Data Science Institute

Gajendra Kumar

Assistant Professor of Molecular Biology, Cell Biology and Biochemistry (Research)

Biography

My research area aims to decipher the descending motor neural circuit in neurodegenerative diseases utilising electrophysiology, optogenetics, neurocomputation, AI and machine learning. 

In my recent research, i discovered real-time field-programmable gate array-based closed-loop deep brain stimulation platform targeting cerebellar circuitry rescues motor deficits in a mouse model of cerebellar ataxia (CNS Neuroscience & Therapeutics 2024), developed artificial intelligence-assisted graphical user interface for early detection of depression using EEG and facial expression recognition (Neural Computing Applications 2024), computation model of Cerebellar ataxia (Neural Networks 2023), artificial intelligence and machine learning model to predict the finger kinematics (Neurocomputing, 2023), deep brain stimulation to ameliorate the ataxia (Molecular Neurobiology, 2022), elucidate the target-specific neural activity and visual function recovery after optic nerve injury (npj Regenerative Medicine, 2022; PNAS, 2022), dissect the mechanism of glutamatergic system impacts on spontaneous motor recovery (npj Regenerative Medicine, 2022).

How does your research, teaching, or other work relate to data or computational science?
In pre-clinical neurobehavioral and cognitive studies, AI and machine learning models are integral to analyzing complex data patterns from experiments. They enable the identification of subtle behavioral changes, correlations between biological markers and behavior, and prediction of outcomes based on data-driven insights. These models utilize statistical techniques, pattern recognition algorithms, and data visualization tools to uncover meaningful relationships in large datasets, enhancing the precision and efficiency of experimental outcomes. By leveraging data science principles, such as data preprocessing, feature selection, and predictive modeling, AI facilitates deeper understanding and interpretation of neurobehavioral data, advancing research in neuroscience and therapeutic development.