Ashley Babjac

Artificial intelligence – ERIC QA TEST

PhD Computer Science
University of Tennessee, Knoxville (2024)

Bachelors of Statistics
University of Tennessee Knoxville (2020)

Associates of Science
Dallas County Community College (2017)

 

Artificial intelligence and machine learning
Computational biology
Applied deep learning (CNNs, RNNs, Transformers)
Explainable AI
Generative modeling
Multi-agent systems
Bioinformatics

CITI Program Certification – Biomedical Responsible Conduct of Research (2023 – 2028)

DARPA Project Award (2025)>
Alexander von Humboldt Fellowship (2024)
NSF GRFP (2022-2025)
Gonzales Outstanding Research Award, University of Tennessee, Knoxville (2024)
Gonzales Outstanding Teaching Award, University of Tennessee, Knoxville (2022; finalist in 2021, 2023, 2024)

*Babjac, A. and *Oduwole I., Royalty T.M., Hibbs M., Lloyd K.G., Emrich S.J. & Steen A.D., 2025. Functional genomic signatures predict microbial culturability across the tree of life. BioRXiv.

Babjac, A., & Emrich, S. J. (2024, December). CodonT5: A Multi-task Codon Language Model to Perform Generative Codon Optimization. In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 12-18). IEEE.

Golob, J.L., Oskotsky, T.T., Tang, A.S., Roldan, A., Chung, V., Ha, C.W., Wong, R.J., Flynn, K.J., Chai, R., Dubin, C. and Parraga-Leo, A., 2024. Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research. Cell Reports Medicine, 5(1).

Rafi, A.M., Nogina, D., Penzar, D., Lee, D., Lee, D., Kim, N., Kim, S., Kim, D., Shin, Y., Kwak, I.Y. and Meshcheryakov, G., 2024. Evaluation and optimization of sequence-based gene regulatory deep learning models. bioRxiv, pp.2023-04.

*Babjac, A., and *Queen, O., Barhorst, S. P., Kalhor, K., Steen, A. D., & Emrich, S. J. (2023, December). Adapting Protein Language Models for Explainable Fine-Grained Evolutionary Pattern Discovery. In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2609-2616). IEEE.

Babjac, A., Zhiziu Lu, and Emrich, S.J., 2023, August. CodonBERT: Using BERT for Sentiment Analysis to Better Predict Genes with Low Expression. In Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (pp. 1-7).

Erawijantari, P.P., Kartal, E., Liñares-Blanco, J., Laajala, T.D., Feldman, L.E., Challenge, T.F.M.D., Carmona-Saez, P., Shigdel, R., Claesson, M.J., Bertelsen, R.J. and Gomez-Cabrero, D., 2023. Microbiome-based risk prediction in incident heart failure: a community challenge. medRxiv.

Babjac, A., Royalty, T., Steen, A.D. and Emrich, S.J., 2022, August. A Comparison of Dimensionality Reduction Methods for Large Biological Data. In Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (pp. 1-7).

Babjac, A., Li, J. and Emrich, S., 2021, December. Fine-Grained Synonymous Codon Usage Patterns and their Potential Role in Functional Protein Production. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2187-2193). IEEE.

 

 

My research focuses on machine learning and artificial intelligence for genomics and healthcare data science, with an emphasis on interpretable and generative deep learning models.