Born Institute for Nonlinear Optics and Short Pulse Spectroscopy, leading the department A1: Strong Field Processes at Extreme Wavelengths. His research focuses on ultrafast phenomena in strong-field physics, nanoplasma dynamics, and quantum coherent imaging. He has pioneered work on attosecond electron emission, laser-driven nanoplasma expansion, and light-induced dynamics in nanoscale systems. His educational background includes advanced studies in physics and optics, though specific degrees are not detailed here. His lab specializes in experimental and theoretical investigations of extreme light-matter interactions, particularly at the nanoscale. Key techniques include time-resolved spectroscopy, diffraction imaging, and ultrafast laser systems. Recent publications explore topics like subcycle metallization of nanoparticles, attosecond electron emission from metal tips, and quantum coherence in nanostructures. These contributions highlight his leadership in advancing ultrafast science and nanoscale optics. No awards are explicitly mentioned, though his prolific publication record indicates significant contributions to the field. He collaborates extensively with international teams on projects involving femtosecond imaging, plasma dynamics, and strong-field ionization. His work bridges fundamental physics with applications in nanotechnology and quantum photonics, positioning him as a key figure in modern ultrafast science.

Vinkle Srivastavمشاهده پروفایل

پژوهشگر

• Surgical Data Science
• 3D Medical Image Analysis
• Multi-modal Pre-training
• +۷ مورد دیگر

Vinkle Srivastav is a Research Scientist (Chargé de recherche R&D) at the CAMMA group, a collaborative research team between IHU Strasbourg and the University of Strasbourg, where he focuses on advancing surgical data science through novel computer vision and machine learning approaches. His work bridges the gap between clinical practice and artificial intelligence, developing methods for surgical video analysis, 3D medical imaging, and surgical workflow understanding. Education PhD in Computer Science (2018-2021) from University of Strasbourg, France. Thesis: "Unsupervised Domain Adaptation Approaches for Person Localization in the Operating Rooms." Master of Science in Computer Science (2014-2017) from Indian Institute of Technology, Delhi, India. Thesis: "Computerized evaluation of neurosurgery skills using image processing and computer vision techniques." Bachelor of Technology in Electronics and Communication (2007-2011) from Punjab Technical University, Jalandhar, India. Research Interests Vinkle's research spans surgical data science, with particular focus on multi-modal learning approaches for surgical computer vision. His work addresses fundamental challenges in medical AI including domain adaptation, self-supervised learning, and privacy preservation in clinical environments. He develops methods for 3D medical image analysis, multi-view human pose estimation in operating rooms, and surgical activity recognition. His recent work emphasizes multi-modal pretraining frameworks that leverage both visual and textual information to improve surgical workflow understanding. He also investigates scientific simulation techniques, particularly for therapeutic ultrasound applications, where physics-aware deep learning models can accelerate computational processes while maintaining accuracy. Publication Trends Vinkle's recent publications demonstrate a strong trajectory toward multi-modal surgical AI systems that integrate vision, language, and physics-based modeling. His work increasingly focuses on few-shot and zero-shot adaptation techniques to address the data scarcity problem in surgical AI. The publications reveal a progression from basic pose estimation to holistic surgical scene understanding, incorporating team communication analysis and surgical safety protocols. Scientific Awards IPCAI 2024 Best paper award (co-author) IPCAI 2019 Runner-up award in the bench-to-bedside category (co-author) Joint winner for the best paper award in the machine learning for CAI track, IPCAI 2025 Advising and Grants Vinkle actively mentors multiple PhD students and research interns at various levels, supervising thesis work on topics including large-scale multi-modality learning, holistic surgical scene analysis, and self-supervised video representation learning. He serves as Co-PI on two ITI-HealthTech projects: one focused on multi-modality learning for 3D medical imaging (2023), and another on physics-aware deep-learning approaches for therapeutic ultrasound simulation (2024). Laboratories and Teams Vinkle is a key member of the CAMMA research group at IHU Strasbourg, a collaborative team focused on computer-assisted medical modeling and analytics. He co-organizes the Surgical Data Science Summer School, an interdisciplinary program that brings together clinicians and computer scientists to develop AI-driven solutions with clinical impact. His work involves close collaboration with surgical teams at University Hospitals of Strasbourg and international partners including Johns Hopkins University and Technical University of Munich.

