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About me
Hello, world! I am Ishan Khurjekar.
I am a postdoctoral scholar at the Hopkins Extreme Materials Institute (Johns Hopkins University), where I work with Lori Graham-Brady and Somdatta Goswami. At JHU, I work on enhancing the reliability and applicability of neural operators for surrogate modeling in computational mechanics.
Before this, I was a postdoc at the Scripps Institute of Oceanography (University of California San Diego) working with Peter Gerstoft on reliable machine learning for acoustical estimation problems. I earned my PhD from the University of Florida where I worked on uncertainty-aware machine learning for structural health monitoring, working with Joel Harley.
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My research goal is to enhance the reliability and utility of machine learning methods for sensing applications with a focus on uncertainty quantification. I am always open to new collaborations and am generally excited to talk about research! Feel free to reach out.
Over the years, I have pursued research directions with 1 aspect in common: sensing.
Sensing methods are the mainstay of scientific and engineering innovation used for locating submarines in the sea or damages in structures or even imaging objects which are invisible to the eye. I enjoy researching and developing methods that enhance the utility of machine learning in various sensing-based applications. My past + current work has focused on:
Uncertainty quantification: Assess the reliability of ML methods in the presence of external uncertainty sources.
Uncertainty management: Develop ML methods that are robust to external variations present during data collection.
Utility enhancement: Obtain interpretable insights from ML models trained on sensing data to elucidate the underlying complex physical processes.
News ...
[Feb'24] Joined JHU as a postdoctoral scholar in the Hopkins Extreme Materials Institute. Excited to work on scientific machine-learning challenges!
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