💼Laufer Center welcomes Prof. Ramon Grima as Endowed Professor !
Prof. Ramon Grima
Endowed Professor of Laufer Center for Physical and Quantitative Biology
Department of Applied Mathematics and Statistics
The Laufer Center is delighted to announce the appointment of Prof. Ramon Grimaas its newest Endowed Professor. Prof. Grima will join the Department of Applied Mathematics and Statistics and the Laufer Center beginning at the end of this summer.
Prof. Grima comes to Stony Brook from the University of Edinburgh, where he has served as Full Professor and Chair of Mathematical Biology since 2019. Trained as a physicist and applied mathematician — with a PhD from Arizona State University and postdoctoral experience at Indiana University Bloomington and Imperial College London — he has spent the past two decades building a internationally recognized research group at the intersection of stochastic modeling, computational biology, and machine learning.
His research focuses on understanding gene expression dynamics at the single-cell level through the development of novel analytical frameworks for stochastic biochemical systems. The methods pioneered by his group — including the Linear Mapping Approximation, the Holimap algorithm, and generating-function-based inference — now enable researchers to extract kinetic parameters from single-cell RNA sequencing data with previously unattainable accuracy, accounting for technical noise and biological complexity in equal measure.
Beyond his research, Prof. Grima brings a strong commitment to interdisciplinary education. He has designed and taught courses integrating mathematics, physics, computation, and biology at both the undergraduate and postgraduate levels, and has mentored 19 PhD students alongside 7 postdoctoral researchers, many of whom have gone on to independent faculty positions at leading institutions in Europe and North America.
https://scholar.google.com/citations?user=zkpL2H8AAAAJ&hl=en
125 Refereed publications
49 H-index
6910 Citations
Research areas:
Stochastic gene expression, Chemical master equation, Single-cell RNA sequencing, Mechanistic modeling, Machine learning inference, Gene regulatory networks, Systems biology, Computational biology
