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Versatile Data Science Professional and Industrial Engineer with hands-on experience across analytics, mathematical modeling, and data engineering. Skilled in building scalable pipelines and translating complex models into actionable insights for decision-making. Proficient in Python, SQL, Power BI, and cloud tools. Strong interest in applying data science and operations research to real-world challenges
Education#
M.S. in Industrial and Operations Engineering
- Linear Programming, Network Optimization, Integer Programming
- Stochastic Process, Queuing Theory, Numerical Optimization
- Data Envelopment Analysis, Risk Analysis, Reinforcement Learning
- Dynamic Programming, Scheduling, Supply Chain Facility, Statistical Learning
Graduate Certificate in Data Science
- Database Systems & Application Design
- Information Retrieval
- Spatial Analysis
B.S. in Statistics and Data Science
- R Programming, SAS Programming, Python Programming -Probability Theory, Regression Analysis, Bayesian Data Analysis, Non-parametric Statistics, Experimental Design
- Data Science Concept and Analysis, Time Series Analysis, Machine Learning, Predictive Modeling in Linguistic, Deep Learning
- Mathematics of Fixed Income Markets, Financial Market Risk and Modeling
- Cloud Computing & Big-Data Analytics
B.A. in Mathematics
- Linear Algebra, Discrete Mathematics, Vector Calculus
- Differential Equation, Real Analysis, Numerical Analysis
- Operations Research, Number Theory
Professional Experience#
Data Management Consultant
- Integrated Oracle database with Primavera Unifier using RESTful web services to automate data updates and streamline project tracking workflows.
- Developed Python-based QA/QC validation scripts to identify data entry issues and enforce schema integrity, improving input reliability for downstream reporting.
- Created interactive dashboards in Power BI for senior management, tracking project milestones, financial metrics, and schedule adherence
Process Improvement Consultant
- Applied Lean Tools (Value Stream Mapping, 5S, and Poka-yoke Principles) to map the sterlie compounding workflow, quantify cycle times, and identify bottlenecks at powder reconstitution and gravimetric verification.
- Built SARIMA models to forecast infusion workloads, improving demand prediction and identifying peak preparation periods for better capacity planning
- Proposed a hybrid flow shop scheduling model to optimize the chemotherapy preparation workflow, integrating the newly introduced ultrasound sonication step that operates concurrently with other tasks, reducing makespan and bottlenecks while enhancing sterile compounding accuracy by 5–10%
Engineering Design Trainee → Intern Lead
Project Management System Overhaul: Consolidated 200+ spreadsheets into a Power App, SharePoint & Oracle Database workflow, cutting manual data entry by 70% and trimming three days off each monthly reporting cycle
Enterprise Data Integration: Built an enterprise Master Project List by designing a Pentaho Kettle ETL pipeline and an NLP ensemble (BM25, TF-IDF, Transformer) that reached 92% precision in entity matching across three legacy sources
Financial Analytics Automation: Automated budget analytics for a $6B Bay-Area infrastructure portfolio; Power BI dashboards flagged phase-level cost variances and informed reallocations in FY 23
Workforce Planning Tool: Built a Python–Django forecasting tool that visualized staff availability and workload projections 12 weeks ahead, helping Project Managers reduce last-minute reassignments and improve planning accuracy
Team Leadership: Led intern training and knowledge transfer: produced SOPs and live coding workshops for six interns, reducing ramp-up time from two weeks to three days
Data Analyst
- Engaged in the app development process of GoGaucho, a mobile app serving over 10,000 users monthly
- Retrieved academic curriculum data using UCSB API, analyzed historical course enrollment patterns
- Collaborated with development team to implement data-driven features and improvements
Research Data Assistant
- Assisted library users in cleaning and preparing data for analysis, improving data accuracy and usability
- Maintained statistics on the lab and identified IT issues, ensuring smooth operation and timely issue resolution
- Answered student’s questions in the Data Science Carpentry workshop and develop workshop materials
- Multilingual Audio Transcription Trained and fine-tuned Azure custom speech models with language detection and speaker diarization, reducing WER in bilingual audio transcripts.
- Dianping Web Scraper Engineered large-scale web crawler to collect children’s library data from 2000+ Chinese cities; automated CAPTCHA bypass and structured HTML parsing into clean CSV datasets.
Research Experience#
Student Researcher
Developed Python scripts to implement efficient sub-sampling techniques, including reservoir sampling and chunk-based methods, enabling analysis of large datasets (40GB) without loading the entire dataset into memory
Applied the SuperLearner algorithm to small data samples, evaluating and ranking prediction models based on accuracy metrics
Statistical computing, large-scale data analysis, and ensemble learning methods
Undergraduate Research Assistant
Scraped Twitter data using the Twarc2 API.Cleaned and reorganized tweet data using regex and glob.
Streamlined existing code and performed sentiment analysis on LGBTQ tweets using Textblob; visualized the polarity and subjectivity of each tweet using the Altair package.
Technical Skills#
Programming Languages#
Python (Pandas, Scikit-learn, PyTorch), R, C++, SQL, SAS, JavaScript, HTML/CSS, VBA, Git, Bash, LaTeX
Tools & Technologies#
Power BI, PowerApp, Power Automate, SharePoint, Pentaho Kettle, Azure, AWS, Django, Gurobi, Docker, Kubernetes
Languages#
English (Fluent), Cantonese (Native), Mandarin (Native)
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