Dipanjan Roy — Product Manager

Currently : Dipanjan Roy is currently affiliated with School of AI and Data Science at Indian Institute of Technology, Jodhpur. He is leading Brain Dynamics, Connectivity and Cognition Lab working on various research aspects related to Neuroimaging, Multi-scale computational modeling, EEG, and Behavior. The specific areas in which the group contributes computational models and methods pertained to learning and memory, Perception, and attention, Aging, and Multi-sensory processing. His group is also involved in the connectomics dynamical systems and machine learning-based approach to determine age effects on cognition, neurodegenerative disorders, and reorganization of neurocognitive brain networks. In particular, the group is looking at the relationship between structural perturbations, lesions in patients, and investigating mechanisms of the reorganization of functional connectivity using Computational Modeling and noninvasive probes. He has made several key contributions to understanding the computational role of time delay, time-scale separation, structure-function relationship, and plasticity that unfolds in a dynamical landscape in the brain. His research combines developing methods to analyze EEG, fMRI recordings at rest and task conditions along with whole-brain computational modeling. Research methods also cover the acquisition of behavioral response under naturalistic, psychophysical stimuli from participants, and investigating neural correlates of behavior. Software : Python, C++, Root. Used, SciPy, NumPy, Cython, and Boost. Data analysis experience with R, Octave, Pandas, Scikit-learn, Pylearn2 Map-reduce, Hadoop and JAVA. Algorithm development : TVB virtual brain platform for simulation of large scale synthetic brain data. Connectomics graphical probabilistic models and analysis. Graph theoretical topological analysis. Gradient Descent algorithms. Monte Carlo particle physics simulations for data modeling. Pattern recognition and Boltzmann machine algorithms. Signal processing of nonstationary data. EEG, fMRI, MEG signal analysis using multivariate time series approach and Granger Causality techniques. Effective connectivity analysis and big data visualization. Applications of Supervised and Unsupervised Machine Learning: Linear/Logistic Regression, GLM analysis, Support Vector Machines, K Means Clustering. Nonlinear PCA, Statistical analysis using Bayesian and ML estimators. Distributed computing and implementing data analysis on clusters (Grid computing environment).

Stackforce AI infers this person is a leading expert in Computational Neuroscience and AI-driven research.

Location: Jodhpur, Rajasthan, India

Experience: 17 yrs 9 mos

Skills

Computational Neuroscience
Neuroimaging
Cognitive Neuroscience
Machine Learning
Neuroinformatics
Data Science

Career Highlights

Leading innovative research in AI and Neuroscience.
Expert in computational modeling and neuroimaging techniques.
Significant contributions to understanding brain dynamics.

Work Experience

Indian Institute of Technology Jodhpur

Associate Professor (4 yrs 5 mos)

National Brain Research Centre

Associate Professor (4 yrs 3 mos)

IIIT Hyderabad

Assistant Professor (1 yr 4 mos)

Charité

Research Associate (1 yr 4 mos)

MIT

Postdoctoral Researcher (2 yrs 1 mo)

Department of computer science Technical University Berlin

Postdoctoral Research Associate and project lead (2 yrs 4 mos)

CNRS

Researcher (3 yrs)

CERN

Research Assistant (1 yr)

Education

Doctor of Philosophy (PhD) at CNRS

Master of Science (MS) at The University of Texas at Arlington

Bachelors of Science at Savitribai Phule Pune University

BSc at Fergusson College

Dipanjan Roy

Product Manager

Jodhpur, Rajasthan, India17 yrs 9 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Leading innovative research in AI and Neuroscience.
Expert in computational modeling and neuroimaging techniques.
Significant contributions to understanding brain dynamics.

Stackforce AI infers this person is a leading expert in Computational Neuroscience and AI-driven research.

Contact

Skills

Core Skills

Computational NeuroscienceNeuroimagingCognitive NeuroscienceMachine LearningNeuroinformaticsData Science

Other Skills

AI methodsBig data analysisBig data analyticsBrain networksCC++Computational Neuroscience and NeuroimagingData analysisData-driven algorithmsEEGElectroencephalography (EEG)FractalsGPU computingGrid ComputingGrid computing

About

Experience

Indian institute of technology jodhpur

Associate Professor

Oct 2021 – Present · 4 yrs 5 mos · Jodhpur, Rajasthan, India

We are setting up a lab at IIT Jodhpur working on the interface of AI and Neuroscience. The lab that I have created called Brain Connectivity, Dynamics and Cognition lab. This lab is presently working on exciting projects involving running behavioral experiments, data fusion using EEG, fMRI data collected from human participants and applying AI methods, big data analytics to address Neuroscience of Speech and Language processing, Emotion and cognition interactions with naturalistic stimuli, Attention, Perception, Working and Episodic memory. We also study human aging and quantify observational models using signal processing, Nonlinear dynamics to track developmental changes, cognitive flexibility and Brain Network Dynamics. Finally, mapping human brain functions requires data drive approaches leveraging on open source neuroimaging data, Computational Neuroscience techniques, developing signal processing algorithm and advanced network analysis methods to study human representation and learning of the sensory and cognitive processes by large-scale neurocognitive brain networks impacting human behavior over the entire lifespan.

