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About Me
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Published:
Today is a fresh start.
git config --global credential.helper store
sudo apt-get install mosh
kill $(ps --no-headers --sort=start_time -C mosh-server -o pid | head -n -1)
jupyter notebook --no-browser --port=8889
ssh -N -f -L localhost:8888:localhost:8889 username@serverIP
Published in AI Systems Workshop at SOSP, 2019
This paper presents a preliminary technique for accelerating ML inference on sparse inputs by modifying the convolution operator to be sparsity-aware.
Recommended citation: Wei Hao and Shivaram Venkataraman, "Sparse Convolutions for Faster Object Recognition", AI Systems Workshop at Symposium on Operating Systems Principles(SOSP), 2019. http://learningsys.org/sosp19/assets/papers/21_CameraReadySubmission_sparse_conv_aisys19_final.pdf
Published in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020
This paper talks about a novel trainable network diffusion model that infers the propagation dynamics of amyloid pathology, which conditioned on individual-level structural connectivity network.
Recommended citation: Wei Hao, Nicholas M. Vogt, Zihang Meng, Seong Jae Hwang, Rebecca L. Koscik, Sterling C. Johnson, Barbara B. Bendlin, and Vikas Singh, "Learning Amyloid Pathology Progression from Longitudinal PiB-PET Images in Preclinical Alzheimer’s Disease", International Symposium on Biomedical Imaging (ISBI), 2020. https://ieeexplore.ieee.org/abstract/document/9098571
Published in 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2020
In this paper, starting with the predictable execution times of individual DNN inferences, we adopt a principled design methodology to successively build a fully distributed model serving system that achieves predictable end-to-end performance.
Recommended citation: Arpan Gujarati, Reza Karimi, Safya Alzayat, Wei Hao, Antoine Kaufmann, Ymir Vigfusson, Jonathan Mace, "Serving DNNs like Clockwork: Performance Predictability from the Bottom Up", 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2020. https://arxiv.org/abs/2006.02464
Published:
This presentation talks about a novel trainable network diffusion model that infers the propagation dynamics of amyloid pathology, which conditioned on individual-level structural connectivity network.
Published:
This presentation talks about a fully distributed DNN model serving system that achieves predictable end-to-end performance.
Undergraduate course, Columbia University, Computer Science Department, 2020
Ohhh my first TA job