جست‌وجوی استاد و پژوهشگر

Zixiang Xiongمشاهده پروفایل

استاد

• Machine Learning
• Image/Video Processing
• Network Information Theory
• +۷ مورد دیگر

Zixiang Xiong is a Professor and Associate Department Head in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Robert M. Kennedy '26 Endowed Professorship II. He earned his Ph.D. in Electrical Engineering from the University of Illinois at Urbana-Champaign in 1996. His career includes roles at Princeton University, University of Hawaii, and Texas A&M since 1999. Education: Ph.D., Electrical Engineering, University of Illinois at Urbana-Champaign, 1996 Visiting Research Associate, Princeton University, 1995–1997 University of Hawaii, 1997–1999 Research Interests: Focuses on machine learning, image/video processing, federated learning, network information theory, biomedical engineering, and communications. His work spans distributed source coding, genomic signal processing, and energy-efficient systems. Publications & Awards: Over 200 publications, including seminal works on distributed video coding and network information theory. Notable awards include the NSF Career Award (1999), ONR Young Investigator Award (2001), IEEE Fellow (2006), and the ECE Outstanding Faculty Award (2024). His research has led to patents in video compression and multimedia systems. Grants & Advising: Active in NSF-funded projects on coding theory and energy-delay tradeoffs. Advises numerous PhD and MS students, with over 50 alumni in academia and industry. Collaborates on biomedical imaging, remote sensing, and federated learning initiatives. Labs & Teams: Leads a dynamic research group at Texas A&M, focusing on cutting-edge projects in signal processing and machine learning applications. Collaborates with industry and governmental agencies on applied research.

• Computational Biology
• Structural Biology
• Bayesian Inference
• +۱۰ مورد دیگر

Dr. Roy Lederman is an Assistant Professor at the Department of Statistics and Data Science , Yale University. He is affiliated with the Quantitative Biology Institute (QBio) , the Applied Math Program , the Institute for Foundations of Data Science (FDS) , and the Wu Tsai Institute (WTI) . He was awarded the Sloan Research Fellowship (2023) . He previously held a Gibbs Assistant Professorship at Yale (2014-2015) and a postdoc at Princeton University (2015-2018) . Education: PhD in Applied Mathematics, Yale University (2014); dual BSc in Physics and Electrical Engineering, Tel-Aviv University. Teaching: Courses include Computational Tools for Data Science, Signal Processing, and Mathematical Machine Learning. Research Areas: Dr. Lederman works at the intersection of computational biology , structural biology , Bayesian inference , numerical analysis , and machine learning . His recent work focuses on cryo-EM and hyper-molecules for studying molecular heterogeneity, alternating diffusion for common variable recovery, and Zernike polynomials for 3D imaging. He also develops Hamiltonian Monte Carlo methods and randomized DNA sequencing algorithms . Publications Trends: His publications (15 most recent) emphasize structural biology and cryo-EM applications, machine learning (Bayesian deep learning, diffusion maps), numerical analysis (Fourier/Laplace transforms), and computational biology (DNA sequencing algorithms). Key sub-fields include heterogeneity analysis , manifold learning , Hamiltonian Monte Carlo , and Zernike polynomials . Scientific Awards: Sloan Research Fellow (2023) Dr. Lederman actively mentors graduate students and postdocs at Yale, and co-organizes the One World Cryo-EM seminar series . His lab develops open-source software (e.g., prolate function implementation ) and explores theoretical bounds on transforms and common variable recovery in multi-sensor experiments.

