A passionate researcher bridging the gap between cutting-edge AI theory and real-world applications, with a focus on making deep learning more efficient and accessible.
I am an experienced Deep Learning Research Engineer with a diverse background spanning embedded systems, wireless communications, and artificial intelligence. I graduated from IIT Kharagpur with a dual degree in Electronics and Electrical Communications Engineering (E&ECE), where I built a strong foundation in both theoretical and applied aspects of engineering. Currently working at NeuroPixel.AI, I focus on developing innovative solutions that push the boundaries of what's possible with neural networks.
My research journey has taken me from the fundamental challenges of embedded systems engineering at Analog Devices to the cutting-edge frontiers of AI research. This unique combination of hardware expertise and AI research gives me a distinctive perspective on building efficient, deployable AI systems.
I believe in research that makes a tangible impact. Whether it's optimizing neural networks for edge deployment, advancing computer vision capabilities, or exploring new paradigms in machine learning, my work is driven by the desire to solve real problems and create technologies that benefit society.
NeuroPixel.AI
Leading research initiatives in neural network optimization and computer vision applications. My work focuses on developing novel architectures and training methodologies that improve model efficiency while maintaining high performance.
Key Contributions:
RPAD Labs, Carnegie Mellon University
Collaborated with world-class researchers on cutting-edge projects in autonomous driving and perception systems. This experience exposed me to the intersection of AI research and robotics applications.
Research Focus: Active Perception using Light Curtains for autonomous vehicle navigation and obstacle detection.
Analog Devices
Built expertise in hardware-software integration and real-time systems. This role provided crucial insights into the practical constraints and opportunities for deploying AI systems on embedded platforms.
"The best research doesn't just advance our understanding-it creates tangible value that improves people's lives and solves real problems."
I believe in interdisciplinary research that brings together insights from multiple fields. My experience spans from low-level embedded systems to high-level AI algorithms, giving me a unique perspective on the full stack of technology development.
I'm particularly passionate about making AI more efficient and accessible. This means developing algorithms that can run on resource-constrained devices, creating tools that democratize AI development, and ensuring that the benefits of artificial intelligence reach everyone.
I'm always interested in discussing new research opportunities, collaborative projects, and innovative applications of AI technology.