Practicum Experience(s)
Los Alamos National Laboratory (2025)
Los Alamos National Laboratory (2023)
Annual Program Review Abstracts
Publications
Tynes, M., Chard, K., Foster, I., & Ward, L. (2025, July). Will It Blend? Mixing Numerical and Machine-Learned Physics Quantities for Accurate on-the-Fly Surrogate Modeling. In International Conference on Computational Science (pp. 270-284). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-97632-2_19
Tynes, M., Taylor, M. G., Janssen, J., Burrill, D. J., Perez, D., Yang, P., & Lubbers, N. (2024). Linear graphlet models for accurate and interpretable cheminformatics. Digital Discovery, 3(10), 1980-1996. https://doi.org/10.1039/D4DD00089G
Tynes, M., Gao, W., Burrill, D.J. Batista, E.R., Perez, D., Yang, P., and Lubbers, N. "Pairwise difference regression for uncertainty quantification and candidate selection." Journal of Chemical Information and Modelling. 61, 8, 3846-3857 (2021). (2019 IF: 4.5) https://doi.org/10.1021/acs.jcim.1c00670
Schrier, J., Tynes, M., and Cain, L. "Determining the Activity Series with the Fewest Experiments using Sorting Algorithms" Journal of Chemical Education. 98, 5, 1653-1658 (2021). (2019 IF: 1.7) https://doi.org/10.1021/acs.jchemed.1c00043
Yip, T., Smith, P., Tynes, M., Mirpuri, S., Weems, A., and Cheon, Y. M. "Discrimination and hair cortisol concentration among asian, latinx and white young adults." Comprehensive Psychoneuroendocrinology. 6, 100047 (2021). (2019 IF: 4.7) https://doi.org/10.1016/j.cpnec.2021.100047
Pendleton, I.M., Caucci, M.K., Tynes, M., Dharna, A., Nellikkal M.A., Li, Z., Chan E.M., Norquist, A.J., and Schrier, J. "Can Machines "Learn" Halide Perovskite Crystal Formation without Accurate Physicochemical Features?" Journal of Physical Chemistry C. 124, 25, 13982-13992 (2020). (2019 IF: 4.2)
https://doi.org/10.1021/acs.jpcc.0c01726
Jones, L., Tynes, M., and Smith, P. "Prediction of models for ordered solvent in macromolecular structures by a classifier based upon resolution-independent projections of local feature data." Acta Crystallographica Section D: Structural
Biology. 75, 8, 696-717 (2019). (2019 IF: 7.7) https://doi.org/10.1107/S2059798319008933
Zheng, W., Tynes, M., Gorelick, H., Mao, Y., Cheng, L., and Hou, Y. "FlowCon: Elastic Flow Configuration for Containerized Deep Learning Applications." Proceedings of the 48th International Conference on Parallel Processing Article 87, 1-10 (2019). (Conference Acceptance Rate: 20%) https://doi.org/10.1145/3337821.3337868
Awards
LANL Seaborg Fellowship, 2021-2022: Award to support interpretable machine learning for autonomous discovery in Actinide science
LANL ISTI Fellowship, 2021-2022: Award to support development of software infrastructure for autonomous discovery in laboratory science at LANL
LANL SPOT Award, 2021: Recognition of distinguished performance in research.
LANL CNLS Fellowship, 2020-2021: Award to support the development of Pairwise Difference Regression (PADRE)
Fordham GSAS Graduate Research Fellowship, 2019-2020: Funding to support development of tensor factorization based reaction recommendation systems in chemical science. Funded by Fordham GSAS and the DARPA SD2 program (PI: Joshua Schrier).
Fordham Centennial Graduate Research Fellowship, 2018-2019: Funding from Fordham GSAS granted to the top 10% of students admitted to the MSDS program
Fordham Senior Leadership Award, 2017: Awarded each year to six to eight seniors who exhibit the ability to lead, organize, empower and inspire fellow students.
Fordham James C. Higgins Memorial Award in Psychology, 2017: One of three named awards given annually to a graduating senior for distinguished performance and contributions to the department
Fordham Undergraduate Research Grant, Fall 2016: Continued research support from Fordham College at Rose Hill for hormone quantification pipeline development
Fordham Undergraduate Research Grant, Summer 2016: Initial research support from Fordham College at Rose Hill for hormone quantification pipeline development