about me.

We are living in an exciting era of machine learning (ML) and artificial intelligence (AI), where sensing has become ubiquitous and cost-effective and machines have become well-capable of learning complex patterns. While sensors feed the data, machines learning techniques learn complex patterns from that data to either generate actionable insights or automate the process and make direct impact on us – the people. Whether you consider healthcare, transportation, or energy analytics, AI-enabled feedback loop is transforming the way we live. My research interest precisely lies at the intersection of Sensor Network, Applied Machine Learning, and Human.

I am a Data Scientist at Pacific Northwest National Laboratory, USA. Prior to joining the lab, I completed my Ph.D. under the supervision of Dr. Amarjeet Singh and Dr. Vikas Chandan in Data Driven Thermostats: For Feedback, Comfort, and Reliability from Indraprastha Institute of Information Technology Delhi (IIIT-Delhi). In my research work, I worked on analyzing multimodal sensory information to generate insights, and later use those insights to develop machine learning framework for real-world applications.

In my Ph.D., I collaborated with some of the top research labs across the world, including, MIT Media Lab, University of Waterloo, and IBM Research Lab. I visited the Optimization and Control Group at PNNL as an Alternate Sponsored Fellow on IUSSTF BHAVAN Fellowship – an initiative by the Government of India and the United States of America.

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02/2023: Paper accepted on An ML Framework to Deconstruct the Primary Drivers for Electricity Market Price Events in 2023 IEEE PES General Meeting for oral presentation.

02/2023: Poster presentation on Training Machine Learning Models to Characterize Temporal Evolution of Disadvantaged Communities in 2023 AAAI Workshop on Artificial Intelligence for Social Good.