Rate My Professor A. Shahina

Dr. A. Shahina serves as Professor and Head of the Department of Information Technology at Sri Sivasubramaniya Nadar College of Engineering (SSN). She earned her M.Tech. and Ph.D. in 2008 from the Department of Computer Science and Engineering at IIT Madras, India. With more than 25 years of teaching and research experience, her career commenced with five years as a project officer on a DRDO-sponsored speech processing project at IIT Madras. At SSN, she leads the Machine Intelligence Research Lab (MIRL), focusing on fundamental and applied machine intelligence to address societal challenges, particularly in healthcare and speech technology. MIRL's efforts have secured funding from agencies including ANRF, ST-Microelectronics, Nvidia, DST-NCSTC, and MeitY, supporting projects such as brain-computer interfaces for speech-impaired individuals, sleep disorder classification, and multi-agent nutritional systems.

Dr. Shahina's research specializes in machine learning, deep learning, and reinforcement learning applications for neuro-cognitive systems, computational pathology, medical imaging, and speech processing. Her work encompasses brain-computer interfaces for speech conversion and neurorehabilitation, epilepsy detection and localization, brain tumor identification, neurodegenerative disease diagnostics, speaker recognition, person authentication, speech recognition, and Lombard effect compensation. She has guided multiple Ph.D. scholars, including Mohamed Hashim C (2023, dependent speech recognition), Umamaheswari S (2021, Lombard effect compensation), and Radha N (2020, multimodal speech recognition). Key publications include 'Fusion of Multimodal Audio Data for Enhanced Speaker Identification Using Kolmogorov-Arnold Networks' (IEEE Access, 2025), 'Intelligent Multigrade Brain Tumor Identification in MRI: A Metaheuristic-Based Uncertain Set Framework' (IEEE Transactions on Artificial Intelligence, 2024), 'Recurrence plot embeddings as short segment nonlinear features for epileptic seizure detection' (Scientific Reports, 2024), 'Deep learning approach to detect seizure using reconstructed phase space portraits for detecting brain diseases' (Biomedical Signal Processing and Control, 2022), and 'Understanding Lombard Speech: A Review of compensation techniques' (Artificial Intelligence Review, 2021). Her contributions appear in high-impact Q1 journals.