Corinna Hooseمشاهده پروفایل

استاد

• Aerosol-Cloud Interactions
• Mixed-Phase Clouds
• Heterogeneous Ice Nucleation
• +۳ مورد دیگر

Prof. Dr. Corinna Hoose leads the Cloud Physics research group at the Institute of Meteorology and Climate Research - Tropospheric Research (IMKTRO) within Karlsruhe Institute of Technology (KIT) . She has held this W3 Professorship since 2013 and previously led a Helmholtz Young Investigators Group (2010-2016). Her work focuses on aerosol-cloud interactions , mixed-phase cloud processes , and numerical modeling of atmospheric systems . Professor of Theoretical Meteorology at KIT (2013-present) Former Helmholtz Group Leader (2010-2016) University of Oslo & ETH Zurich Postdoc experience Co-Editor of Atmospheric Chemistry and Physics Research Interests center on ice nucleation mechanisms , cloud dynamics , and climate sensitivity studies . Her group develops parameterizations for heterogeneous ice nucleation and investigates precipitation formation across different cloud types. Notable methodological contributions include SEVIRI satellite data analysis and ICON model modifications for microphysical-dynamic coupling. Publication Trends show consistent leadership in mixed-phase cloud modeling (2013-2025), with particular emphasis on Arctic cloud systems , heterogeneous ice formation , and aerosol impacts on precipitation . Her work bridges laboratory ice nucleation studies (e.g., dust-ash parameterizations) and regional climate modeling at various spatial resolutions. Teaching includes core courses in Theoretical Meteorology , Numerical Methods , and Cloud Physics at KIT. She has mentored multiple early-career researchers as evidenced by co-authorships with advisees like A. Oertel and L. Ickes. Collaborations span institutions including ETH Zurich, University of Oslo, and EU projects like EUCAARI. Her research group interacts with observational teams through field campaigns like Swabian MOSES and utilizes both in situ and remote sensing data for model validation.

Zhaoxia Puمشاهده پروفایل

استاد

• Numerical Weather Prediction
• Data Assimilation
• Machine Learning
• +۵ مورد دیگر

Dr. Zhaoxia Pu is a Professor in the Department of Atmospheric Sciences at the University of Utah and an Adjunct Professor at the School of Computing . Recognized as a Fellow of both the American Meteorological Society and the Royal Meteorological Society, she serves on the NOAA Science Advisory Board and has led 38 federally funded projects from agencies including NOAA, NASA, NSF, DOE, and ONR. Specializes in numerical weather prediction , data assimilation , and AI/machine learning for high-impact weather systems Developed advanced methods integrating satellite/radar data (GOES-R, CYGNSS, TROPICS) with Earth system models (UFS, E3SM, WRF) Recipient of the 2024 Excellence in Research Award and 2023 Provost's Banner Project recognition Research Trends : Her recent publications focus on: Machine learning approaches for precipitation retrieval using GOES-R data Cold fog microphysics and visibility parameterization in complex terrain Tropical cyclone dynamics through radar and lidar data assimilation Boundary layer turbulence in landfalling storms Drought mechanisms linked to synoptic-scale circulation New particle formation in mountainous regions Scientific Leadership : Lead scientist for CFACT NSF field campaign (2021–2025) Editorial board member of leading journals Active reviewer for NSF, DOE, NOAA, and NASA Teaching & Mentorship : Teaches Numerical Weather Prediction , Atmospheric Dynamics , and Introduction to Atmospheric Sciences courses. Has supervised 28 graduate students to completion.

