Due to the nature of mathematical research, all authors are considered on equal footing and are always listed in alphabetical order, i.e., there is no 'first author'. This convention contrasts to my collaborative work with domain scientists, where a 'first author' naming convention exists in manuscript writing.
S.Moon, D.Maguire, A.Spannaus, J.Tuccillo, M.Alam, S.Seal, J.Gounley, H.Hanson, “LLM- powered reasoning in agent-based modeling”, submitted: Journal of the Royal Society Interface, 2026.
A. Shivanna, A. Spannaus, J. Tschida, J. Gounley, H. Hanson, “Ensuring Equity in AI Healthcare: A Study of Racial Bias in Cancer Site Classification Models”, JCO Clinical Cancer Informatics, 2026. https://doi.org/10.1200/CCI-25-00250
A. Deas, A. Spannaus, D. Maguire, J. Trafton, A. Kapadia, V. Maroulas, “Investigating the importance of social vulnerability in opioid-related mortality across the United States”, Informatics in Medicine Unlocked, 2026. https://doi-org.ornl.idm.oclc.org/10.1016/j.imu.2026.101759
M. Alam, S. Seal, J. Tuccillo, L. McBride, R. Mishra, A. Spannaus, S. Moon, J. Nutaro, J. Gounley, H. Hanson, “ENABLE: A Hybrid CPU-GPU Framework for Data-Driven Agent-Based Population Health Simulations”, accepted: International Journal on High Performance Computing Applications.
S. Dhaubhadel, J. Mohd-Yusof, B. H. McMahon, T. Estrada, K. Ganguly, A. Spannaus, J. Gounley, X. Wu, E. B. Durbin, H. Hanson, T. Bhattacharya, “Global explainability of a deep abstaining classifier for cancer pathology reports”, submitted with revisions: IEEE Journal of Biomedical and Health Informatics.
K.Eversman, A.Spannaus, R.Campbell, L.Pearcy J.Trafton, A.Kapadia, W.C.Strickland, “A modeling study of the opioid epidemic for vulnerable communities in Knoxville, Tennessee”, submitted: BMC Public Health, 2025.
A.Spannaus, S.Moon, J.Gounley, H.Hanson, “Data Assimilation for Robust UQ Within Agent- Based Simulation on HPC Systems”, PASC ’25: Proceedings of the Platform for Advanced Scientific Computing Conference, 2025. https://doi.org/10.1145/3732775.3733582
A. Shivanna, H. Hanson, J. Gounley, A. Spannaus, “Multi-Label Classification with Constraint-Based Learning for Hierarchical Consistency”, CAI 25 – IEEE Conference on Artificial Intelligence, 2025. https://doi.org/10.1109/CAI64502.2025.00098
A. Deas, A. Spannaus, H. Fernando, A. Kapadia, J. Trafton, V. Maroulas, “Investigating opioid vulner- ability profiles through a spatial Kalman filter: insights from social and environmental factors”, BMC Public Health, 2025. https://doi.org/10.1186/s12889-025-23044-0
A. Spannaus, H. Hanson, L. Penberthy, G. Tourassi, “Topological Interpretability for Deep Learning”, PASC ’24: Proceedings of the Platform for Advanced Scientific Computing Conference, 2024. https://doi.org/10.1145/3659914.3659935
A. Spannaus, et al., “FrESCO: Framework for Exploring Scalable Computational Oncology.” Journal of Open Source Software, 2023. https://joss.theoj.org/papers/10.21105/joss.05345.
A. Peluso, I. Danciu, H. Yoon, A. Spannaus, J. Yusof, T. Bhattacharya, N. Schaefferkoetter, E. Durbin, X. Wu, A. Stroup, J. Doherty, S. Schwartz, C. Wiggins, L. Coyle, L. Penberthy, G. Tourassi, S. Gao, “Deep learning uncertainty quantification for clinical text classification”, Journal of Biomedical Informatics, 2023. https://doi.org/10.1016/j.jbi.2023.104576
A. Spannaus, T. Papamarkou, S. Erwin, and J. B. Christian, Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling. Scientific Reports, 2022. https://doi.org/10.1038/s41598-022-14979-0
A. Spannaus, K. J. H. Law, D. J. Keffer, V. Maroulas, Variational Atomic Sequencing. In preparation.
A. Spannaus et al., Topological Interpretation of Deep Learning Models, Computational Approaches for Cancer Workshop, SC21, 2021 – accepted.
A. Spannaus, K. J. H. Law, D. J. Keffer, V. Maroulas, et al., Materials Fingerprinting Classification. Computer Physics Communications, 2021. https://doi.org/10.1016/j.cpc.2021.108019.
V. Maroulas, C. Micucci, A. Spannaus, A Stable Cardinality Distance for Topological Classification, Advances in Data Analysis and Classification. 2019. https://doi.org/10.1007/s11634-019-00378-3
A. Spannaus, K. J. H. Law, D. J. Keffer, V. Maroulas, Bayesian Point Set Registration, 2017 MATRIX Annals, Editors: David R. Wood, Jan de Gier, Cheryl E. Praeger, Terence Tao. MATRIX Book Series, Volume 2, Springer. https://doi.org/10.1007/978-3-030-04161-8_8.
A. Spannaus, Testing for New Better than Used: Oxygen Monitoring at the Spallation Neutron Source as a Test Case, SIAM Undergraduate Research Online. http://dx.doi.org/10.1137/15S014010.
Invited Talks
Data Assimilation for Robust UQ Within Agent-Based Simulation on HPC Systems, PASC 25, June 2025.
Topological Learning in Health Sciences, SIAM Mathematics of Data Science, October 2024.
Topological Interpretability for Deep Learning, PASC24, June 2024.
Enhancing Cancer Registry Data with MOSSAIC: Deep Learning Solutions for Reportability Screening, NAACCR Annual Conference, June 2024.
Topological Interpretability for Deep Learning, NCI Cancer Moonshot series, August 2023.
Topological Interpretability for Deep Learning, Monterey Data Conference, August 2023.
FrESCO: Framework for Exploring Scalable Computational Oncology, NAACCR Annual Conference, June 2023.
University of Tennessee Department of Mathematics Data Science Seminar, November 2022.
Privacy Considerations within Biomedical Deep Learning, Digital Futures Workshop: Privacy Preserving Data Analysis, Manchester, UK May 2022.
University of Tennessee Bredesen Center Energy Science and Engineering Colloquium, April 2022.
Topological Interpretation of Deep Learning Models, Computational Approaches for Cancer Workshop, SC21, November 2021.
Topological Interpretation of Deep Learning Models, Oak Ridge Postdoctoral Research Symposium, July 2021.
Atomic Sequencing for High-Entropy Alloys, SIAM Mathematical Aspects of Materials Science, May 2021.
University of Tennessee Data Science Colloquium, October 2020.
Topological Classification of Crystal Structures through Statistical Learning. Oak Ridge Chapter of the ASM, Knoxville, TN, October 2019.
Uncertainty Quantification Minisymposium, SIAM Annual Meeting, Portland, OR, July 2018.
Data Analysis and Workflows for Structural Descriptions of Complex Materials, SIAM Conference on Computational Science and Engineering, Atlanta, GA, February 2017.
Contributed Talks
Materials Fingerprinting Classification. Applied Topology: Theory and Applications, AMS Southeast Sectional, Gainesville, FL, November 2019.
Accelerator Reliability Workshop, Knoxville, TN, April 2015.
UT Undergraduate Math Conference, Knoxville, TN, April 2015.