A scalable I/O runtime that unifies disparate I/O stacks for converged HPC, Big Data, and ML workflows.

An I/O framework that determines near-optimal checkpoint schedules and accelerates delivery of the latest model updates for DNN training and inference.

An AI-powered I/O tuning framework that reduces tuning time while achieving the same performance gain through RL-driven early stopping.

An intelligent storage resource provisioning and auto-configuration service for HPC multi-storage deployments.

A study on feature reduction of Darshan counters using evolutionary algorithms to identify pertinent I/O performance predictors.

A dynamic and elastic multi-layered data forwarding system leveraging a real-time data movement paradigm for HPC.

A low-latency monitoring service that provides direct access to relational telemetry data for HPC middleware and applications.

A poster exploring characterization and approximation of I/O behavior in HDF5 applications using genome-like sequences.

A distributed shared multi-tiered log store using physical time to achieve scalable, parallel, high-performance log workloads.

A system using sentiment and subjectivity scores of cashtag tweets to model and predict opening stock market prices.

A system using sentiment and subjectivity of cashtag tweets to predict opening and closing stock market prices.