Dr. Jerrin Joy Varughese

Assistant Professor

Dr. Jerrin Joy Varughese is an academician, researcher, and engineering professional with over 17 years of experience spanning higher education, advanced research, academic administration, and industry. He is currently serving as an Assistant Professor in the Department of Mechanical Engineering, School of Engineering, Dayananda Sagar University (DSU), Bengaluru, contributing to teaching, research, student mentoring, curriculum development, and interdisciplinary academic initiatives. His professional profile is strongly positioned at the intersection of Mechanical Engineering, Artificial Intelligence, Machine Learning, Deep Learning, and Advanced Materials. He completed his Ph.D. in Advanced Polymer Nanocomposites from Vellore Institute of Technology (VIT), Vellore, with doctoral research focused on Modified Exfoliated Hexagonal Boron Nitride (hBN) Epoxy Nanocomposites and the application of Machine Learning and Deep Learning techniques for predictive modelling of material properties and exfoliation yield. His research demonstrates the use of data-driven modelling to complement experimental materials science, enabling prediction, optimisation, and intelligent interpretation of engineering behaviour.

In alignment with DSU's AI-first institutional vision, Dr. Varughese is actively developing his academic and research engagement in AI-enabled Mechanical Engineering. His interests include AI/ML-assisted materials engineering, predictive modelling, intelligent manufacturing, engineering data analytics, AI-enabled design and optimisation, and the integration of emerging digital technologies into engineering education. He has undertaken AI-focused professional development initiatives, including recent Faculty Development Programme activities and sessions covering AI applications in engineering education and NVIDIA-aligned learning and certification pathways. He also holds the AICTE HEIs Engagement AI Pre-Summit Certificate. He has actively contributed to AI-oriented knowledge dissemination through Faculty Development Programmes, technical sessions, and academic outreach activities. His recent professional development includes exposure to Artificial Intelligence and emerging digital technologies in engineering, along with sessions related to NVIDIA-aligned learning and certification pathways. These activities have strengthened his ability to connect AI/ML concepts with Mechanical Engineering, materials research, predictive modelling, and future-ready engineering education.

Dr. Varughese has published several SCI-indexed journal articles in advanced materials and nanocomposites and has received the Raman Research Award from VIT University on three occasions for his research contributions. He has also received seed grant support for research in polymer nanocomposites and intelligent materials engineering. His research and scholarly activities reflect a strong emphasis on combining experimental engineering, computational intelligence, and data-driven prediction. Prior to joining DSU, he served as Assistant Professor and Head of Student Affairs at Providence College of Engineering, where he mentored students and research projects involving materials science, epoxy nanocomposites, and AI/ML-based property prediction using Python. He also served for more than 11 years at St. Thomas College of Engineering & Technology as Assistant Professor and later as Head of the Department of Mechanical Engineering, contributing to academic development, NBA and NAAC accreditation, industry collaboration, professional-body initiatives, FDPs, and student innovation. Earlier, he gained industrial experience as a Production Engineer at Impression Systems & Engineers Pvt. Ltd., associated with Pipavav Shipyard.

His areas of expertise include Artificial Intelligence in Engineering Applications, Machine Learning, Deep Learning, AI/ML-assisted Materials Engineering, Advanced Polymer Nanocomposites, Materials Science, Mechanical Design, Manufacturing Engineering, Advanced Material Characterization, and predictive modelling. He is particularly interested in developing AI-enabled engineering education, industry-relevant curricula, interdisciplinary research, and intelligent engineering solutions that bridge experimental science with computational intelligence.

AI & Emerging Technology Focus

• AI/ML assisted Materials Engineering and predictive modelling
• Machine Learning and Deep Learning for engineering property prediction and process optimisation
• AI-enabled Mechanical Engineering education and interdisciplinary curriculum development
• Intelligent manufacturing, engineering data analytics, and AI-assisted design/optimisation
• NVIDIA-aligned AI learning and certification-oriented professional development
• Python-based data-driven engineering applications