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Denis WirtzView profile

Professor

• 3D Cell Motility
• Aging and Cancer
• Tumor Microenvironment
• +7 more

Denis Wirtz is the Vice Provost for Research and Theophilus Halley Smoot Professor at Johns Hopkins University. He is affiliated with the Whiting School of Engineering, the Data Science and AI Institute, and holds joint appointments in Pathology and Oncology. His research spans mechanobiology, cancer metastasis, and 3D biomedical imaging. PhD, Chemical Engineering, Stanford University (1993) MSc, Chemical Engineering, Stanford University BSc, Physics Engineering, Free University of Brussels (1988) Wirtz’s research focuses on cell motility , tumor microenvironment dynamics , and AI-driven 3D tissue reconstruction . His lab developed CODA , a groundbreaking pipeline for cm-scale 3D tissue mapping, and pioneered work in mechanobiology and high-throughput cell phenotyping. Recent projects include analyzing pancreatic precancerous lesions, skin aging, and immune cell organization in tumors. Key research trends include 3D cell migration models , machine learning applications in pathology , and mechanotransductive feedback in cancer progression. His work bridges physics , oncology , and biomedical engineering . NSF CAREER award (1995) Theophilus H. Smoot Professor (2009) Fellow, AIMBE (2007), AAAS (2009), APS (2010) Royal Academy of Medicine of Belgium Wirtz has advised numerous PhD students, including Kyu Sang Han, and mentors a lab team working on metastasis , extracellular vesicles , and immunology . His lab collaborates with the NCI-funded Physical Sciences-Oncology Center and Cellular Cancer Biology Imaging Research Center. The Wirtz Lab, part of the Johns Hopkins Institute for NanoBioTechnology (INBT), focuses on quantitative methods like particle-tracking microrheology and CODA for 3D tissue analysis. Recent additions include Master’s students from Instituto Superior Técnico and new PhD cohorts in 2023–2024.

Yuling YanView profile

Professor

• Biomedical Imaging
• Machine Learning for Medical Applications
• Biosignal Analysis
• +7 more

Yuling Yan serves as Professor and Founding Chair of the Department of Bioengineering at Santa Clara University's School of Engineering, holding the Phil and Bobbie Sanfilippo Professorship. She maintains an affiliation as Consulting Faculty at Stanford University's Department of Otolaryngology. Her academic journey includes faculty positions at the University of Hawaii-Manoa (2002-2005), University of Wisconsin-Madison, and University of the Ryukyus (1997-1999), with postdoctoral training at McGill University and Max Planck Institute for Biochemistry. Professor Yan's research focuses on AI-driven medical diagnostics, specializing in machine learning applications for early disease detection including vocal pathologies, cardiac arrhythmia, skin cancer, brain aneurysms, and breast cancer. Her work integrates biomedical imaging (MRA, ultrasound, CT), biosignal analysis, and fluorescence microscopy with molecular switch probes. Current projects involve collaboration with radiologists at Santa Clara Valley Hospital to develop deep learning systems for medical image analysis. Her publication trends reveal an evolution from foundational work in vocal fold dynamics and optical probe development (2011-2015) toward AI/ML applications in medical diagnostics (2017-present), with increasing emphasis on deep neural networks for cancer detection and cardiac monitoring. Key contributions include novel methodologies for high-speed laryngeal imaging analysis and optical lock-in detection microscopy. Active research funding includes NIH Multi-Investigator Awards for high-contrast single-molecule imaging and NSF grants for laryngeal imaging tools development. She teaches graduate courses including Machine Learning for Biomedical Applications and Deep Learning for Medical Image Analysis, alongside undergraduate bioengineering fundamentals.

