Project: Computer Vision for Agritech Assembly LineResponsibilities:- Developing AI-driven machine vision software to efficiently detect and classify agricultural products, enhancing the assembly line's accuracy and productivity.- Implementing real-time performance monitoring tools, ensuring continuous optimization of the vision system.- Researching and implementing explainable AI (XAI) techniques to improve the transparency and interpretability of the computer vision models, enabling better decision-making by offering insights into how AI systems make classifications and predictions.Project: ESG Analytics for Real Estate SustainabilityResponsibilities:- Designing and implementing algorithms to accurately compute and ESG performance metrics for large-scale real estate datasets.- Engineering robust data pipelines to streamline sustainability reporting for real estate portfolios.- Applying predictive modeling techniques to forecast ESG performance, aiding clients in strategic planning and compliance.

Bright Box provides external audits of algorithms. We assess model performance and optimization, but also resilience to external shocks, validation, and compliance with fairness and interpretability regulations.

- Designing and implementing algorithms to accurately compute and ESG performance metrics for large-scale real estate datasets.- Engineering robust data pipelines to streamline sustainability reporting for real estate portfolios.- Applying predictive modeling techniques to forecast ESG performance, aiding clients in strategic planning and compliance.

- Predicting house prices using machine learning- Modelling and forecasting building energy consumption- Formulating data-driven strategies for clients- Automating data processes within the organisation

Graduation Internship at the Financial Crime Analytics team. Thesis subject: Detection of Deepfakes using Deep Learning (Grade: 8.0).

Building an economic forecasting model with user interface.