Image Deconvolution Professors and Supervisors | ApplySharif

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Showing 91-105 of 402 supervisors

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Alisa YurovskyView profile

Lecturer

Stony Brook UniversityUnited States

Alisa Yurovsky is a Lecturer and IDEA Fellow at the Department of Biomedical Informatics at Stony Brook University. Her research focuses on computational biology at the intersection of computer science, genetics, and statistics to advance precision medicine. She earned her PhD in Computer Science from Stony Brook University under Steven Skiena, an M.S. in Computational Biology from EPFL under Bernard Moret, and a B.S. in Computer Science with a Mathematics minor from Carnegie Mellon University. Research Interests: Alisa develops computational algorithms for precision medicine applications, particularly addressing racial disparities in health data. Her current projects include compartment deconvolution in mixed tissue samples, spatial transcriptomics analysis, small-size differential expression studies, and machine learning in biomedical informatics. Publications Trends: Her recent work spans spatial transcriptomics, survival analysis, and machine learning applications in genomics. She has contributed to algorithms for cell-cycle phase detection, ribosomal frameshift identification, and non-negative matrix factorization. Scientific Awards: 2023 IDEA Fellowship 2020 NSF/CRA/CCC Computing Innovation Postdoctoral Fellow 2017 CEWIT Best Poster Award 2016 NSF Graduate Research Fellowship 2010 Prix Annaheim-Mattille for Master’s Thesis 2007 Cadence Design Systems Award Advising and Grants: She mentors undergraduate and high school students through Stony Brook’s VIP Webgen team and Simons Summer Research Program. Her research has received support from NSF and institutional grants.

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Gengsheng L. ZengView profile

Adjunct Professor

University of UtahUnited States

Gengsheng L. Zeng, PhD, is an Adjunct Professor in the Department of Radiology & Imaging Sciences at the University of Utah. He holds a PhD in Electrical and Computer Engineering from the University of New Mexico and completed postdoctoral training at the University of Utah Medical Center. His research focuses on advanced imaging techniques in nuclear medicine, particularly SPECT and CT reconstruction algorithms. Education: PhD, University of New Mexico MS, University of New Mexico BS, Xidian University Research Interests: Medical imaging reconstruction algorithms Attenuation and scatter compensation Truncated data handling in tomography Geometric blurring correction Collimator design optimization Dynamic cardiac imaging techniques His work spans three decades, with over 50 peer-reviewed publications and 12 U.S. patents related to SPECT instrumentation and image processing. Zeng's algorithms address challenges in non-uniform attenuation correction , iterative reconstruction , and hybrid tomographic systems . His patents cover innovations like skew-slit collimators, rotating slat-hole detectors, and depth-dependent sensitivity corrections.

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Daniele MarinazzoView profile

Professor

Ghent UniversityBelgium

Daniele Marinazzo is a Full Professor in the Department of Data Analysis at Ghent University (Belgium), with a background in statistical physics and a focus on computational neuroscience and complex systems. He serves as Deputy Editor at PLOS Computational Biology , Co-Editor-in-Chief at Neurons, Behavior, Data Analysis, and Theory , and academic editor at Network Neuroscience and Imaging Neuroscience. His work spans theoretical physics, neuroscience methodology, and experimental neurophysiology. Academic Affiliation: Ghent University (Belgium) Editorial Roles: 3+ journals Research Initiatives: EBRAINS representative Research Interests combine computational neuroscience, complexity science, and network analysis. He investigates higher-order interactions in brain networks, synergistic information processing, and methodological advancements in neuroimaging. Recent work includes MEG/EEG analysis, gene expression modeling, and developing open-source tools like SPM-Python. Scientific Contributions include 15+ recent publications on topics ranging from individual brain fingerprinting to multisensory integration models. His work appears in venues like Nature Communications , Physics of Life Reviews , and Entropy , with methodological innovations in hidden factor localization and network synchronization analysis. Current projects include PhD/postdoc recruitment for fast-sampled fMRI analysis and organizing the Erice workshop Higher-Order Interactions: Mechanisms, Behaviors, and Networks (August 2025).

