My work centers on second-order federated optimization with differential-privacy guarantees, making distributed training converge faster while keeping formal privacy budgets tight. In plain terms, I'm building ways for institutions like hospitals, banks and data centres to train shared AI models together without ever handing over the private data behind them, so that as machine learning scales, personal privacy doesn't have to be the price. Below: peer-reviewed work and preprints, then the research appointments behind them.
Co-authored work detailing the convergence behaviour and communication efficiency of second-order federated-learning methods — benchmarking Newton-CG, L-BFGS and quasi-Newton schemes against first-order baselines on heterogeneous clients, with Hessian-free and low-rank approximations to cut communication overhead.
Privacy-preserving second-order federated optimization with (ε−δ) differential-privacy guarantees for heterogeneous data distributions. A regularized Fisher Information Matrix preconditions updates while a federated privacy accountant tracks the budget under subsampling amplification.
A novel federated domain-adaptation approach addressing limited target data and client distribution heterogeneity, introducing functional-distance-based aggregation that aligns target and source models via mean gradient fields — 15–20% accuracy gains over state-of-the-art FL, PFL and FDA methods on CIFAR-10, PACS and Caltech-10.
Optimized computational object slicing and multi-axis G-code design for support-free extrusion printing — part of an industry–university collaboration with SONY on India's first 5-axis FDM printer.