Chaojian Li

I am a 5th-year Ph.D. student at Georgia Tech, advised by Prof. Yingyan (Celine) Lin. My research interests are in deep learning and computer architecture, with a focus on 3D reconstruction and rendering in an algorithm-hardware co-design approach and deep learning on edge devices.

Before coming to Georgia Tech, I received my B.Eng. from Department of Precision Instrument at Tsinghua University.

I was a part-time Research Engineer Intern at Meta Mobile Vision Team from 2021 to 2022, mentored by Dr. Bichen Wu and Dr. Peizhao Zhang.

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Publications

Mixing a low-quality mesh, a view-dependent-displacement map, and a compressed NeRF model to achieve real-time rendering speeds on edge devices.

Training NeRF in Near-Memory Processing hardware architecture.

The first algorithm-hardware co-design acceleration framework that achieves instant on-device NeRF training.

Simply scaling ViT's depth, width, input size, and other basic configurations, we show that a scaled vanilla ViT model without bells and whistles can achieve comparable or superior accuracy-efficiency trade-off than most of the latest ViT variants.

The first algorithm-hardware co-design acceleration of NeRF rendering.

A framework for boosting semantic segmentation efficiency during both training and inference, leveraging the hypothesis that maximum model accuracy and efficiency should be achieved when the data and model are optimally matched.

The first public dataset for HW-NAS research aiming to (1) democratize HW-NAS research to non-hardware experts and (2) facilitate a unified benchmark for HW-NAS to make HW-NAS research more reproducible and accessible.

A practical meta optimizer dedicated to resource-efficient on-device adaptation.

Design and source code from Jon Barron's website.