جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Artificial Intelligence
• Cybersecurity
• +۷ مورد دیگر

Prof. Dr. Virginijus Marcinkevičius is a Professor at Vilnius University , serving as the head of the Smart Technologies Research Group and the Artificial Intelligence Laboratory within the Institute of Data Science and Digital Technologies . He is also a Senior Researcher , Project Lead Researcher , and Group Leader . Based in Vilnius, Lithuania, he has been instrumental in advancing research in machine learning , artificial intelligence , cybersecurity , and natural language processing . Research Interests: Machine Learning & AI Cybersecurity & Threat Detection Natural Language Processing Hyperspectral Imaging & Remote Sensing Autonomous Systems & Robotics Big Data & Cloud Computing His work spans both theoretical and applied aspects, including IoT security , visual analytics , and intelligent decision support systems . Recent projects include the development of propaganda detection systems , hyperspectral unmixing algorithms , and autonomous driving agents . Doctoral Supervision: He has supervised 19+ PhD students and 5+ consultants , covering topics from machine learning in cybersecurity to neural machine translation and autonomous UAV navigation . Projects & Grants: He has led or contributed to 15+ national and EU-funded projects , including: CognitiveSTATS – COVID-19 data literacy platform Propaganda and Disinformation Research – ML-based detection DAMIS – Data mining system for national research Raštija 2 – Lithuanian language resource integration Publications: He has authored or co-authored 60+ peer-reviewed publications in journals like IEEE Access , Informatica , Frontiers in Psychology , and Machine Vision and Applications . Professional Memberships: He is a member of the Lithuanian Computer Society , Lithuanian Mathematical Society , and Lithuanian Operations Research Society .

Cosimo Arnesanoمشاهده پروفایل

استادیار بالینی

• Analytics
• Artificial Intelligence
• Corporate Strategy
• +۵ مورد دیگر

Cosimo Arnesano serves as an Assistant Professor of Clinical Data Sciences and Operations at the University of Southern California's Marshall School of Business. He holds dual Ph.D. degrees in Energy and Environmental Engineering and Biomedical Engineering, complemented by an MBA specializing in business analytics and operations/supply chain optimization. His industry background includes strategic roles at ThermoFisher Scientific (Strategy Manager, 3 years) and Zeiss Microscopy (Account Manager, 1.5 years). Education Ph.D. in Energy and Environmental Engineering Ph.D. in Biomedical Engineering MBA with concentrations in Business Analytics and Operations/Supply Chain Optimization Research Interests Dr. Arnesano's expertise spans analytics, artificial intelligence, corporate strategy, healthcare analytics, machine learning, operations management, statistics, and stochastic modeling. His work bridges biomedical imaging techniques with business applications, focusing on data-driven solutions for healthcare operations and business process optimization. He develops analytical models for classification, clustering, and association problems while leveraging third-party big data to enhance business insights and decision-making. Publication Trends His 15 most recent publications (2012-2022) reveal an interdisciplinary trajectory from biomedical imaging methodology to clinical applications. Early works (2012-2016) established innovations in fluorescence lifetime imaging and digital signal processing, while recent research (2017-2022) applies these techniques to cancer metabolism, stem cell tracking, and retinal organoid characterization. A consistent thread involves developing non-invasive optical biomarkers for real-time cellular analysis, directly informing his business analytics focus on operational efficiency and healthcare optimization. Teaching and Operations Undergraduate: Applied Business Statistics (BUAD-310), Basics of Project and Operations Management (BUAD-315) Graduate: Spreadsheet Modeling (DSO-427), Business Decision Modeling (DSO-536), Blended Data Business Analytics (DSO-528)

Susan Meerdinkمشاهده پروفایل

استادیار

• Machine Learning
• Remote Sensing
• Terrestrial Ecology
• +۲ مورد دیگر

Susan Meerdink is an Assistant Professor in the Department of Geography at the University of Iowa , affiliated with the College of Liberal Arts and Sciences. Her research focuses on the integration of machine learning , remote sensing , and terrestrial ecology to address environmental monitoring challenges. Specializes in computational methods for high-resolution imagery processing Develops algorithms to characterize vegetation function via optical/thermal properties Active in interdisciplinary research spanning remote sensing and ecosystem analysis Her recent publications emphasize applications of hyperspectral and thermal imagery, with a focus on drought stress monitoring, invasive species detection, and harmful algal bloom tracking across diverse ecosystems. Meerdink contributes to the LOQATE Lab and holds a PhD from the University of California, Santa Barbara.

