I worked on improving algorithms within an inner source time series anomaly detection package and onboarding its new features to the Machine Learning as a Service (MLaaS) platform. MLaaS is a machine learning workflow orchestration platform which turns algorithms and other components of a model pipeline into microservices.

My team's goal was to build multimodal machine learning models which could predict end of semester depression from both passively sensed data and ecological momentary assessment for undergraduate students. My work was primarily focused on feature extraction and validation as well as database reconfiguration.

Functional Programming (15-150) is an introductory course for the theory and practice of functional programming taught in Standard ML. I led weekly programming labs for 25+ students, held regular office hours, developed course materials, and graded assignments.

My team's goal was focused on determining the reproducibility of research on the entrainment of brain waves using binaural beats. I designed the research proposal and conducted exploratory data analysis with an emphasis on the visualization of multidimensional time series data.