Find Academic Supervisors

• Image Processing
• Neuroscience
• Adaptive Smoothing Methods
• +4 more

Dr. Karsten Tabelow is a researcher at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin, Germany, affiliated with the group Stochastic Algorithms and Nonparametric Statistics . His work bridges mathematical statistics and biomedical imaging through advanced computational methods. PhD in physics (2001) and Diplom in physics (1997) from Freie Universität Berlin Key projects: Matheon grants F10 and A3 on biomedical imaging Current focus: adaptive smoothing , quantitative MRI , and nonparametric statistical modeling His research has produced foundational contributions in neuroimaging software , including R packages adimpro , dti , fmri , and qmri , as well as SPM toolboxes like ACID-Toolbox and aws4SPM . He extends these methods to inverse problems in magnetic resonance and electron microscopy. Collaborations span neuroscience (Charité), semiconductor physics (Leibniz Institute), and biomedical engineering (Max Planck Institute). Recent work includes physics-informed data assimilation for intracranial pressure estimation and mathematical research data infrastructure through the MaRDI initiative under Germany's NFDI. Publications emphasize open science principles using R/knitr workflows. Grants include Matheon projects and MaRDI development. Software contributions are distributed under GNU GPL within R and SPM ecosystems. Talks highlight applications in presurgical planning , in-vivo histology , and cross-disciplinary data management .

Benjamin BerkelsView profile

Professor

• Variational Image Processing
• Electron Microscopy
• Non-Rigid Registration
• +3 more

Prof. Benjamin Berkels is affiliated with the Aachen Institute for Advanced Study in Computational Engineering Science (AICES) at RWTH Aachen University. His research focuses on advanced image processing techniques tailored for electron microscopy data, including innovations in non-rigid registration, denoising algorithms, and multi-phase segmentation. His work addresses critical challenges in atomic-scale imaging, such as sub-picometre precision measurements, leveraging crystalline periodicity through primitive unit cell extraction, and analyzing materials with discontinuous periodicity via high-dimensional feature vectors. These methods are being extended to hyperspectral data like EELS/EDX, demonstrating their applicability to complex material analyses.

Amanda FernandezView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +8 more

Amanda Fernandez is an Assistant Professor in the Department of Computer Science at The University of Texas at San Antonio (UTSA), where she contributes significantly to research, teaching, and interdisciplinary collaboration. She will be a faculty member in the newly launching College of AI, Cyber and Computing in Fall 2025, reflecting her central role in advancing UTSA's strategic focus on emerging technologies. Her research lies at the intersection of artificial intelligence, deep learning, computer vision, and computer science education . She directs the UTSA Vision and AI Lab (VAIL) and serves as Machine Learning & Deployment Thrust Lead for MATRIX: The AI Consortium for Human Well-Being , and as a Principal Investigator in the Consortium on Nuclear Security Technologies (CONNECT) . Her work emphasizes explainable and robust AI, with applications in nuclear materials, VR/AR, and biomedical signals like ECG. The 15 most recent publications reflect a strong trend in explainable AI, adversarial robustness, and AI-enhanced education . Her work spans technical AI research (e.g., ICML, CVPR workshops) and educational innovation (e.g., SIGCSE, RESPECT), demonstrating a dual commitment to advancing both the science and pedagogy of computing. Themes include model attribution, training optimization, semantic segmentation, and the integration of LLMs in introductory CS courses. Senior Member of the IEEE Senior Member of the National Academy of Inventors (NAI) Funded by the National Science Foundation (NSF) Supported by the U.S. Department of Energy (DOE) Supported by the U.S. Department of Defense (DOD) Fernandez is deeply committed to mentoring undergraduate researchers through her CS-CURE course, which provides hands-on research experience. She advises student organizations including ACM-W and GDSC , and leads initiatives such as CS4SA to increase Latinx participation in computer science. Her grants support both research and educational outreach, emphasizing inclusivity and workforce development. She leads the Vision and AI Lab (VAIL) at UTSA and is an integral member of two major research consortia: MATRIX and CONNECT , fostering collaboration across institutions including Los Alamos National Lab, Argonne National Lab, and UNLV.

