Computational Modeling and Analysis

Projects range from multifractal image analysis to wavelet-based segmentation and characterization of chromosome territories and mammograms to the study of the interstellar medium.

Take a look at CompuMAINE’s research and get to know our lab members. Let us know if you’re interested in working with us: CompuMAINE_Undergrad_Internship_2020.

Computational Modeling, Analysis of Imagery & Numerical Experiments

CompuMAINE Lab members, May 2019.

Grant Alert!!!

We were recently awarded a 3-year $423K grant from the National Cancer Institute (start date Sept 4, 2020). The work is mostly centered on CompuMAINE lab’s work on the computational analyses of mammograms, but we also successfully proposed an extension of this work to breast tissue analyses from biopsies, lumpectomies, and mastectomies, thanks to Karissa Tilbury’s involvement.

Of note: Of all institutes at NIH, NCI is one of the most competitive to get funding from. While the funding rate for NIH as a whole is ~21% (substantially lower than NSF’s ~28%), the specific mechanism we went through has a funding rate of only 9.9% at NCI. Based on data obtained from UMaine’s ORA, this is the first grant to UMaine from NCI in 16 years, only the fourth overall in UMaine’s history, and the first ever in the College of Engineering.

The Computational Modeling, Analysis of Imagery and Numerical Experiments (CompuMAINE) lab is an image and signal processing, analysis, and modeling laboratory located at the University of Maine flagship campus. It was founded by Dr. Andre Khalil.

By developing and implementing novel signal processing & image analysis techniques, and computational modeling, CompuMAINE integrates mathematics, physics, artificial intelligence, machine learning, data mining, and computational engineering approaches to study a wide variety of applications. Focused research projects are centered on radiomics, a new field of medical study that aims to extract large amounts of quantitative features from medical images using data-characterization algorithms. Applications include Medicine (cancer, neuroscience, muscular dystrophy), Biophysics (neuro-development, cell nucleus architecture), Biomedical Engineering (artificial bone implants, protein modeling, astrobiology), Physics / Geophysics / Astrophysics (climate change,  surface science, solar physics, interstellar medium, cosmology), and Pure Mathematics (fractal structures in Pascal’s Triangle).