Bio
I am a Ph.D student in the Department of Computer Science at Virginia Tech. I am also affiliated with the Sanghani Center for Artificial Intelligence and Data Analytics. My advisor is Prof. Naren Ramakrishnan.
My research interests lie broadly in the span of machine learning, data mining and language models.
My research goals are leveraging ML to influence and accelerate knowledge discovery, addressing problems of scientific and societal relevance and building intelligent systems that assist human intelligence.
Publications
Y Jiao, S Li, P Luo, X Huo, YK Li. "Unbalanced Encoding in Synchronous Weight Quantization-Compression for Low-Bit Quantized Neural Network." 2021 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT). IEEE, 2021.
S Li, King Hung Chiu and Dongpeng Wang. "A Quantized Convolutional Neural Network Optimization Method and System with Non-linear Weight Quantization and Sharing for Digital Circuit Implementation." China Patent, 2021.
Y Jiao, S Li, X Huo, YK Li. "Synchronous Weight Quantization-Compression for Low-Bit Quantized Neural Network." 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021.
MPC Mok, CH Chan, WCS Chow, Y Jiao, S Li, P Luo, YK Li, M Ieong. "Chiplet-based System-on-Chip for Edge Artificial Intelligence." 2021 5th IEEE Electron Devices Technology & Manufacturing Conference (EDTM). IEEE, 2021.
Y Jiao, X Huo, Y Lei, S Li, YK Li. "Weight Compression-Friendly Binarized Neural Network." 2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT). IEEE, 2020.
S Zhang, SMH Bamakan, Q Qu, S Li. (2018). Learning for personalized medicine: a comprehensive review from a deep learning perspective. IEEE reviews in biomedical engineering
News
Our paper is accepted - December 8, 2021