activity
20032026
most citedThe Effect of Faults on Network Expansion

33 citations · 48 across the 5 of their papers we have counts for

collaborators

6 papers

cs.LG2026

FlexiFlow: Bandit-based Model Switching in ML Workflows

Abhilash Jindal, Todd Nief, Bhanu Prakash Vangala +6

Model optimizations help improve inference performance and accuracy of ML workflows. However, relying on a single model to perform inference across all data batches often fails to…

cs.DB2022

CHEX: Multiversion Replay with Ordered Checkpoints

Naga Nithin Manne, Shilvi Satpati, Tanu Malik +3

In scientific computing and data science disciplines, it is often necessary to share application workflows and repeat results. Current tools containerize application workflows, and…

cs.DC2010

A Dynamic Data Middleware Cache for Rapidly-growing Scientific Repositories

Tanu Malik, Xiaodan Wang, Philip Little +2

Modern scientific repositories are growing rapidly in size. Scientists are increasingly interested in viewing the latest data as part of query results. Current scientific middlewar…

cs.DB2010

Providing Scalable Data Services in Ubiquitous Networks

Tanu Malik, Raghvendra Prasad, Sanket Patil +2

Topology is a fundamental part of a network that governs connectivity between nodes, the amount of data flow and the efficiency of data flow between nodes. In traditional networks,…

cs.DC2004★ 33 cited

The Effect of Faults on Network Expansion

Amitabha Bagchi, Ankur Bhargava, Amitabh Chaudhary +2

In this paper we study the problem of how resilient networks are to node faults. Specifically, we investigate the question of how many faults a network can sustain so that it still…

cs.CG2003★ 15 cited

Deterministic Sampling and Range Counting in Geometric Data Streams

Amitabha Bagchi, Amitabh Chaudhary, David Eppstein +1

We present memory-efficient deterministic algorithms for constructing epsilon-nets and epsilon-approximations of streams of geometric data. Unlike probabilistic approaches, these d…