• Biomedical Signal Processing
• Medical Imaging
• Deep Learning
• +3 more

Devrim Unay is a Professor in the Department of Electrical-Electronics Engineering at İzmir Democracy University. His research focuses on biomedical signal processing, medical imaging, and AI-driven solutions for healthcare and agriculture. He leads the AIDA (Artificial Intelligence and Data Analytics) Research and Application Center, advancing interdisciplinary projects such as automated cell analysis, tumor suppression nanomedicines, and fruit grading systems. Unay has authored over 50 peer-reviewed publications and contributed to initiatives like the EURASIP Journal on Advances in Signal Processing as an Associate Editor. His work includes datasets like WHAD/CAMAD and software frameworks like BIAFLOWS. He holds roles such as Chair of the IEEE Signal Processing Society’s Challenges and Data Collections Subcommittee. Education: Not explicitly stated in provided texts, inferred via academic rank and publications. Research interests span AI applications in microscopy, CT imaging for disease detection (e.g., COVID-19), and nanomedicine. He leads projects funded by TEYDEB, including cloud-based image analysis tools for industrial applications. His articles emphasize image segmentation, deep learning pipelines, and interdisciplinary collaborations in neuroscience and agriculture. Key Projects: SSNOMBACTER database, Neubias bioimage analysis framework, and AI-driven fruit grading systems. Unay’s contributions include organizing conferences like TıpTekno’22 and serving on technical committees such as IEEE BISP TC and Tübitak ARDEB.

Yan ZhuView profile

Research Fellow

• Photovoltaic systems
• Solar cell technology
• Semiconductor characterization
• +3 more

Dr. Yan Zhu is a Research Fellow at the University of New South Wales (UNSW), affiliated with the School of Photovoltaic and Renewable Energy Engineering. She is currently part of the ACDC research team, focusing on advanced characterization techniques for photovoltaic materials and devices. Dr. Zhu holds a PhD in Photovoltaic Engineering from UNSW, complemented by international education including a Master's from École Polytechnique (France) and a Bachelor's from Shanghai Jiao Tong University. Her research specializes in photovoltaic systems and semiconductor characterization, with expertise in: Development of novel characterization methods for solar materials Defect analysis in silicon and semiconductors using photoluminescence Performance optimization of monocrystalline and multijunction solar cells Advanced metrology for photovoltaic device quality improvement Her publication portfolio demonstrates strong focus on photovoltaic innovation (60% of recent works), complemented by interdisciplinary contributions in computer vision applications for biometrics (25%) and medical imaging (15%). The research shows consistent emphasis on measurement accuracy, material reliability, and efficiency enhancement in renewable energy systems. Awards and Honors: Dean's Award for Outstanding PhD Theses (UNSW) Best Student Award - 7th World Conference on Photovoltaic Energy Conversion Steve Robinson Memorial Prize (UNSW) Dr. Zhu leads research within the ACDC photovoltaics characterization laboratory at UNSW, collaborating on projects developing next-generation solar cell diagnostic techniques. Her work bridges fundamental semiconductor research and industrial applications for renewable energy solutions.

Erik MeijeringView profile

Professor

• Biomedical Imaging
• Computer Vision
• Machine Learning
• +5 more

Erik Meijering is a Professor of Biomedical Image Computing at the University of New South Wales (UNSW), School of Computer Science and Engineering. His research focuses on developing deep learning and machine learning methods for biomedical imaging analysis, including cell tracking, neuron reconstruction, and image segmentation. He holds an IEEE Fellowship and has served in key roles such as Chair of the IEEE Signal Processing Society's Bio Imaging and Signal Processing Technical Committee. Educations: MSc in Electrical Engineering (Delft University of Technology, 1996), PhD in Medical Image Analysis (Utrecht University, 2000). He previously worked at Erasmus University Medical Center (2002–2019) and Swiss Federal Institute of Technology (2000–2002). Research interests include biomedical image computing, computer vision, and the application of AI in microscopy and clinical imaging. Notable projects include the Cell Tracking Challenge and BigNeuron initiative, which benchmark computational methods in bioimage analysis. His work spans over 300 publications, including high-impact journals like Nature Methods and IEEE Transactions on Medical Imaging. He has supervised numerous PhD, Master's, and Bachelor's students, contributing to advancements in medical image analysis and AI-driven diagnostics.

