I'm Yining Yuan.

Complete my degree with GPA over 3.8, University honor for multiple semester. Taken courses related to data structure and algorithms, computer vision, machine learning, database management systems, web system, and human centered software engineering.

Worked on an end-to-end collaborative machine learning project that predicted the success of newly emerging topics for after a decade, using the ProQuest dissertation database and Web of Science database data. Developed a model incorporating a feature extraction system, feature engineering system, truth label engineering system, and a model training and prediction system. Currently able to predict with a precision at the top 200 for an around 50% accuracy.

Utilized Python as our development programming language. Managed the database using dask and pandas dataframe. In this project, I worked extensively with machine learning packages such as seaborn, scikit-learn, PyTorch, and matplotlib, as well as data preprocessing packages like numpy and math. Gained valuable experience working with AWS virtual machines, managing teamwork effectively, and handling large-scale datasets. This project not only enhanced my technical skills in machine learning and data processing but also developed my abilities in collaborative teamwork and big data management.

Conducted weekly instructor team meetings, managed and edited class materials, created homework and midterm questions, graded assignments, and provided student support during office hours.

Led a beginner programming study group once a week, met with my team once a week and designed a study group agenda. Reviewed and helped the professor with editing the ROB101 textbook during summer using latex, wrote the section that explained and compared Julia(the programming language used in class) to C++. Hosted office hours and lab sessions once a week, and graded student’s homework coding assignment. Meet and communicate with instructors once a week, discussing about teaching strategy and students performance in class.

A research project aimed to analyze the relationship between stakeholders in western forest wildfire management using data from https://inciweb.nwcg.gov/ website. Implemented an automated web scraping program that collected various types of fire incident data from over 2000 web pages using the bs web-scraper, developed a database of actors involved in wildfire management using pandas dataframe. Preprocessed data using NLP model, extracting named-entities from overview section, providing critical data for future analysis.

Trained three machine learning models on data from the National COVID Cohort Collaborative, predicted COVID severity in pediatric patients with an impressive 82% AUROC. Acquire hands-on experience with essential Python libraries such as sklearn, pandas, and numpy. Project help with exploring and experimenting machine learning applications within a critical healthcare context in a newly developed data corhort.