Konstanze F. WinklhoferView profile

Professor

• Neurodegeneration
• Ubiquitin system and proteostasis
• Mitochondrial biology and stress signaling
• +4 more

Professor Konstanze F. Winklhofer is a full Professor of Molecular Cell Biology at the Institute of Biochemistry and Pathobiochemistry, Faculty of Medicine, Ruhr University Bochum, Germany. She additionally serves as Vice-Speaker of the Research Department of Neuroscience and heads the Winklhofer Lab, which is equipped with state-of-the-art imaging core facilities including super-resolution microscopy, lattice-SIM, holotomography, and high-content screening platforms. Education & Career: Current: Professor (W3) at Ruhr University Bochum, Faculty of Medicine, Institute of Biochemistry and Pathobiochemistry. Role: Vice-Speaker, Research Department of Neuroscience. Research Focus: The Winklhofer laboratory investigates molecular, cellular, and systems-level mechanisms underlying neurodegenerative diseases. Central themes include: Ubiquitin system & proteostasis: understanding how linear ubiquitination and E3 ligases such as Parkin and LUBAC control neuronal integrity. Mitochondrial biology: deciphering mitochondria-centric stress response pathways, inter-organellar communication, and innate immune signaling (NF-κB axis). Phase separation & condensate biology: studying how proteins such as NEMO, α-synuclein, and prion protein undergo liquid-liquid phase separation and how this impacts aggregation and clearance by autophagy. Advanced imaging: exploiting super-resolution microscopy (SIM, PALM/dSTORM), live-cell lattice-SIM, FRAP/FRET, and holotomography to visualize dynamic cellular processes in real time. Publications & Trends: Across more than 150 peer-reviewed articles since 2000, the group has progressively shifted from elucidating basic ubiquitin biochemistry to integrating these insights with emerging concepts of phase separation and mitochondrial signaling hubs. Recent high-impact works (2023-2025) in Nature Communications , EMBO Journal , Cell , and Life Science Alliance highlight novel roles for NEMO, PACRG, and LUBAC in aggregate clearance and neuroinflammation. Scientific Awards: No specific awards detailed in the provided text. Funding & Collaborations: The lab is funded by the German Research Foundation (DFG) through major consortia including: RESOLV Cluster of Excellence (EXC 2033) SPP2453 “Integration of mitochondria into the cellular proteostasis network” FOR2848 “Nanoscale Architecture and Heterogeneity of the Mitochondrial Inner Membrane” RTG2862 “Monoaminergic Neuronal Networks and Disease” IMPRS for Living Matter Lab & Imaging Core: The Winklhofer group operates an open-access Imaging Facility housing Zeiss Elyra PS.1, Elyra7 Lattice-SIM, Nanolive CX-A holotomography, Cytation 5 MPW multimode reader, and Sapphire Biomolecular Imager. Custom pipelines in Imaris and CellProfiler enable advanced segmentation, particle tracking, and machine-learning-assisted analysis.

• Artificial Intelligence
• Biomedical Imaging
• Computer Vision
• +5 more

Andreas Uhl is a University Professor in Artificial Intelligence and Human Interfaces at the Department of Computer Science, University of Salzburg. With a prolific research career spanning from 1996 to present, he has authored or co-authored 534 publications and led or participated in 59 research projects. His work demonstrates sustained academic productivity with recent publications and projects extending through 2025. Professor Uhl's research interests span multiple domains at the intersection of artificial intelligence and practical applications. His primary focus areas include computer vision, biometrics, biomedical imaging, and digital forensics, with significant contributions to pattern recognition and image analysis. His work bridges theoretical computer science with practical applications in cultural heritage preservation, medical diagnostics, and security systems, demonstrating a versatile research portfolio that addresses both fundamental challenges and real-world problems. His recent publications reveal a strong emphasis on temporal image forensics, biomedical image analysis, and biometric security. The research shows a clear trajectory toward increasingly sophisticated applications of AI in specialized domains, with particular attention to validation methodologies and limitations of current approaches. His work on cultural heritage applications demonstrates an innovative application of computer vision techniques to historical artifacts. Best paper award @ 25th ACM Symposium on Applied Computing (Applications Track), 2010 Best Paper award @ 2nd European Workshop on Visual Information Processing (EUVIP'10), 2010 IEEE Biometrics Council Best Paper Award (TBIOM), 2022 Kurt Zopf Preis, 2023 Professor Uhl actively leads multiple significant research initiatives, including the CDL-POSA project on People and Object Surface Authentication (2025-2032), Artificial Intelligence driven Biomedical Imaging Innovation (2025-2029), and the AIBIA Research and Transfer Junior Lab (2023-2025). His research group maintains active collaborations with institutions like Carnegie Mellon University, as evidenced by his recent research stay there in September 2024. The scope and duration of his current projects indicate substantial grant funding and institutional support for his research agenda. His laboratory activities focus on AI applications in biomedical imaging, border security through vehicle-integrated technologies (AutoBorder project), and cultural heritage analysis. The research environment appears to integrate academic inquiry with practical transfer through initiatives like the FFG Student Internships program, suggesting a strong commitment to both fundamental research and real-world implementation.

