جست‌وجوی استاد و پژوهشگر

• Transmission Electron Microscopy (TEM) Method Development
• Charge Particle Optics and Scattering Theory
• Magnetic Nanotextures
• +۳ مورد دیگر

Dr. Axel Lubk is a Group Leader at the Institute for Solid State Research (IFW Dresden) , specializing in advanced electron microscopy techniques for materials science. His research spans four key areas: (1) TEM method development (high-resolution imaging, tomography, holography, and in-situ techniques), (2) charge particle optics and scattering theory , (3) magnetic nanotextures (domain walls, skyrmions), and (4) plasmonics (mode hybridization in heterogeneous structures and semiconductor heterostructures). Dr. Lubk’s work focuses on three-dimensional magnetic texture analysis using electron holography and tomography, particularly in systems like skyrmion tubes , FeGe , and Cr2O3 thin films . He has pioneered techniques for vector-field electron tomography and phase retrieval under varying boundary conditions, advancing nanoscale magnetic imaging. His recent studies include plasmonic properties in AgAu nanosphere chains , thermoelectric multilayer systems , and topological insulators like NiRh2Sb and TaTMTe4 . Dr. Lubk has published extensively in high-impact journals such as Nature Communications and Advanced Materials , with a focus on TEM instrumentation and quantitative analysis . He frequently presents at international conferences like the International Microscopy Congress and European School of Magnetism , emphasizing applications in spintronics , quantum materials , and nanostructured systems . His contributions to holographic vector-field electron tomography and machine learning for spectrum-image data have set new standards in electron microscopy.

Lisa Duizerمشاهده پروفایل

استاد

• Sensory Science
• Food for Older Adults
• Texture Analysis
• +۳ مورد دیگر

Lisa Duizer serves as Professor and Department Chair in the Department of Food Science at the University of Guelph, where she has been faculty since 2009 after previous appointments at Acadia University's School of Nutrition and Dietetics and Massey University's Institute of Food Nutrition and Human Health. Her research addresses critical challenges in food for aging populations, focusing on sensory properties of foods designed for older adults with dysphagia or malnutrition. Education BASc in Applied Human Nutrition, University of Guelph (1990) MSc in Food Science, University of Guelph (1992) PhD in Food Technology, Massey University (2003) Research Interests Professor Duizer's work centers on sensory science applications for geriatric nutrition, investigating age-related changes in taste and texture perception. She develops evidence-based strategies for improving nutritional quality and acceptability of foods for elderly populations, including pureed food optimization, nutrient fortification techniques, and 'supermenu' development for long-term care settings. Her research integrates human subjects testing with rheological and tribological analysis to establish science-based standards for modified texture foods. Publication Trends Her recent publications (2022-2025) demonstrate expanding interdisciplinary scope, with increasing focus on emotional food responses, space food sensory perception, and plant-based alternatives. The predominant themes involve texture modification for dysphagia management, salt reduction strategies for older adults, and stakeholder-driven interventions for malnutrition in long-term care. This trajectory reflects growing integration of sensory science with healthcare delivery systems and innovative food product development. Scientific Awards No scientific awards were documented in the provided source materials. Advising and Grants Lisa Duizer mentors graduate students with rigorous emphasis on ethical conduct in human subjects research, requiring comprehensive understanding of participant interaction protocols. Her students from nutrition and food science backgrounds have pursued careers as registered dietitians, food product developers in multinational corporations, and research scientists. Her work with the Agri-Food for Healthy Aging group and University Health Network affiliation indicates substantial grant funding from health and agriculture sectors for projects targeting malnutrition intervention in aging populations. Labs and Teams She leads sensory evaluation laboratories within the Department of Food Science and serves as Research Scientist for the Agri-Food for Healthy Aging initiative. Her collaborative network includes the University Health Network, Ontario Ministry of Health and Long-Term Care, long-term care home operators, food distributors, and agricultural producers, facilitating translational research from laboratory to institutional meal service implementation.