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Wei SunView profile

Professor

Fred Hutchinson Cancer CenterUnited States

Dr. Wei Sun is a Professor in the Biostatistics Program within the Public Health Sciences Division at Fred Hutchinson Cancer Center, with additional appointments as Member of the Immunotherapy Integrated Research Center (IIRC) and Translational Data Science Integrated Research Center (TDS IRC). He holds affiliated professor positions in Biostatistics at both the University of North Carolina at Chapel Hill and the University of Washington. His educational background includes: PhD in Statistics from University of California at Los Angeles (2007) MS in Statistics from University of California at Los Angeles (2004) BS in Statistics from Peking University, Beijing (2002) Dr. Sun specializes in developing statistical and computational methods for multi-omics data integration, with emphasis on cancer genomics, proteomics, and immunomics. His work pioneers deep learning applications for T cell receptor analysis, HLA typing, neoantigen prediction, and tumor microenvironment characterization using single-cell and spatial transcriptomic technologies. Current research bridges biostatistics with AI-driven cancer diagnostics. His recent publications (2021-2023) demonstrate leadership in cell-type-specific computational frameworks for RNA-seq and spatial omics, with significant contributions to neural network models for immunogenomics and foundational methods for eQTL mapping. Work consistently targets translational applications in tumor heterogeneity and liquid biopsy development. Major recognitions include: Elected Fellow of the American Statistical Association (2017) Junior Faculty Development Award from UNC-Chapel Hill (2010) As Principal Investigator, Dr. Sun leads multiple NIH-funded projects including deep learning for single-cell transcriptomics, statistical methods for gene-phenotype associations using MorPhiC resources, and liquid biopsy consortium coordination for cancer early detection. His grants emphasize cross-institutional collaboration with UW and UNC. He directs the Sun Lab at Fred Hutch—a MorPhiC-affiliated research group focused on creating open-source computational tools for tumor immunology and precision oncology, with active partnerships across the Immunotherapy and Translational Data Science Integrated Research Centers.

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Olaf DimigenView profile

Assistant Professor

Humboldt UniversityGermany

Olaf Dimigen is a tenured Assistant Professor of Experimental Psychology at the University of Groningen, Netherlands. Previously, he held a visiting professorship for Biological Psychology at Humboldt University Berlin (2018-2022) and conducted research at the Max Planck Institute for Human Development. His research focuses on understanding how the brain integrates eye movements with visual perception and cognition, leveraging EEG/eye-tracking co-registration and advanced signal analysis techniques. Key affiliations: Faculty of Behavioural and Social Sciences, Department of Experimental Psychology Education: PhD in Psychology from Humboldt University Berlin (2014) Research interests include: Neural mechanisms of active vision Fixation-related potentials (FRPs) and microsaccade-related activity EEG artifact correction and deconvolution modeling Cross-linguistic reading processes Development of open-source analysis tools (EYE-EEG, UNFOLD, OPTICAT) Selected contributions: Pioneered methods for analyzing temporally overlapping EEG signals using deconvolution (UNFOLD toolbox) Optimized ICA-based ocular artifact removal for free viewing EEG (OPTICAT method) Discovered cognitive modulation of microsaccade-related potentials as attention markers His work bridges cognitive psychology, neuroscience, and computational methods, with applications in language processing, scene perception, and cultural neuroscience.

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Wayne HockingView profile

Professor

Western UniversityCanada

Dr. Wayne Hocking is a Full Professor at Western University's Faculty of Science in the Department of Physics & Astronomy. His research focuses on atmospheric physics, with particular emphasis on mesosphere-stratosphere-troposphere dynamics, tornado genesis, and meteor physics. He contributes to Earth observation and exploration technology innovations as a Western Space Investigator. Research Interests : Atmospheric radar scattering processes Gravity wave-turbulence interactions Polar mesospheric phenomena Radio meteor physics Stratosphere-troposphere exchange The 15 most recent articles highlight his work in radar windprofiling, planetary wave dynamics, meteor trail analysis, and polar atmospheric phenomena. His studies employ meteor radars, ozonesondes, and satellite data to investigate middle-atmosphere coupling, severe weather detection, and dusty plasma in the mesosphere. Methodologically, he advances deconvolution techniques and error analysis for radar systems.

