Implementation of software utilizing Machine Learning (ML) and Artificial Intelligence (AI) for advanced data analysis, enhancing product capabilities and insights.Developed software and cloud infrastructure architecture for various products.Managed software development projects by defining requirements, goals, and timelines aligned with the product development roadmap.Ensured secure and scalable data processing through a deep understanding of cloud storage, API integration, and data lifecycle management on cloud platforms.Collaborated on the development of automated systems that leverage cloud infrastructure for real-time processing and analysis, enhancing the efficiency of geospatial data management.Designed forward-looking strategies for upcoming development initiatives, considering overarching objectives and resource allocation.Collaborated with external stakeholders to integrate new tools and systems.Key outcomes:1. Automated tile-based preprocessing system in cloud empowering organization to easily handle and deliver any EO data project.2. The system was improved the consecutive year reducing the processing time by 70%.3. Optimized storage by utilizing S3 more effectively, resulting in reduced storage costs by 30%.4. Automated biomonitoring system was implemented using AWS Lambda with minimal cost.5. Developed a Python library for efficient geospatial data processing, integrating CREODIAS and Sentinel-2, with advanced GIS tasks and robust error handling, significantly reducing development time for large-scale projects and encouraged colleagues to use it.6. Promoted using LLMs and GUI-based development environment in the organization, significantly reducing development time.

As a Bootcamp Instructor, I had the opportunity to teach and mentor aspiring data scientists and machine learning enthusiasts through a comprehensive curriculum. My role involved delivering in-depth, hands-on sessions that covered a broad range of topics critical to mastering data science and machine learning: Machine Learning Classification Techniques: Led sessions on classification models, including Decision Trees, Random Forests, Adaboost, Gradient Boosting, and Support Vector Machines. Additionally, I focused on evaluating classification models and introduced clustering methodologies. Neural Networks & Deep Learning: Guided students through the fundamentals of neural networks, from basic perceptrons to advanced architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Residual Neural Networks. Emphasized the importance of backpropagation, regularization, and avoiding overfitting, while also introducing popular deep learning frameworks. Time Series Forecasting: Conducted sessions on time series analysis, covering essential techniques such as ARIMA models and machine learning applications for time series forecasting. Discussed the unique challenges of time series prediction and introduced foundational cloud technologies for scalable solutions. Data Science Implementations & Best Practices: Provided insights into web scraping techniques, essential libraries for data scientists, and the intricacies of version control in data science projects. Highlighted common pitfalls in implementing data-driven solutions and shared strategies to avoid them.Throughout the bootcamp, I conducted surveys to gauge student progress and satisfaction, ensuring that the learning experience was both impactful and aligned with industry standards.

My team and I crafted content and teaching strategies focused on guiding students' mental activities towards intentional thinking rather than mere rote memorization. We aimed to foster a deep love of learning among students, nurturing their curiosity, creativity, and critical thinking skills. Most importantly, we encouraged them to be open to revising or refuting their existing ideas when presented with compelling evidence.

Numerical simulation of two way fluid structure interaction in Elasto-hydrodynamic lubrication. An existing solver for line contact is extended to simulate point contact. Effect of the neglected inertial effects on the pressure predictions is investigated.

Investigation of feasibility of new module in vertical washing machine.Study of characteristics of an existing system in a washing machine that removes water post-wash and validate a newly designed system.

Matlab codes for visualisation and data interpretation of results from simulation of solitary shock waves in granular system. Modification of the code to include restitution loses and the study their influence on mitigation of shock waves.