Ruan Elliottمشاهده پروفایل

مدرس ارشد

• Nutritional Metabolism
• DNA Damage and Repair
• Nutrigenomics
• +۴ مورد دیگر

Dr Ruan Elliott is Dean of the Doctoral College and Senior Lecturer in Nutrition at the University of Surrey's School of Biosciences. He holds a PhD in Nutritional Metabolism (2017) and BA in Natural Sciences from Cambridge (1988). His research focuses on nutritional modulation of DNA repair mechanisms, functional genomics in nutrigenomics, and micronutrient homeostasis of iron, copper, selenium, and zinc. He has led studies on vitamin D efficacy, food bioavailability, and oxidative stress biomarkers. Education: PhD (University of Surrey, 1992), BA (University of Cambridge, 1988) Previous Roles: Senior Scientist (Institute of Food Research, 1994-2009), Postdoctoral Fellow (University of British Columbia, 1992-1994) His research integrates molecular biology and nutrition to address health impacts of dietary components. Recent work explores oat-based milk alternatives' sensory profiles, vitamin D supplementation efficacy, and DNA repair pathways in cardiovascular disease. He collaborates internationally on projects like the D2-D3 Study and NuGO initiatives. Publications emphasize translational research, including systematic reviews on insect protein efficacy and meta-analyses of vitamin D supplementation. His work bridges lab-based models (e.g., Caco-2 cells for iron studies) and clinical applications. No scientific awards were explicitly listed in the provided texts. He advises on doctoral training programs and has contributed to grant-funded projects like the University of Surrey's food and infection biosciences initiative (2024). Collaborations include studies on probiotics' immune effects and transcriptomic analysis of metabolic conditions.

Peter J. Kohlerمشاهده پروفایل

استادیار

• Visual Neuroscience
• Mid-Level Vision
• Perceptual Organization
• +۹ مورد دیگر

Peter J. Kohler is an Assistant Professor in the Department of Biology within the Faculty of Science at York University, Toronto. He is eligible to supervise graduate students in the Biology Graduate Program and leads the Kohler Visual Neuroscience Lab, which is part of the Centre for Vision Research at York University. His research lies at the intersection of cognitive neuroscience and visual perception, focusing on mid-level visual processing. This involves understanding how the brain, within the first few hundred milliseconds of visual input, constructs representations of shape, motion, location, and perceptual organization—including figure-ground segregation, grouping, and constancy. His work integrates functional MRI (fMRI) , electroencephalography (EEG) , and visual psychophysics to probe the neural mechanisms underlying these processes in humans. Recent publications reveal a strong focus on symmetry processing, perceptual grouping, numerical estimation, and multisensory integration. His work often involves advanced neuroimaging techniques and collaborative international research, particularly with teams in Belgium and Luxembourg. The articles span high-impact journals such as PNAS , Nature Communications , Current Biology , and Journal of Vision , indicating a robust and influential research program in visual neuroscience. Scientific Awards and Funding: NSERC Discovery Grant (awarded April 2020) VISTA Research Grant (funded June 2023) Prof. Kohler actively mentors students, including graduate students such as Rachel Moreau, Sara Chaparian, Yara Iskandar, Shaya Samet, and Shenoa Ragavaloo, as well as undergraduate researchers. His lab has presented at major conferences including the Vision Sciences Society (VSS) and the Lake Ontario Visionary Establishment (LOVE), where he joined the organizing committee in 2024. The Kohler Visual Neuroscience Lab also develops experimental tools, as evidenced by GitHub repositories for stimulus generation and behavioral testing using jsPsych.

Ting-Chung Poonمشاهده پروفایل

استاد

• Optical Scanning Holography
• Digital Holography
• 3D Imaging
• +۳ مورد دیگر

Ting-Chung Poon is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research focuses on optical scanning holography (OSH), digital holography, and 3D imaging applications. He leads the Optical Scanning-Holographic Imaging Group (OSIG), which explores OSH for 3D imaging, processing, and display, emphasizing 2D optical heterodyne scanning techniques. Education: Ph.D., University of Iowa, 1982 M.S.E.E., University of Iowa, 1979 B.A., University of Iowa, 1977 Research Interests: Optical Scanning Holography (OSH) and its applications in 3D imaging Computer-Generated Holography (CGH) Quantitative Phase Imaging Optical Cryptography Efficient Hologram Algorithms His work spans theoretical advancements and practical implementations, including encryption systems, noise reduction techniques, and high-resolution 3D reconstruction. Recent Research Trends: Focus on polygon-based CGH algorithms for faster rendering Integration of machine learning for speckle noise reduction and hologram classification Development of adaptive and compressive holography methods for industrial and biomedical applications Labs & Teams: Optical Scanning-Holographic Imaging Group (OSIG) at Virginia Tech Collaborations with institutions globally, including conferences on digital holography and photonics