• Optical Imaging
• Biomedical Device and Instrumentation
• Medical Image Analysis
• +۲ مورد دیگر

Jungeun (Jenny) Won is an Assistant Professor of Research in the Department of Biomedical Engineering at the School of Engineering and Applied Sciences, University at Buffalo. Her research focuses on optical imaging , biomedical device development , medical image analysis , and artificial intelligence in OCT . She leads the Translational Biophotonics Laboratory , where she develops advanced OCT techniques for medical applications such as diabetic retinopathy , otitis media , and biofilm analysis . Contact: 215J Bonner Hall, Buffalo NY 14260, jungeunw@buffalo.edu Related Links: CV PDF , Google Scholar , Lab Website Her recent work involves high-resolution OCT for longitudinal studies on retinal degeneration, VISTA OCTA for blood flow analysis, and 3D motion correction algorithms to enhance image quality. She also explores multimodal imaging combining OCT with Raman spectroscopy for bacterial differentiation and microplasma-based therapies for ear infections.

Jiju Poovvancheriمشاهده پروفایل

دانشیار

• Computer Graphics
• 3D Vision
• Machine Learning
• +۸ مورد دیگر

Dr. Jiju Poovvancheri is an Associate Professor in the Department of Math & Computing Science at Saint Mary’s University, Halifax, Canada. He holds affiliations with the Graphics & Spatial Computing Lab and previously held postdoctoral positions at the University of Victoria and University of Calgary. His research focuses on computer graphics, 3D vision, and machine learning, with applications in virtual/augmented reality, autonomous robotics, urban planning, and bio-mechanical studies. Key research areas include point cloud processing, semantic surface reconstruction, spatial data structures, and geometric deep learning. Education: PhD from Indian Institute of Technology Madras (2011–2014), supervised by Prof. Ramanathan Muthuganapathy. Postdoctoral work at University of Calgary (EYES-HIGH Fellowship, 2015–2017) and University of Victoria (MITACS Elevate Fellowship, 2018). Research & Awards: Winner of MITACS Elevate Fellowship (2018), EYES-HIGH Fellowship (2015–2017), and multiple grants including NSERC DG (2019–2026). His work has been supported by NVIDIA GPU hardware, NSERC, CFI, and industry partners like Modest Tree Media and Caterpillar. Professional Activities: Associate Editor for IEEE Access , Guest Editor for special issues in Remote Sensing and Sensors , and reviewer for top conferences like CVPR, ICCV, and ECCV. Member of ACM SIGGRAPH, Solid Modeling Association, and IEEE Geoscience & Remote Sensing Society. Lab & Collaborations: Leads the Graphics & Spatial Computing Lab, collaborating with institutions globally. Current projects include "Interaction and navigation in virtual spaces" and industry partnerships with Modest Tree Media for real-time object recognition. Advising: Supervised over 20 graduate and undergraduate students, with notable alumni advancing to roles at ReelData AI, Royal Canadian Air Force, and academic institutions like Dalhousie University.

• Plant Phenotyping
• Precision Agriculture
• Machine Vision
• +۶ مورد دیگر

Dr. Huajian Liu is a Grant-Funded Researcher (B) at the University of Adelaide's School of Agriculture, Food and Wine, within the Faculty of Sciences, Engineering and Technology. He is affiliated with The Plant Accelerator, the Adelaide node of the Australian Plant Phenomics Facility. His research focuses on machine vision and machine learning for plant phenotyping and precision agriculture, particularly using hyperspectral imaging for plant nutrient estimation, disease detection, stress tolerance analysis, and pest monitoring. Dr. Liu holds a PhD in machine vision for invertebrate detection from the University of South Australia (2018) and has been a post-doctoral researcher at The Plant Accelerator since then. His expertise spans hyperspectral imaging, bio-inspired machine vision systems, 3D model reconstruction, and optical sensing system design. He is eligible to supervise Masters and PhD students in plant phenotyping and precision agriculture. Key research areas include crop stress detection (drought, salt, nutrient deficiencies), invertebrate pest identification, and automation in agricultural monitoring. Affiliations: The Plant Accelerator, School of Agriculture, Food and Wine Research Themes: Plant phenotyping, precision agriculture, machine learning applications Grants: Ongoing grant-funded research in plant phenomics and hyperspectral technologies Publications highlight innovations in hyperspectral imaging for crop yield prediction, disease diagnostics, and nitrogen content analysis. His work bridges computational methods with agricultural challenges, emphasizing sustainable farming solutions.

