Kedar Grama is an Algorithm Engineer specializing in computer vision and machine learning with 16 years of experience building production-grade algorithms for medical devices, semiconductor inspection, and healthcare automation. Based in San Francisco, he has driven FDA-related work on a portable stroke detection device, delivered vision-language models to automate radiology reporting, and reduced critical inspection latencies through C++ and TensorFlow innovations. His background spans academic research in microscopy and PhD-level training in electrical and computer engineering, enabling him to bridge rigorous modeling with practical system design. Notably, he combines low-level DSP and high-level LLM alignment skills—optimizing signal processing pipelines while integrating vision models like dinoV2 with large language models for real-world clinical workflows.
16 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD Electrical and Computer Engineering, Doctor of Philosophy - PhD Electrical and Computer Engineering at University of Houston
Bachelor's degree Biomedical/Medical Engineering, Bachelor's degree Biomedical/Medical Engineering at Visvesvaraya Technological University
Master's degree Bioengineering and Biomedical Engineering, Master's degree Bioengineering and Biomedical Engineering at Rensselaer Polytechnic Institute
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Kedar Grama - Algorithm Engineer, ML at Applied Materials