• Biomedical Engineering
• Medical Diagnostics
• Microfluidics
• +7 more

Prof. Hayden Oliver holds the Heinz Nixdorf Chair of Biomedical Electronics at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology. His research focuses on interdisciplinary biomedical engineering, combining electronics, optics, microfluidics, and materials science to develop novel in-vitro diagnostic methods. Current work emphasizes magnetic and optical methods for functional diagnostics of blood cells, with applications in point-of-care testing and quantitative phase imaging. Education: Studied biochemistry at the University of Vienna (PhD, 1999). Postdoctoral research at Harvard University, followed by habilitation in Analytical Chemistry at the University of Vienna. Prior to TUM, he worked at IBM Research Zurich (2005–2007) and Siemens Corporate Technology/Siemens Healthcare (2007–2017) on organic electronics for medical imaging and diagnostics. Appointed to TUM in 2017. Research Themes: Digital holographic microscopy, magnetic flow cytometry, label-free cell analysis, AI-driven diagnostics, and microfluidic device development. His work bridges fundamental science (e.g., cell mechanics) with clinical applications (e.g., rapid disease diagnostics). Awards: European Inventor Award (2017), AMA Innovation Award (2016), Siemens NTF Award (2013), Young Investigator Award (2002). Lab/Teams: Leads the Biomedical Electronics research group at TUM, collaborating with industry partners (e.g., Siemens) and academic institutions globally. Grants/Funding: Not explicitly listed in text, but implied through high-profile publications and awards.

• Optometry
• Ophthalmology
• Eye Care
• +4 more

Jenny Roth is a Research Fellow and PhD student in Optometry at the Department of Medicine and Optometry, Linnaeus University, Kalmar campus. She is affiliated with the Faculty of Health and Life Sciences and the Research in Eyes, Vision and Optometry (REVO) group. Her research focuses on the anterior segment of the eye, with emphasis on infectious keratitis, corneal nerve imaging, and AI-driven diagnostic tools. Research Interests : Jenny’s work bridges clinical optometry and biomedical research. Her studies explore bacterial keratitis epidemiology, AI applications for pathogen detection (e.g., Acanthamoeba Keratitis), and ocular impacts of systemic conditions like mild COVID-19. She employs advanced imaging techniques such as in vivo confocal microscopy to analyze corneal nerve structures and dendritic cells. Key Projects : Current projects include automated nerve segmentation algorithms and antibiotic susceptibility analyses. Completed work includes the AIDAK study (AI for Acanthamoeba diagnosis) and a pilot on dry eye syndrome prevalence in Sweden. Publications : Her articles span clinical ophthalmology journals and AI-driven diagnostic studies. Recent work addresses bacterial infection patterns, AI decision-support systems, and post-COVID ocular nerve changes. Labs/Teams : Active member of the REVO group, collaborating on interdisciplinary eye research projects.

• Music Information Retrieval
• Machine Learning
• Audio Signal Processing
• +3 more

Alicja Wieczorkowska is a Professor affiliated with the University of Gdańsk, specializing in Music Information Retrieval, Machine Learning, and Audio Signal Processing. She has edited multiple books and special issues in journals like Sensors and Springer's Studies in Computational Intelligence series. Her research focuses on audio processing applications in healthcare, music recommendation systems, and environmental monitoring. Education Background: Not explicitly stated in text. Research Interests: Development of audio and image sensing techniques for healthcare and environmental applications Machine learning models for music and speech analysis Data mining and pattern recognition in polyphonic audio Interpretable models for medical survival analysis Recent Research Trends: Her recent work emphasizes practical applications of machine learning in music recommendation systems, medical diagnostics (e.g., liver transplantation prognosis), and environmental pollen monitoring through image processing. She has pioneered methods for audio-based vehicle speed detection and pipe organ acoustics modeling. Labs/Teams: Actively collaborates with interdisciplinary teams including computer scientists, medical researchers, and acoustic engineers. Key collaborators include Z.W. Ras, E. Kubera, and M. Klec.

Lily LiangView profile

Professor

• AI
• Cybersecurity
• Computer Science Education
• +3 more

Dr. Lily Liang is a Professor in the Department of Computer Science and Information Technology at the University of the District of Columbia (UDC), affiliated with the School of Engineering and Applied Sciences. She holds leadership roles including Director of the Master of Science in Computer Science Program (2010-14; 2017-Present) and former Department Chair (2014-16). Education: PhD in Computer Science and Engineering from the University of Nevada, Reno. Research focuses on AI, cybersecurity, and computer science education with an emphasis on K-12 outreach, student mentorship, and broadening STEM participation. Notable projects include Minecraft-based learning initiatives and community partnerships to engage female students in robotics. Publications emphasize educational innovation and cybersecurity solutions, such as deepfake detection methods and curriculum development for underrepresented groups. Awards: 2014 Outstanding University Service Award, 2012 Myrtilla Miner Faculty Fellow Grants: Multiple NSF grants for cybersecurity education and HBCU partnerships Leadership: Led UDC's Learning and Development initiatives (2014-15) and served on NAS/NAE/IOM committees Labs/Teams: Active in interdisciplinary initiatives promoting computational literacy and inclusive tech ecosystems.

