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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: ECG, EEG, Type 1 Diabetes biometric data

 

Data ScienceBig data, Deep learning, Data Visualization, 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

                                                    

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

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