I'm bringing AI applications to the manufacturing facility to improve day-to-day operations.
I'm a Computer Science student at UW-Madison with a passion for technology, automation, and AI applications in manufacturing. With an Associate Degree completed before high school graduation and 109 credits transferred, I bring both academic excellence and practical experience to every project.
Completed 120 college credits while still in high school.
Earned an AAS degree in IT-Software Development before high school graduation.
Entered UW-Madison as a freshman with senior academic credits (109 credits transferred).
Articulate and clear in presenting ideas and concepts.
Prioritize team goals and foster collaborative environments.
Strong customer service aptitude with excellent communication skills.
BS Computer Science • September 2023 - Current
Associate Degree, IT – Software Developer • December 2022 • GPA 4.00
*Completed before graduating from High School
Non-degree seeking high school student • 42 credits completed • GPA 3.97
Courses: Calculus I-III, Probability & Statistics for Engineering, Differential Equations, Linear Algebra, Chemistry, and other General Education courses.
Industrial Automation, Robotics and PLC
UW-Madison • August 2023 - July 2025
UW-Madison • August 2024 - May 2025 • Student Web Assistant
iQuality Services LLC, Menomonie, WI • July 2022 - Current
University of Wisconsin – Stout • September 2022 - May 2023
INMGT300 - Engineering Economy & INMGT314 - Enterprise Resource Planning Practicum
Multiple Leadership Roles • 2022-2023
Membership Chair, Nominating Committee Chair, Program Chair
A vision inspection station was installed on a medical device production line to replace manual inspection. This upgrade significantly improved inspection accuracy and enabled automated collection of quality-related data to support ISO 13485 Quality Management System (QMS) compliance.
The project was presented at the ASQ BOSCON 2022 Conference.
Generative AI model was uploaded to the automotive vehicle that carries a collaborative robotic arm. The device will take verbal orders from the human operator and complete tasks in manufacturing floor. This user case will be included in the textbook “AI Applications in Manufacturing”, which will be published in Spring 2026.
An AI deep learning algorithm was trained to monitor molds on a medical device production line. Images captured from multiple cameras at the production station are analyzed by the algorithm to detect defects before the molds are used in production, helping prevent defective parts and reducing waste.
A machine learning (ML)–based AI algorithm was developed to assist a manufacturer's sales department by automatically reading purchase orders (POs) and converting them into production execution documents used for quotation, budgeting, inventory checks, and production planning. The system processes orders received via email, fax, and phone, replacing the manual order-entry process previously performed by staff.
The algorithm will also communicate with the ERP system to issue production orders to the manufacturer floor.
The SPC Minitab training curriculum covers key Statistical Process Control (SPC) functions and applications in Minitab, including: Control charts, Process capability analysis (Cp/Cpk, SixPack, PPM), Non-normal data handling, Design of Experiments (DOE), Quality loss functions, and Gauge R&R studies.
Multiple medical device companies have sent employees to this training, including participants from their international facilities in China, Ireland, and Finland.
Quality data — including sample measurements, CMM reports, and gauge evaluations — were processed by a machine learning (ML)–based AI algorithm. The system automatically generates comprehensive quality reports with key parameters such as Cp/Cpk, PPM, control charts, and nonconformance reports.
These reports serve as standardized records for the organization's Quality Management System (QMS).
A 200 pages paper-based training materials were used to train an AI model that supports new employee onboarding. New hires can interact with the AI model to learn the material; the system automatically generates various types of assessment questions to evaluate their understanding and provides personalized feedback until the employee fully grasps the training content.
