Saqib Javed

Saqib Javed is a doctoral researcher and Researcher at the Computer Vision Laboratory (CVLab) at EPFL, supervised by Prof. Pascal Fua and Dr. Mathieu Salzmann. His research focuses on energy-efficient deep networks, 3D reconstruction, quantization-aware training, and domain generalization. He holds a master’s degree from TU Munich and ETH Zurich. He is affiliated with the School of Computer and Communication Sciences (IC) and the Department of Computer Science at EPFL.

His current projects include compressed Gaussian splatting for dynamic scenes, quantized diffusion models, and reducing inference time for vision-language models. He has been awarded the EPFL IC Distinguished Service Award and is a Global Leaders PhD Fellow. His work spans theoretical research and practical applications in low-power device optimization.

Teaching roles include serving as a Teaching Assistant for courses like Introduction to Machine Learning (CS-233) and Probability and Statistics (MATH-232). He has supervised multiple students, including Chengkun Li and Ahmad Jarrar Khan, on projects related to quantization and 3D pose estimation. His research also involves collaborations with industry partners like BMW, Siemens, and Intel, focusing on hardware-friendly neural networks.

Awards include the EPFL IC Distinguished Service Award (2024) and recognition for his contributions to efficient deep learning. His recent publications address domain generalization, Gaussian splatting, and modular quantization techniques, reflecting his interdisciplinary approach to advancing machine learning efficiency and applicability.