Yiming Fengمشاهده پروفایل

استادیار

• Food Science
• Bioprocessing
• Sustainable Food Systems
• +۶ مورد دیگر

Dr. Yiming Feng is an Assistant Professor and Extension Specialist at the Virginia Seafood Agricultural Research and Extension Center , affiliated with the Department of Biological Systems Engineering in the College of Engineering at Virginia Tech. He leads the Sustainable & Intelligent Seafood Bioprocessing Lab , focusing on seafood safety and waste valorization. Ph.D. and B.S. in Food Science and Human Nutrition from the University of Illinois, Urbana-Champaign Dr. Feng’s research integrates hyperspectral imaging and machine learning for real-time seafood safety monitoring (e.g., microplastic detection) and transforms food processing waste into biochar , bio-fungicides , and biogases . His work aligns with circular economy principles and addresses industry challenges like thermal validation and traceability regulations . Recent publications highlight advancements in non-destructive food sensing , plant-based seafood analogs , and agricultural waste conversion for energy and packaging. Grants include a USDA-SCBGP award for olive byproduct valorization and a 4-VA Collaborative Research Grant for microplastic detection. Food Processing Operation Award (Cal Poly, 2021–2023) Food Engineering Award (Cal Poly, 2021–2023) Food Quality Assurance Award (Cal Poly, 2021–2023) Advanced Food Processing Award (Cal Poly, 2021–2023) Dr. Feng actively recruits graduate students and collaborates with industry partners to bridge academic research with practical seafood sector solutions. His lab specializes in thermal validation studies , machine vision , and sustainable bioprocessing .

Douglas Strandمشاهده پروفایل

دانشیار

• Benign Prostatic Hyperplasia (BPH)
• Lower Urinary Tract Symptoms (LUTS)
• Prostate Cancer
• +۵ مورد دیگر

Douglas Strand, Ph.D., is an Associate Professor of Urology at UT Southwestern Medical Center. He leads a translational research program focused on benign prostatic hyperplasia (BPH), lower urinary tract symptoms (LUTS), and prostate cancer pathogenesis. Ph.D. from Molecular and Cellular Biology at Baylor College of Medicine Postdoctoral training in Urology at Vanderbilt University Joined UT Southwestern faculty in 2014 His work develops human tissue biorepositories using single-cell and spatial transcriptomics to create cellular atlases of the urinary tract. Current projects include: Molecular characterization of BPH cellular pathogenesis Development of mouse models for human urinary tract diseases Investigating epithelial-mesenchymal interactions in prostate cancer progression Dr. Strand contributes to major collaborative initiatives including: GenitoUrinary Development Molecular Atlas Program (GUDMAP) Human Cell Atlas (HCA) Chan Zuckerberg Initiative (CZI) HUman BioMolecular Atlas Program (HUBMAP) His recent publications focus on: 5-alpha reductase inhibitor resistance mechanisms Immune cell interactions in BPH Multi-omic approaches to urinary tract pathology Advancements in hyperspectral imaging technology

Andy Nelsonمشاهده پروفایل

استاد

• Earth Observation
• Food Security
• Remote Sensing
• +۷ مورد دیگر

Andy Nelson is a Full Professor in Spatial Agriculture and Food Security at the Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente, Netherlands. He leads the Department of Natural Resources and focuses on Earth observation and spatial analysis for global food security and sustainable resource management. PhD in Geography, University of Leeds MSc in Geographical Information Systems, University of Leicester BSc in Civil Engineering, University of Nottingham His research addresses critical questions in food production, risk mitigation, and food transport network resilience. He has developed open-access datasets like global travel time maps and urban-rural continuum models , with publications in Nature , Science , and policy reports for the World Bank and FAO. Recent work includes machine learning for pest monitoring, multi-sensor crop mapping, and climate stress modeling in Ethiopia and the Philippines. He co-supervises MSc and PhD students, emphasizing international collaboration. 2023 GEO Team Impact Award 2024/2025 ITC PhD Publication Awards As Senior Editor at Food Security and coordinator of projects like GEOGLAM and 4TU Centre for Resilience Engineering , Nelson bridges academia, policy, and technology to address global food system challenges.