• Machine Learning
• Computer Vision
• Medical Imaging
• +۷ مورد دیگر

Ali Gooya is a Senior Lecturer (Associate Professor) in Machine Learning at the School of Computing Science, University of Glasgow, UK. His research focuses on probabilistic deep learning applied to medical imaging, particularly in cardiology and oncology, emphasizing semi/unsupervised methods due to sparse expert annotations. He holds a PhD in medical image analysis from the University of Tokyo (2007) and has held academic positions at the University of Leeds and Sheffield before joining Glasgow in 2022. Affiliations: Senior Lecturer in Machine Learning, University of Glasgow (2022–present) Lecturer in Computing, University of Leeds (2018–2022) Lecturer in Computing, University of Sheffield (2016–2018) Postdoctoral Researcher, University of Pennsylvania (2008–2011) Research Interests: Deep learning for medical imaging, probabilistic modeling, cardiac and cancer imaging, computational anatomy, and marker discovery. Key applications include motion analysis, segmentation, and predictive modeling in healthcare. Key Achievements: Won prestigious fellowships including Allen Touring Institute (2022), JSPS Short-Term (2020), Marie-Curie IIF (2014), and JSPS-PDRA (2008). Pioneered Bayesian deep learning frameworks for cardiac motion assessment and generative models in medical imaging. Grants & Supervision: EPSRC Impact Acceleration Award (PI) EPSRC New Investigator Grant (EP/S012796/1) Actively supervising PhD students in areas like Bayesian deep atlases for cardiac motion analysis. Labs & Teams: Leads research in medical AI within the School of Computing Science, collaborating on projects integrating imaging and patient metadata for clinical decision support.

Diego Patiñoمشاهده پروفایل

استادیار

• Machine Learning
• Geometric Computer Vision
• 3D Reconstruction
• +۷ مورد دیگر

Diego Patiño is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), a position he began in September 2024. He earned his Ph.D. in Computer Engineering from the National University of Colombia in 2020, following M.S. and B.S. degrees from the same institution. Prior to joining UTA, he served as a Postdoctoral Fellow at Drexel University and a Postdoctoral Researcher at the GRASP Laboratory, University of Pennsylvania. B.S. in Computer Engineering, National University of Colombia, 2010 M.S. in Computer Engineering, National University of Colombia, 2012 Ph.D. in Computer Engineering, National University of Colombia, 2020 Dr. Patiño's research centers on geometric computer vision and machine learning, with applications in robotics and 3D vision. His primary interests include 3D reconstruction, graph neural networks, symmetry detection, physics-informed machine learning, and reinforcement learning. He develops algorithms that integrate geometric priors and physical constraints into deep learning models to improve robustness and generalization in real-world robotic systems. His recent publications demonstrate a strong trend in leveraging implicit neural representations for 3D shape reconstruction, applying graph neural networks to swarm robotics, and enhancing computer vision tasks with self-supervised and physics-informed learning. Work spans high-impact venues such as IEEE RA-L, ICRA, ICPR, and MICCAI, showing a consistent focus on geometric reasoning, robotic perception, and medical imaging applications. His scientific contributions have been recognized with awards from the UTA Division of Student Affairs for exceptional dedication and positive impact (2024 and 2025). He is actively involved in securing research funding, with multiple grants under review from NSF, Air Force SBIR, and industry partners like Sony. Exceptional dedication and positive impact recognition, UTA Division of Student Affairs (December 9, 2024) Exceptional dedication and positive impact recognition, UTA Division of Student Affairs (April 30, 2025) Dr. Patiño advises and serves on committees for multiple graduate students in computer science and engineering, including doctoral and master’s candidates. He is also leading or co-leading several research grants under review, covering topics such as aerial swarm navigation, neuromorphic sensing, and industrial computer vision. He teaches graduate courses in computer vision and is involved in service roles including PhD admissions and faculty appointments committees. He is affiliated with research initiatives at UTA, including the UTARI Research Institute, where he has presented on geometric modeling and physics-informed learning. His lab focuses on developing next-generation computer vision algorithms for robotics, industrial inspection, and safety-critical systems.

