Held tutoring and review sessions for Data Structures and Program Development course taught in C++ and Introduction to Computer Science course taught in Python.

- Worked on a proof-of-concept prototype of an offline cost-effective system capable of predicting roadway concentrations of ultrafine particles (UFP) using a low-power camera and a combination of sensors.- Created an algorithm to count the number of cars in each frame in Python using openCV.- Preprocessed and condensed different time series parameters such as car count, wind speed, and particulate matter concentrations into a unified time series using pandas.- Built a recurrent neural network (RNN) using the unified time series data to predict the UFP concentration on roads using PyTorch.- Conducted data exploration and experimentation to model the UFP concentrations as multiple decaying functions using pandas and NumPy.

- Upgrading existing features and implementing novel ones in VR environment for MBARI's ROVs.

- Worked with the Engineering team to deploy 2 projects that reduces the time of analyzing test reports. - Created a Python script that processes data logs from different test fixtures by taking in multiple parameters and determining patterns in failures. - Standardized test files to allow regex search, reducing the number of complete file scans for large workloads.