CV
Research Statement
My dissertation builds pipelines for recognizing activities in smart homes, by observing data collected from residents using nearable sensors. As such I propose a ‘lifespan of a human activity recognition system in smart homes’, which includes (a) initial bootstrapping procedure - aimed at `jump-starting’ the activity recognition system in the home, with minimal supervision from the resident, without considerable wait times; (b) updating and extending the activity recognition system - aimed at improving the recognition capabilities of the initial bootstrapped system; (c) routine assessment - aimed at behavior monitoring of residents over extended periods of observation.
Education
- Ph.D in Computer Science, Georgia Institute of Technology, 2024 (expected)
- M.S. in Computer Science, Georgia Institute of Technology
- B.Tech. in Computer Science, Vellore Institute of Technology
Work experience
- Aug 2018 - Present: Graduate Research Assistant
- Georgia Institute of Technology
- I developed data-driven technologies for wearable and nearable based application scenarios to build deployable human activity recognition sys- tems, with a focus on providing health monitoring.
- Published at venues: IMWUT, ISWC, Sensors, International Conference on Activity and Behavior Computing
- Supervisor: Dr. Thomas Ploetz
- Aug 2018 - Present: Graduate Teaching Assistant
- Georgia Institute of Technology
- CS 6601: Artificial Intelligence - design assignments and exams
- CS 7470: Mobile & Ubiquitous Computing - design exams
- May - July 2016 : Data Science Intern
- ADP Solutions
- My work involved development of data analysis procedures to analyze and gather insights from collected payroll data.
- Aug 2014 - July 2015: Software Developer
- Deloitte USI (India)
- I was involved in building a tool that served as an interface between end users and project management activities(PMC) aimed as reducing the number of manual tickets raised for changes to a project team. The tool successfully contributed to a 70% reduction in costs associated with handling tickets, thereby achieving significant cost savings
Skills
- Quantitative: Applied Machine Learning, Deep Learning, Statistical Model, Time-Series Data Analysis
- Programming Languages: Python (numpy, pandas, scikit-learn), Java, R
- Database Systems: MYSQL, SQLite, Oracle
- Deep learning frameworks - PyTorch, Keras.