• Augmented Reality
• Computational Geometry
• Computer Vision and Pattern Recognition
• +12 more

Andrea Bottino is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) at the Polytechnic University of Turin. He has been actively involved in the Computer Graphics and Vision Group and leads various VR@POLITO initiatives. Chair of the Master HUMANAIZE program Coordinator of multiple Machine Learning for Vision and Multimedia courses Research Focus : Augmented and Virtual Reality for education and safety Computer Vision with applications to medical imaging and kinship analysis Human-Computer Interaction in immersive environments Multimodal Learning systems XR for Cultural Heritage Publication Trends : Recent works focus on AI applications , XR training systems , and computer vision techniques applied to medical diagnostics and cultural preservation . Leadership Roles : Scientific Director for MEI - Interactive Egyptian Museum Coordinator of PNRR Mission 4 projects Principal Investigator for Holo-BLSD and ALPTECH initiatives Labs and Collaborations : Active in Visionary LAB and VR@POLITO Collaborates with Balletto Teatro di Torino for cultural XR applications Partners with Fondazione Museo Egizio and Robin Studio

Manuel Fernández DelgadoView profile

Associate Professor

• Machine Learning
• Pattern Recognition
• Computer Vision
• +3 more

Manuel Fernández Delgado is an Associate Professor at the University of Santiago de Compostela (manuel.fernandez.delgado@usc.es). His research spans Machine Learning , Pattern Recognition , and Computer Vision with applications in medical diagnostics, agricultural monitoring, and gender-inclusive education. PhD in Computer Science (1999) Developed software tools: Govocitos , CystAnalyser , STERapp Research highlights: Medical Imaging : Breast cancer and oral leukoplakia analysis via image segmentation/classification Agricultural AI : Nutrient deficiency detection in wheat, marbling analysis in ham Gender Equality : Pioneering computational thinking education with gender perspectives Robotics : Fault diagnosis systems for antenna arrays Key methodologies include Extreme Learning Machines, Support Vector Machines, and Deep Learning. He collaborates with teams in TELGalicia and TecAnDaLi networks.

Daria IlatovskayaView profile

Associate Professor

• Cardiorenal pathophysiology
• Ion channel biophysics
• Mitochondrial bioenergetics
• +3 more

Dr. Daria Ilatovskaya is a tenured Associate Professor of Physiology at the Medical College of Georgia, Augusta University where she also serves as Graduate Program Director for the Physiology Graduate Program. She leads the Kidney and Hypertension Research Group and has adjunct roles in The Graduate School. Education: Ph.D. Cellular Biology, Institute of Cytology, Russian Academy of Sciences, 2012 M.S. Physics, Peter the Great Saint Petersburg Polytechnic University, 2009 B.S. Physics, Peter the Great Saint Petersburg Polytechnic University, 2007 Research Focus: Dr. Ilatovskaya’s laboratory integrates in vivo, ex vivo and in vitro approaches to dissect how ion channels, mitochondrial bioenergetics and sex hormones govern cardiorenal homeostasis. Areas of emphasis include: Salt-sensitive hypertension and blood pressure control Polycystic kidney disease (ARPKD & ADPKD) Mitochondrial dysfunction in podocytes and tubular epithelia Sex-specific pathobiology and precision medicine Her team employs state-of-the-art techniques such as single-channel electrophysiology on freshly isolated nephron segments, live-cell confocal imaging, high-resolution mitochondrial respirometry and chronic blood-pressure telemetry. Publication Impact: Across 93 scholarly outputs (h-index 29), her 2024–2025 portfolio reveals a surge in translational studies elucidating ANP-mediated mitochondrial protection, sex-specific redox control of podocytes, and community-based hypertension interventions in rural Georgia. Awards & Honors: Outstanding Early Career Basic Science Faculty Award, Augusta University (2025) Young Investigator Award, APS Renal Section (2024) Lazaro J Mandel Young Investigator Award (2019) FAHA distinction, American Heart Association (2018) Edward J. Lennon Award for Outstanding Woman Postdoc (2014) Funding & Mentorship: Dr. Ilatovskaya is PI or Co-I on five active NIH grants totaling >$4 million, including two NHLBI R01s investigating ANP signaling and women’s cardiovascular health. She mentors graduate students, postdocs and undergraduates, and champions diversity through CUR’s STR program. Laboratory & Team: The Ilatovskaya Lab (Lab CA-3091) currently includes an Assistant Scientist (track), senior research assistant, three graduate students and an honors undergraduate, fostering an inclusive environment centered on rigorous science and trainee success.