• Computer Vision
• Machine Learning
• General AI
• +۶ مورد دیگر

Dr. Maya Aghaei is a Lecturer and Researcher in Computer Vision & Data Science at NHL Stenden University of Applied Sciences, part of the Academy Technology & Innovation. She holds a M.Sc. in Artificial Intelligence and a Ph.D. in Computer Vision from the University of Barcelona. Her academic role includes supervising Minor and Master students while focusing on applying cutting-edge AI techniques to real-world challenges. Prior to her current position, she served as a Postdoctoral Researcher at the Italian Institute of Technology, developing AI solutions for industrial applications. Her research spans Computer Vision, Machine Learning, and General AI with a focus on surveillance systems, autonomous drones, hyper-spectral imaging for environmental analysis, and social signal processing through egocentric data. Notable projects include crime scene classification via trajectory analysis, obstacle detection for BVLOS drones, and psychological trait prediction based on clothing analysis. Dr. Aghaei's work emphasizes real-world applicability, bridging theoretical advancements with practical implementations in industries like agriculture, waste management, and public safety. Her interdisciplinary approach combines technical innovation with societal relevance, addressing challenges from plastic recycling to social distancing compliance through computer vision systems.

Moo Chungمشاهده پروفایل

استاد

• Computational Neuroimaging
• Topological Data Analysis
• Brain Networks
• +۵ مورد دیگر

Moo Chung is a Professor of Biostatistics and Medical Informatics at the University of Wisconsin-Madison, affiliated with the School of Medicine and Public Health. He holds additional appointments in the Department of Statistics and the College of Letters and Science. Chung earned his Ph.D. in Mathematics and Statistics from McGill University under Professors Keith J. Worsley and James O. Ramsay. His research focuses on computational neuroimaging, leveraging MRI, fMRI, and DTI to study brain dynamics through topological and geometric methods. Key areas include persistent homology, brain network analysis, and statistical modeling of high-dimensional imaging data. He has pioneered methods like hyper-network construction and exact topological inference for paired brain networks, addressing computational challenges in analyzing large-scale neuroimaging datasets. Chung’s work integrates advanced mathematical techniques such as Hodge Laplacian, spectral graph theory, and Wasserstein distances to analyze brain connectivity. He has secured NIH funding for projects like the Brain Initiative (2017-2020) and current grant MH133614 (2023), focusing on geometric data analysis and topological dynamics. His contributions include three books on brain imaging and network analysis, with ongoing research on topological data analysis applications. Awards: Vilas Associate Award (2013-2014), NIH Brain Initiative Award, Editor's Award (2011) Labs/Teams: Leads brain imaging workshops globally, including Seoul National University (2024) and POSTECH (2024), and organizes conferences like ISBI and MICCAI special sessions. Grants: NIH EB022856 (Brain Initiative), MH133614 (2023), and collaborations with institutions like Vanderbilt University and University of Chicago. Chung actively mentors students and postdocs in biomedical data science, offering fellowships through the CIBM program. His group maintains a Google mailing list for brain image analysis discussions and hosts regular seminars on methodological advancements.

