I am an Applied Research Scientist and Senior Algorithm Engineer at Carl Zeiss, Inc. , where I bridge advanced machine learning research with enterprise-grade production software. My core expertise spans Computer Vision, Computational Imaging, Agentic AI, and Multi-modal Understanding. I specialize in utilizing multimodal AI for complex image analysis, developing robust systems for automated feature and defect detection during cross-material inspection.

I hold an M.S. in Computer Science and Engineering from Washington University in St. Louis. My academic research focused heavily on deep generative modeling, including the development of state-of-the-art GAN architectures for ill-posed image restoration and 3D computed tomography reconstruction.

At Zeiss, I design and automate systems to solve complex vision and imaging tasks. I architect high-throughput C++ inference engines , and I leverage AI agents to architect and develop autonumous scanning systems, automate image analysis pipelines and streamline volumetric imaging algorithms. By combining dynamic scene representations - such as 3D Gaussian Splatting and Neural Radiance Fields (NeRF) - with scalable Azure ML cloud infrastructure, I have successfully opened up paths for scene simulation and understanding.

My research has been published in leading conferences and journals, including work on deep-learning-based resolution recovery, progressive neural compression and scalable cloud infrastructure. Outside of work, I enjoy skiing, paddling, cycling, and playing ping pong, as well as hitting the gym for a workout.