Hello, World.

I'm Peggy Wang.

Engineer Roboticist Entrepreneur

More About Me

Let me introduce myself.

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I am a recent graduate (B.S. and M.S. '20) from Stanford University in Computer Science: Artificial Intelligence Track. I'm interested in autonomous driving, robotics, and artificial intelligence.


I currently work full time as a software engineer at Facebook. I'm interested in autonomous driving, robotics, and artificial intelligence projects. Feel free to contact me at peggy.yuchun.wang@cs.stanford.edu !


I have a wide variety of skills spanning Artificial Intelligence, Robotics, Computer Science, Machine Learning, and Computer Vision. I am particularly interested in Robot Perception and Decision Making. Below are some highlights of my technical skills.

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More of my credentials.

Here are some of my technical experiences.

Technical Experience

Software Engineer

August 2020 - Present


Interested in AR/VR, Robotics, Computer Vision, AI/ML

Software Engineering Intern

June 2020 - August 2020

Lyft Autonomous Driving Division (Level 5)

Behavior Planning and Decision Making at Intersections

Software Engineering Intern

June 2019 - September 2019

Facebook, Oculus Core Technology Team

AI Systems Team - Designed and created end-to-end pipeline for camera reprojection of ground truth depth data and integrated into data collection system, improved efficiency by ~230%, Created algorithm to speed up data processing by ~30%, Created visualization frontend and backend system to compare different depth sensing algorithms using QT and OpenGL

Software Engineering Intern

July 2018 - September 2018

Lyft Autonomous Driving Division (Level 5 Office)

Created pedal map model for vehicle modeling in autonomy motion planning and controls team by: Building Python plotting tools for scatter plot after linearly interpolating timestamps of different fields, Building control service in C++ with publisher/subscriber system to automatically test throttle and brake system at test site, Fitting and validating function model using Python and Matlab. Implemented sensor fusion to integrate vehicle model into trajectory planning on test vehicles and simulation

Stanford Intelligent Systems Lab, Stanford University

Used hierarchical reinforcement learning and utility value decomposition to develop a city-level policy for autonomous driving agents composed of low level policies trained on micro-scenarios

Machine Learning (CS229) Final Project

September 2019 - December 2019

Stanford University

Improving LiDAR Point Cloud Classification of Urban Objects.
Used Sydney Urban Objects Dataset, VoxNet, and Pytorch

Stanford University

Improving Training for End-to-End Autonomous Driving
Used Udacity Self Driving Car Simulator and Pytorch, algorithms using Behavior Cloning and DAgger

Stanford University

Semantic Image Segmentation for Autonomous Driving Scenarios Combining FCNs, DeepLab, and Attention

Visiting Researcher

June 2018 - July 2018

Advanced Robotics Lab, University of Edinburgh

Performed analysis of Deep Reinforcement Learning Networks for Robotic Controls by: Writing Python and Bash scripts to automatically collect data from OpenAI simulation environment for humanoid robot balancing, Conducting systematic data analysis using Matlab by creating phase plots and modeling control policy of agent

Stanford Unmanned Aerial Vehicle (UAV) Club

Detected, localized, and classified the shape, color, and alphanumeric character of a poster object from an aerial image by: Generating unique dataset of over 10,000 images by augmenting shapes and alphanumeric characters from EMNIST dataset onto aerial background image, Training YOLO network and implemented OpenCV SURF algorithm to localize objects, Developing a Convolutional Neural Network using Keras to classify alphanumeric characters, Utilizing k-means clustering to segment objects from background for color classification

Stanford University

Improved inverse reinforcement learning model to learn reward function of human drivers to predict human driving behavior by: Adding features (position of car, distance to lane boundaries, etc.) to human reward function, Integrating test data on human drivers in driving simulation world (CARS), Designing and creating new worlds in simulation, Quantitatively analyzed the performance of AI agents with different models of the human reward function by comparing metrics such as speed, steering angle, position, trajectory, and acceleration


Master of Science Candidate

September 2019 - June 2020

Stanford University

Computer Science Major: Artificial Intelligence Track

Bachelor of Science Candidate

September 2016 - June 2020

Stanford University

Computer Science Major: Artificial Intelligence Track


Check Out Some of My Works.

Below are some of the projects I created.

Engineering Courses at Stanford

Classes Taken

MATH 51: Linear Algebra
CS 234: Reinforcement Learning
CS 221: Artificial Intelligence: Principles and Techniques
CS 230: Deep Learning
CS 110: Principles of Computer Systems
CS 238: Decision Making Under Uncertainty
CS 161: Design and Analysis of Algorithms
CS 248: Interactive Computer Graphics
AA 274: Principles of Robotic Autonomy
CS 103: Mathematical Foundation of Computing
CS 231N: Convolutional Neural Networks for Visual Recognition
MATH 104: Applied Matrix Theory
CS 229: Machine Learning
CS 336: Robot Perception and Decision Making
CS 131: Computer Vision: Foundations and Applications
CS 224U: Natural Language Understanding
CS 361: Algorithms for Optimization
CS 168: Modern Algorithmic Toolbox
CS 193X: Web Design


Activities and Leadership

Here are a few activities I choose to get involved in during my free time.


Assignments Completed


Tests Taken


Lines of Code Written


Crazy Ideas


Tea Cups



I'd Love To Hear From You.

Please email me at peggy.yuchun.wang@cs.stanford.edu if you have any questions!

Where to find me

Stanford, CA
94309 USA

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