Dipanjan Roy (Ph.D.)
Associate Professor and DBT Ramalingaswami fellow
Principal Investigator – Dipanjan Roy
Dipanjan Roy currently leads a Cognitive Neurodynamics group focusing on research areas related to Neuroimaging, Brain Connectivity, Multiscale computational modeling, EEG, fMRI, and behavior. The specific areas in which the group contributes computational models and methods pertained to learning, Perception, Working memory, Aging, and Multisensory processing..… Read Full Bio
Moumita Das (Ph.D.) (Postdoctoral Research Associate)
Moumita did her Ph.D. work from Indian Statistical Institute and currently doing her research in developing statistical methods and models to understand resting state and task fMRI data in the context of aging and development. Designing benchmark statistical tools based on nonlinear time series analysis and Bayesian approach to understanding the relationship between neuronal activity recorded using EEG, MEG and the metabolic hemodynamic response recorded using fMRI.
Shubham Kumar (Ph.D.) (Postdoctoral Research Associate)
Shubham did his Ph.D. work from the Polytechnic University of Catalunya Barcelona in nonlinear dynamics and currently doing his research in Perceptual decision making and learning using EEG recordings and modeling oscillatory network dynamics. For instance, time-series data from real-time EEG recordings of a subject performing a certain cognitive task can be very effectively processed by machine learning algorithms, leading to the design of more accurate and even predictive models of brain functioning. Such techniques could be very effective in tasks involving simple cognitive, perceptual or decision making tasks. Thus by this integrated machine learning and nonlinear dynamical system approach, many novel experiments could be designed, each revealing different functional aspects of the brain, which together would help in the eventual understanding of the big picture, that is, the dynamics of consciousness.
Fahd Yazin (Project Assistant)
Fahd did his Bachelor of Medicine and Bachelor of Surgery- Calicut University, Kerala. He did an internship following his MBBS. Currently, he is working in understanding episodic memory retrieval and encoding using behavioral experiments along with Brain signal recording using EEG, fMRI. His longterm interest to understand memory in the context of aging, development, and learning.
Bikash Sahoo (Project Assistant)
Bikash finished did his master’s degree in data engineering and his thesis from Centre for Neuroscience IISC Bangalore. He is currently developing a pipeline for the analysis of MEG lifespan data to understand the role of multiple timescales in behavior and reorganization of multi-scale Brain Dynamics in cognitive processes related to attention, perception, and cognition in healthy aging. His long-term research interests include large-scale neuroimaging data analysis and neurostimulation procedures.
Vatika Harlalka (MS research)
IIIT Hyderabad (Jointly with Dr. P.K.Vinod)
ASD functional connectivity at rest using fMRI Simulating large-scale brain network dynamics using mean field models and constrained structural connectivity between brain areas. Simulated BOLD activity is constrained by neuroimaging resting state functional data acquired using fMRI. Designing benchmark simulations to understand the relationship between neuronal activity and metabolic hemodynamic response as measured by fMRI brain signals in Autism Spectrum Disorders (ASD).
Snigdha Dagar (MS research)
How endogenous brain states interact with therapeutic mechanisms where antagonizing these subject-specific maladaptive alterations by regulation of cortical excitability/activity and induction of beneficial plasticity are crucial for re-installing efficient information transfer in the brain during neurorehabilitation. In this perspective article, we propose that innovative technologies of portable electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) neuroimaging will be able to objectively quantify the individual brain state with computational neural mass models in order to understand the impact of Brain State Dependent Electrotherapy (BSDE) in post-stroke rehabilitation.
Govinda Surampudi (MS research)
IIIT Hyderabad (Jointly with Dr. Avinash Sharma)
Multiscale diffusion kernels and machine learning techniques to learn functional connectivity features in the Human Cortex
Govinda is a Research Scholar at IIIT-since July 2015. He graduated from Nirma University, Ahmedabad in computer science in 2013. He has worked at Samsung Research Institute, Noida for 2 years on Android OS and applications. He is interested in Brain-Computer Interface, Computer Vision, and Machine Learning. Currently, he is presently working with CVIT and Cognitive Science Lab at IIIT-Hyderabad on Multiscale diffusion kernel to learn functional connectivity features in the Human Cortex.