Publications

Twenty-five years after the Bhuj earthquake, India has strengthened its disaster systems—but seismic risk remains high. Artificial intelligence offers a decisive shift from reactive response to anticipatory risk management through real-time seismic analytics, predictive damage modelling, and smarter emergency logistics. With the India AI Mission providing national infrastructure and coordination, India can embed AI into disaster governance at scale—building cities, systems, and institutions resilient to the earthquakes of the future.

This study examines the readiness of the inhabitants of Bengaluru, a metropolitan city located in the State of Karnataka in India, to shift to electricity-based residential cooking. The study also touches upon the critical knowledge gaps regarding the energy transition from an established LPG-based ecosystem to the new electric cooking ecosystem. Based on a household survey conducted in Bengaluru focusing on low-income and medium-income households, the study briefly discusses the complex interplay between culinary habits and sustainable practices that would dictate the transition readiness in the residential cooking sector on a mass scale.

This study discusses a methodological framework in conjunction with the existing probability-based risk metrics for assessing the resource adequacy of the electricity generation mix, considering the generation capacities of different sources, their average availability levels, and the possible outages that these sources may suffer from. The simulations show that the available capacity decreases with an increasing number of discrete risk events, which limits the capability of the power system to meet the demand. For a 2-day test simulation (considering a loss of load expectation value of 2 event-days), the loss of load event is found to be 4 events, and the expected unreserved energy is estimated to be 1400 MWh over the predefined, representative 2-day period.

This study discusses a methodological framework in conjunction with the existing probability-based risk metrics for assessing the resource adequacy of the electricity generation mix, considering the generation capacities of different sources, their average availability levels, and the possible outages that these sources may suffer from. The simulations show that the available capacity decreases with an increasing number of discrete risk events, which limits the capability of the power system to meet the demand. For a 2-day test simulation (considering a loss of load expectation value of 2 event-days), the loss of load event is found to be 4 events, and the expected unreserved energy is estimated to be 1400 MWh over the predefined, representative 2-day period.

This study discusses a methodological framework in conjunction with the existing probability-based risk metrics for assessing the resource adequacy of the electricity generation mix, considering the generation capacities of different sources, their average availability levels, and the possible outages that these sources may suffer from. The simulations show that the available capacity decreases with an increasing number of discrete risk events, which limits the capability of the power system to meet the demand. For a 2-day test simulation (considering a loss of load expectation value of 2 event-days), the loss of load event is found to be 4 events, and the expected unreserved energy is estimated to be 1400 MWh over the predefined, representative 2-day period.

It’s taken for granted that every researcher, after their PhD, moves to academics. Very few are interested in moving into industry.