Gözde YOLCU ÖZTELView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +5 more

Gözde YOLCU ÖZTEL serves as Assistant Professor in the Department of Software Engineering at Sakarya University's Faculty of Computer and Information Sciences. With a Ph.D. in Computer and Information Engineering completed in 2019, she has established herself as an active researcher in artificial intelligence with significant contributions to computer vision applications. Her educational journey includes: Ph.D. in Computer and Information Engineering (2014-2019) with thesis on deep learning-based interest detection through facial analysis M.Sc. in Computer and Information Engineering (2012-2014) focusing on Kinect-based virtual mirror design B.Sc. in Computer Engineering (2007-2011) from Sakarya University Dr. YOLCU ÖZTEL's research bridges deep learning with practical healthcare and public safety applications. Her work demonstrates exceptional adaptability to emerging challenges, particularly in developing AI systems for pandemic response (masked face detection, social distancing monitoring) and medical diagnostics (monkeypox/skin lesion classification). She specializes in creating efficient mobile-compatible deep learning models that maintain accuracy while operating on resource-constrained devices. Analysis of her 15 most recent publications reveals a clear trajectory toward healthcare-oriented computer vision, with increasing focus on mobile medical applications since 2020. Her research consistently combines novel deep learning architectures with real-world implementation constraints, particularly smartphone deployment. The integration of traditional computer vision techniques (like LBP) with modern deep networks represents a distinctive methodological signature across her work. She actively contributes to research through TÜBİTAK 2209 projects as student advisor and leads institutional research initiatives including SAYZEK-ATP (2024) and MAG Engineering Research Support Group. Her project portfolio spans from mathematical assistant software development to emergency drone-based object detection and MRI-based brain tumor classification. Dr. YOLCU ÖZTEL teaches core software engineering courses including Database Management Systems, Artificial Intelligence Fundamentals, and Data Mining while maintaining active research output. She previously organized the 2017 Facial Expression Recognition workshop, demonstrating ongoing commitment to academic community building.

• Radiation Oncology
• Medical Physics
• Computational Physics
• +5 more

Dr. Yulong Yan is a Professor in the Department of Radiation Oncology at UT Southwestern Medical Center, where he serves as Director of Computational Physics and faculty in the Division of Medical Physics & Engineering. His career spans academic, clinical, and technical domains in radiation oncology and biomedical engineering. Bachelor's & Master's: Nanjing University of Aeronautics and Astronautics Ph.D.: Biomedical Engineering, Southeast University, Nanjing Fellowship/Residency: Stanford University School of Medicine Dr. Yan's research focuses on Medical Physics , Adaptive Radiotherapy , and Deep Learning applications in clinical oncology. Key areas include: Functional lung avoidance techniques (FWAS, virtual bronchoscopy) Deep learning for synthetic CT generation and tumor segmentation Dose verification platforms (ART2Dose) Radiation toxicity modeling for brain and lung treatments Bluetooth-based localization systems for clinical applications MR-only radiotherapy workflows His publications demonstrate expertise in Image-Guided Radiotherapy , Treatment Planning Optimization , and Radiation Dose Modeling , with a 2013–2024 publication record showing sustained contributions to Medical Physics , Radiotherapy and Oncology , and PLOS One . Dr. Yan has served as Associate Editor for Medical Physics and Journal of Clinical Medical Physics , while chairing the Southwest Chapter AAPM Communications and IT Committee since 2014. As an educator since 2013, he mentors medical physics residents and postdoctoral fellows while delivering lectures in the Medical Physics Certificate Program.

