PhD Candidate · EECS, Penn State
Srijit Mukherjee
Disease-informed, interpretable machine learning for biomedical image analysis, built for clinics with scarce, noisy, imperfect data.
2026 Dr. Nirmal K Bose Dissertation Excellence Award, Electrical Engineering Department, Penn State. Dissertation: Hydrocephalic Etiology Guided Low-Field MRI Analysis.
Research
My research focuses on developing disease-informed and interpretable machine learning and statistical methods for biomedical image analysis, particularly in clinical settings with scarce, noisy, and imperfect data. I incorporate disease etiology, anatomical structure, geometry, and multi-modal information directly into computational models to achieve a balance of performance, robustness, and interpretability.
My current research focuses on neonatal hydrocephalus diagnosis and quantitative analysis from low-field MRI, generalizing the framework to histopathology disease grading and biomedical signal (ICP, ECG) analysis.
I mentored two Master’s and two Ph.D. students on biomedical AI projects involving hemi-brain growth analysis, high-field MRI/CT segmentation, histopathological disease grading (DBIBD-Net), and intracranial-pressure (ICP) analysis, guiding them in problem formulation, literature review, methodology, experimentation, and technical communication. I also contributed to NIH and NSF proposals through research framing, methodological design, and technical writing.
- Computer Vision
- Medical Image Analysis
- Multimodal AI
- Interpretable Machine Learning
- Shape Analysis
- Biomedical Signal Analysis
Penn State
- Program
- PhD Candidate, 4th year, EECS Department
- Group
- Information Processing & Algorithms Laboratory
- Advisors
- Vishal Monga (Penn State), Steven Schiff (NIH)
