Sina Mohseni is a Senior Software Engineer and PhD candidate specializing in interpretable ML, deep learning safety, and human-AI interaction with a decade of experience bridging research and production. Currently at NVIDIA, he designs safety-focused deep learning techniques for autonomous systems, including self-supervised out-of-distribution error detection published at AAAI. His academic work at Texas A&M under DARPA XAI spans explainable interfaces, robustness evaluation with saliency maps, and human-grounded explanations that inform practical engineering design. He has a track record of shipping tooling and crowdsourced data pipelines from internships at Bosch to engineering prototypes for LiDAR annotation and analytic provenance visualizations. Comfortable across research and systems engineering, he blends rigorous empirical evaluation with deployable solutions that improve trust and dependability in open-world ML. Based in Campbell, California, he pairs deep domain knowledge with hands-on implementation experience across industry and academia.
9 years of coding experience
11 years of employment as a software developer
PhD in Computer Science, Explainable AI, Visual Anaytics, Human-AI Interaction, PhD in Computer Science, Explainable AI, Visual Anaytics, Human-AI Interaction at Texas A&M University
Bachelor of Science (B.Sc.), Electrical and Electronics Engineering, Bachelor of Science (B.Sc.), Electrical and Electronics Engineering at University of Isfahan
Master's degree, Electrical and Electronics Engineering, Master's degree, Electrical and Electronics Engineering at Babol Noshirvani University of Technology (NUT)
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