
Asif Iqbal Ahangar
Post-Doctoral Research Fellow
Université de Lille
France

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 Science: Big 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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