Iacopo Mochiمشاهده پروفایل

پژوهشگر

• EUV Lithography
• Nano-imaging
• X-Ray Metrology
• +۶ مورد دیگر

Iacopo Mochi is a Group Leader for Advanced Lithography and Metrology and beamline scientist at the Paul Scherrer Institute (PSI), specifically working at the Laboratory for X-ray Nanoscience and Technologies within the PSI Center for Photon Science. He has extensive experience in EUV lithography, X-ray optics, and semiconductor metrology, with a career spanning multiple prestigious research institutions including Lawrence Berkeley National Laboratory and imec. Dr. Mochi received his physics degree from the University of Florence and earned a PhD in Methods and Technologies for Environmental Monitoring. His career path has included significant contributions to LIDAR systems, astrophysical instrumentation, and ultimately EUV technologies for semiconductor manufacturing. At PSI since 2016, he has established himself as a leading researcher in advanced lithography techniques. His primary research focuses on the development of EUV instrumentation for semiconductor metrology, particularly the XIL-II metrology end station - a lensless microscope dedicated to EUV photomask inspection. His work spans nano-imaging, interferometry techniques in the X-Ray spectrum, and the characterization of advanced materials for semiconductor manufacturing. Dr. Mochi has made significant contributions to the field through the development of RESCAN, an actinic pattern inspection platform based on coherent diffraction imaging. Analysis of his publication record reveals a strong trajectory of innovation in EUV lithography, with recent work pushing resolution limits to 5 nm, developing novel metrology techniques, and applying machine learning approaches to improve image reconstruction and defect detection. His research directly addresses critical challenges in semiconductor manufacturing as the industry moves toward smaller technology nodes. Dr. Mochi's scientific contributions include groundbreaking work on EUV pellicles for mask protection, characterization of absorber and phase defects on EUV reticles, and advancements in interference lithography techniques. His collaborative research demonstrates strong partnerships with industry and academic institutions worldwide. As the beamline scientist for the XIL-II beamline, Dr. Mochi coordinates and supervises experiments that support cutting-edge research in semiconductor manufacturing and nanotechnology. His leadership extends to mentoring junior researchers and contributing to the development of next-generation scientists in the field of advanced lithography.

• Galaxies
• Data Astronomy
• High-redshift Galaxies
• +۵ مورد دیگر

Rhythm Shimakawa is an Associate Professor at Waseda University's Center for Data Science and Institute for Advanced Study, with affiliations to the National Astronomical Observatory of Japan (Subaru Telescope). His career spans roles as a JSPS Overseas Fellow at UC Santa Cruz and NAOJ Fellow at Subaru Telescope. Education: Doctor of Philosophy (2017) from The Graduate University for Advanced Studies (School of Physical Sciences, Department of Astronomical Science), B.Sc. in Science (2012) from Osaka University. His research bridges galaxy formation/evolution, machine learning applications, and citizen science projects like GALAXY CRUISE. Key interests include protoclusters, superclusters, Lyα emitters, environmental effects on galaxies, and gas recycling in high-redshift systems. Recent publications (2024) focus on galaxy size-environment correlations, JWST observations of protocluster AGN activity, and discovery of z=4 quiescent galaxy concentrations. His team uses Subaru Hyper Suprime-Cam data for large-scale structure mapping and molecular gas studies in protoclusters. Scientific awards: SOKENDAI Research Award (2017), SOKENDAI Future Scientist Award (2015) Media coverage includes findings on supermassive black holes in protoclusters, galaxy void kinematics, and citizen-astronomy collaborations. He leads observational campaigns with ALMA, Keck/MOSFIRE, and HST data to study protocluster gas properties and galaxy morphologies.

• Optoelectronics
• Hyper-Spectral Imaging
• Biomedical Engineering
• +۴ مورد دیگر

Professor Balas Costas is a full Professor and Director of the Electronics Lab at the School of Electrical and Computer Engineering, Technical University of Crete. His expertise spans optoelectronics, biomedical imaging, and medical device innovation. He earned his Ph.D. in medical physics from the University of Patras (1992) and has led pioneering research in hyper-spectral imaging and non-invasive diagnostics. Key roles include founder of DySIS-Medical, a medical device company, and inventor of the DySIS imaging system for cervical neoplasia detection. Awards include the 2013 Greece Innovates Applied Research & Innovation Competition. Research interests focus on optical biopsy, biomedical spectral imaging, and diagnostic device development. His work bridges academia and industry, with contributions to global health through cost-effective diagnostic tools. Notable projects include the development of the DySIS device, validated through multinational clinical trials, and hyperspectral imaging systems for art conservation. Publications span biomedical optics, diagnostic technologies, and material analysis. Grants and venture capital funding (€15M+) support his research. He is a member of IEEE Photonics Society, SPIE, and Optical Society of America.

