Research
Our research focuses on context-specific, high-altitude science that is often overlooked by global models. All of our datasets are open to the public.
Atmospheric Science
From the Terai at 60m to the high Himalaya above 8,000m, Nepal's air is almost entirely unmeasured. Pollution from the Indo-Gangetic Plain crosses these mountains every winter. We don't know what reaches altitude, how it changes on the way up, or what it does to the people and ecosystems living there.
Climate-Responsive Passive Solar
Heating a school at 4,000m without imported fuel means designing for this altitude, this sun angle, these materials. The empirical thermal data for this doesn't exist anywhere in Nepal. Crystal Mountain School in Dolpo has 20 sensors embedded in its structure during construction — the first long-term building thermal dataset at high Himalayan altitude.
Cosmic Ray Muon Physics
Muon flux — cosmic ray particles that shower through the atmosphere and reach the ground — rises sharply with altitude. Nepal's altitude range is the most extreme on Earth and has never been systematically measured for muon flux. HICS is building the detector array to do it.
Systematic measurement of cosmic ray muon flux across Nepal's altitude gradient — 60 m to 5,000+ m — using custom scintillator-SiPM detectors.
PlannedNepal's contribution to the Global Meteor Network. All-sky camera observations, fireball trajectory triangulation, and spectroscopy from Himalayan altitudes.
PlannedDense MEMS seismometer networks across the Kathmandu Valley. Subsurface imaging, site response characterisation, and earthquake early warning feasibility.
PlannedMonitoring of Himalayan glaciers relevant to regional water security. Muon tomography for non-invasive ice-bedrock imaging, black carbon deposition measurement, and glacier mass balance studies — contributing to GLOF risk assessment.
PlannedSystematic documentation of indigenous astronomical and environmental knowledge, cross-validated with instrument data, with communities as co-owners of results.
PlannedMachine learning tools for automated analysis of environmental time-series data. Published openly for use by any institution globally.
PlannedAll research outputs — publications, datasets, instrument designs — are published openly. Data is freely downloadable. Code and designs are on GitHub.