Yuanhang Luo

royluo@stanford.edu

A graduate student in computer science at Stanford University. Experience as software engineer @Waymo, strategy consultant @PwC Strategy&, and research assistant. Passionate about technology.

Education

Stanford University

Master of Science
Computer Science

GPA: 4.00 / 4.00

Sep 2019 - Present

University of Michigan - Ann Arbor

Bachelor of Science
double major: Computer Science & Data Science

GPA: 3.95 / 4.00

August 2016 - May 2019

Chinese University of Hong Kong, Shenzhen

School of Management and Economics

GPA: 3.84 / 4.00, ranked the 1st

September 2014 - May 2016

Working Experiences

Strategy&, PwC

Consulting Summer Associate
  • Performed due diligence for a private equity megafund to evaluate the feasibility of investment in a high-tech field
  • Investigated and co-designed the user operation strategy for a company to facilitate growth of users, and composed slides
    • June 2021 – Aug 2021

    Kuaishou Technology

    Strategy Analyst Intern
  • Investigated and evaluated to-business technology applications, interviewed 10+ experts, and composed notes and reports
    • May 2021 – Jun 2021

    Waymo

    Software Engineering Intern
  • Collaborated with 3 different teams (simulation, perception, and research) to do multi-pedestrian tracking from LiDAR data
  • Conducted in-depth research on state-of-the-art papers, wrote reports, and presented to different teams
  • Analyzed LiDAR and camera data to generate heuristics for tracking algorithms and formulated the optimization problem
    • Jun 2020 – Sep 2020

    Research Experiences

    Medical Dialogue Topic Classification

    Advisor: Prof. Rada Mihalcea
  • Built learning models to classify the topic of medical dialogues between nurse and patient.
  • Utilized traditional linguistic features and deep neural network features for classification.
    • May 2019 – Aug 2019

    Finding optimal treatment for patients with ARF using reinforcement learning

    Advisor: Prof. Jenna Wiens
  • Designed state, action, and rewards for the reinforcement learning task to find an optimal treatment.
    • Feb 2019 – Apr 2019

    Learning optimal representation for EHR data for ML prediction tasks

    Advisor: Prof. Jenna Wiens
  • Developed a pipeline to represent patient state using Electronic Health Record data.
  • Utilized different statistical and machine learning methods to represent the features.
  • Achieved AUROC of 0.86 on prediction tasks.
    • May 2018 - Jan 2019

    Multimodal Sensing of Human Behavior

    Advisor: Prof. Rada Mihalcea
  • Extracted features from face videos of human subjects.
  • Processed different features using various statistics and methods.
  • Built shallow and deep models to predict the stress of human subjects based on facial features.
  • Achieved 0.73 AUROC.
    • Jan 2018 - Apr 2019

    Projects

    Mini Shell

  • Built a shell with concurrency in C that supports process control, job lists, signals, pipelines, and I/O redirection functions
    • Apr 2020 – June 2020, Stanford, CA

    RecommendPro Chrome Extension

  • Published the chrome extension written in JavaScript to assist college students in choosing courses
  • Built the extension to scrape professor and course ratings and comments and show them in a mouseover window
    • Jan 2019 – Apr 2019, Ann Arbor, MI

    Mini Instagram

  • Built a client-side and server-side dynamic website using Jinja2 & Flask in Python and React in JavaScript
  • Implemented timeline, login, post, delete, comment, follow and like functions to imitate Instagram features
    • Mar 2019 – Apr 2019, Ann Arbor, MI

    Projects - AI Related

    StackOverflow User Prediction

  • Analyzed and visualized StackOverflow user data in BigQuery to find relationships between user profiles and reputation
  • Implemented machine learning models in BigQuery to predict user’s reputation and achieved 0.91 accuracy score
    • Sep 2020 – Dec 2020, Stanford, CA

