Present Position:       Postdoctoral Research Fellow, Université de Lille, France

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Previous Positions:   

                                            Postdoctoral Research Fellow, CEA, Saclay, France

                                            Post-Doctoral Research Fellow, Raman Research Institute, India

                                            Junior Research Fellow, University of Kashmir, India

                                            Lecturer,  Department of Education,Jammu and Kashmir, India

 

Research Interests:

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Early Universe: Physics of early universe and Inflation, Low CMB power anomaly and infrared cut-off in the primordial power spectrum, Cosmological parameter estimation using Markov chain Monte Carlo (MCMC) algorithm and CMB data.

 

Late Universe: X-ray studies of galaxy clusters, Sunyaev-Zel'dovich effect in galaxy clusters, Non-gravitational feedback in intra-cluster medium (ICM), Dark matter profile, Strong gravitational lensing.

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Biometric Modeling: CGM, ECG, EEG, Type 1 Diabetes modeling

 

Data Science:  Big data, Deep learning, Data Visualisation, Computing.

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Skills:

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Programming:                           C, C++, Fortran, Python, IDL, Matlab

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Specialty software:                   X-rays: HEASARC: XMM Newton, Chandra
                                                    CMB: COSMOMC, HEALPIX, CAMB, MCMC

 

Machine learning:                     Supervised Learning, Unsupervised Learning Semi-supervised Learning
                                                    Core Concepts: Linear Regression, Logistic Regression, Decision Trees,                                                                Random Forests, Support Vector Machines, k-means clustering and                                                                        Hierarchical Clustering, Principal Component Analysis, Bayesian Analysis                                                            and  various neural network architectures, etc.
 

Neural Networks:                      Pytorch, JAX, TensorFlow
                                                    Core Concepts: FFN and CNN networks, Computer Vision, Natural Language                                                      Processing, Generative Modeling, etc.

 

Parallel Programming:            OpenMP (C), multiprocessing (Python)
                                                    Core Concepts: Concurrency vs. Parallelism, Parallel Programming Models,                                                        Synchronization Load Balancing, etc.

 

Cluster Computing:                 Slurm
                                                   Core Concepts: Job Scheduling and Submission, Monitoring and Reporting,                                                         Resource Manmanagement, etc.

 

Scripting:                                  Bash and Tcsh
 

Database:                                  SQL (MySQL), Power BI, GitHub, Latex
 

Cloud Computing:                   AWS, Docker
                                                   Core Concepts: Compute and storage services, Database Services, Monitoring                                                     and Management Tools, etc.

                                                    

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Codes Developed:

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Galaxy clusters:                        Developed a numerical code for the Non-radiative model of galaxy clusters to                                                      study the degree and nature of non-gravitational feedback.

                                                    

                                                    Developed parametric and non-parametric code for deconvolution of observed                                                      X-ray temperature profiles.

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                                                    Numerical code for calculating SZ power spectrum for given cosmology and                                                        cluster physics (to be integrated with CosmoMC

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                                                    Deep learning modeling (Graph convolution network) of galaxy clusters using                                                      hydrodynamical simulations to estimate accurately the mass of galaxy clusters

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                                                    Deep learning modeling of galaxy clusters using hydrodynamical simulations                                                       to learning the mapping between the intra-cluster medium and dark matter                                                             profiles

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CMB:                                         Developed a numerical code to calculate the primordial power spectrum using 

                                                   Adiabatic approximation (Bunch-Davis boundary condition) and integrated it                                                       with publically available software like COSMOMC. 

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Type 1 Diabete                          Played a major role developing explainable model for glycaemic variations                                                           during and following physical activity sessions in children with type 1                                                                   Diabetes.

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Generic                                      Numerical code for Bayesian parameter estimation using MCMC and MC                                                           simulations.
 

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