About Me

Howdy! My name is Wangyang He(何汪洋), I am a master student in Department of Computer Science and Engineering at Texas A&M University. My advisor is Prof. Xia “Ben” Hu. My master thesis topic is Automated Deep Learning for Time Series Outlier Detection. I worked as a Research Scientist Intern at Adobe in 2022, mentored by Tong Sun and Jiuxiang Gu. I also received my bachelor’s degree from the Department of Electrical and Computer Engineering at Texas A&M University in 2020.

Download my resumé.

  • Machine Learning
  • Deep Learning
  • Data Mining
  • Time Series Outlier Detection
  • Natural Language Processing
  • Automated Machine Learning
  • MSc in Computer Science, 2022

    Texas A&M University

  • BSc in Computer Engineering, 2020

    Texas A&M University


Research Scientist Intern
May 2022 – Dec 2022 San Jose, USA

Responsibilities include:

  • Internal Document Understanding Multi-Modal Framework with HuggingFace Ecosystem
  • API Documentation
  • Guides for Data Loading, Pretraining, Finetuning, etc.
  • Explored AutoML’s Potential in Text/Vision Transformer Models
  • Content-based Recommendation Feature for Existing Document Recommendation System in Adobe Acrobat
DATA Lab at Texas A&M University
Research Assistant
Dec 2020 – Dec 2022 College Station, USA

Responsibilities include:

  • Neural Architecture Search Space
  • Supervised/Unsupervised/Semi-supervised Detection Algorithms
  • BlockChain Transaction Analysis
  • Payment Fraud Detection
  • Cyber Security Intrusion Detection
XiaoShui Intelligence
Software Development Intern
May 2019 – Aug 2019 Beijing, China

Responsibilities include:

  • Action Detection System for elderly care facilities
  • Safety Gear Detection System for construction companies


Dec 2020 – Present
  • An end-to-end system that supports easy pipeline construction with more than 70 primitives for automated machine learning.
  • Top 3 contributor.
  • Mentor for new team members;
  • Explored neural architecture pipelining combination.
  • Open sourced on GitHub, with 450+ stars and 50+ forks.
Smart Homes
Jan 2021 – May 2021
  • Deep learning project for action and emotion detection used in ”Smart Homes”.
  • Detected actions including coughing, hand washing, falling, cleaning windows, cleaning bathroom and washing feet.
  • Implemented with Keras, used Kinetics 700 dataset, built VGG16 and Xception CNNs for base model.
  • Found in total of 23000+ clips from 800+ YouTube videos, average accuracy 91.2%, ranked top three overall in the project competition.
COVID-19 Analysis
May 2021 – Dec 2021
  • Data analysis and visualization on COVID-19 dataset from CSSE at John Hopkins University.
  • Implemented with Scikit-Learn, Plotly, TODS and Tensorflow.
  • Utilized algorithms including K-Means, DeepLog(Outlier Detection).
Masked Trigger Exploration in Neural Cleanse
May 2021 – Dec 2021
  • Re-implemented the Neural Cleanse baseline system with Tensorflow.
  • Explored several types of masked trigger including local and global features.
  • Found global masked triggers to successfully inject models while not getting detected.
Aug 2019 – Dec 2019
  • Detection system to generate personal playlist based on age and mood.
  • Used Flickr & YouTube Music APIs.
Jan 2020 – Dec 2020
  • Web, iOS and Andriod application for tracking & delivery of fuel trucks, deployed on Google Play Store.
  • Implemented using Google Maps API and OpenWeather API, programmed in Dart language.


Convolutional Neural Networks
See certificate
Hyperparameter Tuning, Regularization and Optimization
See certificate
Structuring Machine Learning Projects
See certificate
Neural Networks and Deep Learning
See certificate


  • hewangyang@yahoo.com
  • 435 Nagle St, College Station, TX 77840
  • Enter Peterson Building and take the stairs/elevator to Room 430 on Floor 4