Andrew Jamieson, Ph.D. Assistant Professor School Medical School Department Lyda Hill Department of Bioinformatics Graduate Programs Biomedical Engineering Biography Since 2019, Andrew R. Jamieson, Ph.D. has been appointed as an Assistant Professor in the Lyda Hill Department of Bioinformatics. Recently, Prof. Jamieson served as a co-leader of the Bioinformatics Core Facility (BICF), responsible for leading campus-wide collaborative research engagements with emphasis on computational image analysis. In 2020, as the global pandemic emerged, Prof. Jamieson’s group led development of the UTSW COVID-19 forecast model. Prior to returning to academia, Andrew worked in Pharma Services, Operational Excellence, and the MultiOmyx group at Clarient, a GE Healthcare molecular diagnostics company (later sold to NeoGenomics). In 2012, Andrew dove into industry as the first employee and Data Scientist for a Plano, TX -based Big Data Analytics start-up, Oculus360. Andrew completed his Ph.D at the University of Chicago under the supervision of Maryellen L. Giger, Ph.D., a pioneer in Breast Cancer Computer-Aided Diagnosis. Education Undergraduate University of Chicago (the) (2006), Physics Graduate School University of Chicago (the) (2012), Medical Physics Research Interest Embedded Large Langauge Models Spatial Biology (2D/3D, multimodal, & highly-multiplexed tissue images) Video-based machine learning (e.g., intraoperative robotic surgery analysis, team dynamics, medical education) Working at the intersection of science, technology, and medicine Publications Featured Publications Heterozygous Mutation of Vegfr3 Reduces Renal Lymphatics Without Renal Dysfunction. Liu H, Hiremath C, Patterson Q, Vora S, Shang Z, Jamieson A, Fiolka R, Dean K, Dellinger M, Marciano D, J Am Soc Nephrol 2021 Sep Rethinking Autonomous Surgery: Focusing on Enhancement over Autonomy. Battaglia E, Boehm J, Zheng Y, Jamieson AR, Gahan J, Majewicz Fey A, Eur Urol Focus 2021 Jul Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma. Zaritsky A, Jamieson AR, Welf ES, Nevarez A, Cillay J, Eskiocak U, Cantarel BL, Danuser G, Cell Syst 2021 May A Silver Lining? Fewer non-SARS-CoV-2 Respiratory Viruses during the COVID-19 Pandemic. Most ZM, Holcomb M, Jamieson AR, Perl TM, J Infect Dis 2021 Apr What the Coronavirus Disease 2019 (COVID-19) Pandemic Has Reinforced: The Need for Accurate Data. Arvisais-Anhalt S, Lehmann CU, Park JY, Araj E, Holcomb M, Jamieson AR, McDonald S, Medford RJ, Perl TM, Toomay SM, Hughes AE, McPheeters ML, Basit M, Clin Infect Dis 2021 03 72 6 920-923 Robust and automated detection of subcellular morphological motifs in 3D microscopy images. Driscoll MK, Welf ES, Jamieson AR, Dean KM, Isogai T, Fiolka R, Danuser G, Nat Methods 2019 10 16 10 1037-1044 Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers. Li H, Giger ML, Sun C, Ponsukcharoen U, Huo D, Lan L, Olopade OI, Jamieson AR, Brown JB, Di Rienzo A Med Phys 2014 Mar 41 3 031917 Enhancement of breast CADx with unlabeled data. Jamieson AR, Giger ML, Drukker K, Pesce LL Med Phys 2010 Aug 37 8 4155-72 Exploring nonlinear feature space dimension reduction and data representation in breast Cadx with Laplacian eigenmaps and t-SNE. Jamieson AR, Giger ML, Drukker K, Li H, Yuan Y, Bhooshan N Med Phys 2010 Jan 37 1 339-51 Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset. Li H, Giger ML, Yuan Y, Chen W, Horsch K, Lan L, Jamieson AR, Sennett CA, Jansen SA Acad Radiol 2008 Nov 15 11 1437-45 Results 1-10 of 10 1
