Research Areas:
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Collective Intelligence:
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I study how animals coordinate and make decisions in groups, examining patterns like collective movement, escape dynamics, and synchrony in species such as fireflies and birds.
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My research delves into computational and theoretical models that drive swarm intelligence, investigating how animal behavior can inspire solutions to complex environmental and societal challenges.
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I use multi-modal data collection (UAVs, sensors, GPS) and network analysis to uncover the principles behind collective decision-making in natural systems.
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Eco-Informatics:
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I apply AI, machine learning, and data science to study animal populations, habitat quality, and community ecology. My work involves automating wildlife monitoring with advanced tools like drones, remote sensing, and deep learning.
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I focus on building models that use animal behavior as early warning indicators for environmental stress, such as habitat degradation or the impacts of climate change.
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By integrating ecological theory with computational methods, I strive to develop scalable, data-driven solutions for wildlife conservation and ecosystem management.
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Through this interdisciplinary approach, my research not only pushes the boundaries of collective behavior and computational ecology but also contributes to real-world applications that address biodiversity and environmental sustainability challenges.
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Join me in exploring the synergy between collective intelligence and eco-informatics as we work toward a deeper understanding of the natural world and its future.
