SagarmathaIQ started as a weekend exercise: could a probabilistic model — built on public data, zero-sum vote
share distributions, and Monte Carlo simulation — forecast Nepal's 2082 parliamentary elections at the
constituency level?
We are not political scientists or professional forecasters. We are researchers with backgrounds in data
science, statistical modeling, and materials science who share an interest in Nepal and a belief that
transparent, quantitative analysis is more useful than punditry. All methodology and results are published
openly.
The Team
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Dipak Rimal, PhD
Lead Modeller
Dr. Dipak Rimal is a data scientist with 10+ years across academia and industry. Dr. Rimal holds PhD in Physics from the Florida International University, Miami, FL (2014). Training in particle physics taught him to find signal in noisy data — the same approach now drives his work in AI and analytics. Has led interdisciplinary projects across domains from particle collisions to bee colony sizes to student learning patterns, always focused on understanding complex systems and creating measurable impact. For this project, Dipak built the FPTP constituency model, and National PR Model, Monte Carlo simulation pipeline, and all published dashboards.
Dr. Puskar Chapagain received his Ph.D. in Physics from the Texas Christian University (TCU) in 2015. Before joining Southern Arkansas University (SAU) in 2016, he taught physics in Nepal and at North Central Texas College. He currently teaches physical sciences, astronomy, solid state physics, and semiconductor devices at SAU, with a growing research interest in applying AI to physics education. Dr. Chapagain brings 15+ years of combined research and teaching experience to the team.
Dr. Nabraj Bhattarai earned his PhD in Physics from the University of Texas at San Antonio in 2014. Since then, he has built extensive experience in materials science engineering and technology node development in leading semiconductor industry. His work integrates advanced data analytics, machine learning, and deep learning techniques to analyze complex defect mechanisms, support advanced failure analysis, and improve semiconductor process development at leading technology nodes. Dr. Bhattarai contributes rigorous quantitative research methodology and ground-level electoral context for Nepal.