👋 I’m a second-year PhD student in computer science, working with Prof. Marco Canini at SANDS Lab. My research interests lie between computer systems and machine learning, devoting to speedup deep learning training and inference via efficient communication. My recent work focuses on gradient compression and optimized GPU communication. I’m also a member at KAUST Graduate Student Council(12/1683).
Paper
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(2024).
Immediate Communication for Distributed AI Tasks.
HotInfra'24.
(2023).
Kimad: Adaptive Gradient Compression with Bandwidth Awareness.
DistributedML'23.
(2023).
Global QSGD: Practical Floatless Quantization for Distributed Learning with Theoretical Guarantees.
Preprint (Poster accepted at Eurosys'23).
(2022).
A Convolutional Neural Network Based Maximum Power Point Voltage Forecasting Method for Pavement PV Array.
TIM'22 (JCR Q1).
(2021).
OpenFunction for Software Defined IoT.
ISNCC'21 Best Paper.
Education
Ph.D. student in Computer Science
Master of Science in Computer Science (Dean’s List Award) (GPA = 3.90/4.0)
Master of Science in Applied Computational Science and Engineering (Distinction)
Bachelor of Engineering in Computer Science and Technology (GPA = 89.34/100)
Internship
Research Intern
Microsoft Research Asia
Research Intern
ISTAustria
TopK gradient compression with efficient threshold selection by CUDA/C++.
Engineering Intern
Tencent Cloud
Tencent international website backend developer by GoLang.
Research Intern
The University of Western Australia
YOLO-based splash detection of Western Australia’s coastal highway by Python.
Experience
TA - Saudi Ministry of Interior Program CS229: Machine Learning
TA - UESTC & KAUST Summer School
TA - UESTC & KAUST Summer School
Robotics & AI Winter School - Imperial College London Hamlyn Center
I’m into …
Linux
C++/CUDA
Python/PyTorch
Gallery of KAUST
Contact
- 966560569149
- KAUST Campus, Thuwal, Mekka Province 23955
- 4307-WS18, 4th Floor, Building 1