Accomplished, forward-thinking Lead Healthcare Data Scientist with 15 years of experience delivering predictive & prescriptive analytics, health outcomes research, machine learning solutions, and data visualization & reporting systems that drive evidence-based decision-making, innovation, improved patient outcomes, and greater efficiency across healthcare systems. Committed to advancing health equity and responsible AI through rigorous data science, statistical modeling, and health economics research. Adept at managing multiple projects in fast-paced environments with proven ability to lead teams, collaborate across disciplines, and translate complex analytical results into actionable insights for clinicians, policymakers, and stakeholders.

• Big-picture strategist with strong execution skills, able to distill complex healthcare analytics into actionable insights for data-driven and evidence-based decision-making.
• Proven track record in leading interdisciplinary healthcare analytics teams, building stakeholder relationships, and mentoring junior researchers.
• Programming proficiencies: Python, R, SQL, VBA, MATLAB, ARENA, and ASP.NET with foundational knowledge in Java.
• Ethical AI & Responsible Data Science advocate: fairness, transparency, interpretability in healthcare AI implementation.
• Effective communicator and collaborator with clinicians, policymakers, and academic partners to translate complex data science results into actionable healthcare strategies.
• Published in leading journals and conferences on health informatics, operations management, AI, and outcomes research. 
• Skilled in Fair Machine Learning & AI: DALEX, Model Studio, Fairness, TensorFlow, PyTorch, Scikit-learn, XGBoost, SHAP, and fairness-aware ML frameworks.
• Polished presenter, educator, and facilitator, experienced in teaching R/Python, health analytics, and data-driven decision-making to diverse audiences

My Research Interest:

My research interests lie at the interface of Data Sciences (particularly, responsible data science) and Operations Research, focusing on healthcare applications. I explore important and contemporary problems characterized by underlying data that must be clearly understood before any subsequent decision-making process. I am a strong proponent of data-driven analytical models and their applications in healthcare Operations Management and clinical decision-making.

My research interests can be categorized as follows:

a. Methodology

• Data Science/Analytics: Artificial Intelligence (AI)/Machine Learning (ML), and Statistical Modeling for descriptive, predictive, and prescriptive analytics
• Fair AI, Responsible Data Science
• Data-Driven Optimization
• Trajectory Analytics

b. Context