Kumar Avinash Chandra specializes in Brain-Computer Interface (BCI), EEG signal processing, and neurotechnology, with a particular emphasis on motor imagery-based classification and cognitive state analysis. His research integrates artificial intelligence, deep learning, and quantum-inspired machine learning to enhance the interpretation and reconstruction of neurophysiological signals. He has contributed significantly to the development of advanced methods in graph signal processing and dynamic functional connectivity analysis for brain-state monitoring. He is also involved in exploring non-invasive neuromodulation approaches, such as acupressure-evoked EEG, for mental health and neurorehabilitation applications.

He holds a Ph.D. (pursuing) in Electrical Engineering, with a focus on EEG-based Brain-Computer Interfaces and neurotechnology applications. He completed his Master’s degree (M.Tech) in Electronics and Communication Engineering, specializing in Signal Processing and Artificial Intelligence. His academic foundation was laid with a Bachelor's degree (B.Tech) in Electrical Engineering. Throughout his academic journey, He has consistently focused on interdisciplinary research combining neuroscience, engineering, and computational intelligence.

Avinash brings extensive academic and research experience in the fields of biomedical signal processing, artificial intelligence, and neurotechnology. He has served as an Assistant Professor and research supervisor in reputed engineering institutions, where he has guided numerous undergraduate and postgraduate students in projects related to Brain-Computer Interfaces, EEG analysis, and AI-driven healthcare systems. He has been actively involved in interdisciplinary collaborations, contributing to funded research projects focused on neural signal decoding, cognitive state monitoring, and non-invasive therapeutic technologies. As an editorial board member and peer reviewer for several reputed journals, he consistently evaluates manuscripts in the areas of biomedical engineering, computational neuroscience, and intelligent systems. His experience spans both academic instruction and applied research, with a strong commitment to innovation and translational impact.