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Hello, World.

I'm Peggy Wang.

Engineer Roboticist Entrepreneur

More About Me
About

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.

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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 !

Skills

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.

  • 80%
    Python
  • 70%
    C++
  • 60%
    Pytorch
  • 70%
    Matlab
  • 70%
    Git
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Resume

More of my credentials.

Here are some of my technical experiences.

Technical Experience

Software Engineer

August 2020 - Present

Facebook

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

Computer Science Research Assistant

January 2019 - Present

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

Robot Perception and Decision Making (CS 336) Final Project

September 2019 - December 2019

Stanford University

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

Computer Vision (CS 231N) Final Project

April 2019 - June 2019

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

Computer Vision Team Member/Deep Learning (CS230) Project

January 2018 - June 2018

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

Artificial Intelligence (CS221) Final Project

April 2018 - June 2018

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

Education

Master of Science Candidate

September 2019 - June 2020

Stanford University

Computer Science Major: Artificial Intelligence Track
Courses
Activities
Projects

Bachelor of Science Candidate

September 2016 - June 2020

Stanford University

Computer Science Major: Artificial Intelligence Track

Projects

Check Out Some of My Works.

Below are some of the projects I created.

vacAItionary

CS 361 Project

VacAItionary: An AI Travel Itinerary Planner
CS229

CS 229 Machine Learning Project

Improving LIDAR Point Cloud Classification of Urban Objects
CS336

CS 336 Robot Perception and Decision Making Project

Improving End to End Learning for Autonomous Driving
CS230

CS 230 Deep Learning Project

Standardized Object Detection and Classification for Unmanned Aerial Vehicles
SUAVE

Stanford Unmanned Aerial Vehicle Club (SUAVE)

AUVSI Competition Computer Vision Team Member
CS234

CS 234 Deep Reinforcement Learning Project

Hierarchical Deep Reinforcement Learning through Scene Decomposion for Autonomous Urban Driving
AI Project

CS 221 AI Project

Learning Predictive Models of Human Driving Behavior
AA93

University of Edinburgh Research Project

Analysis of Robotic Control Policies Learned by Deep Reinforcement Learning Networks
Research

CS 231N Computer Vision Final Project

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

Infuse

ME 203: Design and Manufacturing Final Project - Tea Infuser
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

Extracurriculars

Activities and Leadership

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

1500

Assignments Completed

900

Tests Taken

100000

Lines of Code Written

120

Crazy Ideas

1500

Tea Cups

7200

Hours
Contact

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

Email Me At

peggy.yuchun.wang@cs.stanford.edu

Connect With Me At

https://www.linkedin.com/in/yuchun-peggy-wang/