About Me
I am a Principal Member of Staff at AMD, where I work on the applications of Generative AI on AMD hardware since April 2026.
Previously, I was a Machine Learning Scientist at Amazon Prime Video, where I developed Video-Language Foundational Models that bridge visual and linguistic understanding at scale.
Previously, I was a Postdoctoral Research Fellow at the Stanford AI Lab (Stanford University), working under the guidance of Dr. Stefano Ermon. I am deeply grateful to Dr. Ermon for his exceptional mentorship and support.
My research at Stanford spanned efficient convolutional networks optimized for run-time complexity, unsupervised & weakly supervised learning for improved sample efficiency, generative models, and machine learning for computational sustainability.
I earned my Ph.D. from the Chester F. Carlson Center for Imaging Science at Rochester Institute of Technology, advised by Dr. Matthew J. Hoffman.
🎓 Education
Ph.D.
Chester F. Carlson Center for Imaging Science, RIT
2011 – 2016
Aerial Vehicle Detection and Tracking using a Multi-modal Adaptive Sensor
M.S.
Electrical & Computer Engineering, University of Bridgeport
2009 – 2011
Non-speech Environmental Sound Classification with Pitch Range-based Features
B.S.
Electrical & Electronics Engineering, Eskişehir Osmangazi University
2004 – 2009
Autonomous Parallel Parking of Non-holonomic Vehicles
💼 Professional Experience
AMD
Applications of Generative AI on AMD hardware
Amazon Prime Video
Video-Language Foundational Models
Samsung Research America
Vision Transformer Compression · Multimodal Understanding
Stanford University — Stanford AI Lab
Self-Supervised Learning · Dynamic Models · Generative Models · Computational Sustainability
Planet Labs
Convolutional Object Detection in Low-Resolution Aerial Imagery
Autel Robotics
High-Speed Object Tracking on Low-End Embedded Systems
Huawei R&D
Unsupervised Semantic Role Assignment in Photo Albums
🔬 Research Interests
🎥 Video-Language Understanding ⚡ Efficient Deep Learning & Model Compression 🤖 Multimodal Machine Learning 🌱 Computational Sustainability 🎨 Generative Models
