I am a second-year Ph.D. student in Computer Science & Engineering at the University of Connecticut (UConn). My research focuses on Machine learning and computer vision for large-scale biomedical and biomechanical systems, with emphasis on scalable experimentation using high-performance computing (HPC). Core interests include image segmentation, representation learning, semi-supervised learning, and data-driven surrogate modeling for computationally expensive scientific pipelines.
Currently, I serve as a Graduate Research Assistant at the University of Connecticut (UConn), contributing to data-driven biomechanics and machine learning projects in collaboration with research teams across UConn and UConn Health. GPA: 3.8. Qualifying Exam: passed.
⭐ Selected Projects
Machine Learning for Biomechanics
Developed machine learning models (Regression, SVR, Decision Trees, Random Forests, MLPs) to predict knee joint and muscle forces. Applied PCA and Neighborhood Component Analysis for dimensionality reduction, evaluated cross-subject generalization using RMSE, MAE, and R², and analyzed accuracy-latency-scalability trade-offs. Led to a first-author manuscript under review (npj Digital Medicine) and a review manuscript submitted to Communications AI.
Microscopy and Probabilistic Segmentation
Conducted large-scale microscopy image segmentation using CPU/GPU HPC clusters. Reproduced deterministic baselines, implemented standardized evaluation metrics (Precision, Recall, F1, Dice, AP), and developed probabilistic workflows to assess robustness and diversity. Automated CSV-based metric aggregation and generation of publication-quality figures. Manuscript in preparation.
HPC Automation and Scientific Data Post-Processing
Developed Python-based automation pipelines for HPC job submission and post-processing of large-scale molecular dynamics trajectories and imaging datasets. Performed spatial and statistical analyses and organized reproducible tables, figures, and summaries for collaborative research.
📚 Latest Publications
- [In Press] Sharifi L, Ghasemi J, et al. “Impact of Salt on AAV8 Capsid Aggregation with Single-Stranded DNA: Insights from Coarse-Grained Molecular Dynamics Simulations.” Int J Pharm, 2025.
- [Under Review] Ghasemi J, et al. “Machine Learning for Gait Analysis in Rehabilitation: A Scoping Review of Models, Modalities, and Clinical Applications.” npj Digital Medicine.
- [Under Review] Ghasemi J, et al. “Toward Real-Time Knee Joint Force Prediction via Machine Learning: Outpacing Traditional Simulations.” Nature Bioengineering.
- [Under Review] Ghasemi J, Bradford P. “Survey of Dynamic Circuit Breakers for Resilient Microservices.” CMOC Journal.
🔬 Interests
Data-Driven Modeling | Machine Learning | Biomechanics | Packaging Dynamics (Shock/Vibration) | Sensor Data Analytics | Risk & Damage Prediction | HPC & Reproducible Pipelines | Microservices Resilience | IoT