Andrew Kalukin | Remote Sensing | Best Researcher Award
Dr. Andrew Kalukin, Through Sensing LLC, United States.
Dr. Andrew Kalukin ๐ is a leading expert in computer vision ๐ค, artificial intelligence ๐ก, remote sensing ๐ฐ๏ธ, and signal processing ๐ก. With over two decades of experience across industry and government R&D sectors, he has developed pioneering techniques in machine learning training data simulation, automated target detection, and multimodal data fusion. Currently, he serves as a SAR Scientist at Barone Consulting and Founder of Through Sensing, LLC ๐. His interdisciplinary work combines AI, radar systems, atmospheric modeling, and computer vision, earning recognition through patents, publications, and collaborations with government, academia, and industry. ๐ง ๐ฌ๐
Publication Profiles
ย Education & Experienceย
๐ Education
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๐งช Ph.D. in Physics โ Rensselaer Polytechnic Institute
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๐ป M.S. in Computer Science โ Rensselaer Polytechnic Institute
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๐จโ๐ป B.S. in Computer Science โ Central Connecticut State University
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๐ง B.A. in Psychology โ University of Dallas
๐ผ Experience
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๐ฐ๏ธ SAR Scientist, Barone Consulting (2024โPresent)
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๐ Founder, Through Sensing, LLC (2023โPresent)
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๐ง Senior Lead Scientist, Booz Allen Hamilton (2014โ2023)
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๐ Senior Image Scientist, BAE Systems (2005โ2014)
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๐ Staff Scientist, Aretรฉ Associates (2002โ2005)
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๐ท Image Scientist, Eastman Kodak (2000โ2002)
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๐ก๏ธ Target Recognition Engineer, SAIC (1999โ2000)
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๐งฌ Research Associate, NIST/Rensselaer (1995โ1999)
Suitability for the Award
Dr. Andrew Kalukin, a seasoned expert in computer vision, artificial intelligence, remote sensing, and data fusion, is an exemplary nominee for the Best Researcher Award. With over 25 years of pioneering contributions across government, industry, and academia, Dr. Kalukinโs research has profoundly influenced applications in defense, environmental monitoring, autonomous systems, and semantic understanding through multi-modal AI.
ย Professional Development
Dr. Kalukin has consistently advanced the frontiers of automated image analysis, presenting at SPIE and IEEE AIPR conferences ๐ค๐. He has co-chaired IEEE AIPR and led collaborative projects involving CubeSats ๐ฐ๏ธ, polarimetric radar, and atmospheric simulation for AI detection systems. His efforts have resulted in successful partnerships with academic institutions, government labs, and industry innovators ๐ค. Through both individual research and managing scientific teams, heโs contributed to strategic defense technologies and geospatial intelligence applications. His consulting and entrepreneurship continue to shape future AI-enabled sensing systems. ๐ผ๐ฌ๐
ย Research Focusย
Andrew Kalukin’s research is centered on Artificial Intelligence for Remote Sensing Applications ๐๐ค. His work spans machine learning model development, image and video analysis, semantic segmentation, and signal processing using multispectral, radar, and hyperspectral data ๐ฐ๏ธ๐ง . Heโs developed AI models for autonomous navigation, wildfire detection, and global deforestation mapping. His interdisciplinary methods combine physics-based modeling with advanced neural networks, enabling AI applications in both defense and environmental monitoring ๐ณ๐ฅ. Through simulation, automation, and ontology-based learning, Kalukin is contributing to the evolution of real-world AI for edge platforms like drones and CubeSats ๐๐งฉ.
ย Awards & Honorsย
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๐ง Patented novel AI-based ontology transformation for multimodal data
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๐ Lead Principal Investigator for U.S. Army Phase I AI research project
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๐ฐ๏ธ Received AWS Cloud Credit awards for wildfire and deforestation AI research
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๐ Multiple IEEE AIPR and SPIE presentations and invited talks
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๐ฌ Recognized for generating novel machine learning training datasets simulating real atmospheric and sensor conditions
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๐ก Developed patented techniques used in satellite and defense AI systems
Publication Top Noted
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Applications of Deep Learning Neural Networks for Clustering Mesoamerican Historical Art Objects and Documents โ 2023 ๐จ
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Coโdomain transformation to represent abstract information for machine learning in visual scenes โ 2022 ๐ง
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Modeling and assessing VNIIRS using inโscene metrics โ 2019 ๐ฐ๏ธ
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Automating video interpretability assessment through automatic target recognition โ 2018 ๐น
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Motion image data collection simulation for structureโfromโmotion 3D target reconstruction โ 2018 ๐
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Automated generation of convolutional neural network training data using video sources โ 2017 ๐ค
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Automated mensuration of inโscene targets for video quality assessment โ 2016 ๐
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Automated video quality measurement based on manmade object characterization and motion detection โ 2016 ๐งญ
ย is a dedicated doctoral student at Northeast Normal Universityย
, specializing in Condensed Matter Physics. Her research is centered on flexible organic
ย and near-infrared (NIR) photodetection technologiesย
. With a solid foundation in semiconductor physics and hands-on experience in device fabrication, she has authored 2 SCI-indexed papersย
ย and holds a granted invention patentย
. Yu’s innovation in NIR photoelectric synaptic transistors mimics the human visual systemย
ย and promises advancements in neuromorphic imaging and AI vision systemsย
. Her passion and precision mark her as a rising researcher in optoelectronics and flexible electronicsย
.
ย Gained expertise inย semiconductor physics,ย organic field-effect transistors, andย flexible device fabrication
ย Publishedย 2 SCI-indexed research articles
ย Grantedย 1 invention patentย in the field of optoelectronics
ย Experience in synthesizing and testing flexible photodetector structures
ย has developed a strong research profile through her commitment to innovation in flexible organic electronicsย
. Her academic journey has been defined by continuous learningย
ย and interdisciplinary skill-building in materials science, electronics, and photophysicsย
. Her doctoral work includes collaborative research, device prototyping, and publishing in high-impact journalsย
. With a focus on neuromorphic and bio-inspired photodetection systemsย
ย and AI-enhanced visual systemsย
.
ย and their integration intoย bionic optoelectronic synapse devicesย
, and multifunctionality in real-world environments. Yuโs work significantly contributes to next-generationย artificial vision,ย intelligent night vision systemsย
, andย bio-inspired electronicsย
.
ย 2 SCI-indexed journal publicationsย in advanced materials and optoelectronics
ย Recognition for innovation in bionic synaptic devices for AI vision
ย 2024ย |ย Online Analysis of Coal Slime Water Particle Size Based on RRSB Characteristic Parametersย |ย
ย 2023ย |ย Comparison and Analysis of Powder Removal and No Powder Removal in Daliuta Coal Preparation Plantย |ย
ย 2022ย |ย Application of Equipment Operation and Efficiency System in Intelligent Coal Preparation Plantย |ย