Kaiguang Zhaoمشاهده پروفایل

دانشیار

• Remote sensing
• GIS
• Spatial modeling
• +۷ مورد دیگر

Kaiguang Zhao is an Associate Professor of Environmental Modeling and Spatial Analysis at The Ohio State University's School of Environment and Natural Resources. His research focuses on mapping, monitoring, modeling, and managing terrestrial environments across scales, especially in the context of global environmental changes. He employs a combined toolset including geotechnology, spatial analysis, machine learning, biophysical & climate modeling, ecological modeling, Bayesian statistics, and eddy-covariance to characterize ecosystem status and changes. Ph.D. in Forestry, Texas A&M University, 2008 M.S. in Geography, Beijing Normal University, 2004 B.S. in Physics, Beijing Normal University, 2001 Dr. Zhao's research interests span remote sensing & GIS, spatial modeling, data analytics, environmental modeling, climate change, and machine learning applications to environmental systems. His work particularly emphasizes developing geospatial applications using hyper spectral imaging, high-resolution imagery, hyper-temporal remote sensing, and lidar to characterize ecosystem structure and functioning. His research addresses critical questions about carbon storage in forests, ecosystem disturbance patterns, vegetation community mapping, climate change impacts on vegetation activities, and climate regulation services from forestry and land-use activities. His extensive publication record demonstrates strong trends in applying advanced statistical and machine learning methods to environmental remote sensing data. His work increasingly focuses on integrating multiple data sources and developing novel algorithms for environmental monitoring, with particular emphasis on crop modeling, water quality assessment, carbon cycling, and climate change impacts. The research shows a clear trajectory toward more sophisticated modeling approaches that combine physical principles with data-driven techniques. 2022 Best Paper award for 'Mapping crop types in fragmented arable landscapes using AVIRIS-NG imagery and limited field data' published in the International Journal of Image and Data Fusion Dr. Zhao actively seeks self-motivated graduate students with quantitative and computational backgrounds to apply their skills to environmental issues in natural or human-dominated ecosystems. His research has been supported by numerous grants focused on environmental monitoring, climate change impacts, and sustainable resource management. While specific grant details aren't listed in the provided text, his extensive publication record suggests substantial research funding across multiple environmental domains. His work involves collaboration with various research teams and laboratories focused on remote sensing, environmental modeling, and climate change research. He has developed significant computational tools including the Rbeast package for Bayesian Change-Point Detection and Time Series Decomposition, indicating leadership in developing methodological approaches for environmental data analysis.

• Nanotechnology
• Atomic, Molecular and Optical Physics
• Quantum Physics
• +۳ مورد دیگر

Jordan Gerton is a Professor of Physics & Astronomy at the University of Utah, holding appointments since July 2021. He earned a BS in Engineering Physics from the University of Arizona (1994), and MS/PhD in Physics from Rice University (1998/2001), followed by postdoctoral work at Caltech. His research bridges nanophotonics, quantum physics, and STEM education reform. He led the Center for Science and Mathematics Education (2014–2022) and is an advocate for anti-racist educational practices. Education: Bachelor's: Engineering Physics, University of Arizona (1994) Master's: Physics, Rice University (1998) Doctorate: Physics, Rice University (2001) Research interests include nanotechnology, quantum physics, and science education. His nanotechnology work focuses on optical force sensors and super-resolution microscopy. In education, he spearheaded graduate program reforms post-tragedy and developed evidence-based teaching methods. He co-taught courses like 'Being Human in STEM' and 'The Way Things Work.' Grants include NSF funding for inclusive REU programs, graduate education policy studies, and STEM teacher training. He also led projects on solar-driven water splitting (Research Corporation for Science Advancement). Awards: Cottrell Scholar (2007), a key milestone supporting his career's collaborative research and education initiatives.

