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Dr. Nitika Nigam
Assistant Professor
Profile Summary
Dr. Nitika Nigam’s primary focus is on Machine Learning and Deep Learning within the sphere of system deployment. Prior to her current role, she engaged in research activities as a scholar at the Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India. In 2023, she successfully completed her doctoral studies, which revolved around Computer Vision and Applied Artificial Intelligence, with specific concentration on Deep Learning. Complementing her academic journey, she earned an M.Tech degree from MMMUT Gorakhpur and a B.Tech degree from UPTU Lucknow.
Work Experience
Dr. Nitika Nigam joined UPES as an Assistant Professor in July 2023. She was a Research Scholar at IIT(BHU) from July 2018 till June 2023.
Research Interests
Computer Vision| Image and Video Processing| Machine Learning| Deep Learning
Teaching Philosophy
Dr. Nitika Nigam’s teaching methodology focuses on the understanding that cultivating a supportive and motivating ecosystem holds paramount significance in fostering student growth. She holds a strong belief in her capacity as an educator to cultivate an atmosphere that not only stimulates but also inspires a sense of curiosity and aspiration within students. Her passion stems from the core belief that an impactful pedagogical philosophy should steer students from a novice perspective to one of adept and advanced cognition, nurturing their advancement and refinement throughout their educational expedition.
Courses Taught
Dr. Nitika Nigam teaches subjects such as Python and C Programming.
Awards and Grants
Dr. Nitika Nigam has been awarded with University Grants Commission examination for Lectureship (NET) as Assistant Professor in 2017. She also qualified for the Graduate Aptitude Test in Engineering (GATE) in 2016.
Scholarly Activities
Dr. Nitika Nigam has published journals in IEEE Transactions and posters in A* and A conferences. She has also been the reviewer of IEEE Transactions on Geoscience and Remote Sensing with an impact factor of 8.2 and IEEE Transactions on Neural Networks and Learning Systems with an impact factor of 10.4.
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