I currently work at Apple as a Research Engineer on the Apple Media Products Analytics team. I am a recent graduate of the Duke University Class of 2018, where I earned a B.S. in Computer Science and a B.S. in Statistical Science.
Hummus aficionado, animal lover, future proprietor of a cauliflower farm.
Python | Java
Relevant Undergraduate Coursework
Probabilistic Machine Learning | Statistical Computing | Design & Analysis of Algorithms | Software Design & Implementation | Operating Systems | Introduction to Databases | Bayesian & Modern Statistics | Statistical Case Studies | Statistical Decision Analysis | Statistical Inference | Regression Analysis | Probability | Algorithms & Data Structures | Computer Architecture | Discrete Mathematics | Linear Algebra | Multivariable Calculus
Exclusive development program to strengthen technical skills, through an online technical development curriculum and final programming project. Program culminates in an all-expense paid summit at Google in August 2016. Matched with a Google mentor to participate in mock interviews and meet regularly with throughout the program.
Apple Media Products Analytics | Research Engineer (2018)
Responsible for collecting requirements from data scientists and analysts, translating them into engineering specs, working with other teams on instrumentation and data quality, and developing datasets and machine learning features used for analytics and customer experience research.
AT&T Research Labs | Research Intern (2017)
Worked to apply machine learning technology in software defined networking (SDN). Implemented Optical Character Recognition (OCR) and Long Short Term Memory (LSTM) neural networks to automate day-0 configuration and set-up of Virtualized Network Functions (VNFs). Applied deep convolutional neural networks to build a facial recognition tool for secure multi-factor authentication.
AT&T Big Data | Technical Intern (2016)
Applied graph database Neo4j (first in AT&T Big Data to utilize this technology), Hive, Python, and other statistical tools to data-driven analyses of
employee productivity, automation opportunities, and resource management.
Duke CS | Teaching Assistant (2016-2017)
Worked as a TA for Computer Architecture. Responsibilities include holding weekly office hours (both in-person and online), teaching lab sections, and grading.
Selected to work alongside Duke Medical School and BME faculty to better understand the potential risks associated with healthcare centers, environmental microbes, and infectious diseases through mapping the distribution of antibiotic-resistance genes across the Duke campus. Led the computational team in developing tools for data analysis and dissemination. Personally set up data pipeline and developed project website.
Assisting Duke CS and Statistics faculty in developing an interactive tool that will improve online big data database query performance analysis through the use of large-scale, multi-level, directed acyclic dependency graphs. Specifically, researching the use of probabilistic reasoning to define an intelligent method of large-scale data-stream collection and analysis.
Selected by the Information Initiative at Duke (i.i.D.) to research risk behavior with the Duke Center for Cognitive Neuroscience. Applied machine learning algorithms such as linear discriminant analysis, random forests, and k-means to predict individual risk behavior preference, culminating in a predictive model 40% more accurate than previously available models.