• Caused a decrease in medical test result turnaround time by optimizing worker schedules using previous incubation time data• Decreased time for workers by automating finding tests that needed to be thrown out or retested• Created a python script to identify and prevent blockages of test tubes on the automation line and identifying faulty machines•Automated irreplaceable sample tracking system to ensure irreplaceable samples were not lost in the lab

• Achieved 97% accuracy by training a deep learning ANN end to end (data processing, architecting, training) to diagnose fractures from x‑rays• Model resulted in writing a paper that is expected to publish on PeerJ (Peer‑reviewed journal)• Worked with the Program Director of CS @ MSOE to benchmark and establish processes for using the University supercomputer for AI purposes• Model was designed in Python using Keras as the modeling software with DenseNet‑121 as the base model

- Taught kids aged 5-16 the fundamentals of tennis through group sessions, and how to excel individually and work with each other as a team.

• Generated $500k in expected profit by combining previous sales data and results from response forms to optimize promotions• Collected >1.2k data points on consumer preferences by creating and deploying a nationwide consumer response form for Home Depot• Ran data analysis on collected response and sale data using Pandas and Matplotlib and presented our findings to Milwaukee Tools executives