Odelia SchwartzView profile

Associate Professor

• Machine Learning
• Computational Neuroscience
• Medical Informatics
• +4 more

Odelia Schwartz is an Associate Professor in the Department of Computer Science at the University of Miami, College of Arts and Sciences. She also serves as Director of Undergraduate Studies for Computer Science and holds a secondary faculty appointment in Biology. Her research focuses on computational neuroscience, machine learning applications in healthcare and biology, and the intersection of artificial intelligence with visual and neural processing systems. Key research areas include machine learning analysis of medical signals (e.g., ECG for atrial fibrillation prediction), computational modeling of biological systems (e.g., endosymbiont population dynamics via microscopy image analysis), and hierarchical neural network models for visual cortex understanding. Her work integrates statistical methods with deep learning to bridge computational models and biological/neurological phenomena. Publications highlight applications in cardiology, neurotrauma recovery prediction, and visual cortex modeling. While no specific awards are listed, her contributions span interdisciplinary fields at the university and collaborative research institutions. Advising no listed students, but actively engages in graduate training through her faculty roles. No specific labs/teams are mentioned, though collaborations with medical and biological departments are evident.

Xiaotang LuView profile

Assistant Professor

• Biochemical tools for brain imaging
• Connectomics
• Spatial omics
• +4 more

Xiaotang Lu is an Assistant Professor in the Department of Chemistry at the University of Illinois Urbana-Champaign, with affiliations at the Beckman Institute for Advanced Science and Technology and the Carl R. Woese Institute for Genomic Biology. She holds a PhD in Materials Science and Engineering from the University of Texas at Austin and completed postdoctoral training in neuroscience at Harvard University under Jeff Lichtman. Her research focuses on developing biochemical tools and advanced imaging techniques to study brain connectivity, particularly in connectomics and correlated light-electron microscopy. Research interests include biochemical tools for brain imaging, spatial omics, and systems neuroscience. She leads the Lu Lab, which works at the chemistry-neuroscience interface to address challenges in understanding brain circuitry and neuropsychiatric diseases. Notable achievements include the development of NATIVE (Nanobody-assisted tissue immunostaining for volumetric EM) and contributions to NIH BRAIN Initiative projects. Education: PhD in Materials Science and Engineering, University of Texas at Austin M.S. in Chemistry, Tsinghua University Postdoctoral Fellowship, Harvard University Awards include the NIH BRAIN Initiative K99/R00 Award (2022–2024), Cornell FIRST Scholar (2022), and Leading Edge Fellow (2022). She teaches Chem 588: Physical Methods for Materials Chemistry and actively recruits graduate students, postdocs, and undergraduates for interdisciplinary research projects. Key publications focus on multiplexed molecular imaging, connectomics methodologies, and advanced microscopy techniques to map brain circuitry. Her work bridges chemical innovation with neurobiological applications, aiming to clarify how brain structure enables function and disease.

Jing YangView profile

Professor

• Developmental Biology
• Cytoplasmic Structure and Dynamics
• Reproductive Biology
• +4 more

Jing Yang is a Professor of Comparative Biosciences at the University of Illinois at Urbana-Champaign, affiliated with the Carl R. Woese Institute for Genomic Biology. She holds dual roles within the College of Liberal Arts & Sciences and School of Molecular & Cellular Biology. Her research focuses on cytoplasmic structure dynamics, developmental biology, reproductive systems, and RNA mechanisms, alongside interdisciplinary work in robotics and medical imaging. Research Interests: Yang’s biological research explores germ plasm dynamics in vertebrate development, RNA phase transitions, and reproductive cell biology. Her robotics work integrates vision-guided systems, surgical navigation, and human-robot interaction, with applications in precision agriculture and medical robotics. Key themes include adaptive control, SLAM algorithms, and immersive learning frameworks. Recent Trends: Articles emphasize robotics advancements (e.g., telerobotics, SLAM systems) and biological studies on Xenopus and zebrafish embryology. Cross-disciplinary projects bridge molecular mechanisms with engineering solutions, such as robotic platforms for strawberry harvesting and AI-enhanced surgical tools. Labs & Teams: The Yang lab develops technologies at the intersection of biology and robotics. Ongoing projects include automated micromanipulation systems and educational frameworks for engineering curricula. Collaborations span departments in genomic sciences, mechanical engineering, and computer vision.