• Volunteered Geographic Information (VGI)
• OpenStreetMap
• Spatial Data Analysis
• +۳ مورد دیگر

Peter Mooney is a Lecturer in the Department of Computer Science, Faculty of Science & Engineering at Maynooth University. His research focuses on Volunteered Geographic Information (VGI), OpenStreetMap, spatial data analysis, and geospatial data integration in applications such as environmental monitoring and pervasive health systems. Institution: Maynooth University School: Faculty of Science & Engineering Department: Computer Science Role: Lecturer Mooney's research explores the use of crowdsourced geospatial data, particularly through OpenStreetMap, analyzing data quality, community roles, and integration into location-based services. His work bridges technical analysis with policy considerations in geospatial data management. Recent publications highlight his contributions to understanding spatial data dynamics, including attribute changes in OpenStreetMap, characteristics of edited objects, and applications of VGI in environmental systems. He also investigates the intersection of haptics and GIS for novel interaction methods. Contact: peter.mooney@mu.ie

Leif Kobbeltمشاهده پروفایل

استاد

• Computer Graphics
• Geometry Processing
• Interactive Visualization
• +۳ مورد دیگر

Leif Kobbelt serves as a University Professor at RWTH Aachen University, leading the Computer Graphics Group within the Department of Computer Science (Informatik 8). His research focuses on advancing geometry processing, interactive visualization, and computer graphics through innovative algorithmic solutions and interdisciplinary collaborations. Professor Kobbelt's research program centers on geometry acquisition and processing, with significant contributions to mesh generation, surface reconstruction, and neural rendering techniques. His work bridges theoretical geometry with practical applications in computer vision, photo-realistic image synthesis, and multimedia data transmission, often involving collaborations with industry partners and international research teams funded by DFG and EU sources. Recent publications (2023-2025) reveal a strategic integration of deep learning with traditional geometry processing, particularly in Gaussian splatting for real-time rendering, NeRF-based 4D content generation, and robust mesh Boolean operations. His group maintains leadership in quad mesh optimization and surface mapping while expanding into immersive visualization techniques for complex data analysis. The group has earned recognition through prestigious awards: Günter Enderle Best Paper Award at Eurographics 2023 Best Paper Award (1st place) at Symposium on Geometry Processing 2022 Honorable Mention for Best Paper at ACM Symposium on Virtual Reality Software and Technology Funding from Deutsche Forschungsgemeinschaft and European Union programs supports the group's research infrastructure and international collaborations. The team actively supervises graduate theses while developing open-source software tools that translate theoretical advances into practical industry applications, particularly in digital fabrication and immersive visualization systems. The Computer Graphics Group operates as a central hub for visual computing research at RWTH Aachen, maintaining strong ties with both academic institutions and technology companies. Their recent work on virtual reality educational tools and high-fidelity 3D reconstruction systems demonstrates commitment to knowledge transfer and real-world impact beyond traditional publication venues.

Sylvain Faisanمشاهده پروفایل

استادیار

• Image Processing
• Machine Learning
• Geometry
• +۶ مورد دیگر

Sylvain Faisan is a permanent Assistant Professor at ICube - MIV (University of Strasbourg, France). His research focuses on image processing, statistical modeling, and geometry, with applications in medical imaging and neuroscience. He works on advanced methodologies integrating machine learning and mathematical frameworks. Key Research Areas: Polarimetric image processing, retinal image registration, 3D statistical model comparison, topology-preserving image deformation, and fMRI brain mapping Technical Expertise: Bayesian inference, non-local means filtering, reversible jump MCMC algorithms, causal modeling, and constrained optimization His publications demonstrate interdisciplinary applications in optics, biomedical imaging, and computational anatomy. He contributes to developing algorithms that maintain physical admissibility and topological integrity in complex imaging problems.

• Cosmetic Science
• Pharmaceutics
• Dermatology
• +۴ مورد دیگر

Dr. Deborah A. Kayode serves as Assistant Professor of Pharmaceutics and Industrial Science and Full-time Director of Cosmetic Science at Long Island University, specializing in evidence-based cosmetic product development with particular expertise in cellulite pathophysiology and formulation science. Her academic credentials include: Ph.D. in Cosmetic Science, University of Sunderland (2021) M.Sc. in Drug Discovery and Development, University of Sunderland (2015) PGDip. in Clinical Pharmacology, University of Aberdeen (2014) B.Sc. in Biochemistry, Ajayi Crowther University (2012) Dr. Kayode's research program centers on the human integumentary system with emphasis on gynoid lipodystrophy mechanisms and novel anti-cellulite treatments. She pioneers rheological characterization techniques for cosmetic formulations while developing innovative quality control methodologies that bridge laboratory testing with consumer sensory experiences. Her work establishes scientific foundations for cosmetic claim substantiation through rigorous in vivo validation protocols. Analysis of her publication record reveals consistent advancement in cosmetic formulation science, particularly regarding oil-in-water emulsion systems. Her research demonstrates sophisticated integration of rheological analysis with sensory evaluation, develops stability assessment frameworks for natural oil-containing products, and creates novel quality control metrics linking refractive index measurements to skin hydration outcomes - collectively enhancing scientific rigor in cosmetic product development. Dr. Kayode has mentored 15 undergraduate and master's students through research projects while maintaining significant industry engagement through product validation services for cosmetic and pharmaceutical companies. Her notable BBC collaboration on The Truth About...Looking Good featured rigorous in vivo testing of anti-cellulite treatments, demonstrating real-world application of her research methodology. She additionally established comprehensive quality assurance guidelines for laboratory operations and continues to lead the Cosmetic Science program as Director.

