PhD student in Computer Science and Engineering at the University of Connecticut, with a background in Electrical Engineering. My work combines machine learning, computational modeling, and kinematics for soft and deformable systems.
- Geometric and kinematic modeling of soft and shape-changing systems
- Machine learning for physical, biomedical, and imaging data
- Scientific computing, numerical analysis, and reproducible research workflows
- Data-driven modeling of deformation and time-varying behavior
I am developing computational methods to quantify and model shape deformation in soft robotic systems. The current direction connects analytical centerline kinematics with parameter estimation from images, experiments, and simulations.
- Soft Robot Kinematics — tested planar centerline reconstruction and deformation metrics for synthetic curvature fields.
- Jahan BioOmics — validated DIA proteomics analysis with imputation, PCA, Welch tests, FDR correction, and pathway enrichment.
- Segmentation Comparison Panels — configurable microscopy segmentation comparison panels with synthetic tests.
- DIA Proteomics Preprocessing in R — reproducible filtering, log2 transformation, and per-sample median normalization.
L. Sharifi, J. Ghasemi, et al., “Impact of salt on AAV8 capsid aggregation with single-stranded DNA: insights from coarse-grained molecular dynamics simulations,” International Journal of Pharmaceutics, vol. 681, 125867, 2025. doi:10.1016/j.ijpharm.2025.125867
Python, R, NumPy, pandas, SciPy, scikit-learn, Matplotlib, Plotly, Streamlit, and GitHub Actions.