Benedikt WirthView profile

Professor

• Optimisation and calculus of variations
• image processing
• scientific computing
• +9 more

Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.

• Mitochondrial Dynamics
• Neurological Diseases
• 4D Microscopy
• +3 more

Johannes Schöneberg serves as Assistant Professor of Pharmacology at the University of California San Diego's School of Medicine, holding joint appointments in Chemistry & Biochemistry. His research integrates cutting-edge microscopy, computational biology, and stem cell technologies to quantitatively analyze cellular behavior in native tissue environments. His laboratory focuses on mitochondrial dynamics and their role in neurological disorders including Parkinson's Disease and epilepsy. Key methodologies include stem cell-derived organoids, 4D adaptive optics lattice light-sheet microscopy, high-performance computing, and machine learning applications. Research spans computational modeling of cellular processes through tools like ReaDDy software to advanced image analysis techniques for tracking subcellular structures. The group's publication record demonstrates expertise across biophysics, computational biology, and neuropharmacology, with recent work emphasizing mitochondrial network tracking and 4D cellular imaging. Though newly established at UCSD, the lab shows strong interdisciplinary connections through collaborations with microscopy pioneers like Eric Betzig. No scientific awards are currently listed in available sources. The laboratory maintains active research in neurological disease mechanisms with emphasis on mitochondrial dysfunction pathways. As a newly formed research group, current activities focus on establishing organoid models and advanced imaging pipelines. The lab leverages UCSD's strong infrastructure in computational biology and neuroscience research, with potential for future grant development in mitochondrial disease mechanisms and imaging technology.

Robert HaaseView profile

Lecturer

• Bioimage Analysis
• Medical Informatics
• GPU-Accelerated Image Processing
• +6 more

Dr. Robert Haase is a Lecturer and Training Coordinator at the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) under Leipzig University , with prior leadership roles at the DFG Cluster of Excellence 'Physics of Life' at TU Dresden . He specializes in Bioimage Analysis , GPU-Accelerated Image Processing , and Large Language Models (LLMs) for life sciences. His research focuses on democratizing bioimage analysis through open-source tools like CLIJ , clesperanto , and bia-bob , aiming to bridge microscopy with data science . Recent projects explore LLM-driven code generation for image analysis and interactive workflow design in platforms like napari . He leads initiatives such as the NFDI4BioImage consortium for research data management in Germany and GloBIAS , a global society for bioimage analysts. Funded by organizations including the Chan Zuckerberg Initiative (CZI) and DFG , his work emphasizes reproducibility , open science , and interdisciplinary collaboration in bioimaging. As an educator, he conducts training programs like "Large Language Models for Bioimage Analysis" and "Collaborative Working with Git" , and contributes to workshops at institutions such as EMBO , Institut Pasteur , and ScaDS.AI Summer Schools .

• Neuroscience
• Biomedical Engineering
• Neuroimaging
• +3 more

Nikolay Kutuzov serves as an Assistant Professor in the Department of Neuroscience at the University of Copenhagen's Faculty of Health and Medical Sciences. His research employs two-photon microscopy and deep learning to investigate cerebrovascular dynamics, blood-brain barrier function, and pericyte biology in health and aging. Kutuzov's work centers on neurovascular coupling, with key interests including blood-brain barrier transport mechanisms, capillary pericyte function, and the role of precapillary sphincters in regulating cerebral blood flow. He integrates experimental neuroscience with computational approaches, particularly deep learning applications for nanoparticle tracking and image analysis in two-photon microscopy. His recent publications (2018-2025) demonstrate a progression from foundational studies on myelin organization to innovative work combining deep learning with in vivo imaging. Current research emphasizes aging-related cerebrovascular changes and nanoparticle diffusion modeling, reflecting a multidisciplinary approach that bridges molecular neuroscience and biomedical engineering. Though specific advising details are unreported, his collaborative publications with leading neuroscientists suggest mentorship activities. His laboratory likely utilizes advanced imaging facilities at the University of Copenhagen for studying neurovascular unit dynamics, with potential focus on developing new computational tools for brain barrier research.