Proven Software Engineer with a track record of delivering scalable solutions and robust code at Freddie Mac. Excelled in optimizing algorithms and enhancing team collaboration, showcasing strong problem-solving and communication skills. Expert in Javascript and Agile methodologies, consistently exceeding project expectations without compromising quality. Complex problem-solver with analytical and driven mindset. Dedicated to achieving demanding development objectives according to tight schedules while producing impeccable code.
Anomaly Detection in Bill of Materials for GE Aviation
Fall 2020
● Prototyped and compared several anomaly detection methodologies like DBSCAN, Isolation Forests, Support Vector Machine for tree structured data (Bill of Materials) provided by GE Aviation
● Detected anomalous patterns using Pandas, NetworkX and scikit-learn without any false-positives
Analysis of a Boolean Model of the Yeast Cell-Cycle Control Network
May 2019- August 2019
● Workedwith Dr. T. M. Murali and Dr. John Tyson on cell-cycle progression of budding yeast and created a software to construct and analyze Boolean models
● Analyzed the dynamical properties of a published Boolean model of the control system and compared across perturbed networks; learning the complexity of cell-cycle control by modeling protein interactions
● Presented the research at Virginia Tech Research Symposium