Find Academic Supervisors

Sebastian SchmelzleView profile

Researcher

• Functional Morphology
• Animal Morphology
• Arthropod Structure
• +5 more

Dr. Sebastian Schmelzle is a researcher at the Department of Biology, Technische Universität Darmstadt , where he works on functional morphology and biomechanics of arthropods. Since 2022, he has also served as CEO of Small World Vision GmbH . His academic career includes a magna cum laude PhD in Biology (2015) and research roles in DFG- and BMBF-funded projects like LIS, Waldklimafonds, NOVA, and ASTOR. He specializes in 3D imaging techniques, particularly synchrotron microtomography , and has contributed to open-source platforms like Biomedisa . Education : PhD in Biology (2015, TU Darmstadt), Diploma in Biology (2008, Eberhard Karls University Tübingen) Research interests focus on arthropod defensive mechanisms , notably the ptychoid defense in oribatid mites. His work explores how evolutionary lineages independently develop exoskeletal and muscular adaptations for survival. He bridges entomology , materials science , and computational imaging to study biomechanics, surface chemistry, and structural efficiency. Publications highlight his expertise in 3D microtomography (e.g., analyzing ant mandibles, insect cuticular hydrocarbons, and primate retinal structures). Collaborative projects span biomedical image segmentation , insect biomechanics , and synchrotron radiation applications . His recent work integrates machine learning and real-time data filtering for enhanced visualization. Additional contributions include public engagement initiatives like the AR Museum Station and Insect Excursions , aiming to democratize scientific knowledge through interactive exhibits and field studies.

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.

María Virumbrales-MuñozView profile

Assistant Professor

• Microphysiological Systems
• Organ-on-a-Chip
• Cancer Biology
• +12 more

María Virumbrales-Muñoz is an Assistant Professor in the Department of Obstetrics and Gynecology at the University of Wisconsin–Madison, with an affiliate appointment in Biomedical Engineering. She is also the Principal Investigator of the Virumbrales-Muñoz Lab, where she leads research in microphysiological systems to model cancer and reproductive disorders. Position: Assistant Professor, Obstetrics & Gynecology Affiliation: Biomedical Engineering (Affiliate Faculty) Lab: Virumbrales-Muñoz Lab, University of Wisconsin–Madison Email: virumbralesm@wisc.edu She holds a PhD in Biomedical Engineering from the University of Zaragoza, Spain, and completed postdoctoral training at UW-Madison in Biomedical Engineering and Pathology & Laboratory Medicine. Her research lies at the intersection of bioengineering and women's health, focusing on 3D biomimetic modeling of diseases such as high-grade serous ovarian cancer (HGSOC) and preeclampsia. Using microfluidic technologies, her lab develops organ-on-a-chip models to study tumor microenvironments, metabolic heterogeneity, immune-endothelial interactions, and vascular dysfunction in reproductive diseases. The lab's recent publications reveal a strong focus on advancing microphysiological systems for cancer and vascular biology, with applications in drug screening, immune response modeling, and patient-specific disease modeling. Her work integrates molecular biology, bioengineering, and clinical data to improve understanding of disease mechanisms and therapeutic responses. Scientific Awards: Chester B. Martin Graduate Training Program Mentorship Award (2021) Dr. Virumbrales-Muñoz is an active mentor and educator, advising graduate and undergraduate students in biomedical engineering and reproductive sciences. She teaches in courses such as AN SCI 875 and OBS&GYN 710 and collaborates on major research grants, including NIH R01 proposals. Her lab is funded by the American Cancer Society, University of Wisconsin Carbone Cancer Center, and Meriter Foundation, among others. Research Labs and Teams: Virumbrales-Muñoz Lab: Focuses on microphysiological systems for cancer and preeclampsia Collaborations: UW-Madison Department of Biomedical Engineering, Pathology & Laboratory Medicine, and UnityPoint Health-Meriter Foundation Team Members: Includes postdoctoral researchers, PhD students, and research technicians working on ovarian cancer, breast cancer, and vascular modeling projects

