Gabriel Maliakal is a research-driven machine learning engineer with 10 years of experience applying deep learning and computer vision to medical imaging and clinical problems. Currently a Graduate Research Assistant at Michigan State University and a PhD candidate in Computational Science, he blends academic rigor with industry experience from Cleerly and Weill Cornell, where his work on automated cardiac segmentation and TAVR design was presented at SCCT2019. He has interned at NVIDIA on end-to-end autonomous driving models and brings a practical knack for comparing ML outputs to human labels, producing ROC/AUC analyses that inform clinical validity. Comfortable at the intersection of clinicians’ needs and technical solutions, he fills the gap between medical expertise and scalable imaging automation.
10 years of coding experience
4 years of employment as a software developer
Master of Science, Electrical and Electronics Engineering, Master of Science, Electrical and Electronics Engineering at Columbia University in the City of New York
Artificial Intelligence Nano Degree, Specialization in NLP, Artificial Intelligence Nano Degree, Specialization in NLP at Udacity
Doctor of Philosophy - PhD, Computational Science Maths and Engineering, Doctor of Philosophy - PhD, Computational Science Maths and Engineering at Michigan State University
Bachelor of Technology, Electrical and Electronics Engineering, Bachelor of Technology, Electrical and Electronics Engineering at National Institute of Technology Karnataka
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