B.S. in Statistics and Mathematics · 2023
University of Wisconsin–Madison
Yi Ma
My work connects statistical modeling, software engineering, and manufacturing operations data. I hold a B.S. in Statistics and Mathematics and an M.S. in Statistics from the University of Wisconsin–Madison, and I am currently pursuing an M.S. in Computer Science at the University of Southern California.
My experience spans data analysis, predictive modeling, full-stack development, and mobile applications, extending to manufacturing challenges in cost, inventory, and supply. I aim to combine reliable analytical methods with software that makes results understandable, verifiable, and useful.
6Manufacturing software copyrights
8Selected engineering & data projects
2Business statistics & operations awards
1Manufacturing implementation white paper
About Yi Ma
My academic path began in computer science, developed through statistics and mathematics, and returned to software and systems development. Statistical training taught me to examine evidence, models, and uncertainty; computer science helps me turn analytical methods into working applications.
In industry, I have contributed to data preparation, predictive modeling, and inventory visualization. I also exchange technical ideas through forum presentations and project reviews. These experiences have broadened my focus from model outputs to usability, business context, and implementation outcomes.
M.S. in Statistics · 2024
University of Wisconsin–Madison
M.S. in Computer Science · In progress
University of Southern California
From Data Analysis to Implementation
- 01
Define the Problem
Identify the business objective, constraints, and success criteria.
- 02
Prepare the Data
Align data entities, time periods, and status definitions.
- 03
Build the Analysis
Select robust, interpretable methods that can be validated.
- 04
Support Decisions
Present options, evidence, expected effects, and uncertainty.
- 05
Put Plans into Practice
Integrate responsibilities, approvals, and exception handling.
- 06
Validate and Refine
Review actual outcomes and refine models and rules.
Six Registered Manufacturing Software Works
View ProjectsIndustrial Cost Forecasting and Optimization System V1.0
Identifies key cost drivers to support trend forecasting, variance explanation, and optimization analysis.
Registration No.: 2025SR1594104Dynamic Expense Analysis Platform V1.0
Connects goods movements, transport status, and expense records to identify cost changes and reconciliation discrepancies.
Registration No.: 2025SR1605165Intelligent Inventory Structure Forecasting System V1.0
Uses time-series analysis to examine inventory composition, replenishment needs, and capital tied up in stock.
Registration No.: 2025SR1605187Intelligent Supply Anomaly Monitoring Platform V1.0
Detects unusual changes in supply, inventory, and production data and generates risk alerts.
Registration No.: 2025SR1594094Raw Material Purchasing Strategy Optimization Software V1.0
Quantifies the operational effects of purchasing timing, inventory conditions, and procurement strategies.
Registration No.: 2025SR1605201Enterprise Profit Optimization Decision Support System V1.0
Integrates data from multiple business sources to explain drivers, compare scenarios, and review performance.
Registration No.: 2025SR1605166
Manufacturing Software Applications
Pharmaceuticals
Cost forecasting, inventory composition, and working capital.
Industrial Gases
Inventory monitoring, goods tracking, and expense alerts.
Aluminum Processing
Purchasing timing, inventory turnover, and return simulations.
Packaging
Coordinated monitoring of costs, inventory, and supply anomalies.
Other Manufacturing
Adaptation to business processes and data maturity.
Industry Awards and Professional Service
View Honors
2023 Outstanding Individual Award in Business Statistics
Individual professional recognition
2024 Innovative Project Award in Business Operations Statistics
Enterprise Profit Optimization Decision Support System V1.0
Published White Paper
An Implementation Framework for Operational Data Intelligence and Decision Support in U.S. Small and Medium-Sized Manufacturing Enterprises
Focused on operational data in U.S. small and medium-sized manufacturers, the paper examines data preparation, model validation, phased pilots, and ongoing evaluation, linking statistical analysis to business decisions.
Original Chinese PDF · 38 pages