• Machine Learning
• Bioinformatics
• Biomedical Applications
• +6 more

Marc Vaisband is a researcher at the Life and Medical Sciences Institute (LIMES) of the University of Bonn, where he pursues his PhD under Prof. Hasenauer. His research focuses on machine learning applications in biomedical imaging and oncology, supported by funding from the Salzburg Cancer Research Institute. Education B.Sc. and M.Sc. in Mathematics at the University of Bonn Specialized in stochastics and numerical analysis, with theses on KPZ theory and Totally Asymmetric Simple Exclusion Process Research Interests His work bridges machine learning and biomedical challenges, particularly in age-related macular degeneration diagnostics, cancer therapy optimization, and stochastic modeling. He develops neural networks for medical imaging and survival analysis in hematologic malignancies. Scientific Contributions Marc's 15 most recent publications include machine learning-driven AMD detection via fluorescence microscopy, precision dosing in anticancer therapy, and deep learning validation for NGS genetic variants. His articles emphasize statistical modeling, image analysis, and clinical outcome prediction. Professional Context As part of the Hasenauer Lab, Marc contributes to computational medicine projects. His interdisciplinary approach integrates mathematical rigor with clinical applications.

Antoni Jaume-i-CapóView profile

Professor

• Artificial Intelligence
• Explainable Artificial Intelligence
• Computer Vision
• +3 more

Dr. Antoni Jaume-i-Capó is a Full Professor in the Department of Mathematics and Computer Science at the University of the Balearic Islands (UIB) , affiliated with the Computer Graphics and Vision and Artificial Intelligence Group (UGiVIA) and serving as Director of the Artificial Intelligence Applications Laboratory (LAIA@UIB) . His work bridges Artificial Intelligence , Computer Vision , and Medical Imaging with applications in Motor Rehabilitation and Explainable AI (XAI) . Research Interests : Artificial Intelligence, Explainable AI, Computer Vision, Human-based Computation, Intelligent Systems for Motor Rehabilitation, Medical Imaging Processing. Teaching : Courses include Algorithmics and Data Structures , Final Degree Projects , and Practical Placements for Informatics Engineering degrees across multiple campuses. Scientific Contributions span 15 recent publications focusing on XAI fidelity metrics , medical image analysis , and crowdsourced diagnostics . These works integrate machine learning for Sickle Cell Disease classification and kinect-based rehabilitation systems . Scientific Awards : Premio al mejor trabajo JENUI 2010 Students : Supervised over 11 PhD/Master’s students, including Gabriel Moyà-Alcover and Pedro Marrero-Fernández. Projects : Leads EU-funded initiatives like EXPLAINME and EUGAIN , alongside regional efforts such as People4Sicklemia .

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.

Audrey SallesView profile

Researcher

• Cell Biology
• Immunology
• Super-Resolution Microscopy
• +2 more

Audrey Salles is a senior expert research engineer at the Unit of Technology and Service Photonic BioImaging (UTechS PBI) within Institut Pasteur in Paris, France. Since 2014, she has specialized in super-resolution microscopy and fluorescence correlation spectroscopy (FCS) techniques, supporting researchers through training and collaborative projects. Her work bridges biophysical modeling with advanced imaging to study plasma membrane mechanics and pathogen interactions. Education: PhD in Immunology (2011), CIML, Marseille, France Her research focuses on plasma membrane organization , ESCRT machinery in cell division , and actin cortex dynamics during viral budding . She collaborates on projects involving Saccharolobus islandicus cytokinesis, HIV-1 release mechanisms, and Staphylococcus aureus pathogenesis. Her publications span Nature Methods , Developmental Cell , and eLife , reflecting interdisciplinary applications of microscopy. Audrey leads quality assurance for the ISO9001 certification at UTechS PBI, ensuring rigorous standards in a facility that integrates equipment management, repair logistics, and staff upskilling. Her methodological innovations include enhanced super-resolution radial fluctuation (eSRRF) and three-dimensional live-cell imaging frameworks.

