I am experienced in identifying questions and defining problems. Leveraging agile frameworks, I design analytic workflow to understand given biological data and its context to extract meaningful features and answer underlying phenotypic changes and biomolecular mechanisms involved in Human Aging and Cancer.
As a member of Hackathon team, I deployed the backend of an iOS app built on react-native to read electroencephalogram(EEG) brain signals sent from a hairband called MUSE originally developed for assisting meditation and trained a deep-learning model to predict the emotional status of the user wearing the hairband. We developed this app to help the caregivers of autistic children keep track of their children's emotional level and quickly react to any potential danger.
As a TA, I helped designing coursework and lead discussions from basic to the state-of-art practices in Machine Learning in different domains including time-table, image, and language.
Research Topic: Machine-learning, Human Aging, Digital Pathology
Research Topic: Machine-learning, Cancer Cell Morphology and Phenotyping
GPA: 3.6
Apart from conducting research, I enjoy most of my time being outdoors. In the winter, I am an avid skier. During the warmer months, I enjoy road cycling, rock climbing, and scuba diving.
When forced indoors, I follow a number of sci-fi and fantasy genre movies and novels, I am an aspiring chef, and I spend a large amount of my free time exploring the latest technology advancements in human tissue image analysis.