• Exoplanetary Atmospheres
• Astrophysics
• Planetary Science
• +۳ مورد دیگر

Eva-Maria Ahrer is a postdoctoral researcher in the Atmospheric Physics of Exoplanets department, focusing on exoplanetary atmospheres and data analysis techniques for space-based telescopes like JWST and HST. Her work bridges observational astronomy with computational methods to advance planetary science. Her research spans exoplanet atmospheric characterization , planetary formation models , and JWST spectroscopic data analysis . Key projects include the BOWIE-ALIGN comparative survey and the KRONOS initiative, emphasizing the link between migration history and atmospheric composition. Recent publications highlight her contributions to understanding hot Jupiter alignment , metallicity , and chemical signatures using JWST’s NIRSpec and NIRISS instruments. She co-developed the open-source Eureka! pipeline for time-series observations and advocates for equity in STEM through the Equitea forum.

Maria Moruzمشاهده پروفایل

پژوهشگر

• Romanian linguistics
• Philology
• Phraseology
• +۳ مورد دیگر

Maria Moruz serves as a Researcher at the Department of Social Sciences and Humanities, Alexandru Ioan Cuza University of Iasi, a position she has held since February 2009. Her academic profile centers on Romanian linguistic research with strong interdisciplinary connections to biblical studies and computational methods. She earned her PhD from Alexandru Ioan Cuza University of Iasi in December 2009 with a dissertation analyzing phraseological units in parallel bilingual corpora, establishing her foundational expertise in comparative linguistics. Maria Moruz's research program integrates traditional philological scholarship with modern computational techniques . Her primary domains include Romanian linguistics , phraseology , and computational linguistics , with significant contributions to Biblical philology through critical editions of historical texts. She examines national specificity in phraseological units while developing linguistic resources for Romanian language processing , bridging historical textual analysis with contemporary NLP applications. Her publication trajectory reveals distinct thematic phases: early computational linguistics work (2004-2012) on Romanian lexical resources and question-answering systems evolved into specialized biblical philology research (2014-present) within the Monumenta linguae Dacoromanorum project. Recent publications focus on Romanian lexicography and biblical marginalia, demonstrating sustained engagement with both digital humanities methodologies and historical textual criticism. Maria Moruz has secured significant research funding as Project Director for a CNCSIS TD grant and served as a POSDRU postdoctoral researcher. Her collaborative work includes membership in the international "Widespread Idioms in Europe and Beyond" project and leadership in multiple Romanian linguistics initiatives. Project Director: CNCSIS TD grant POSDRU Postdoctoral Researcher Collaborator: "Widespread Idioms in Europe and Beyond" project Co-organizer: International Symposium "Explorations in the Romanian and European Biblical Tradition" Founding Member: Romanian Association of Biblical Philology and Hermeneutics She maintains active scholarly infrastructure through editorial work on the Monumenta linguae Dacoromanorum series and organization of specialized symposia, fostering interdisciplinary dialogue between computational linguists, philologists, and biblical scholars.

Ming Yanمشاهده پروفایل

دانشیار

• Optimization Methods
• Sparse Recovery
• Inverse Problems
• +۷ مورد دیگر

Ming Yan is an Associate Professor and Assistant Dean at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), where he has been serving since 2022. Prior to this position, he was an Associate Professor at Michigan State University from 2021 to 2023 (on leave from 2022 to 2023) and an Assistant Professor from 2015 to 2021. His academic journey includes postdoctoral positions at UCLA and Rice University. His educational background includes: Ph.D. in Mathematics from University of California, Los Angeles (UCLA), 2012 M.S. in Mathematics from University of Science and Technology of China (USTC), 2008 B.S. in Mathematics from University of Science and Technology of China (USTC), 2005 Ming Yan's research focuses on optimization methods , particularly for sparse recovery and inverse problems . His work extends to federated and machine learning algorithms , parallel and distributed algorithms for large-scale data , and variational techniques in image processing . His approach often combines theoretical analysis with practical applications, developing algorithms that address computational challenges in modern data science. His recent publications demonstrate a strong focus on decentralized optimization , primal-dual algorithms , and sparse recovery techniques . These works span theoretical developments in convergence analysis and practical applications in machine learning and signal processing. His research has evolved from foundational work in image processing to more recent contributions in distributed learning and federated systems, reflecting the changing landscape of data science. Ming Yan is actively involved in mentoring students and has indicated that he is recruiting Ph.D. students, postdocs, and visiting students at CUHK-Shenzhen. His teaching portfolio includes courses in calculus, optimization, and computational methods across multiple institutions. His research is supported by various grants, though specific details are not provided in the available information. He has developed several algorithms and software implementations, including the PD3O framework for convex optimization algorithms.

