Sida Peng
Senior Principle Economist at Microsoft
Newton, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolSida Peng is a Senior Principal Economist at Microsoft with nine years of experience translating advanced econometric and statistical methods into software-industry insights, grounded in a Ph.D. in Economics from Cornell and an M.S. in Statistics from UVA. At Microsoft she has advanced from Senior Economist to Principal and now Senior Principal Economist in the Office of the Chief Economist, applying strong programming skills in Python, Matlab, and Stata to large-scale empirical problems. Her GitHub contributions reveal hands-on machine learning and computer vision engineering—implementing GPU RANSAC layers and extending CVPR-caliber projects like PVNet and NeuralBody—demonstrating a rare blend of causal economic analysis and practical ML systems work. Colleagues rely on her for rigorous quantitative design, reproducible analysis, and production-minded model refinement that bridge academic research and product decisions. Based in Newton, MA, she brings both deep theory and tooling expertise to interdisciplinary teams shaping data-driven strategy.
9 years of coding experience
Ph.D, Economics, Ph.D, Economics at Cornell UniversityM.S, Statistic, M.S, Statistic at University of Virginia
Github Skills (18)
pytorch10
python10
machine-learning10
deep-learning10
trainings10
instance-segmentation10
data-processing10
cuda10
computer-vision10
modeling10
3d-reconstruction10
evaluation9
visualization9
ransac9
visualizations9
Programming languages (7)
Github contributions (5)
zju3dv/neuralbody
Dec 2020 - Jan 2023
Code for "Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans" CVPR 2021 best paper candidate
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:74 commits, 3 PRs, 78 pushes in 2 years
Contributions summary:Sida's contributions focused on modifying and extending the `neuralbody` repository, likely related to novel view synthesis of dynamic humans. They worked on updating configurations, modifying visualization scripts, and adjusting renderer and dataset files. These changes indicate a focus on improving data processing, rendering capabilities, and potentially integrating the models with new datasets or features.
pytorchimplicitrepresentationsstructureddeep-learning
zju3dv/snake
Dec 2019 - Mar 2021
Code for "Deep Snake for Real-Time Instance Segmentation" CVPR 2020 oral
Role in this project:ML Engineer
ML EngineerContributions:42 commits, 1 PR, 49 pushes in 1 year 2 months
Contributions summary:Sida contributed significantly to the project by adding and modifying various evaluation and training scripts. They integrated COCO evaluation metrics for instance segmentation and incorporated cityscapes evaluation capabilities. The user also added code for visualizing the model's output, demonstrating a focus on model development and result analysis. Furthermore, they integrated SBD and KINS datasets suggesting model training across a range of datasets.
pytorchsegmentationdeep-learningsnakecvpr-2020
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