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    Binu Nair

    True perception can only be solved through integration of computer vision, machine learning and text analytics.

    Binu Nair is a professional expert in image and video processing, machine learning, and deep learning, particularly focusing on text and video analytics.

    With a solid background in statistics and probability theory, Binu excels in solving detection, estimation, and regression problems.

    He possesses a high level of skill in implementing deep learning algorithms such as Deep Belief Nets, CNN, and LSTM for tasks like person/object detection, gait analysis, and action localization.

    Binu has a proven track record of submitting proposals as a technical lead for prestigious programs like NSF STTR, DoD SBIR/STTR, and IARPA.

    Proficient in utilizing computer vision and deep learning open-source libraries like Torch/Lua and OpenCV within Linux environments, Binu showcases a deep understanding of these tools.

    Familiar with Agile and Lean methodologies, Binu actively engages in code collaboration using platforms such as GitHub and Bitbucket to facilitate rapid software development.

    Highly regarded for exceptional analytical and communication skills, Binu is known for his dedication to fostering a positive team environment.

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    Location

    Berkeley, California