• Artificial Intelligence
• Machine Learning
• Biomedical Imaging
• +۵ مورد دیگر

Despina Kontos, PhD is the Herbert and Florence Irving Professor of Radiological Sciences at Columbia University Irving Medical Center (CUIMC), with appointments in the Department of Radiology and the Herbert Irving Comprehensive Cancer Center. She serves as the Chief Research Information Officer for CUIMC, Vice Chair of Artificial Intelligence and Data Science Research in the Department of Radiology, and Director of Biomarker Imaging at NewYork-Presbyterian Hospital. Additionally, she holds appointments in the Departments of Biomedical Informatics and Biomedical Engineering. Dr. Kontos received her educational training from prestigious institutions: BS in Engineering from the University of Patras, Greece MSc and PhD in Computer and Information Sciences from Temple University Postdoctoral training in Radiology at the University of Pennsylvania Certificates in Biostatistics and Epidemiology from UPenn, Cancer Biology from Harvard, and AI for Decision Making from Wharton As a computer scientist with expertise in artificial intelligence and machine learning, Dr. Kontos focuses on developing computational methodologies to leverage imaging as quantitative biomarkers for personalized disease prediction, particularly in cancer. Her research program investigates how imaging data can be mined to extract sophisticated phenotypic signatures with diagnostic, prognostic, and predictive value. While her primary focus has been on breast cancer, her lab also pursues related research in lung cancers, evaluating the integration of CT radiomic features with liquid biopsy data to characterize tumor heterogeneity. Dr. Kontos founded and directs Columbia University's Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), a multidisciplinary center dedicated to developing and integrating quantitative imaging and non-imaging biomarkers for personalized disease prediction. Through CIMBID, she has built a vibrant scientific ecosystem that brings together expertise across Columbia's campuses, linking basic science, engineering, clinical medicine, public health, and health services research. Analysis of Dr. Kontos's publication record reveals a strong focus on applying AI and machine learning to biomedical imaging, particularly for cancer risk prediction and personalized treatment. Her work demonstrates a progression from foundational methodological development to clinical translation, with increasing emphasis on multi-modal biomarker integration. Recent publications show expansion into new disease areas including Alzheimer's disease prediction, while maintaining her strong focus on breast and lung cancer applications. Dr. Kontos has received significant recognition for her contributions to the field: Academy for Radiology and Biomedical Imaging Research Distinguished Investigator Award (2020) Eastern Cooperative Oncology Group - American College of Radiology Imaging Network ECOG-ACRIN Young Investigator Award of Distinction for Translational Research (2014) Dr. Kontos has been highly successful in securing research funding, with numerous grants from federal agencies including the National Institutes of Health (NIH) and the Department of Defense (DOD), as well as private foundations such as the American Cancer Society (ACS) and the Radiological Society of North America (RSNA). Her leadership extends to mentoring students and postdoctoral researchers through her roles at CIMBID and the Department of Radiology. As the founding director of CIMBID, Dr. Kontos leads a multidisciplinary team that includes the Computational Imaging Biomarker Group (CBIG), the Laboratory of AI and Biomedical Science (LABS), and several other affiliated research labs. The center leverages Columbia's institutional strengths in engineering, data science, and clinical medicine to advance personalized healthcare through AI and imaging technologies.

