Romit Maulik
Assistant Computational Scientist,
Mathematics and Computer Science Division,
Argonne National Laboratory.

Assistant Professor (Starting July 2023),
Department of Information Science and Technology,
Pennsylvania State University.

I am an Assistant Computational Scientist at the Mathematics and Computer Science division (MCS) at Argonne National Laboratory. Previously, I was the 2019 Margaret Butler Postdoctoral Fellow at Argonne National Laboratory and obtained my Ph.D. in Mechanical & Aerospace Engineering from Oklahoma State University. My interests are scientific machine learning, stochastic processes, high performance computing with applications to engineering, geoscience, plasma physics. An updated list of publications can be found on my Google Scholar profile and some of my software contributions can be found on Github. If you're interested in a high-level overview of some of my research, check out these recordings of recent talks [1], [2], [3], [4]. If you are a student interested in an internship at Argonne along the lines of my research interests - please email me.

Starting July 2023, I will be an Assistant Professor in the Department of Information Science and Technology (IST) at Pennsylvania State University. I will also be jointly appointed at Argonne National Laboratory (Argonne) as a faculty scientist. I have several fully funded PhD positions in the Interdisciplinary Scientific Computing Laboratory, starting Fall 2023. Please contact me if you are interested. Note - applicants must apply online (deadline December 15) to the IST graduate program here.

In addition to the team at Penn State, the group will also be composed of postdoctoral fellows, graduate, and undergraduate students at Argonne and allow for research at the intersection of academia and National Labs. Successful applicants will have a unique PhD experience with access to Argonne's state-of-the-art supercomputing resources and the ability to work on large-scale research projects of strategic importance.


Research information

The research manifesto of the interdisciplinary scientific computing group.

Grand challenge problems

Improving geophysical forecast models with data science.

Tokamak disruption mitigation for nuclear fusion (image taken from Boozer et al., 2012).

Scientific ML algorithm development

An algorithm that builds deep learning function approximation for sparse, unstructured, and time-varying observations (Nature Machine Intelligence 3 (11), 945-951, 2021).

Applied machine learning

Building a wind-turbine wake model for on-shore wind farms in the Texas panhandle using LIDAR and meteorological data (Neural Computing and Applications 34 (8), 6171-6186, 2022).

High performance heterogeneous computing

Building a scalable and reproducible ecosystem for scientific machine learning research (Journal of Computational Science 62 (2022): 101750).

Plain Academic