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Dr. Hrant Gharibyan is a prominent figure in the field of Artificial Intelligence and Computer Vision, currently serving as the Co-founder and Chief Technology Officer (CTO) at SuperAnnotate. He brings a strong academic background, holding a PhD, and effectively bridges the gap between theoretical research and practical industry applications. At SuperAnnotate, he spearheads the technological vision and development of their AI-driven data annotation platform, empowering companies to build more accurate and efficient AI models. Alongside his entrepreneurial pursuits, Hrant also contributes to academia as an Adjunct Lecturer at the American University of Armenia (AUA), where he shares his expertise in computer science, machine learning, and AI with the next generation of innovators. His work focuses on advancing computer vision technologies and making sophisticated AI tools more accessible.
Hrant Gharibyan, PhD's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Co-founded SuperAnnotate, a leading AI-powered platform for data annotation and model training. As CTO, he has been instrumental in developing the core technology, scaling the engineering team, and driving the company's product innovation in the competitive AI landscape.
Earned a Doctor of Philosophy (PhD), specializing in areas related to artificial intelligence and machine learning, which provided a strong theoretical foundation for his subsequent work in the AI industry.
Serves as an Adjunct Lecturer in the Zaven & Sonia Akian College of Science and Engineering at AUA, teaching courses and mentoring students in computer science, artificial intelligence, and related fields, fostering local talent in Armenia.
Under his technical leadership, SuperAnnotate has developed and launched innovative features for image, video, text, and LiDAR annotation, incorporating AI assistance to significantly improve annotation speed and quality for computer vision tasks.
Frequently shares his insights and expertise at industry conferences, workshops, and through publications, contributing to the broader discourse on AI, computer vision, and data-centric AI development.
Stanford University - Year 2014
Stanford University - Year 2014
M.I.T. - Year 2010
PhysMath - Year 2003
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