Rate My Professor N Pappa

Pappa has supervised 13 PhD theses, demonstrating substantial influence in the field. Notable doctoral works under her guidance include "Investigations on Hardware realization of Soft Sensors" (2024), "Machine learning based adaptive control of reactor core for PWR type Nuclear Reactor" (2022), "Modeling and Control of Fed-batch bioreactor" (2020), "Support vector machine based modeling and control of a batch reactor" (2013), and "Image processing for flame monitoring in power station boilers" (2011). Her publications reflect this expertise, with key works such as "Design of a self-tuning regulator for temperature control of a polymerization reactor" (ISA Transactions, 2012), "Estimation of fungal biomass using multiphase artificial neural network technique" (Process Biochemistry, 2019), "Application of deep neural techniques in predictive modelling of bioprocesses" (Journal of Applied Microbiology, 2021), "Deep Learning based Soft Sensor for Bioprocess Application" (2021), and "Deep-learning framework for state of charge (SoC) estimation of electric vehicle batteries" (Ingeniería Investigación y Tecnología, 2024). She has contributed to over 80 research items, accumulating hundreds of citations.

Beyond research, Pappa has organized short-term training programs on Deep Learning for Modelling and Control and Artificial Neural Networks at Anna University. She has served as convener for national conferences like Recent Trends in Instrumentation and Control (RTIC) and coordinated RUSA 2.0 projects on AI-integrated systems. Additionally, she has been a member of the university's Grievances Committee for teaching and non-teaching staff, fostering academic and administrative excellence in instrumentation engineering.