Associate Professor
Department of Mathematics and Statistics
Email: fmotta@fau.edu
Education
Ph.D. in Mathematics, Colorado State University, 2014
Research Interests
Topological Data Analysis
Machine Learning and Artificial Intelligence
Dynamical Systems
Mathematical Biology
Research Description
I am a mathematician specializing in topological data analysis (TDA), dynamical systems, and mathematical biology. I am interested in every aspect of the expansion of the applicability of TDA to existing machine learning & statistical modeling approaches, especially those concerned with the analysis of data derived from spatiotemporal systems. I thrive on interdisciplinary collaboration & I am involved in both the design, implementation, & application of novel computational data analysis methods to solve problems from multiple scientific domains including synthetic and systems biology, condensed matter physics, and neuroscience. Currently my work involves 1) using topological methods together with machine learning tools to understand principles of protein stability, and 2) modelling the dynamics of host-parasite interactions of Plasmodium, to understand genetic regulatory networks in the causative agent of malaria.
Recent Publications
Mireles James, J. D., Motta, F. C., & Naudot, V. (2024). State Dependent Delay Maps: Numerical Algorithms and Dynamics of Projections.
Experimental Mathematics, 1–24. https://doi.org/10.1080/10586458.2024.2337910
Mishra A. & Motta F.C. (2023). Stability and Machine Learning Applications of Persistent Homology Using the Delaunay‑Rips Complex.
Front. Appl. Math. Stat. 9:1179301. doi: 10.3389/fams.2023.1179301
Motta, F. C., McGoff, K., Moseley, R. C., Cho, C.‑Y., Kelliher, C. M., Smith, L. M., …Haase, S. B. (2023). The Parasite Intraerythrocytic Cycle
and Human Circadian Cycle are Coupled During Malaria Infection. Proceedings of the National Academy of Sciences, 120(24),
e2216522120. doi:10.1073/pnas.2216522120
Cummins, B., Motta, F. C., Moseley, R. C., Deckard, A., Campione, S., Gameiro, M., Gedeon, T., Mischaikow, K., & Haase, S. B. (2022).
Experimental Guidance for Discovering Genetic Networks Through Hypothesis Reduction on Time Series. PLOS Computational Biology,
18(10), 1–31. doi: 10.1371/journal.pcbi.1010145
Motta, F.C., Moseley, R.C., Cummins, B., Deckard, A., & Haase, S. B. (2022) Conservation of Dynamic Characteristics of Transcriptional
Regulatory Elements in Periodic Biological Processes. BMC Bioinformatics 23, 94. doi: 10.1186/s12859‑022‑04627‑9
