Baiyan Ren

adiw0w@r.postjobfree.com 917-***-**** LinkedIn: www.linkedin.com/in/baiyanren

Github: https://github.com/BaiyanRen Kaggle: https://www.kaggle.com/baiyanren Summary

{ Three years of hands-on experience in dealing with both structured and unstructured data, performing data wrangling and visualization, and applying machine learning approaches to business problems

{ Experienced Ph.D. researcher solving complicated problems, designing research process and managing projects Skills

{ Data Science:

- Programming: R, Python, SQL

- Data analysis: Microsoft Office, Tableau (Desktop Specialist Certificate), Power BI

- Machine learning: Regressions, Decision Trees, Support Vector Machines (SVMs), ensemble methods

{ Research: Experimental design, project management, scientific writing and public speaking. Education

{ University of Nebraska Medical Center, Omaha, NE Expected Aug. 2021 Ph.D. in Neuroscience GPA: 3.5/4.0

{ Udacity

The School of Data Science, Data Analyst Nanodegree Nov. 2020

{ China Pharmaceutical University, Jiangsu, China Jul. 2016 B.S. in Pharmaceutical Science GPA: 3.3/4.0

Relevant courses: Linear algebra, Probability, Biostatistics, Data structure and algorithm, Machine Learning Projects

{ Predicting Medical Appointment Attendance

- Applied supervised machine learning to predict appointment absence

- Performed univariate, bivariate, and multivariate explorations on 110,527 medical appointment records containing 14 features (age, gender, financial and physical conditions, etc.) of patients; conducted features selection, algorithms selection, and parameters tuning using Python statsmodel and scikit learn libraries

- Built Random Forest Classification to predict appointment absence using age, physical conditions, and financial support, with accuracy 0.66, precision 0.31, recall 0.55

- https://github.com/BaiyanRen/Medical-appointment-absence-prediction

{ A/B Testing on Webpage Design

- Analyzed 290,584 A/B test records collected in 3 weeks run by an e-commerce website

- Built Logistic regression model to assess whether page view, living country, and their interactions could predict whether a user decides to pay for the product (convert); performed hypothesis testing to analyze whether the new page increases the conversion rate

- Reported that page view and country do not predict the conversion of users, avoiding unnecessary expense on launching the new webpage

- https://github.com/BaiyanRen/AB-test-of-e-commerce-website

{ Modified SEIR Model for COVID-19 Cases Prediction

- Built a modified SEIR (Susceptible - Exposed - Infectious - Recovered) model using Python to forecast the COVID-19 cases

- Processed daily records of COVID-19 in the US, France, and Italy from global data; modified the SEIR model considering infectious viral carriers occasionally do not exhibit symptoms; applied Least Square to optimize the parameters: infection rate, recovery rate, exposed rate, and reinfection rate, in the modified SEIR model

- This model fits the real-world COVID-19 data in the US, France, and Italy very well

- https://www.kaggle.com/baiyanren/modified-seir-model-for-covid-19-prediction-in-us Experience

Research Assistant, University of Nebraska Medical Center, Omaha, NE Aug 2016 - now

{ Determined cell cycle dynamics of human stem cell-derived astrocytes programmatically; Analyzed and visualized proteomics profiles of human stem cell-derived astrocytes using Python

{ Delivered presentation on global and regional conference: Neuroscience 2018 and Neuroscience 2019, Midwest Student Biomedical Research Forum in 2017, 2018, 2019, and 2020

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