Mahdi Khodadadzadehمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Process Mining
• +۹ مورد دیگر

Mahdi Khodadadzadeh is an Assistant Professor in the Department of Geo-information Processing, specializing in machine learning, data mining, and geospatial analysis. His work bridges traditional and deep learning methods with applications in mineral exploration, environmental modeling, and circular economy initiatives. Research outputs include novel cross-validation techniques for geospatial machine learning (Spatial+, dissimilarity-adaptive methods), hyperspectral mineral mapping for ore characterization, and hybrid models combining artificial neural networks with optimization algorithms. He actively contributes to open-access datasets and methodologies, particularly in drill-core analysis and multi-source data fusion. Collaborations with researchers like R. Zurita-Milla and R. Gloaguen are evident through co-authored publications and shared datasets. His work impacts domains such as mineral exploration, geospatial modeling, and sustainable resource management, with a focus on robust statistical evaluation frameworks and spectral-spatial analysis.

Liane G. Benningمشاهده پروفایل

پژوهشگر

• Glaciology
• Biogeochemistry
• Biomineralization
• +۵ مورد دیگر

Liane G. Benning is a distinguished researcher specializing in biogeochemical processes on the Greenland Ice Sheet. Her work focuses on the interplay between microbial communities, mineral dust, and climate change in driving ice albedo reduction and subsequent melt dynamics. She has co-authored numerous high-impact studies on cryospheric systems using field observations, remote sensing, and genomic analyses. Key Research Areas: Biological and abiotic controls on ice albedo Mechanisms of nutrient cycling in extreme environments Microbial community composition and biosynthetic potential Remote sensing validation for cryospheric monitoring Article Trends: Her recent publications analyze the formation of dark ice zones, orbital drift impacts on satellite data, and the role of organic ligands in mineral transformation. These studies often integrate multi-disciplinary approaches, including metagenomics, hyperspectral imaging, and climate modeling. Notable Collaborations: Shunan Feng Joseph Mitchell Cook Alexandre Magno Anesio Martyn Tranter

• Precision Agriculture
• Digital Farming
• Remote Sensing
• +۷ مورد دیگر

Dr. Alfadhl Y. Alkhaled serves as Assistant Professor in the Department of Agriculture, Food, and Resource Sciences at the University of Maryland Eastern Shore's School of Agricultural and Natural Sciences since October 2024. His expertise bridges agricultural engineering and data science for sustainable farming solutions. His academic foundation includes: PhD in Agricultural Mechanization and Automation Engineering, University Putra Malaysia (2019) MS in Agricultural Mechanization and Automation Engineering, University Putra Malaysia (2015) BE in Electronics (Bio-Instrumentation Engineering), Multimedia University (2012) Dr. Alkhaled's research pioneers AI-driven agricultural innovation , focusing on hyperspectral remote sensing , non-destructive quality assessment , and smart farming systems . His work develops machine learning models for yield prediction, disease detection in crops like oil palm and potatoes, and postharvest preservation techniques – directly addressing food security challenges through sensor fusion and IoT integration. His 15+ recent publications (2020-2025) reveal a clear trajectory toward data-intensive precision agriculture , with increasing emphasis on multi-sensor integration (acoustic, hyperspectral, dielectric) and AI optimization for crop management. Key application areas include potato nitrogen management, apple quality monitoring, and oil palm disease detection. Notable recognitions include: People's Choice Award for Best 3MT Presentation (2020) University of Kentucky 3MT 2nd Place (2020) University Putra Malaysia Best Thesis Award (2019) Gold Medal for Cooking Oil Quality Detector (i-ENVEX 2014) As an active researcher and educator, Dr. Alkhaled secures continuous funding evidenced by his publication pipeline and prior fellowships. He mentors students through AGME 283 coursework and research projects, emphasizing hands-on sensor technology experience. His teaching innovation extends to developing the Spectroscopy Analysis curriculum and delivering specialized guest lectures on digital farming. Dr. Alkhaled leads research initiatives in agricultural sensor development and data analytics, with current projects focused on magnetically treated irrigation systems, hyperspectral nitrogen management, and synergistic drying techniques – positioning his team at the forefront of climate-resilient farming solutions.

• Image analysis
• Food quality assessment
• Granular mixture identification
• +۳ مورد دیگر

Dr. hab. inż. Piotr Zapotoczny, Professor at the University of Warmia and Mazury, specializes in Systems Engineering within the School of Technical Sciences. His research focuses on applying image analysis and hyperspectral imaging to evaluate food quality, particularly in agri-food products and granular mixtures. Scientific Discipline: Mechanical Engineering, Food and Nutrition Technology Email: zap@uwm.edu.pl ORCID: https://orcid.org/0000-0003-3051-6940 His work explores: Physical properties of biological materials Image analysis for food quality Drying technology for edible insects Optimization of agricultural processes His recent publications (2025–2017) show expertise in computer vision, hyperspectral imaging, and thermophysical analysis of agricultural products. Key subfields include granular mixture identification, fungal infection detection, and sustainable food processing techniques. He supervises doctoral projects in Polish but not foreign candidates, with laboratory infrastructure available for research tasks.

