Contact Information
1205 W. Clark St., Urbana
M/C 257
Research Areas
Research Interests
Formation of the first stars and galaxies
Primordial chemistry
High-performance computing and computational simulations
Analysis and visualization of astrophysical data
Education
BA from Northwestern in 2003
PhD from Stanford in 2009
Additional Campus Affiliations
Assistant Professor, School of Information Sciences
Assistant Professor, National Center for Supercomputing Applications (NCSA)
Affiliate, Center for Social and Behavioral Science
External Links
Recent Publications
Liao, W. T., Turk, M., & Schive, H. Y. (2021). Large-scale Dynamo in a Primordial Accretion Flow: An Interpretation from Hydrodynamic Simulation. Astrophysical Journal, 909(1), [37]. https://doi.org/10.3847/1538-4357/abd9b7
Narayanan, D., Turk, M. J., Robitaille, T., Kelly, A. J., McClellan, B. C., Sharma, R. S., Garg, P., Abruzzo, M., Choi, E., Conroy, C., Johnson, B. D., Kimock, B., Li, Q., Lovell, C. C., Lower, S., Privon, G. C., Roberts, J., Sethuram, S., Snyder, G. F., ... Wise, J. H. (2021). Powderday: Dust Radiative Transfer for Galaxy Simulations. Astrophysical Journal, Supplement Series, 252(1), [12]. https://doi.org/10.3847/1538-4365/abc487
Roca-Fàbrega, S., Kim, J. H., Hausammann, L., Nagamine, K., Lupi, A., Powell, J. W., Shimizu, I., Ceverino, D., Primack, J. R., Quinn, T. R., Revaz, Y., Velázquez, H., Abel, T., Buehlmann, M., Dekel, A., Dong, B., Hahn, O., Hummels, C., Kim, K. W., ... Turk, M. J. (2021). The Agora High-resolution Galaxy Simulations Comparison Project. iii. Cosmological Zoom-in Simulation of a Milky Way-mass Halo. Astrophysical Journal, 917(2), 917-964. https://doi.org/10.3847/1538-4357/ac088a
Benes, B., Guan, K., Lang, M., Long, S. P., Lynch, J. P., Marshall-Colón, A., Peng, B., Schnable, J., Sweetlove, L. J., & Turk, M. J. (2020). Multiscale computational models can guide experimentation and targeted measurements for crop improvement. Plant Journal, 103(1), 21-31. https://doi.org/10.1111/tpj.14722
Chard, K., Gaffney, N., Hategan, M., Kowalik, K., Ludäscher, B., McPhillips, T., Nabrzyski, J., Stodden, V., Taylor, I., Thelen, T., Turk, M. J., & Willis, C. (2020). Toward enabling reproducibility for data-intensive research sing the whole tale platform. In I. Foster, G. R. Joubert, L. Kucera, W. E. Nagel, & F. Peters (Eds.), Parallel Computing: Technology Trends (pp. 766-778). (Advances in Parallel Computing; Vol. 36). IOS Press BV. https://doi.org/10.3233/APC200107
