Leveraged stochastic processes on networks using differential equations to determine disease susceptibility rates and recovery probabilities, employing sophisticated mathematical and epidemiological models for better analysis and modelling of infectious diseases spread.

Developed a Hybrid Feature Selection Algoirthm to improve the performance of machine learning model. Combination of filter-wrapper techniques with feature clustering using a key <b>cluster quality metric - Rand Index</b> to achieve optimal feature selection while maintaining computational efficiency. Further reduced time complexity and improving computational effiency by employing multi-processing and parallel programming methods. Making more practical for real-world predictive modelling.

A state-of-the-art payload system was designed for a rocket launched at an estimated speed of 4,600 feet per second. The purpose of this system is to execute a range of image processing techniques, all controlled by commands received from a Software-Defined Radio (SDR) Receiver. Custom functions were developed to efficiently parse these commands. The heart of this system is a Raspberry Pi microcontroller, which serves as the main processing unit. The result is an advanced camera system that seamlessly integrates radio frequency capabilities to capture and process images during the rocket's mission.

Developed algorithms for automated scheduling and coordination of advanced weapon systems in naval warfare by integrating Markov Chains and Naïve Bayesian Classification. The methods involve using a Markov chain to track variables and predict future states, and employing supervised learning and statistical classification to classify data and optimize weapon pairings.