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Lee RobertsView profile

Professor

University of LeedsUnited Kingdom

Professor Lee Roberts serves as Professor of Molecular Physiology and Metabolism at the University of Leeds within the Faculty of Medicine and Health, School of Medicine. He leads the Discovery and Translational Science Department at the Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), directing research on metabolic disease mechanisms and therapeutic development. His academic foundation includes: BSc in Biochemistry from University of Bristol PhD in Biochemistry from University of Cambridge Postdoctoral training at Massachusetts General Hospital/Harvard Medical School Research Fellow at Wolfson College, Cambridge Roberts' research centers on metabolic regulation in adipose tissue and skeletal muscle, with emphasis on beige/brown adipose thermogenesis, fatty acid oxidation, and metabolite-mediated inter-organ signaling. His laboratory investigates how small molecules mediate cross-talk between tissues to influence systemic metabolism, particularly in obesity and diabetes pathogenesis. Current projects explore endothelial-adipocyte interactions, metabokine signaling, and novel therapeutic targets for cardiometabolic diseases. Analysis of his 15 most recent publications reveals dominant themes in adipose tissue biology, endothelial mechanotransduction, and multi-omics approaches to metabolic disease. Key methodological trends include transcriptomic deconvolution (CIBERSORTx/BayesPrism), metabolomic/lipidomic profiling, and in vivo modeling of inter-organ signaling. Major disease contexts span type 2 diabetes, obesity, heart failure, and fatty liver disease. His scientific recognition includes: BBSRC New Investigator Award Diabetes UK RD Lawrence Fellowship Leducq Foundation Career Development Award Elsie Widdowson Fellowship Roberts actively mentors PhD students including Shaimaa Gad, Hannah Smith, Aysha Ali, and Anna Malicka, while supervising postdoctoral researchers Dr. Amanda MacCannell and Dr. Raquel Fernando. His research group operates within LICAMM's LIGHT Laboratories, utilizing advanced metabolomics, lipidomics, and molecular techniques. Current projects include brown adipose tissue-derived signaling in cardiometabolic disease and endothelial IGF-1R mechanisms in adipose adaptation. The laboratory maintains strong collaborations with clinical departments and participates in British Heart Foundation-funded cardiovascular research initiatives through the Multidisciplinary Cardiovascular Research Centre.

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Marianna PenskyView profile

Professor

University of Central FloridaUnited States

Marianna Pensky is a Professor of Mathematics at the University of Central Florida (UCF), affiliated with the College of Sciences. She holds a PhD from Moscow State University (1988) and has been at UCF since 1995. Her research focuses on Data Science, Machine Learning, and Mathematical Statistics, with specializations in Bayesian methods, nonparametric statistics, inverse problems, and network models. She has published over 100 refereed articles in top journals and is an elected Fellow of the Institute of Mathematical Statistics, American Statistical Association, and International Statistical Institute. In 2023, she was awarded the Pegasus Professorship, UCF's highest honor. Dr. Pensky serves as Chief Editor of the Journal of Statistical Planning and Inference and Associate Editor of the Annals of Statistics. She has mentored 13 PhD and 9 MS students, all of whom secured post-degree employment. Her work spans theoretical and applied statistics, including contributions to stress-strength models, empirical Bayes estimation, and statistical inverse problems. Recent research emphasizes network models and multiplex clustering, with applications in bioinformatics and engineering. Dr. Pensky’s methodologies address high-dimensional inference, wavelet-based techniques, and robust statistical frameworks.

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Lori CollinsView profile

Academic

University of South FloridaUnited States

Dr. Lori Collins is a Research Associate Professor at the University of South Florida (USF), holding appointments in the School of Geosciences (College of Arts and Sciences) and the Department of Anthropology. She serves as Co-Director of the Digital Heritage and Humanities Collection at the USF Library and leads the Alliance for Integrated Spatial Technologies (AIST), a center dedicated to preserving cultural heritage through advanced technologies. Her work bridges archaeology, geosciences, and digital humanities, focusing on heritage preservation strategies using LiDAR, terrestrial laser scanning, and 3D visualization. Dr. Collins earned her Ph.D. and M.A. in Applied Anthropology from USF and a B.S. in Agriculture from the University of Florida. She teaches courses on technologies for heritage preservation and museum visualization. Her research emphasizes Florida and Southeast U.S. archaeology, Mesoamerican iconography, and rock art preservation, with projects spanning sites in Mexico, national parks, and historic landmarks. Key methodologies include GIS, GPS, and integrated spatial technologies for documenting landscapes, monuments, and cultural resources. Her publications address ethical considerations in 3D digitization, sinkhole analysis, and heritage management. She collaborates with institutions like NASA, national parks, and international archaeological teams. Her work aims to safeguard cultural heritage through innovation while fostering interdisciplinary education and global engagement.

