Implementing AI-driven automation in technical infrastructure, enhancing personalized mental health solutions.

Working on my master’s thesis, on predicting infant mortality using Machine Learning. My research involves analyzing NHS data to uncover causal pathways (used techniques like Causal Bayesian Networks, Granger Causality, and DAGs) and inform decision making. Used techniques like SHAP for transparent predictions

I developed C++ and Python frameworks to enhance the Adobe Content Synchronizer for Adobe Cloud, streamlining asset synchronization from Desktop to Cloud. I led the creation of testing frameworks, significantly improving CPU performance and reliability by up to 40%, while reducing memory leaks by 25%. Additionally, I developed internal tools for seamless pre-production deployment, enhancing issue analysis and overall system efficiency.

I conducted performance, longevity, and stress testing on weekly, fortnightly, and monthly schedules, resulting in a 25% increase in product resiliency. I implemented CI/CD services for longevity, performance, and stress testing, streamlining issue analysis and debugging for in-development features. Collaborating with multiple teams, I deployed CI/CD services, leading to a threefold increase in the rate of issue discovery.

- Worked under the Blockchain Developer Team- Learnt the thick and thins of Distributed Ledger Technology- Got an exposure in Hyperledger Fabric and Composer