SATISH KUMAR PILLAI
Mississauga
Summary
M.S. Data Science candidate with 8+ years of experience as a Registered Nurse and hands-on expertise in machine learning, deep learning, and SQL. Strong background in diagnostic imaging workflows, patient data systems (EPIC, MEDITECH, and healthcare analytics). Proficient in Python, R, and advanced ML algorithms, with academic projects spanning regression, classification, clustering, neural networks, and computer vision. Eager to apply ML to healthcare challenges in real-world, research-driven environments.
Overview
1
1
Certification
Work History
Registered Nurse
- Extensive experience with patient care, diagnostic imaging, and healthcare IT systems.
- Ensured data quality, compliance, and accuracy in EPIC and MEDITECH workflows.
- Collaborated across clinical and technical teams to integrate healthcare data for decision-making.
- 8+ years
Education
M.S. - Data Science
Eastern University
05-2026
Bachelor of Science - Nursing
Rajiv Gandhi University of Health Sciences
India06-2016
Skills
- Python, R, NumPy, pandas, SQL, matplotlib, seaborn, scikit-learn, Regression, classification, clustering, SVM, Random Forest, Gradient Boosting, XGBoost, anomaly detection, dimensionality reduction, PCA, t-SNE, Neural networks, CNNs, transfer learning, ResNet, YOLO, R-CNN, Mask R-CNN, face detection/recognition, object detection, SQL joins, subqueries, relational schemas, transactions, regression in SQL, Diagnostic imaging knowledge, EPIC, MEDITECH, healthcare data privacy & compliance, Jupyter Notebook, Google Colab, Git/GitHub, Flask, logistic regression, GridSearchCV, R², RMSE, Keras, TensorFlow, Multi-instance learning, Self-supervised learning, contrastive learning, Explainable AI (XAI), Time-series, waveform analysis
Languages
English
Full Professional
Certification
- Programming for Everybody (Python)
- University of Michigan (Coursera)
Timeline
Registered Nurse
M.S. - Data Science
Eastern University
Bachelor of Science - Nursing
Rajiv Gandhi University of Health Sciences
Research Interests
- Multi-instance learning for aggregating frame-level to patient-level predictions in medical imaging.
- Self-supervised and contrastive learning for representation learning in limited-label healthcare datasets.
- Explainable AI (XAI) for model interpretability in diagnostic imaging.
- Time-series and waveform analysis for clinical signal data (e.g., ultrasound, ECG).
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