Overview

I am an incoming MSc Machine Learning student at the University of Tübingen and an ELIZA Master’s Scholarship recipient. My background as an Associate ML Scientist - 2 at the Wadhwani Institute for Artificial Intelligence centered on computer vision and agricultural AI. My research focuses on developing innovative deep learning solutions for crop monitoring, pest detection, and yield estimation, applying my experience in model optimization and real-world AI deployment.

My research interests lie in developing human-centric, reliable, and ethical AI for social impact. I translate complex data into practical tools, demonstrating a strong dedication to impactful AI across diverse domains.

My hands-on experience in machine learning, particularly computer vision, includes developing medical AI for Gall Bladder Cancer detection and contributing to the national deployment of the CottonAce system for crop monitoring. I also apply expertise in optimizing ML pipelines and exploring principles for robust AI, including uncertainty-aware inference.

My core research interests are advancing explainable AI (XAI) and efficient edge deployment models. I aim to bridge theoretical AI with practical implementation, enhancing interpretability and performance in real-world applications within medicine, life sciences, and agricultural technologies.

Research Interests

• AI Security & Trustworthy AI: Developing robust and secure AI systems
• Computer Vision: Advanced deep learning for image analysis
• Medical Image AI: Healthcare applications and diagnostic assistance
• AI for Social Good: Agricultural technology and assistive technologies
• Model Explainability: Interpretable AI for real-world deployment