Niranjan Mujumdar
About Niranjan Mujumdar
Niranjan Mujumdar is a Computer Vision Engineer at Mad Street Den in Chennai, India, where he has worked since 2016. He has a strong background in AI-based systems, image recognition, and object detection, supported by his academic achievements and previous roles in research and teaching.
Work at Mad Street Den
Niranjan Mujumdar has been employed at Mad Street Den as a Computer Vision Engineer since 2016. His role involves leveraging his expertise in AI to develop innovative solutions in the field of computer vision. Based in the Chennai Area, India, he has contributed to the company's focus on creating AI-powered products that utilize advanced image recognition and object detection technologies.
Education and Expertise
Niranjan Mujumdar earned a Master of Science (M.S.) in Computer Vision from the Indian Institute of Technology, Madras, where he studied from 2012 to 2016. His academic background includes a Bachelor of Engineering (B.E.) in Computer Science from Don Bosco Institute of Technology, Mumbai, completed from 2006 to 2010. He also studied at Kendriya Vidyalaya, IIT Powai, Mumbai, achieving CBSE certification from 2004 to 2006. His expertise includes developing scalable AI-based systems and a specific interest in shape analysis within computer vision.
Background in Research and Teaching
Prior to his current role, Niranjan Mujumdar worked at the Indian Institute of Technology, Madras, as a Research Scholar from 2012 to 2016. During this time, he focused on various research initiatives in computer vision. He also served as a Graduate Teaching Assistant from 2012 to 2015, where he supported academic programs and contributed to the educational development of students in the field.
Previous Experience at Infosys
Before his tenure at the Indian Institute of Technology, Madras, Niranjan Mujumdar worked as a Systems Engineer at Infosys from 2010 to 2012. In this role, he gained valuable experience in software engineering and system development, which contributed to his foundational knowledge in technology and engineering practices.