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M. Hassan ArbabView profile

Assistant Professor

Stony Brook UniversityUnited States

M. Hassan Arbab is an Assistant Professor in the Department of Electrical and Computer Engineering at Stony Brook University. His research focuses on developing novel terahertz emission, detection, and imaging technologies with applications in biophotonics, medical imaging, non-destructive testing, and material characterization. Key projects include the PHASR (Portable Handheld Spectral Reflection) scanner for burn wound assessment and polarization-sensitive imaging of biological tissues. His work integrates electro-optic theory, instrumentation design, and advanced signal processing techniques. Research Highlights: Development of handheld THz imaging systems for clinical burn triage and material analysis Monte Carlo modeling of Mie scattering effects in tissue imaging Integration of physics-based deep learning models for automated wound classification Advancements in THz time-domain spectroscopy for material characterization Key Technologies: Polarization-sensitive THz spectral imaging Wavelet-based signal processing for scattering mitigation Telecentric optics for high-resolution THz imaging Labs/Teams: His research group collaborates with medical professionals and engineers to translate terahertz technologies into clinical and industrial applications.

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Axel FlinthView profile

Assistant Professor

Umea UniversitySweden

Axel Flinth is an Assistant Professor in the Department of Mathematics and Mathematical Statistics at Umeå University, focusing on mathematical foundations of machine learning for pattern recognition in large datasets. Education: PhD in Mathematics from Technische Universität Berlin (2018) His research centers on compressed sensing—reconstructing signals from incomplete data using structural assumptions—and equivariance in deep neural networks, investigating how data symmetries can be leveraged to enhance model performance. He actively contributes to geometric deep learning and statistical inference for spatio-temporal data through membership in specialized research groups. Recent publications (2022-2025) reveal a cohesive trajectory in mathematical optimization and symmetry-aware deep learning, with applications spanning computer vision (e.g., rotation-equivariant architectures for point clouds), wireless communication security, and signal reconstruction. His work consistently bridges theoretical mathematics with practical machine learning implementations. Scientific Awards: No awards documented in available sources Grants and Projects: Lead Researcher: Trade-offs in Nonconvex Learning (April 2022 - March 2027) Research Affiliations: Geometric Deep Learning Group Statistical Learning and Inference for Spatio-Temporal Data

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Stella CorsettiView profile

Research Fellow

University of St AndrewsUnited Kingdom

Dr Stella Corsetti is a Research Fellow at the University of St Andrews, affiliated with the School of Physics and Astronomy. Her work focuses on advancing optical microscopy techniques, particularly light-sheet microscopy and biophotonics applications. She has contributed to innovations in imaging resolution, reduction of imaging-induced damage in biological samples, and integration of deep learning with microscopy. Her research bridges optics engineering, biomedical imaging, and cellular biology. Her publications highlight advancements in light-sheet microscopy, including suppression of imaging artifacts, optimization of beam propagation, and development of deconvolution methods. Corsetti’s work addresses challenges in visualizing living systems at high resolution while minimizing phototoxic effects. She collaborates on projects involving DNA damage quantification in embryos and neuroimaging applications. Dr Corsetti’s research aligns with biophotonics advancements, contributing to technologies with potential in medical diagnostics and biological research. Her datasets, including those on deconvolution techniques and Airy beam applications, further demonstrate her commitment to open science and methodological transparency.

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Quefeng LiView profile

Associate Professor

University of North Carolina at Chapel HillUnited States

Dr. Quefeng Li is an Associate Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill (UNC-Chapel Hill), part of the Gillings School of Global Public Health. He earned his Ph.D. in Statistics from the University of Wisconsin-Madison (2013) and completed a postdoctoral fellowship at Princeton University (2013–2015). His research focuses on high-dimensional statistics and its applications in biomedical and neuroimaging data analysis, emphasizing robust methodologies and integrative data analysis techniques. Dr. Li's work has contributed to advancements in variable selection, data integration, and statistical methods for complex biomedical datasets. He has held editorial roles at journals like Biometrics and Biostatistics , and serves on multiple professional committees. His research has been supported by grants from the NIH, including R01AG073259 and R01CA282648, and he collaborates extensively with clinical researchers across disciplines. His research interests span high-dimensional statistics, neuroimaging analysis, and robust statistical methods. Notable contributions include developing the ARTdeConv algorithm for cell-type deconvolution and the glmmPen package for penalized generalized linear mixed models. He has advised numerous PhD students, many of whom now work in industry and academia. Awardees include the Gillings Research Excellence Award (2023), Junior Faculty Development Award (2018), and University Research Council Award (2016). His work bridges theoretical statistics with practical biomedical applications, addressing challenges in data heterogeneity and reproducibility.

