Dr. Rajat Garg is an Assistant Professor in the School of Applied Computing (SoAC) with an interdisciplinary foundation in engineering and applied AI. His work focuses on developing reliable machine learning and deep learning methods for SAR/PolSAR remote-sensing data, with emphasis on semantic segmentation, land-cover mapping, and multi-sensor information fusion. He is interested in designing models that balance accuracy with interpretability and practical deployment for geospatial intelligence and environmental monitoring. In the classroom, he promotes a learning-by-doing approach through Python-centric labs, real-world datasets, and research-inspired projects that build strong fundamentals in data-driven problem solving. He actively mentors students in project execution, technical writing, and research thinking.