Who We Are

SagarmathaIQ is an independent hobby project run by three Nepali physicists. In our spare time, we apply data science, statistical modeling, and AI to questions about Nepal using publicly available data and transparent methods.

The Project

SagarmathaIQ began with a simple question: could a probabilistic model forecast Nepal's 2082 parliamentary elections using only public data and transparent assumptions?

Since then, the project has expanded into a broader effort to explore Nepal-related questions through quantitative analysis. Elections, economics, public policy, and other topics are all fair game if they are interesting and data can help illuminate them.

We are not funded, affiliated, or advocating for any agenda. We build models, analyze data, publish our methods, and share what we learn. Everything is done independently as a weekend and leisure-time project.

Use of AI

We use modern AI tools to assist with research, coding, data processing, and drafting. AI-generated outputs are treated as inputs to our workflow, not as authoritative sources. All analysis, modeling choices, and published results are reviewed by us before publication.

We may occasionally publish AI-assisted analyses or experiments. When we do, we will make a reasonable effort to document the methodology, limitations, and the role AI played in the work.

The Team

Dipak Rimal, PhD

Lead Modeller

Data scientist with 10+ years across academia and industry. PhD in Physics, Florida International University (2014). Particle-physics training in finding signal in noisy data now drives his work in AI and analytics — from particle collisions to bee colony sizes. Built the FPTP and PR models, the Monte Carlo pipeline, and every published dashboard.

Puskar Chapagain, PhD

Statistical Modelling

Research scientist and educator with 15+ years in academia and quantitative research. PhD in Physics, Texas Christian University (2015). His work spans physics education and nanotechnology research — both demanding precision and statistical rigor from noisy experimental data. Anchors the statistical modeling of Nepal's electoral and socioeconomic landscapes.

Nabraj Bhattarai, PhD

Research & Analysis

PhD in Physics, University of Texas at San Antonio (2014). Built a career in materials science and semiconductor technology development, applying data analytics, machine learning, and deep learning to complex defect analysis and failure diagnostics at leading technology nodes. Contributes rigorous quantitative methodology and ground-level electoral context for Nepal.

Contact

Questions, corrections, or feedback on the model are welcome.

Email us