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This is my data science and engineering portfolio where I share some of the most exciting project I have engaged with over the course of my academic and industry experience. This portfolio currently encompasses 16 different projects in data science and mechanical engineering.

My data science and engineering competencies include:

- Techniques: Machine Learning (Neural Network: Convolutional, Recurrent, Feed Forward; Ensemble methods: Bagging, Boosting; K-Means Clustering; K-NN; Decision Tree; Regression: Logistic, Linear), Deep Learning (ResNet, Inception Model, MobileNet), Data Warehouse, Natural Language Processing (NLP), Statistical Analysis, Data Visualization

Languages: Python (Keras, Scikit-learn, Beautiful Soup, Numpy, Matplotlib), R, MATLAB, SQL, Hive, Pig, Sqoop

Analytics Tools: TensorFlow, Hadoop, Spark, AWS, NOSQL, Tableau, RapidMiner

- Engineering Tools: SOLIDWORKS, COMSOL, Simulink


I am a master student at Emory University Goizueta Business School pursuing a degree in business analytics. This program is a one-year, immersive program that combines business, data, and technology for Business Data Scientist in a data-driven world. In the fall semester, I initiated several data science and business analytics projects, covering topics ranging from programming-heavy quantitative time series analysis to business-oriented decision analytics. In Binance Trading Bot, I implemented recurrent neural network (RNN) for real-time cross-currency price forecast, based on custom-built data pipeline in AWS by using Binance WebSocket API. In FedEx Business Challenge, I leaded a team of three student consultant and provided multiple analytical frameworks on FedEx Delivery Utility Optimization. In Travelers Insurance Fraud Modeling Competition, I explored the behavior of different available ensemble methods, XGBoost and LightGBM, and produced the best performance model in accuracy. Some other interesting projects include Enron Fraud Case Study, where techniques of social network analytics are used for understanding the origin and cause of financial frauds within large organizations, and Ad-Click Prediction, where I developed embedded layer neural network architecture in TensorFlow for click-through rate (CTR) prediction using a 52 million+ database. Also, as a main contributor, I was involved in designing two interactive Tableau dashboard, NBA Multi-level Analysis Dashboard and Lewis Hamilton Career Dashboard.

During my years in UCSB, I received comprehensive training as a mechanical engineer. I had the chance in working on one of the largest and most complex projects, BabyBOOST Capstone Project. The capstone team consisted of five mechanical engineers and five electrical engineers, and utilized $6,700+ budget in designing the first ever home-use automated ball therapy device for infants with cerebral palsy (CP). During the one-year development stage of BabyBOOST, I conducted several multi-physics simulation for optimizing mechanical design, including Electric Box Heat Transfer Analysis for motor safety and rigid body analysis for structure integrity. Some other interesting projects include Portable Vacuum Cleaner, where I designed and assembled a handheld vacuum cleaner during the pandemic in my home work station; Oil Leak Simulation, where I simulated oil spill using discrete numerical approximation based on advection and diffusion; Design Optimization for Simply Supported Cantilever Beam, where  I used meshed SOLIDWORKS objects in COMSOL to search for the best design of beam with minimal building material under several pressure conditions. I also include four of my favorite mechanical engineering lab work including Vibrating Beam Analysis, Pipe Flow Analysis, Transient Heat Analysis, and Tensile Testing.

Thank you for reading through my data science and engineering portfolio! Please contact me for any potential job opportunities, questions related to my projects or experience, or chat about data science and engineering insights. Talk to you soon!


2021 - 2022

Master of Science, Business Analytics

Emory University 

2017 - 2021

Bachelor of Science, Mechanical Engineering

UC Santa Barbara

Cumulative GPA: 3.61 out of 4.00

Honors and Awards:

- Dean's List, Top 10 percent of the class for the fall 2021 semester


Machine Learning(I, II), Marketing Analytics, Human Resource Analytics, Decision Analytics & Optimization, Managing Big Data, Data Visualization, Social Network Analytics, Business Analytics, Business Statistics, Business Problem Solving

Cumulative GPA: 3.92 out of 4.00

Major GPA: 3.97 out of 4.00

Honors and Awards:

- High Honors, Top 8.5 percent of Class 2021 in College of Engineering

- Dean's Honors, 8 quarters awarded

Fluid Mechanics(I, II), Thermodynamics(I, II & III), Structural Analysis, Vibration Analysis, Design Optimization, Multiphysics Simulation, Control System Design, Numerical Analysis, CAD Design, Material Properties, Mechatronics, Electric/Electronic Circuits, Aerodynamics, Manufacturing Processes, Dynamics, Statics, Strength and Materials


May. 2022 - Present

R&D Engineer

ABB Inc,

San Jose, California

Sep. 2021 - May. 2022

Student Consultant

FedEx Corporation

Atlanta, Georgia

Aug. - Sep. 2020

Data Analyst Intern

China Pacific Insurance Company

Shanghai, China

- Lead design of AI vision solution in ABB Robotic Item Picker using TensorFlow, C++, reaching highly competitive performance

- Develop proof of concept project by building image database and automating workflow

- Work with prospective customers on pilot projects by communicating project updates and collecting feedback and facilitate the acquisition of #1 customer for ABB Robotic Item Picker since its launch

- Implement point cloud cross registration and point cloud to depth projection in Python, improve data density in field of view by up to 300%, and cut down production cost on vision processor.

- Maintained technical documentation, including user manuals, technical specifications, and API documentation

- Report directly to CTO and R&D managers on research updates and facilitate AI adoption to senior executives

- Organized two teams of three business analysts, consulted with FedEx data science team, constructed utility optimization model based on queuing theory, and integrated into FedEx’s $20B+ delivery network
- Leveraged sentiment analysis on 10K extracted online reviews to quantify Amazon Effect and COVID-19 impact
- Adapted three academic research frameworks in marketing to breakdown customer experience into multiple stages
- Showcased process optimization solutions for business applications and model scalability to senior leadership of FedEx

- Applied nested cross validation with grid search methods to develop machine learning models using boosting methods
- Clustered 200K business partners with high insurance retention rate based on purchasing behaviors
- Evaluated feature values of 50K small loan customers’ financial activities for ranking credibility


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