May 2023 - August 2023

RESEARCH DATA SCIENTIST INTERN, EPSILON, CHICAGO, IL

• Optimized hyperparameters of existing frameworks used for household generation based on graph clustering.

• Proposed technical KPIs in terms of graph clustering quality underlying business KPIs for model optimization.

• Developed Spark and Scala code to process large-scale graphical commercial data with billions of vertices.

• Identified and fixed a bug for reading user-specified parameter values from command line in production code.

May 2022 - August 2022

RESEARCH DATA SCIENTIST INTERN, EPSILON, CHICAGO, IL

• Created a rule-based household model with machine learning algorithms, working for different data sources.

• Developed Python and SQL code on Jupyter Notebook to analyze and process billions of commercial data.

• Built Spark Scala jobs to explore the property of the large-scale graphical data with millions of vertices.

• Achieved household plans with good run-over-run consistency and better quality, compared to the existing plan.

May 2021 - August 2021

RESEARCH DATA SCIENTIST INTERN, EPSILON, CHICAGO, IL

• Constructed online households based on information of connected TV associated with individual people.

• Processed and analyzed billions of real-world commercial data utilizing SQL and Python with Hive.

• Increased reach to individuals and households and reduced inconsistency, compared to original household system.

September 2018 - December 2019

RESEARCH ASSISTANT, USC INFORMATION SCIENCES INSTITUTE

• Researched on applying Neural Networks to model and solve sensor failure detection and adaptation problems.

• Collaborated with Charles River Analytics company, for Probabilistic Representation of Intent Commitments to Ensure Software Survival (PRINCESS) project.

• Presented a poster presentation at DARPA BRASS Phase III Demo Workshop at San Antonio, Texas.

January 2019 - Present

TEACHING ASSISTANT, COMPUTER SCIENCE DEPARTMENT OF USC

• Graduate level classes: Machine Learning in Data Science, Foundations of Artificial Intelligence, and Optimization Techniques for Data Science.