Student Information
Graduate Student
Robotics and Autonomous Systems (Artificial Intelligence)
Ira A Fulton Engineering
Education
MS in Robotics and Autonomous Systems, Artificial Intelligence
Research Interests
Computational Protein Evolution and AI for Genotype–Phenotype Modeling.
My research focuses on large-scale protein foundation models and their application to mutational effect prediction, fitness landscape modeling, and evolutionary trajectory inference. I work on fine-tuning protein language models (e.g., ESM, METL) on deep mutational scanning (DMS) datasets and leveraging transfer learning across protein families to improve generalization across divergent sequence spaces.
I am particularly interested in integrating learned sequence embeddings into evolutionary simulation frameworks to model genotype–phenotype–fitness relationships, and in comparing transformer-based architectures with state-space models (e.g., Mamba) for scalable long-protein modeling.
Research Group
Jiang Lab
Biodesign Institute
Arizona State University
The Jiang Lab develops theoretical and computational frameworks to model mutational variation, genotype–phenotype maps, and phenotypic evolution by integrating machine learning with evolutionary simulations. The lab combines deep mutational scanning, protein modeling, and population genetics to predict the functional and evolutionary consequences of genetic variation.
Publications
- A Comparative Study for Early Diagnosis of Alzheimer’s Disease Using Machine Learning Techniques
Bharathi Malakreddy A., Sri Lakshmi Priya D., Madhumitha V., Tiwari A.
International Conference on Innovative Computing and Communication (ICICC), 2023, pp. 191–201.
- Machine Learning Approach for Diagnosis of Schizophrenia Using EEG Signals
Rajesh I.S., Priya D.S.L., Madhumitha V., Sreenivas S.
International Conference on Machine Learning and Advances in Computing, 2023.
Research Activity
Graduate Research Assistant, Jiang Lab, Biodesign Institute, Arizona State University
- Fine-tune large protein language models (ESM, METL) on deep mutational scanning datasets to predict mutational effects and quantitative phenotypes.
- Perform transfer learning across divergent protein families to improve cross-protein generalization in genotype–phenotype prediction.
- Compare transformer-based architectures with state-space sequence models (Mamba) for modeling long-range dependencies in large protein sequences.
- Extract and analyze learned protein embeddings to construct high-dimensional fitness landscapes.
- Integrate embedding-based genotype–phenotype models into Wright–Fisher evolutionary simulations to infer plausible evolutionary trajectories under stabilizing selection.
- Evaluate models using Pearson correlation, out-of-distribution mutation prediction, lineage divergence tests, and robustness analyses.