Heidi Baselerمشاهده پروفایل

مدرس ارشد

• Visual Neuroscience
• Neuroimaging
• Sensory Processing
• +۴ مورد دیگر

Dr. Heidi Baseler is a Senior Lecturer in Imaging Sciences at the Hull York Medical School and the Department of Psychology , University of York . She serves as INSPIRE Programme Lead for undergraduate research and contributes to research governance and public/patient involvement in research through committees like the York Neuroimaging Centre Research Governance Committee and Involvement@York . Education : AB in Biology/Psychology from Dartmouth College (1986), PhD in Vision Science from University of California, Berkeley (1995) Career : Research Fellow at Stanford, Smith-Kettlewell Eye Research Institute, and University of York; Lecturer in Imaging Sciences at Hull York Medical School (2012-2021) Her research focuses on neural mechanisms for central/peripheral vision processing, cortical responses to sensory loss (e.g., AMD, deafness), and neuroprotective strategies like photobiomodulation. Techniques include structural and functional MRI , magnetic resonance spectroscopy , multifocal EEG , and electroretinography . Key findings include cortical reorganization in macular degeneration and superior visual performance in deaf adults. Recent article trends highlight: Neuroimaging biomarkers for AMD progression Cross-modal plasticity in deaf individuals Long-term effects of sensory deprivation on brain structure Photobiomodulation for neurodegeneration Functional connectivity in face-selective regions Contrast perception dynamics across aging Scientific awards include: Exceptional Contribution to Student Experience Finalist , Hull York Medical School (2024) Pursuing Excellence Award , Hull York Medical School (2023) Vice-Chancellor's Teaching Award , University of York (2020) Multiple Teacher of Excellence Finalist recognitions (2016-2018) She teaches undergraduate medical students in Foundations of Medicine and Neurological Diseases , and supervises MSc Cognitive Neuroscience projects. Collaborations span institutions like University of Hull , University of Lille , and University of Regensburg . Current students include Sharyfah Alasiri (PhD, Biomedical Sciences) and Erin English (PhD, Psychology) at the University of York.

Tresa Pollockمشاهده پروفایل

استاد

• Materials Science and Engineering
• Structural Materials
• High Temperature Materials Processing
• +۶ مورد دیگر

Tresa Pollock is the ALCOA Professor of High Tech Materials in the Materials Department at the University of California, Santa Barbara (UCSB), part of the College of Engineering. Her research focuses on structural materials, high-temperature processing, ultrafast laser interactions, alloy design, and 3D characterization techniques. She holds a Ph.D. from MIT and a B.S. from Purdue University. Research Interests: Her work addresses extreme environment materials performance, thermal barrier coatings, cobalt-base superalloys, hypersonic flight materials, femtosecond laser tomography, and computational materials engineering. Recent projects include developing refractory alloys and advanced characterization methods like the TriBeam system. Awards: She is a Fellow of TMS (The Minerals, Metals & Materials Society) and received the 2023 Acta Materialia Gold Medal. Grants & Collaborations: Supported by agencies like ONR, NSF, AFOSR, and industry partners including GE, Boeing, and Rolls-Royce. Her lab includes advanced facilities at UCSB’s Microscopy and Microanalysis Facility. Labs & Teams: Leads a research group with senior scientists like Chris Torbet. Labs are located in Engineering II and Elings Hall, focusing on 3D tomography, laser-material interactions, and high-temperature alloy development.

Xin Zhangمشاهده پروفایل

استادیار

• Smart/Digital Agriculture
• Artificial Intelligence (AI) in Agriculture
• Computer Vision
• +۵ مورد دیگر

Dr. Xin Zhang is an Assistant Professor in the Department of Agricultural and Biological Engineering at Mississippi State University. His research focuses on smart agriculture, AI applications, agricultural robotics, and unmanned aerial systems. He leads the Sensing & Automation in Agrisystems (SAAS) Lab, which develops technologies for crop monitoring, robotic harvesting, and machine vision systems. Dr. Zhang holds a Ph.D. from Washington State University and has held postdoctoral positions at UC Davis. He is a FAA-certified UAS pilot and actively collaborates on projects involving machine learning, computer vision, and precision agriculture. Research interests include digital agriculture frameworks, crop prediction models, and the integration of AI into farming systems. Key achievements include pioneering work in robotic cotton-picking simulations and UAV-based yield estimation. His lab emphasizes interdisciplinary approaches combining robotics, data science, and environmental engineering. Awards include the 2020 Giuseppe Pellizzi Prize for agricultural engineering research and multiple ASABE accolades. Current projects involve scalable field extraction frameworks using satellite imagery and deep learning models for plant phenotyping. He has published over 50 peer-reviewed articles, with a focus on advancing automation in crop handling and environmental sensing technologies. Education: Ph.D., Biological & Agricultural Engineering (WSU), M.S., Agriculture (Northwest A&F University), B.S., Agronomy (Gansu Agriculture University).