• Built and deployed C++ and Python codes to analyze and visualize large and complex nuclear reaction data• Developed Monte Carlo simulations to visualize analysis techniques and train neural networks using Tensorflow/Keras• Implemented different supervised and unsupervised machine learning techniques:– K-means clustering to effectively and accurately separate clusters of nuclear contaminants– Linear Regression to improve detector calibration accuracy– Logistic Regression, Random Forest, and Support Vector Machine to separate light particles without human error– Sequential API to create Keras model that predicts Q-value based on energy and angle of a particle– LSTM model for time series analysis of a radioactive decay• Thrived working with high-paced and innovative scientific collaborations• Demonstrated excellent visualization and presentation skills in several oral and poster presentations• Assisted in directing the research of undergraduate students in nuclear science

• Taught calculus-based introductory Physics recitation and lab sessions, and graded assignments and exams