Labs
Scientific Leader: Dr Alex Dickson
Combining knowledge of pharmacological biophysics, network modeling, and rare event sampling methods, the Dickson laboratory uses computational techniques such as molecular dynamics to simulate the motions of biomolecules (protein, RNA and DNA).
Scientific Leader: Dr. Adam Alessio
MIDI Lab is a multidisciplinary team committed to advancing the analysis and integration of medical imaging information and biomedical data for targeted, precise disease treatment.
Scientific Leader: Huan Lei
Our group focuses on computational mathematics, particularly in scientific machine learning (SciML) and multi-scale modeling related to fluid physics, materials science, and biophysics. As part of the broader field of AI for Science, we develop numerical algorithms for constructing accurate and structure-preserving ML-based models of multi-scale systems directly from the first-principle-based descriptions.
Scientific Leader: Dr. Andrew Christlieb
There are three categories of applications our group considers. The first is related to modeling complex multi physics problems in plasma science. The second area we work on is the development of methods that are aimed at solving interface problems in polymer membranes; think fuel cells, solar cells and batteries. The third area we work on is the development of ultra fast methods (sub-linear methods) for identification of sparse signals.
The Computing Education Research Lab (CERL)
Scientific Leaders: Dr. Danny Caballero, Dr. Brian O'Shea, and Dr. Devin Silva
The Computing Education Research Lab (CERL) at MSU is an interdisciplinary collaboration that studies how students learn to do computational work and their attitudes about doing that work. In particular, we explore how students learn to integrate computing into their education outside of traditional computer science courses.
Scientific Leader: Daniel Appelö
The Appelö High Order Group focuses on the development of novel high order accurate discretization of partial differential equations modeling problems from science and engineering.
Scientific Leader: Danny Caballero
We are building a research activity using modern analytical tools such as machine learning network analysis, natural language processing, and simulation to research science education.
Scientific Leader: Ming Yan
The Ming Yan Lab develops efficient optimization algorithms for large-scale problems in machine learning, signal, and image processing.
