DTIO: Data Stack for AI-driven Workflows
A scalable I/O runtime that unifies disparate I/O stacks for converged HPC, Big Data, and ML workflows.
Research area: ML-Assisted Systems
Viper: A High-Performance I/O Framework for Transparently Updating, Storing, and Transferring Deep Neural Network Models
An I/O framework that determines near-optimal checkpoint schedules and accelerates delivery of the latest model updates for DNN training and inference.
TunIO: An AI-powered Framework for Optimizing HPC I/O
An AI-powered I/O tuning framework that reduces tuning time while achieving the same performance gain through RL-driven early stopping.
Feature Reduction of Darshan Counters Using Evolutionary Algorithms
A study on feature reduction of Darshan counters using evolutionary algorithms to identify pertinent I/O performance predictors.
Apollo: An ML-assisted Real-Time Storage Resource Observer
A low-latency monitoring service that provides direct access to relational telemetry data for HPC middleware and applications.
CashTagNN: Exploiting the use of cashtags to predict stock market prices using convolutional networks
A system using sentiment and subjectivity scores of cashtag tweets to model and predict opening stock market prices.
CashTagNN: Exploiting the use of cashtags to predict stock market prices using convolutional networks
A system using sentiment and subjectivity of cashtag tweets to predict opening and closing stock market prices.