NeuroinformaticsPythonGrid ComputingComputational Neuroscience and NeuroimagingEEGfMRI+6

National brain research centre

Associate Professor

Jul 2017 – Oct 2021 · 4 yrs 3 mos · Gurugram, Haryana, India

Cognitive neuroscience is a field that focuses on the neural correlates of mental processes. This field combines theories of psychology and neuroscience along with computational, mathematical modelling, data driven algorithms and advanced network analysis methods to understand normal and pathological brain functions. This field is still evolving and integration of AI and ML is critical to understand multi-scale and multi-modal complex neural processes giving rise to myriad of Brain functions. The primary interest of our group consists of human cognition, perception and mathematical/computational modelling. Our lab uses techniques such as Electroencephalography (EEG), Magnetoencephalography (MEG), Magnetic resonance imaging (MRI) and eye tracking. Cognitive Brain Dynamics Lab is housed in NBRC, Manesar in the foothills of Aravali and also, IIT Jodhpur School of AIDE.
The laboratory is a joint effort of Arpan Banerjee and Dipanjan Roy. It involves a diverse team with expertise ranging from biology to Mathematics/Statistics/Biomedical Engineering and Computer Science.
Our joint work is funded by the Department of Biotechnology Dementia Science Program, Department of Biotechnology Flagship project on mental health, Department of Biotechnology, Ramalingaswami fellowship, Department of Science and Technology, DST-Cognitive Science Research Initiative and by the NBRC core funding. Other projects are funded by the DST National Postdoctoral Fellowship Scheme, DST-CSRI postdoctoral fellowship Scheme.

Cognitive neuroscienceElectroencephalography (EEG)Magnetoencephalography (MEG)Magnetic resonance imaging (MRI)Mathematical modelingData-driven algorithms+2

Iiit hyderabad

Assistant Professor

Jan 2015 – May 2016 · 1 yr 4 mos · Hyderabad Area, India

I was a former Assistant Professor in Cognitive Science and Computational Natural Science and Bioinformatics at IIIT Hyderbad.
Previously I worked on large scale modeling analysis of Brain networks, Computational Neuroscience. I have specifically contributed in benchmark neuro-informatics platform for clinicians “The Virtual Brain” python based open source informatics platform. Contributed in developing pipeline for structural connectivity data analysis using python based tools.
Machine learning model classifiers, state space detection, gradient descent algorithms towards developing cognitive models based on MATLAB, C++ and R for the project “State dependency of Human decision making and learning”.
Code generation and development for data driven large-scale neural network simulation platform
BRIAN. fMRI (functional MRI), EEG (ELECTROENCEPHALOGRAPHY) based data acquisition from participants under cohorts, conditions and further analysis of acquired large scale data using custom made MATLAB based neuroimaging software EEGLAB, FIELDTRIP.
Supervision of a team of three masters students, one PhD student for the “The Virtual Brain Project” and complex network research.

Large scale modelingComputational NeuroscienceMachine learningData analysisPythonMATLAB+3

Charité

Research Associate

Aug 2013 – Dec 2014 · 1 yr 4 mos · Berlin Area, Germany

Bernstein focus theme State dependency of learning. Developing large scale models of the Brain networks. Investigating effects of structural, functional plasticity in the Human brain function.

Large scale modelsBrain networksPlasticityNeuroscienceComputational Neuroscience

Mit

Postdoctoral Researcher

May 2011 – Jun 2013 · 2 yrs 1 mo · Greater Boston Area

In collaboration between MIT Picower center and TU Berlin computer science department we have developed predictive techniques based onmachine learning algorithms and nonlinear control theory to analyze in-vivo optical imaging data of circuit level neural activity with benchmark precision.

Machine learningNonlinear control theoryOptical imagingMachine Learning

Department of computer science technical university berlin

Postdoctoral Research Associate and project lead

Mar 2011 – Jul 2013 · 2 yrs 4 mos · Berlin Area, Germany

Worked in the capacity of a lead computational data scientist in an US-GERMAN collaborative grant CRCNS grant from NIH (USA).
With a team of eight collaborators (Comprise of two professors, two engineers, three postdoctoral scientists) we have developed a data analysis platform in C, python based scripting framework for optical image processing.

Data analysisOptical image processingCPythonData Science

Cnrs

Researcher

Mar 2008 – Mar 2011 · 3 yrs · France

Scientific research :
Using big data analysis techniques and intuition to find hidden patterns.
Identify model classes and low dimensional parameter space.
Surrogate analysis, classifiers to understand hidden variables.
Bench mark experimental design, post-hoc analysis, testing, implementation.
Acquisition and preparation of unstructured data. Data driven processes and decisions.
Multivariate Time series analysis, multivariate techniques and model fitting approaches
using dynamical systems and Baysian framework.
Graphical representation of data for verbal and visual communication.
Strong mathematical, numerical and statistical problem solving skills.

Big data analysisSurrogate analysisMultivariate time series analysisData Science

Cern

Research Assistant

Feb 2007 – Feb 2008 · 1 yr · Geneva Area, Switzerland

Monte Carlo physics process simulations, Development of benchmark C++ particle tracking algorithm.
Detector algorithms. Phys stat workshop conductor and participation. Grid computing using PANDA.

Monte Carlo simulationsC++Grid computingData Science