Burooj Ghani

My research focuses on developing robust machine learning methods for biodiversity monitoring, with a particular emphasis on sound-based approaches. I aim at creating scalable, field-deployable solutions for detecting and classifying animal vocalizations, enabling real-time and high-throughput ecological assessments. My broader vision is to contribute to the development of multimodal and multi-scale frameworks that integrate diverse data sources for a more holistic understanding of ecosystems. I obtained a master’s and a PhD in Computer Science from the University of Göttingen in Germany, where I ventured into bioacoustics, focusing on bird species classification, the impact of species selection on classification outcomes, and algorithmic analysis of intra-species song variations.

My current work aims to bridge AI and ecological conservation by advancing computational tools that can be effectively applied in real-world biodiversity monitoring. This has the potential to greatly benefit wildlife experts, conservation biologists and ecologists by providing automated tools for long-term environmental monitoring. The central focus of my current work lies in processing extensive datasets of environmental sounds, enabling the extraction of valuable insights into the hidden world of animal communication. In nature, while many animals are visually elusive, their vocalizations offer a wealth of information about their habitats, seasonal changes, and interactions. My goal is to create automated processes that accelerate and streamline the analysis, ultimately contributing to the protection and conservation of our planet’s diverse species and ecosystems.