Yufei Zhao

Yufei Zhao

PhD Candidate

Kuhl Lab of Neuroscience

About me

I am a PhD candidate in cognitive neuroscience at University of Oregon. I combine machine learning techniques and statistical inference to study the neural mechanism of human memory and predict memory related behaviors. Along my doctoral training, I find my enthusiasm in solving problems using both structured and unstructured data, inferring from both classic and state-of-the-art algorithms, and communicating stories and insights derived from the data in business settings.

Detail information about my projects can be found here.


  • Data science
  • Machine learning engineering


  • PhD in Cognitive Neuroscience, 2017 - Expected 2023

    University of Oregon

  • Data Science Specialization, 2021

    University of Oregon

  • MS in Cognitive Neuroscience, 2017 - 2018

    University of Oregon

  • BS in Psychology, 2013 - 2017

    Beijing Normal University



  • Python
    • deep learning (tensorflow, keras, pytorch)
    • natural language processing (huggingface transformers, nltk, gensim, spacy)
    • machine learning (sklearn)
    • multivariate data analysis (numpy, pandas, scipy, statsmodels)
    • data viz (matplotlib, seaborn, plotly)
    • web scraping
  • R   multilevel modeling (lmer), machine learning (tidymodels), data viz (tidyverse, shiny)
    • I am the author and maintainer of R package {roistats} for fast multiple testing data analysis.
    • I accomplished a five-course sequence of Data Science Specialization with R, which covers reproducible data analysis, data visualization, functional programming, and machine learning.
  • Git Singularity



Data scientist intern


Jun 2022 – Sep 2022 Mountain view, CA
  • Built end‑to‑end pipeline for quickbooks semantic search with linguistic features and customized word2vec embeddings (baseline model; covered top 1 use case).
  • Reviewed 20+ papers on spoken language understanding & query intent understanding; Partnered with product team to synthesize labeled data.
  • Applied transfer learning with BERT base and customized top layers for joint intent detect and slot filling; covered all use cases.


DeepLearning.AI TensorFlow Developer Specialization

Four courses sequence; Build and train neural networks with tensorflow and keras
See certificate

Natural Language Processing Specialization

Four courses sequence; Parts-of-Speech Tagging; Sentiment Analysis, Attention Models, Transformers
See certificate

Deep Learning Specialization

Five courses sequence; Artificial Neural Network, Convolutional Neural Network, Tensorflow, Recurrent Neural Network, Transformers
See certificate

Machine Learning Engineering for Production (MLOps) Specialization

Four courses sequence; Managing Machine Learning Production Systems, Deployment Pipelines, Model Pipelines, Data Pipelines
See certificate

Machine Learning

Machine Learning (ML) Algorithms
See certificate