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Sreelekha Guggilam - using data-driven information to assist with national security challenges

Sreelekha Guggilam is a research associate for machine learning with the Geospatial Science and Human Geography group at ORNL. She recently shared how she uses data-driven information to assist with national security challenges.

Can you talk about a project you’re working on that uses data science? 

Statistical analysis, machine learning, and artificial intelligence are crucial in several of my projects where the goal is to extract insights and knowledge from data. I have a particular focus on developing anomaly detection algorithms for multivariate time series data, which can have various applications. From monitoring nuclear non-proliferation to analyzing pandemics/disease outbreaks to tracking human mobility patterns, these algorithms are critical to national security.   

What led you to a career in data science? 

The field of data science has advanced beyond being a mere tool for data analysis and has become a skillful art of developing mathematically sound solutions to address real-world problems. As an individual with a keen interest in theoretical mathematics and statistics, I have found the perfect convergence of theory and practical application in data science. Given the growing demand to bridge the divide between core mathematics and domain-specific applications, the interdisciplinary nature of data science presents a promising avenue for emerging research, attracting me to pursue research in this area.

What brought you to ORNL?

ORNL has an optimal mix of domain researchers and theoretical scientists who are dedicated to work together with a shared focus on solving critical problems. The lab is equipped with both comprehensive data and cutting-edge resources to facilitate complex research, making it a one-of-a-kind environment for tackling intricate research projects and conducting advanced scientific investigations.