François Orieuxمشاهده پروفایل

استادیار

• Inverse Problems
• Bayesian Methods
• Signal Processing
• +۵ مورد دیگر

François Orieux is an Assistant Professor at the University Paris-Saclay, affiliated with the Laboratoire des Signaux et Systèmes (L2S) within CentraleSupélec. His research focuses on Bayesian methods for inverse problems, computational imaging, and signal processing, with applications in astronomy, microscopy, and embedded AI systems. He holds a PhD in Astrophysical Instrumentation (2009) and an HDR in Computational Imaging (2024). Key research areas include: Development of optimization algorithms for hyperspectral and multispectral fusion High-dimensional sampling techniques for large-scale data GPU-accelerated convolution methods for real-time processing Instrument modeling for radio astronomy and terahertz spectroscopy Recent work emphasizes hardware-aware AI implementations on embedded systems and radio interferometric imaging pipelines. He collaborates with institutions like the Institut d'Astrophysique de Paris and leads projects funded by ANR and EU initiatives. Teaching activities include computational imaging and signal processing courses at CentraleSupélec. His open-source contributions include the Q-MM Python toolbox for optimization algorithms.

Shobita Satyapalمشاهده پروفایل

استاد

• Astrophysics
• Galaxy Evolution
• Active Galactic Nuclei (AGN)
• +۳ مورد دیگر

Dr. Shobita Satyapal is a Professor in the Physics & Astronomy Department at George Mason University, specializing in the formation and evolution of supermassive black holes and galaxies. Her work focuses on AGN feedback mechanisms, galaxy mergers, and the interplay between black hole growth and galactic environments. She leverages advanced facilities like JWST, Chandra, and Keck to study obscured AGNs, dual AGN systems, and low-metallicity dwarf galaxies as analogs of early-universe systems. Her research includes the CLASS Quasar Catalog , ORCAS Keck adaptive optics project, and BASS mid-infrared surveys. Key interests span coronal line emission diagnostics, merger-induced AGN activity, and the search for intermediate-mass black holes. Dr. Satyapal’s articles highlight trends in ionized outflows, dual AGN dynamics, and JWST/NIRSpec applications in uncovering obscured AGN populations. Her studies emphasize multiwavelength approaches to understand black hole fueling, merger-driven evolution, and the role of AGN in shaping galactic structures. She is affiliated with the Black Hole Growth and Cosmology (BGC) group and collaborates on projects like ReveaLLAGN to study low-luminosity AGN. Her work addresses foundational questions in astrophysics, including the origins of supermassive black holes and their role in galaxy evolution.

Lev Deychمشاهده پروفایل

استاد

• Optics
• Condensed Matter Physics
• Whispering Gallery Modes
• +۴ مورد دیگر

Lev Deych is a Professor of Optics and Condensed Matter Theory at the Department of Physics, Queens College of the City University of New York. He holds a M.S. from Tajik State University (1982) and a Ph.D. from Kirensky Institute of Physics (1991). His expertise spans theoretical physics, photonics, and nanotechnology. Key research areas include Whispering Gallery Modes (WGM), biosensing applications, optomechanical forces, and optical properties of semiconductor quantum wells. His work on WGM explores their use in biosensors for detecting viruses and nanoparticles, leveraging enhanced field concentrations and spectral shifts. He investigates light-induced mechanical forces in WGM resonators for optical manipulation and cooling. Collaborating with Prof. Lisyansky, he developed theories on Bragg Multiple Quantum Wells, experimentally validated by Prof. Menon in Nature Photonics. Recent publications focus on supercontinuum level-crossing, optomechanical interactions, and hyper-degenerate microspheres. His research bridges theoretical photonics with practical applications in sensing and materials science.