research

Graduate and undergraduate research in machine learning and security, spanning medical image analysis at UCSD and privacy-preserving ML, adversarial attacks, and applied cryptography at Ashoka University.

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Uncertainty Quantification for Medical Image Registration

Investigating methods to assess medical image registration quality through uncertainty quantification and its relation to registration error.
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Privacy-Preserving Neural Network Training

Capstone project researching secure multi-party computation techniques to enable privacy-preserving neural network training across distributed systems using SecureNN protocols.
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PPML Protocol Design and Evaluation

Research internship at Mphasis Lab implementing and evaluating PPML protocols including SecureML and BLAZE, and co-developing an optimized protocol for business-specific applications.
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Secure Machine Learning

Independent study investigating the impact of adversarial attacks including data poisoning and model evasion on ML model reliability and performance.
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Applied Cryptography

Independent study examining security vulnerabilities in OpenPGP and S/MIME email encryption, with analysis of real-world attacks on major email clients and proposed countermeasures.