Research InterestsDr. Michael’s general research interest is to develop mathematical tools to extract information from bio-signals to better model the underlying physiological processes at work. Dr. Michael is an electrical engineer with special training in signal processing and imaging. He received his multidisciplinary training in imaging science from the Chester F. Carlson Center at the Rochester Institute of Technology with emphasis on the physics of image formation, the mathematics of image processing and analysis, and the engineering of imaging instrumentation. He has previously collaborated with cross-disciplinary teams on a broad range of projects including shape formation algorithms for autonomous robots, target detection using hyper-spectral remote sensing images, NASA project for Hubble Space Telescope Data, and algorithm development for interferometry imaging at the national radio astronomy observatory. Although he worked on different types of engineering/imaging projects and acquiring important skills, his true passion was in biomedical imaging and applying technology for better healthcare. As a result, for his doctoral dissertation he chose to work on data fusion approaches to better characterize schizophrenia using magnetic resonance imaging (MRI). In the current clinical setting MRI techniques play an important role in the diagnosis process, but understanding the function of the brain and related disorders is still at the research level. Application of functional MRI and other neuroimaging techniques to better understand cognitive processes is an emerging area of research with potential future applications. At the Autism and Developmental Medicine Institute (ADMI) at Geisinger, Dr. Michael will develop techniques to better model Autism and other developmental disorders using genetic, brain imaging and behavioral datasets. Dr. Michael is excited to be part of a multi-disciplinary team at ADMI that consists of developmental pediatricians, geneticists, psychologists, radiologists, computer scientists and many other professionals all working together to tackle an important area of research.
- Michael AM, Anderson M, Miller RL, Adali T, Calhoun VD.. (2014, June). Preserving subject variability in group fMRI analysis: performance evaluation of GICA vs. IVA.. Front Syst Neurosci. , 8, 106. Full Text
- Michael AM, King MD, Ehrlich S, Pearlson G, White T, Holt DJ, Andreasen NC, Sakoglu U, Ho BC, Schulz SC, Calhoun VD. (2011, Aug). A Data-Driven Investigation of Gray Matter-Function Correlations in Schizophrenia during a Working Memory Task. Front Hum Neurosci , 5, 71. Full Text
- Michael AM, Baum SA, White T, Demirci O, Andreasen NC, Segall JM, Jung RE, Pearlson G, Clark VP, Gollub RL, Schulz SC, Roffman JL, Lim KO, Ho BC, Bockholt HJ, Calhoun VD. (2010, Feb). Does function follow form?: methods to fuse structural and functional brain images show decreased linkage in schizophrenia.. Neuroimage , 49(3), 2626-37. Full Text
- Michael AM, Baum SA, Fries JF, Ho BC, Pierson RK, Andreasen NC, Calhoun VD. (2009, Aug). A method to fuse fMRI tasks through spatial correlations: applied to schizophrenia. Hum Brain Mapp , 30(8), 2512-29. Full Text
- Katuwal GJ, Baum SA, Cahill ND, Michael A, . Divide and Conquer: sub-grouping of ASD improves ASD detection based on brain morphometry. PLoS ONE , Epub ahead of print.
EducationMS Electrical Engineering, Rochester Institute of Technology, 2004
PhD Imaging Science, Rochester Institute of Technology, 2009