• Biomedical Engineering
• Medical Image Processing
• Artificial Intelligence in Healthcare
• +3 more

Marek Wodziński serves as a Lecturer at the Department of Metrology and Electronics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His academic role involves teaching and research at the intersection of engineering and medical diagnostics, with a focus on developing computational solutions for clinical imaging challenges. Contact details include office B-1 room 205, phone +48 12 617 27 11, and email wodzinski@agh.edu.pl. Education: PhD in Engineering (indicated by 'dr inż.' title) His research centers on biomedical image processing and artificial intelligence applications in healthcare, with particular emphasis on medical and veterinary image analysis. Key areas include segmentation of anatomical structures in CT/MRI, explainable AI for diagnostic systems, multimodal assessment of neurodegenerative diseases (especially Parkinson's), and development of large-scale medical datasets. His work bridges computer vision, deep learning, and clinical practice to address real-world diagnostic limitations. Recent publications (2025) demonstrate a cohesive research trajectory focused on advancing AI interpretability in medical imaging, with significant contributions to veterinary radiography, cranial defect reconstruction, and Parkinson's disease assessment through voice/gait analysis. His participation in international challenges like MICCAI, SPPIN, and Fusemycells highlights practical implementations of his work, often involving multimodal data fusion and novel deep learning architectures for segmentation and classification tasks. Scientific Awards: No scientific awards mentioned in available information While specific student names and grant details aren't provided, his extensive publication record in top medical imaging venues indicates active mentorship of graduate researchers and involvement in collaborative international projects. His work frequently appears in challenge competitions, suggesting funding from research consortia focused on AI-driven medical diagnostics. Future directions likely include expanding multimodal approaches for neurodegenerative disease tracking and enhancing cross-species applicability of veterinary AI systems. He collaborates within the Department of Metrology and Electronics' biomedical research group, contributing to projects involving mixed reality diagnostic platforms, cranial implant design, and large-scale medical datasets like MedShapeNet. His team actively participates in global initiatives such as the MICCAI society challenges, focusing on reproducible AI solutions for clinical imaging problems.

• 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.

Shmuel AvidanView profile

Professor

• Object Tracking
• 3D Object Modeling
• Computer Vision
• +3 more

Prof. Shmuel Avidan serves as a Professor in the School of Electrical Engineering at Tel Aviv University's Iby and Aladar Fleischman Faculty of Engineering. Holding a Ph.D. from Hebrew University's School of Computer Science (1999), he brings extensive industry experience from Adobe, Mitsubishi Electric Research Labs, MobilEye, and Microsoft Research to his academic role. His educational trajectory features: Ph.D. in Computer Science, Hebrew University of Jerusalem (1999) Avidan's research centers on pixel-centric computational problems, with seminal contributions in video object tracking and 3D object modeling from 2D images. His work spans computer vision, image processing, and machine learning, emphasizing practical applications in industrial settings. Current investigations explore neural rendering, foundation models, and diffusion-based architectures for visual understanding. Recent publications (2023-2025) demonstrate concentrated innovation in neural radiance fields (NeRF), category-agnostic pose estimation, and texture-aware segmentation. These works increasingly integrate foundation models with domain-specific applications in medical imaging, autonomous systems, and materials science, reflecting a strategic shift toward scalable vision systems. Though specific awards aren't documented in source materials, his prolific publication record and sustained industry partnerships signify substantial field impact. His research group maintains active collaboration with leading technology firms, translating academic discoveries into real-world solutions. Professor Avidan mentors graduate students in computer vision while securing competitive grants for projects at the intersection of theoretical computer vision and industrial implementation. His lab focuses on developing robust algorithms for challenging visual environments, particularly in autonomous driving and medical imaging contexts. Leading an active research group within Tel Aviv University's Electrical Engineering department, he drives innovation in neural rendering and vision-language models. The team regularly contributes to premier conferences including CVPR, ICCV, and ECCV, maintaining strong industry ties through ongoing partnerships with automotive and imaging technology companies.

Eric HowardView profile

Research Fellow

• Quantum Physics
• Machine Learning
• Gravitational Waves
• +4 more

Dr. Eric Howard is a Research Fellow at Macquarie University , affiliated with the School of Engineering , School of Mathematical and Physical Sciences , and School of Computing . His research spans interdisciplinary domains at the intersection of quantum physics, machine learning, and AI-driven systems. Key research themes include: Quantum cryptography for Industry 4.0 security Machine learning in IoT temperature sensing Adversarial AI in cybersecurity 6G wireless communication optimization Quantum information processing Deep learning for data imputation Recent publications demonstrate a focus on emerging technologies, with articles on quantum Bayesian inference , 6G signal processing , and smart city IoT systems . His collaborative work extends to blockchain-enabled supply chain visibility and generative AI applications in programming. Research collaborations span institutions in India (AIP Publishing) and Australia, with technical contributions to quantum dynamics, neural network applications, and nanosensor development.

