Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide. - Ganesh Sivaraman; Anand Narayanan Krishnamoorthy; Matthias Baur; Christian Holm; Marius Stan; Gábor Csányi; Chris J. Benmore; Álvaro Vázquez-Mayagoitia (CoRR, 2019)

Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis. - Maksim Levental; Ryan Chard; Joseph A. Libera; Kyle Chard; Aarthi Koripelly; Jakob R. Elias; Marcus Schwarting; Ben Blaiszik; Marius Stan; Santanu Chaudhuri; Ian T. Foster (XLOOP@SC, 2020)

An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide. - Ganesh Sivaraman; Leighanne Gallington; Anand Narayanan Krishnamoorthy; Marius Stan; Gábor Csányi; Álvaro Vázquez-Mayagoitia; Chris J. Benmore (CoRR, 2020)

Flame Stability Analysis of Flame Spray Pyrolysis by Artificial Intelligence. - Jessica Pan; Joseph A. Libera; Noah H. Paulson; Marius Stan (CoRR, 2020)

HPIPE NX: Boosting CNN Inference Acceleration Performance with AI-Optimized FPGAs. - Marius Stan; Mathew Hall; Mohamed Ibrahim; Vaughn Betz (FPT, 2022)

H2PIPE: High Throughput CNN Inference on FPGAs with High-Bandwidth Memory. - Mario Doumet; Marius Stan; Mathew Hall; Vaughn Betz (FPL, 2024)