Welcome to the group page of the Interdisciplinary Scientific Computing Laboratory (ISCL). ISCL performs research at the intersection of data science, applied mathematics, and high-performance computing for solving grand challenge problems in computational science. ISCL is housed in the Information Sciences and Technology Department at Pennsylvania State University, as well as the Mathematics and Computer Science Division at Argonne National Laboratory. Our team is composed of postdoctoral fellows, graduate students, and visiting students across these two locations (with opportunities to travel between them). A high level overview of our research may be found in the following talks: , , , , . Further information about publications can be found on Google Scholar and our software contributions are available on Github.
Members of our group have a unique platform for impactful research with access to Argonne's state-of-the-art supercomputing resources and the ability to work on large-scale research projects of strategic importance.
A new perspective piece of machine learning for wind energy is published in Theoretical and Applied Mechanics Letters. This work, titled "A Call for Enhanced Data-Driven Insights into Wind Energy Flow Physics", is collaborative with Professor Iungo and Coleman Moss at the University of Texas at Dallas.
New preprint out on a state-of-the-art transformer based model for weather forecasting! This work was collaborative with UCLA (Professor Aditya Grover). Read more here.
New preprint out on how we can construct graph neural networks for surrogate modeling with a-posteriori error tagging - a novel technique that improves the explainability of surrogate models! This work was led by Shivam Barwey. Read more here.
Romit presents a talk on anomaly detection at the Brij Disa Center for Data Science and AI at IIM Ahmedabad. Thank you Anindya Chakrabarti and Debjit Ghatak for the invitation!
Romit has been awarded an "Impact Argonne Award" for "For developing an AI model for climate and creating an innovated data assimilation methodology" by Argonne National Laboratory.
Our paper on interpretable latent subgraph extraction for graph neural networks is published in the Journal of Computational Physics! This work was led by Dr. Shivam Barwey at Argonne National Laboratory. Read more here.
Our paper on using Bayesian online changepoint detection to detect anomalies in complex dynamical systems is published in Chaos! This work was led by Sen Lin (at the University of Houston) in collaboration with Gianmarco Mengaldo at the National University of Singapore. Read more here.