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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About Me
About Me
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Posts
Fresh Start
Published:
Today is a fresh start.
links
Github Cheatsheet
- userid and password store:
git config --global credential.helper store
Mosh for low-bandwidth ssh
- Client side install if you are using Chrome
- Server side insatll if you are using Ubuntu:
sudo apt-get install mosh - Don’t forget adding ssh key info
- Kill past sessions except the existing one:
kill $(ps --no-headers --sort=start_time -C mosh-server -o pid | head -n -1)
Login to Jupyter Notebook remotely
- On server:
jupyter notebook --no-browser --port=8889 - On local terminal:
ssh -N -f -L localhost:8888:localhost:8889 username@serverIP - On local browser: Open http://localhost:8888/ with token
publications
Sparse Convolutions for Faster Object Recognition
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
Learning Amyloid Pathology Progression from Longitudinal PiB-PET Images in Preclinical Alzheimer’s Disease
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
Serving DNNs like Clockwork: Performance Predictability from the Bottom Up
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
talks
Oral Presentation at ISBI 2020
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.
Oral Presentation at OSDI 2020
Published:
This presentation talks about a fully distributed DNN model serving system that achieves predictable end-to-end performance.
teaching
Teaching experience 1
Undergraduate course, Columbia University, Computer Science Department, 2020
Ohhh my first TA job