• Developmental Biology
• Cell Biology
• Image Analysis
• +7 more

Karsten Siller is an Associate Professor at the University of Virginia's School of Data Science. He previously served as an Assistant Professor in the Department of Biology (College of Arts & Sciences) and held the role of Associate Director for Research Computing User Services. His work bridges data science with biology, focusing on scalable, reproducible data workflows and computational methods. Ph.D., Biology , University of Tübingen, Germany Dipl. Biol., Biology , University of Tübingen, Germany His research interests include: Building reproducible and resilient, scalable data analysis workflows for diverse computing environments. Developing user-friendly, extensible interfaces for modular data pipelines with integrated visualization. Advancing open-source software and computational tools for biology and image analysis. Key trends in his publications show expertise in: Fluorescence Lifetime Imaging (FLIM) for cellular metabolism studies. High-performance computing (HPC) for data pipeline management. Mechanistic insights into cell division and neurodegenerative processes via imaging and computational modeling.

Melissa SkalaView profile

Professor

• Biomedical optical imaging
• Cancer metabolism
• Immunotherapy
• +3 more

Melissa Skala, Ph.D., is the Carol Skornicka Chair of Biomedical Imaging at the Morgridge Institute for Research and a full Professor of Biomedical Engineering & Medical Physics at the University of Wisconsin–Madison. Her laboratory pioneers label-free optical imaging technologies—most notably fluorescence-lifetime microscopy and optical coherence tomography—to quantify metabolic heterogeneity in cancer, immune cells and engineered tissues, with direct translation to cell-therapy manufacturing and personalized cancer medicine. Education Ph.D. Biomedical Engineering, 2007, Duke University M.S. Biomedical Engineering, 2004, University of Wisconsin–Madison B.S. Physics, 2002, Washington State University Research Focus The Skala lab develops and applies cutting-edge photonics platforms—including autofluorescence lifetime imaging, optical redox ratio mapping, second-harmonic generation, and light-sheet microscopy—to interrogate metabolic states of single cells and organoids in vitro and in vivo . Major thrusts include: Cancer metabolism and immunotherapy response Immune cell activation and exhaustion Stem-cell and CAR-T manufacturing quality control Micro-physiological disease models Machine-learning–driven image analysis Publication Trends Across >250 peer-reviewed papers since 2004, she has progressed from foundational studies on NAD(P)H/FAD redox imaging in epithelial tissues to recent landmark reports defining metabolic biomarkers for T-cell activation, immune-cell subtyping, and patient-derived cancer organoid drug response. A 2025–2024 cluster emphasizes label-free metabolic monitoring of stem-cell-derived cardiomyocytes, neutrophil functional states, and collagen remodeling during immunotherapy. Scientific Honors Fellow, OSA, SPIE, AIMBE (2019) Carol Skornicka Chair, Morgridge Institute (2022) Daniel M. Albert Chair, Retina Research Foundation (2021) Stand Up To Cancer – Sharp Collaboration Award (2017) NSF CAREER Award (2016) NIH/NCI Pathway to Independence Award (K99/R00) (2010) Grants & Team Mentorship Dr. Skala directs multiple active NIH grants (R01, R35, P30) totaling several million dollars, focusing on metabolic imaging for cancer immunotherapy, stem-cell manufacturing, and infectious disease models. The Skala Laboratory actively recruits and mentors graduate students and post-doctoral researchers, fostering interdisciplinary collaborations across engineering, oncology and immunology. Laboratory & Infrastructure The Skala Lab is equipped with custom-built multimodal optical systems, high-throughput microfluidic platforms, and dedicated animal-imaging suites. Core capabilities include time-correlated single-photon counting, hyperspectral imaging, and AI-driven single-cell analytics.

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.