Laurent Younes’ pioneering shape analysis algorithms enable researchers and clinicians to better interpret medical imaging data to advance the detection and treatment of Parkinson’s disease, Alzheimer’s disease, cardiac disease, mental illness, and other diseases.
A member of the JHU Center for Imaging Science, Younes is a professor in the Department of Applied Mathematics and Statistics and leads research teams in studying the statistical properties of image analysis, deformation analysis, and shape recognition, and Markov Random Fields (a mathematical tool for modeling and making inferences about image data). His work is central to the field of computational anatomy and the ability to analyze diseases by how they affect the shape of organs.
Fifteen years ago, Younes’ early models for 3D scene understanding helped cardiologists and radiologists better understand and interpret the then-novel 3D imaging. His innovative mathematical tools also are vital to understanding early-stage brain disease. Younes is collaborating with Johns Hopkins School of Medicine faculty to create a transdiagnostic approach to deciphering psychotic disorder, for which he received a Hopkins 2019 Discovery Award. Other current areas of computational shape analysis focus on better understanding network neurodegeneration during Alzheimer’s, including Younes’ research work in identifying changepoints in biomarkers during the preclinical phase of Alzheimer’s disease and modeling the shapes and creating algorithms to examine cortical thickness atrophy in mild cognitive impairment.
His book, Shapes and Diffeomorphisms, published by Springer Berlin Heidelberg in 2010 and reprinted in 2019, is the definitive text on the complexities of shapes as mathematical entities for computerized analysis and interpretation, in particular, the interesting connections between shapes and diffeomorphisms, which transform the shapes.
In 2015, Younes was named a Fellow of the Institute for Mathematical Statistics. He is a member of the Society for Industrial and Applied Mathematics (SIAM), American Mathematical Society, and The Institute of Electrical and Electronics Engineers (IEEE). Younes’ service includes organizing an Institute for Mathematics and its Applications (IMA) workshop and a Statistical and Applied Mathematical Sciences Institute workshop (both in 2006) and a workshop in 2010. His invited presentations include the 2017 ICMAT, the 2017 SIAM Conference on Imaging Science, and Harvard’s conference on Elastic Functionals and Shape Data Analysis and its Morphometrics, Morphogenesis, and Mathematics conference (both in 2018). He has created and led several lecture series and tutorials on image deformation and warping, including the European Conference on Computer Vision and the Summer School on Imaging for Medical Applications. Younes is an associate editor of the Annals of Applied Statistics and a former associate editor of Pattern Recognition Letters, Journal of Mathematical Imaging and Vision, and IEEE Transaction in Image Processing.
He received his B.S. (1984) from the Ecole Normale Superieure, Paris, and his M.S. (1985) and Ph.D. (1988), both from the University of Paris XI.