Verified record
Publications & Patents
This page highlights the work I most want people to read first: efficient low-rank training, geometry reconstruction, and applied optimization work that made it into patented systems.
Entries below were checked against conference records, OpenReview, and patent databases.
Research papers
Featured publications
NeurIPS 2025
Poster
Q3R: Quadratic Reweighted Rank Regularizer for Effective Low-Rank Training
Ipsita Ghosh, Ethan Nguyen, Christian Kümmerle
Introduces a low-rank regularization method for training low-rank models directly, instead of relying only on low-rank fine-tuning after dense pretraining.
- Frames Q3R through an IRLS-inspired objective for low-rank optimization.
- Reports strong compression results on Transformer-style models, including ViT-Tiny.
- Positions the work around practical low-rank pretraining rather than only parameter-efficient adaptation.
NeurIPS 2024
Poster
Sample-Efficient Geometry Reconstruction from Euclidean Distances using Non-Convex Optimization
Ipsita Ghosh, Abiy Tasissa, Christian Kümmerle
Studies how to recover geometric structure from sparse pairwise distances using non-convex rank minimization and an IRLS-style algorithm.
- Gives a local convergence guarantee under minimal random distance sampling assumptions.
- Establishes a restricted isometry property tailored to the tangent space of low-rank symmetric matrices.
- Shows stronger data efficiency than prior baselines on simulated and real-world geometry reconstruction tasks.
Intellectual property
Patents from industry work
Inventor order below follows the patent records for each individual grant and related publication.
Granted patent
May 2024
US11994936B2: Automated optimization of error-handling flows in memory devices
Jay Sarkar, Ipsita Ghosh, Vamsi Pavan Rayaprolu
Covers automated reordering of SSD error-handling operations using latency and recovery information, with the goal of improving memory-system performance under real workloads.
Granted patent
June 2024
US12019874B2: Adaptive optimization of error-handling flows in memory devices
Jay Sarkar, Vamsi Pavan Rayaprolu, Ipsita Ghosh
Extends workload-aware optimization of memory-device recovery flows, emphasizing adaptive selection of error-handling behavior under changing operating conditions.