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 other 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.

Recent & Upcoming Speaking Engagements

• Data Tech Conference 2025 (Upcoming, May 16) - Presenting my latest research on transformer architectures in both language and vision applications, focusing on their practical implementation in data science workflows.
• Data Tech Conference 2024 - Delivered "Cracking the Code of Snowy Sidewalk Detection," showcasing real-time computer vision techniques for safety applications and how spatial attention mechanisms improve model performance.
• DREAM Student Organization - Presented two guest lectures: "Navigating Research From Concept to Conference Submission" (October 10, 2024) sharing insights on the academic research process, and "Image Classification From Scratch" (January 23, 2024), a comprehensive workshop on building convolutional neural networks for computer vision tasks.
• CADSCOM 2024 - Presented my award-winning paper "Snow Classification Using Prompt-Based Generated Images," demonstrating how synthetic data generation can improve training of computer vision models.
• MWAIS 2024 - Presented "Image Classification for Snow Detection to Improve Pedestrian Safety," detailing the development and implementation of deep learning models for real-time environmental monitoring.