Andreas Savakis

Andreas Savakis is a Professor in the Department of Computer Engineering at the Kate Gleason College of Engineering, Rochester Institute of Technology (RIT). His expertise spans Artificial Intelligence, Computer Vision, and Machine Learning with a focus on domain adaptation, deep learning, and aerial imagery analysis. He holds a BS and MS from Old Dominion University and a PhD from North Carolina State University.

His research emphasizes robust algorithms for object detection, tracking, and domain adaptation in challenging environments like aerial surveillance and medical imaging. Notable contributions include resilient deep networks, Grassmann manifold optimization, and semantic pose estimation frameworks. He has published extensively in top venues such as CVPR, ICIP, and IEEE journals.

Key achievements include recognition in Stanford’s top 2% scientists (2022) for citation impact. His work bridges theoretical advancements with practical applications in autonomous systems, healthcare, and environmental monitoring. Current teaching includes machine learning fundamentals and analytical methods in computer engineering.

Research trends in his articles highlight domain adaptation for varying conditions (e.g., weather, sensors), efficient neural network quantization, and multi-person pose estimation in complex scenes. His lab explores cross-modal learning (e.g., SAR-optical fusion) and continual learning frameworks for evolving data distributions.