    Mixture of Experts Graph Neural Network

  • Built graph neural networks for node classification and graph classification with PyTorch Geometric in Python.
  • Implemented mixture of experts in GraphSAGE network by including structural roles and gating network.
    • Oct 2019 – Dec 2019, Stanford, CA

    Predicting Airbnb Listing Price Across Different Cities

  • Performed feature engineering and built regression models to predict Airbnb listing price in Python.
  • Utilized transfer learning of neural network with PyTorch to train and test on different cities and achieved 0.77 R-squared.
    • Oct 2019 – Dec 2019, Stanford, CA

    Multiple Deep Metric Adversarial Learning

  • Designed a neural network to learn a better metric between positive and negative samples.
  • Proposed a positive generator network with corresponding loss function.
    • Mar 2019 – Apr 2019, Ann Arbor, MI

    Automatic Sentence Completion

  • Built an RNN model with TensorFlow to predict the next possible word in incomplete sentences.
  • Proposed different models to learn a vector representation of the sentence.
    • Oct 2018 – Dec 2018, Ann Arbor, MI

    Sentiment Classification of Yelp Reviews Data

  • Built different models to classify Yelp reviews into different semantics.
  • Explored and implemented different models including CNN and RNN.
    • Jan 2018 – Feb 2018, Ann Arbor, MI

    Text Based Traffic Sign Detection and Recognition

  • Implemented traffic sign character detector using MSER detector to detect traffic signs from a picture.
  • Built a CNN model with PyTorch to recognize the characters detected.
  • Achieved 78% accuracy rate.
    • Nov 2017 – Dec 2017, Ann Arbor, MI

    Creative Music Generator

  • Developed and trained a music generator in Python with N-gram model.
  • Implemented features like Portamento, Vibrato, changing tunes, and GUI.
    • Oct 2016 – Dec 2016, Ann Arbor, MI

    Teaching Experiences

    Instructional Aide

    EECS486 Information Retrieval
  • Led weekly discussion classes on EECS486
  • Held weekly office hours
  • Designed and graded exam questions
    • Jan 2019 – Apr 2019

    Proof Tutor

    MATH217 Linear Algebra
  • Taught students about MATH217 linear algebra proof problems.
  • Taught for two semesters.
    • Sep 2017 - Apr 2018

    Course Assistant

    Human Factors Engineering Short Course
  • Prepared materials for Human Factors Engineering Short Course.
  • Assisted teaching with more than 30 students.
    • Aug 2017

    Publications & Posters

  • Mimansa Jaiswal, Zakaria Aldeneh, Cristian-Paul Bara, Yuanhang Luo, Mihai Burzo, Rada Mihalcea, Emily Mower Provost. “MuSE-ing on the impact of utterance ordering on crowdsourced emotion annotations.” International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Brighton, England. May 2019.
  • Yuanhang Luo*, Reed Horwitz*, Benjamin Li, Jenna Wiens^, Michael Sjoding^ (*equal contribution, ^: co-senior authors). “Learning How to Represent EHR Data for ML Prediction Tasks.” MIDAS 2018 Data Science Symposium. Ann Arbor, Michigan. Oct 2018.

    • Revelant Courses

  • Artificial Intelligence (A+)
  • Applied Regression Analysis (A+)
  • Linear Algebra (A+)
  • Deep Leaning (A)
  • Computer Vision (A)
  • Deep Meta Leaning (A)
  • Natural Language Processing (A)
  • Advanced Directed Study (A)
  • Operating Systems (A)
  • Data Systems (A)
  • Data Structures and Algorithms (A)
  • Probability (A)
  • Machine Learning (A)
  • Machine Learning with Graph (A-)

    • Skills

    Programming Languages
  • Python
  • C/C++
  • JavaScript
  • Matlab
  • R
    • Languages
  • Chinese
  • English
  • Japanese

    • Awards

    • EECS Scholars —— 2017-18, 2018-19
    • James B. Angell Scholar —— Mar 2018
    • University Honors —— Dec 2016; Apr 2017; Dec 2017