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Patrizio CampisiView profile

Professor

Roma Tre UniversityItaly

Patrizio Campisi is a Full Professor in the Section of Applied Electronics at the Department of Engineering, Roma TRE University, Rome, Italy. He leads cutting-edge research in digital signal and image processing with applications to secure multimedia communications and biometrics. He has held visiting positions at the University of Toronto, Beckman Institute (UIUC), and École Polytechnique de Nantes, and has been a Marie Curie Fellow (2010–2014). His educational background includes a Laurea (summa cum laude) in Electronic Engineering from Sapienza University of Rome and a Ph.D. in Electrical Engineering from Roma TRE University. His research focuses on secure biometric recognition (signature, keystroke, EEG, vein), digital watermarking, blind image deconvolution, HDR imaging, and privacy-preserving technologies. He has contributed significantly to template protection, multimodal biometrics, and forensic image analysis. His work bridges engineering, computer science, and security, with strong applications in mobile authentication, border control, and social media safety. The recent publications reflect a strong trend in biometrics, image forensics, and privacy-enhancing technologies, with a focus on real-world applications in mobile systems, healthcare, and law enforcement. Key themes include secure authentication, de-identification, and computational imaging. IEEE Second International Conference on Biometric Systems 2008 Best Student Paper Award IEEE Biometric Symposium 2007 Best Paper Award IEEE International Conference on Image Processing 2006 Best Student Paper Award Marie Curie Fellow (2010–2014) NATO-CNR Advanced Fellowship (2003) IEEE Senior Member Prof. Campisi has supervised numerous students and leads the BioMedia4n6 lab. He has secured major EU grants including H2020 projects AMBER, COSMOS, and ENCASE. His editorial leadership includes serving as Editor-in-Chief of IEEE Transactions on Information Forensics and Security (2018–2020) and Chair of the IEEE Information Forensics and Security Technical Committee (2017–2018). He is actively involved in research networks such as COST Actions on de-identification and biometrics-forensics integration, and has contributed to EU policy via the BEST Thematic Network on biometrics and fundamental rights. His lab, BioMedia4n6, focuses on biometrics, multimedia forensics, and privacy-aware systems.

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Yuewei LinView profile

Associate Professor

Brookhaven National LaboratoryUnited States

Yuewei Lin is a Senior Computational Scientist at Brookhaven National Laboratory and a Research Associate Professor at Stony Brook University. He holds a Ph.D. from the University of South Carolina, an M.E. from Chongqing University, and a B.S. from Sichuan University. Ph.D., University of South Carolina, Columbia, SC (2019) M.E., Chongqing University, China B.S., Sichuan University, China His research focuses on AI for Science, multimodal foundation models, domain adaptation/generalization, and computer vision techniques applied to large-scale scientific data analysis. He has received major funding from DOE ASCR, NNSA, SciDAC, BER, and BES. His recent publications span areas like adversarial robustness, oxygen vacancy engineering in materials, X-ray imaging, and galaxy deblending. These works integrate machine learning with materials science, computational biology, and astrophysics. Scientific Awards Brookhaven’s Top 10 Discoveries of 2024 DOE NNSA Joule Award (2022, 2020) BNL Top-10 Discoveries (2017) Outstanding Graduate Researcher, CSE Department, University of South Carolina (2016) Best 10% Paper Award, ICIP (2015) IEEE Signal Processing Society Travel Grant (2015) Lin leads the Foundation Model Group at Brookhaven National Laboratory's Computational Science Initiative. He has co-PI'd multiple LDRD projects and secured renewed DOE BES funding ($4M) for 2025–2027. His work bridges machine learning with applications in scientific data, materials, and biomedical imaging.

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