Ryan Fu
Research Scholar at University of Illinois Urbana-Champaign
Champaign, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolRyan Fu is a research scholar and MS/PhD candidate at UIUC specializing in energy-efficient, memory-centric mixed-signal VLSI accelerators that aim to overcome the von Neumann bottleneck for next-generation AI and communication workloads. With nine years of experience across research and engineering roles, he contributes to both academic projects (JUMP2.0/CUbiC PENDA, massive MIMO in-memory computing) and open-source ML tooling, improving ONNX export and refactoring cores in the widely used MMDetection codebase. A recipient of UIUC’s highest undergraduate honors and the CUbiC Undergraduate Research Excellence Award, he joined Professor Naresh Shanbhag’s group as an undergraduate and continued into graduate research. Born in Florida and raised in Xi’an and Shanghai, he brings a multicultural perspective to collaborative hardware-software co-design. Outside research he has competed at a national level in contract bridge, a background that hints at strategic thinking and disciplined teamwork.
9 years of coding experience
Master of Science - MS, Electrical and Computer Engineering, 4.0 / 4.0, Master of Science - MS, Electrical and Computer Engineering, 4.0 / 4.0 at University of Illinois Urbana-Champaign
Chinese, English
Github Skills (8)
object-detection10
computer-vision10
pytorch10
onnx10
python10
sd-mmc10
faster-rcnn7
retinanet6
Programming languages (2)
Github contributions (5)
open-mmlab/mmdetection
Sep 2020 - Nov 2020
OpenMMLab Detection Toolbox and Benchmark
Role in this project:
ML Engineer
ML EngineerContributions:13 reviews, 20 commits, 11 PRs in 2 months
Contributions summary:Ryan's contributions focused on refactoring and improving the codebase related to the integration of MMCV, addressing deprecated functions, and enhancing ONNX export functionality. They made changes to various files, including those related to model heads, testing, and core components of the framework. The user's work involved modifying existing code and adding functions to support the conversion of models into the ONNX format.
retinanetbenchmarkfast-rcnnopenmmlabsemisupervised-learning
RyanXLi/mmdetection
Aug 2020 - Mar 2021
OpenMMLab Detection Toolbox and Benchmark
Contributions:103 pushes, 14 branches in 6 months
pytorchdeep-learningtoolboxobject-detectionbenchmark
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