Andrew Kalukin | Remote Sensing | Best Researcher Award

Dr. Andrew Kalukin 🎓 is a leading expert in computer vision 🤖, artificial intelligence 💡, remote sensing 🛰️, and signal processing 📡. 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 🚀. His interdisciplinary work combines AI, radar systems, atmospheric modeling, and computer vision, earning recognition through patents, publications, and collaborations with government, academia, and industry. 🧠🔬🌍

Publication Profiles

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 Education & Experience 

🎓 Education

• 🧪 Ph.D. in Physics – Rensselaer Polytechnic Institute

• 💻 M.S. in Computer Science – Rensselaer Polytechnic Institute

• 👨‍💻 B.S. in Computer Science – Central Connecticut State University

• 🧠 B.A. in Psychology – University of Dallas

💼 Experience

• 🛰️ SAR Scientist, Barone Consulting (2024–Present)

• 🚀 Founder, Through Sensing, LLC (2023–Present)

• 🧠 Senior Lead Scientist, Booz Allen Hamilton (2014–2023)

• 🌐 Senior Image Scientist, BAE Systems (2005–2014)

• 🔍 Staff Scientist, Areté Associates (2002–2005)

• 📷 Image Scientist, Eastman Kodak (2000–2002)

• 🛡️ Target Recognition Engineer, SAIC (1999–2000)

• 🧬 Research Associate, NIST/Rensselaer (1995–1999)

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. Kalukin’s research has profoundly influenced applications in defense, environmental monitoring, autonomous systems, and semantic understanding through multi-modal AI.

 Professional Development

Dr. Kalukin has consistently advanced the frontiers of automated image analysis, presenting at SPIE and IEEE AIPR conferences 🎤📊. He has co-chaired IEEE AIPR and led collaborative projects involving CubeSats 🛰️, polarimetric radar, and atmospheric simulation for AI detection systems. His efforts have resulted in successful partnerships with academic institutions, government labs, and industry innovators 🤝. Through both individual research and managing scientific teams, he’s contributed to strategic defense technologies and geospatial intelligence applications. His consulting and entrepreneurship continue to shape future AI-enabled sensing systems. 💼🔬🚀

 Research Focus 

Andrew Kalukin’s research is centered on Artificial Intelligence for Remote Sensing Applications 🌍🤖. His work spans machine learning model development, image and video analysis, semantic segmentation, and signal processing using multispectral, radar, and hyperspectral data 🛰️🧠. He’s 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 🌳🔥. Through simulation, automation, and ontology-based learning, Kalukin is contributing to the evolution of real-world AI for edge platforms like drones and CubeSats 🚁🧩.

 Awards & Honors 

• 🧠 Patented novel AI-based ontology transformation for multimodal data

• 📈 Lead Principal Investigator for U.S. Army Phase I AI research project

• 🛰️ Received AWS Cloud Credit awards for wildfire and deforestation AI research

• 🏆 Multiple IEEE AIPR and SPIE presentations and invited talks

• 🔬 Recognized for generating novel machine learning training datasets simulating real atmospheric and sensor conditions

• 💡 Developed patented techniques used in satellite and defense AI systems

Publication Top Noted

• Applications of Deep Learning Neural Networks for Clustering Mesoamerican Historical Art Objects and Documents – 2023 🎨

• Co‑domain transformation to represent abstract information for machine learning in visual scenes – 2022 🧠

• Modeling and assessing VNIIRS using in‑scene metrics – 2019 🛰️

• Automating video interpretability assessment through automatic target recognition – 2018 📹

• Motion image data collection simulation for structure‑from‑motion 3D target reconstruction – 2018 🔁

• Automated generation of convolutional neural network training data using video sources – 2017 🤖

• Automated mensuration of in‑scene targets for video quality assessment – 2016 📏

• Automated video quality measurement based on manmade object characterization and motion detection – 2016 🧭