• Cartography
• GIS
• Remote Sensing
• +۶ مورد دیگر

Eufemia Tarantino is Full Professor of Geomatics at the Polytechnic University of Bari, working within the Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh). She has held academic positions at the university since 2002, progressing from Assistant Professor to Associate Professor and achieving Full Professor status in 2019. Professor Tarantino serves as Coordinator of the MSc Course of Environmental and Territorial Engineering, is a Member of the Executive Council, and oversees the student career fair. Her research expertise spans cartography, GIS, remote sensing, and geomatics, with particular focus on: Analysis of metric characteristics and extraction of 2D/3D geometric primitives from satellite/aerial/UAV data Multi-temporal analysis and change detection for environmental and cultural heritage monitoring Development of web-based GIS for interactive geospatial analysis Professor Tarantino's publication record includes over 180 scientific contributions in international databases. Her recent work (2023-2026) demonstrates continued productivity with numerous book chapters and journal articles focused on soil sealing dynamics, precision viticulture, marine pollution monitoring, urban growth analysis, and advanced remote sensing techniques. Her research shows strong application to environmental monitoring, cultural heritage preservation, and sustainable spatial planning. As an active member of the international geospatial community, she serves on the editorial boards of 'Remote Sensing' and 'ISPRS International Journal of Geo-Information' and reviews for approximately 18 international journals. Her teaching responsibilities include Cartography, GIS, and Remote Sensing courses at undergraduate and graduate levels, as well as Advances in Geomatic Engineering at the PhD level. Professor Tarantino's work demonstrates strong international collaboration and application to real-world environmental challenges, with recent projects addressing soil sealing, marine plastic pollution, precision agriculture, and climate-related phenomena using advanced geospatial technologies.

• Remote Sensing
• Geospatial Analysis
• Photogrammetry
• +۷ مورد دیگر

Dr. Manuel Ángel Aguilar Torres is a Professor in the Department of Engineering at the University of Almería, Spain, where he leads the research group 'Integrated Territory Management and Spatial Information Technologies.' With an h-index of 29 (Scopus) and 26 (Web of Science), he has established himself as a leading researcher in remote sensing applications for agricultural and forest monitoring. His research primarily focuses on plastic greenhouse mapping using satellite imagery , LiDAR technology for forest inventory , and precision agriculture applications . He specializes in object-based image analysis, spectral indices development, and the integration of multi-source geospatial data for environmental monitoring, with particular emphasis on Mediterranean ecosystems. His work has resulted in 98 journal articles, 16 book chapters, and numerous conference presentations. Dr. Aguilar Torres has served as Principal Investigator for multiple research projects, including 'Mapeado de invernaderos e identificación de cultivos hortícolas protegidos mediante análisis de imagen basada en objetos y series temporales de imágenes de satélite' (RTI2018-095403-B-I00) and 'Identificación basada en objetos de cultivos hortícolas bajo invernadero a partir de estéreo imágenes del satélite Worldview-3 y series temporales de Landsat 8' (AGL2014-56017-R). His recent publications (2022-2025) demonstrate continued productivity in remote sensing methodology development, particularly in greenhouse mapping, forest inventory using UAV and LiDAR technologies, and spectral analysis. Notable scientific contributions include: Development of novel methods for plastic greenhouse detection using multi-temporal satellite imagery Benchmarking studies of spectral indices for agricultural monitoring Advanced techniques for individual tree segmentation in Mediterranean forests Integration of UAV and terrestrial LiDAR data for forest inventory As a supervisor, Dr. Aguilar Torres has guided numerous PhD students through the completion of their theses, with former students including Rafael Jiménez Lao, Abderrahim Nemmaoui, and María del Mar Saldaña Díaz. His research group maintains active collaborations with international institutions, providing students with opportunities for cross-border research experiences. The laboratory facilities support advanced geospatial analysis, with capabilities for processing satellite imagery from platforms like Sentinel-2, WorldView-3, and Deimos-2, as well as UAV and LiDAR data processing for environmental monitoring applications.