• Atmospheric Science
• Meteorology
• Remote Sensing
• +۴ مورد دیگر

Jan Cermak is Professor and acting Director of the Institute of Meteorology and Climate Research - Atmospheric Trace Gases and Remote Sensing (IMK-ASF) at Karlsruhe Institute of Technology (KIT). His research focuses on atmospheric science with specialization in fog dynamics, aerosol-cloud interactions, and satellite remote sensing applications. His primary research interests include Atmospheric Science, Meteorology, Remote Sensing, Aerosol-Cloud Interactions, Fog and Low Stratus Dynamics, Satellite Meteorology, and Machine Learning Applications. He employs geostationary satellite data and explainable machine learning to investigate fog life cycles, particularly in the Namib Desert and Po Valley, examining how aerosols influence cloud formation and dissipation processes. His work bridges observational meteorology with computational techniques for environmental monitoring. Recent publications (2023-2025) reveal a concentrated research trajectory on fog life cycle analysis using multi-sensor approaches, with significant emphasis on aerosol impacts in polluted regions and arid environments. Key methodological trends include explainable AI for cloud fraction adjustment analysis and high-resolution satellite-based air quality estimation, demonstrating strong interdisciplinary integration of atmospheric physics and data science. Scientific Awards: No awards were documented in the provided source material. Advising and Grants: Source documentation contains no information regarding student supervision, research grants, or funding sources. His leadership role as acting director of IMK-ASF suggests administrative responsibilities within the institute's research framework. Labs and Teams: Cermak leads the Atmospheric Trace Gases and Remote Sensing (IMK-ASF) group at KIT, conducting field campaigns including NaFoLiCA (Namib Fog Life Cycle Analysis) and FAIRARI (Fog and Aerosol Interaction Research Italy). The team specializes in satellite data analysis combined with ground-based observations to study fog dynamics in extreme environments.

Brendan Kennedyمشاهده پروفایل

استاد

• Optical Imaging
• Biomedical Engineering
• Medical Imaging
• +۴ مورد دیگر

Professor Brendan Kennedy is a leading academic in biomedical engineering and optical imaging, serving as Group Leader of the Bioimaging Research and Innovation for Translational Engineering Laboratory (BRITElab) at the Harry Perkins Institute of Medical Research. He holds a Full Professor position in the School of Engineering at The University of Western Australia (UWA) and is a Visiting Professor in the Institute of Physics at Nicolaus Copernicus University, Poland. B.Sc. in Electronic Engineering (2001) Ph.D. in Electronic Engineering (2006) His research focuses on developing advanced optical coherence tomography (OCT) and optical elastography techniques for clinical applications in surgery, mechanobiology, and tissue engineering. These technologies enable high-resolution, real-time imaging of tissue elasticity and micro-scale mechanics, revolutionizing cancer detection and surgical margin assessment. Recent publications highlight his pioneering work in 3D mechanical characterization of tumors, intraoperative imaging for breast-conserving surgery, and multimodal systems integrating OCT with fluorescence lifetime imaging. His team has also advanced phantom designs for calibration, strain retrieval algorithms, and portable optical palpation devices for telehealth and low-resource settings. West Australian 40under40 Award Elected Optica Fellow (2024) Prof. Kennedy has secured over $15 million in competitive research grants and $5 million in industry contracts, driving translational projects like OncoRes Medical, a startup commercializing his optical imaging innovations. He holds 40+ patents and co-founded BioZone, a UWA initiative fostering interdisciplinary biomedical research.