Dr. Ankush Km. Gaur

Dr. Ankush Km. Gaur

Assistant Professor

Profile Summary

Dr. Ankush Km. Gaur is a dedicated researcher and educator in Computer Science and Engineering with a Ph.D. from the National Institute of Technology Nagaland, specializing in Federated Learning and Blockchain applications for Agriculture 4.0. With a strong academic background and over five years of combined research and teaching experience, Dr. Gaur brings a unique perspective to addressing real-world challenges through innovative technological solutions. His work focuses on secure and privacy-preserving collaborative frameworks that integrate distributed intelligence, blockchain systems, and smart data management. He is passionate about leveraging emerging technologies to build scalable and socially impactful solutions across agriculture and industrial domains.

Over the years, he has guided and advised numerous students on projects, internships, and research, many of whom are now excelling in academia and industry across the globe. Prof. Katti has held several academic and administrative leadership positions, including Program Chair, Head of Department, Research Advisory Committee Member and Coordinator, and Audit Committee Chairman. He has also contributed extensively to the academic profession in roles such as reviewer, program committee member, session chair, resource person, and examiner.

Work Experience

Before joining UPES, Dr. Gaur completed his Ph.D. in Computer Science and Engineering from NIT Nagaland, focusing on federated learning and blockchain-based frameworks for secure Agriculture 4.0 data collaboration. He previously served as an Assistant Professor at Kanpur Institute of Technology, where he taught core Computer Science courses, contributed to NBA accreditation activities, and helped organize an IEEE-CIS sponsored national conference.

Research Interests

Federated Learning and Distributed Machine Learning | Blockchain Technology and Smart Contracts | Privacy-Preserving Data Analytics | Secure Data Collaboration Frameworks | Distributed Clustering and Classification Methods.

Teaching Philosophy

Dr. Gaur’s teaching philosophy focuses on making complex computing concepts intuitive through real-world applications, particularly in agriculture and industrial IoT. He integrates current research in federated learning and blockchain into classroom teaching and encourages collaborative, project-based learning. By emphasizing hands-on work using tools such as Python, Google Colab, and Jupyter Notebook, he creates an experiential learning environment where students connect theory with practice and understand the broader societal impact of technology.

Awards and Achievements

Dr. Gaur qualified GATE in 2016, reflecting strong technical proficiency in Computer Science. During his M.Tech at NIT Nagaland, he received the HTTA (Half Time Teaching Assistantship) fellowship from July 2017 to June 2019. He was later awarded the HTRA (Half Time Research Assistantship) from January 2021 to June 2025 during his Ph.D., recognizing his academic merit and teaching–research contributions. He has also received institutional recognition at NIT Nagaland for impactful contributions to research and academic initiatives.

Courses Taught

Core Computer Science and Engineering subjects, with emphasis on distributed systems, machine learning applications, and emerging technologies such as blockchain and federated learning.

Scholarly Activities

Dr. Gaur actively contributes to research in federated learning, blockchain-based systems, and privacy-preserving data analytics. His scholarly work focuses on developing secure and distributed frameworks for collaborative intelligence, particularly in Agriculture 4.0 environments. He has participated in academic and research initiatives involving distributed machine learning, smart contracts, and data-driven decision-making systems. Alongside his research, he remains actively involved in mentoring students, promoting experiential learning, and supporting academic activities that bridge theoretical computing concepts with practical and socially relevant applications.