Bias-Aware and Explainable AI for Social and Environmental Data Analytics

I am a Computer Science researcher and lecturer specializing in AI for social impact, with extensive experience in bias-aware and interpretable machine learning, natural language processing, and large-scale data analytics. My work includes extracting actionable insights from social media and environmental datasets, modeling social and policy trends, and designing AI frameworks that emphasize fairness, transparency, and accountability.

I am seeking collaboration with research groups, supervisors, or institutions interested in advancing AI for societal applications. Specifically, I aim to contribute to projects focused on explainable AI, predictive modeling for social and environmental outcomes, and development of ethical AI frameworks that guide policy and decision-making. I offer expertise in:

• Bias-aware machine learning and fairness evaluation

• Explainable AI and interpretability methods (e.g., LIME, SHAP)

• NLP, sentiment analysis, and topic modeling on social data

• Data engineering and large-scale analytics pipelines

This cooperation can lead to MSCA postdoctoral projects, joint research initiatives, and impactful publications addressing societal challenges using AI.