Professional Highlights

My research focuses on leveraging AI for computer vision applications, particularly in real-time snow detection and synthetic data generation. My work has been published at multiple conferences, including MWAIS, AIMLA, and CADSCOM, where my paper was recognized as best paper and fast-tracked for journal publication.

Current projects include federated learning for mobile AI applications and integrating spatial attention mechanisms into deep learning architectures. These efforts aim to enhance the efficiency and accuracy of AI models deployed in real-world settings.

I have actively participated in data science hackathons, achieving top placements in competitions such as Data Derby and MUDAC. These events have strengthened my expertise in predictive modeling, time series analysis, and data visualization under real-time constraints.

These competitions have not only sharpened my technical skills but also reinforced my ability to solve complex data-driven problems efficiently within diverse teams.

I have presented at various conferences and workshops, sharing insights on AI, computer vision, and data-driven decision-making. My work has been featured at MWAIS, Data Tech Minneapolis, and academic research conferences.

Engaging with the research and industry community through conferences allows me to contribute to ongoing discussions on AI advancements while gaining valuable feedback to refine my work.