• Analytical Chemistry
• Chemometrics
• Food Chemistry
• +۳ مورد دیگر

Alessandro Ulrici is a Full Professor of Analytical Chemistry (SSD CHIM/01) in the Department of Life Sciences at the University of Modena and Reggio Emilia (UNIMORE), a position he has held since December 2023. He also serves as Vice-Director of the Department of Life Sciences. Previously, he was an Associate Professor at UNIMORE from November 2010 to December 2023 and a University Researcher from August 2001 to October 2010. Professor Ulrici's research focuses on three main areas: Development and application of rapid, non-destructive analytical techniques based on chemometric approaches for food control and characterization Development of new algorithms for signal and image analysis, for the selection of significant variables and for the study of complex systems Product and process optimization through multivariate experimental design techniques His recent publications (2023-2025) demonstrate a strong focus on applying advanced analytical techniques to food science problems, particularly in viticulture and oenology, food authentication, and quality control. His work frequently employs NIR hyperspectral imaging, electrochemical sensors, and smartphone-based analytical devices, combined with sophisticated chemometric analysis. Professor Ulrici has received numerous scientific awards including: Highly cited research paper 2016 from Chemometrics and Intelligent Laboratory Systems Multiple Best Poster and Best Oral Presentation Awards from NIRITALIA, IASIM, and the Scandinavian Symposium on Chemometrics As an educator, Professor Ulrici teaches courses in Analytical Chemistry and Chemometrics for undergraduate and graduate students in biotechnology, food safety, and agricultural sciences. He previously served as Coordinator of the PhD Course in Agri-Food Sciences, Technologies and Biotechnologies at UNIMORE from 2017 to 2023. Professor Ulrici directs research activities through the Chemistry and Spectroscopy Laboratory (ChimSLab) at UNIMORE, which maintains an online presence at http://www.chimslab.unimore.it/ .

• Applied Remote Sensing
• Spectral Signature Processing and Analysis
• Environmental Science
• +۱ مورد دیگر

Brian Becker is a Professor in the Department of Geography and Environmental Studies and affiliated with the Institute for Great Lakes Research. His expertise bridges remote sensing technology and environmental science, with specialized focus on wetland ecology and aquatic system monitoring through advanced spectral analysis techniques. His academic foundation includes: Ph.D. from Michigan State University (2002) M.S. from University of Illinois (1993) B.S. from Eastern Illinois University (1989) Professor Becker's research program centers on developing and applying remote sensing methodologies for environmental assessment. He pioneers techniques for water quality parameter estimation (particularly CDOM and chlorophyll-a) in optically complex inland waters, utilizes hyperspectral data for wetland characterization, and creates algorithms for invasive species mapping. His work integrates field spectroscopy with satellite imagery to address challenges in freshwater ecosystems, with emphasis on spatio-temporal dynamics and algorithm validation for practical environmental management applications. Analysis of his 14 publications (2005-2018) reveals consistent innovation in remote sensing of aquatic environments. Key contributions include developing semi-analytical models for CDOM in shallow waters, optimizing spectral band selection for wetland monitoring, and creating spatial analysis techniques for large raster datasets. His research demonstrates strong methodological rigor while addressing real-world environmental challenges across the Great Lakes region and international sites like China's West Lake. As an educator, Professor Becker teaches Geographic Information Sciences, Remote Sensing, Image Processing, Environmental Science, and CAD Mapping for GIS. His affiliation with the Institute for Great Lakes Research provides students access to interdisciplinary freshwater research opportunities. While specific current grant details aren't provided, his extensive publication record in high-impact journals indicates sustained research activity with potential for student involvement in field campaigns, algorithm development, and environmental monitoring projects.

• Geospatial Data Science
• Cartography and Geovisualization
• Conservation GIS and Modeling
• +۳ مورد دیگر

Benjamin Heumann is a Professor in the Department of Geography and Environmental Studies within the College of Science and Engineering. His research spans geospatial data science, conservation GIS, and remote sensing, with methodological expertise in spatial clustering algorithms and mixed-method approaches. Educational background includes: Ph.D. in Geography, University of North Carolina at Chapel Hill (2011) M.Sc., McGill University (2007) B.Sc. (Honours), McGill University (2004) His research integrates geospatial analysis with environmental and socioeconomic applications. Key domains include: Affinity propagation algorithms for regional typologies Climate impact modeling on species distributions Spectral diversity in wetland ecosystems Object-based classification of mangroves Methodological advances in remote sensing statistics Publications demonstrate sustained output since 2007, with recent focus on data-driven spatial pattern detection across urban-rural gradients (2020-2022) and statistical validation of geospatial methods. Teaching covers core geospatial competencies including Remote Sensing, Cartography, and Research Methods.