James HopgoodView profile

Professor

• Statistical Signal Processing
• Machine Learning
• Data Science
• +5 more

James Hopgood is a Professor of Statistical Signal Processing at the University of Edinburgh's School of Engineering, leading the Institute for Data, Imaging, and Communications. He holds roles as Dean of Quality and Enhancement for the College of Science and Engineering and Director of the EPSRC/MoD CDT in Sensing, Processing & AI for Defence and Security (SPADS). His research focuses on statistical signal processing applications in medical imaging, acoustic sensing, and multi-target tracking. Hopgood earned his M.A., M.Eng., and Ph.D. from the University of Cambridge, joining Edinburgh in 2004. He has developed algorithms for Bayesian signal processing, acoustic source localization, and medical imaging, with over 100 publications. As Editor-in-Chief of the IET Journal of Signal Processing since 2011, he contributes to academic leadership. Teaching includes courses on signal processing, probability, and sensor networks, emphasizing visualization and real-world applications. Current projects span adaptive optical imaging for medical diagnostics and probabilistic graphical models for multi-target tracking. His work bridges academia and industry, collaborating with companies like Leonardo and Agilent Technologies. He supervises numerous PhD students and leads multidisciplinary initiatives in defense, healthcare, and autonomous systems. **Education**: PhD in Statistical Signal Processing, University of Cambridge (2001) MEng in Electrical and Information Sciences, University of Cambridge (1997) MA, University of Cambridge (2000) **Research Interests**: His work spans model-based Bayesian methods, acoustic source localization, medical imaging (e.g., fluorescence lifetime imaging), and multi-target tracking. Recent projects include optimizing UAV trajectories in dynamic environments and developing algorithms for distributed sensor networks. **Grants & Collaborations**: Funded by EPSRC/MoD, GSK, and Leonardo, his projects address defense sensing, drug development, and clinical imaging. Collaborations include Firefinch (software/data science) and the UDRC Summer School. **Labs/Teams**: Leads the Acoustics and Audio Group and the SPADS CDT, fostering innovation in sensing and AI for defense/security applications.

Christian GagnéView profile

Professor

• Deep Learning
• Computer Vision
• Machine Learning
• +7 more

Christian Gagné is a Full Professor in the Department of Electrical and Computer Engineering at Laval University and Director of the Institute for Intelligence and Data (IID). He holds a prestigious Canada CIFAR AI Chair and is an Associate Academic Member at Mila – Quebec Artificial Intelligence Institute. His research spans multiple interdisciplinary domains with significant contributions to machine learning and its applications. Dr. Gagné completed his doctorate in electrical engineering at Université Laval in 2005, followed by a postdoctoral fellowship at INRIA Saclay (France) and the University of Lausanne (Switzerland) from 2005-2006. Prior to his academic appointment, he worked as a research associate in an industrial environment between 2006 and 2008. His research interests focus on deep learning and stochastic optimization, with particular emphasis on the robustness and generalization of deep neural networks, neurosymbolic approaches to enhance interpretability, and the development of multimodal foundational models. His work has practical applications across computer vision, super-resolution microscopy, healthcare, transportation, and energy sectors. Dr. Gagné has developed important methodologies in representation learning, meta-learning, transfer learning, and optimization approaches based on probabilistic models and evolutionary algorithms. Recent publications demonstrate a strong focus on improving the robustness of machine learning models, with significant contributions to data augmentation techniques for medical imaging, adversarial attacks on visual object trackers, and test time adaptation methods. His research group has produced influential work at the intersection of theoretical machine learning and practical applications, particularly in medical imaging and computer vision domains. Canada CIFAR AI Chair As an academic advisor, Dr. Gagné has supervised numerous doctoral and master's students including Frédéric Beaupré, Catherine Bouchard, Fatemeh Nourilenjan Nokabadi, Sabyasachi Sahoo, and Adam Tupper. His research is supported through various grants, including his Canada CIFAR AI Chair position and collaborations with multiple research centers. Dr. Gagné is actively involved with several research entities including the Vision and Digital Systems Laboratory (LVSN), the Center for Research in Robotics, Vision and Machine Intelligence (CeRVIM), the Center for Research in Massive Data (CRDM), as well as strategic groups REPARTI and UNIQUE of the FRQNT, the VITAM center of the FRQS, and the International Observatory on the Societal Impacts of AI and Digital Technology (OBVIA).