Kairui W.

About Kairui W.

Solid understanding of traditional machine learning and deep learning algorithms (e.g. Logistic Regression, Decision Tree, KNN, Naïve Bayes, SVM, K-means, Neural Network, CNN, RNN, Attention Model)- Working knowledge of prevalent machine learning algorithms and techniques in finance industry (e.g. Random Forest, XGBoost, LightGBM, Model Stacking, Bagging, Boosting, K-prototype, Isolation Forest)- Advanced knowledge and implementation experience on technique of Natural Language Processing (e.g. NLTK, Word2Vec, BERT, RoBERTa, XLNet) and Computer Vision (e.g. EfficientNet, ResNeXt)- Frequent user of cloud and distributed computing tools (e.g. H2O, PySpark, Hadoop, Amazon AWS, Google Cloud)- Good understanding of data structure and programming algorithm- Intermediate in R, Java, C++, Scala, SQL, Tableau, SPSS, MATLAB, Azure- Kaggle Competition Expert (Rank: 2,946/158,069)

Experience

1. Applied Scientist Ii

   Amazon

   Feb 2025 — Present · Bellevue, WA, US

Education

• University of Toronto

  Master's degree, Information System

  2017 — 2019

• UCLA

  Multi-/Interdisciplinary Studies, General

  2012 — 2012

• Shanghai Jiao Tong University

  Bachelor's degree, Engineering

  2010 — 2014

• Carnegie Mellon University

  Master of Science - MS, Engineering

  2014 — 2015