About me

I am a computational engineer with a Master’s degree in Computational Materials Science from TU Bergakademie Freiberg, Germany, specialising in high-performance computing (HPC), numerical methods, and GPU-accelerated AI applications. I hold a Bachelor’s degree in Mechanical Engineering from Chaitanya Bharathi Institute of Technology, affiliated with Osmania University, Hyderabad, India, and am currently based in Hyderabad, India. I am fluent in English (C1) and German (B2), enabling effective communication in bilingual professional environments.

Summary

Educational Background

  • Master of Science in Computational Materials Science, TU Bergakademie Freiberg, Germany (Oct 2019 – Sep 2024)
    • Grade: 2.3
    • Focus: Nonlinear Finite Element Methods, Numerical Analysis of Differential Equations, Machine Learning for Materials Scientists, High-Performance Computing, Continuum Mechanics
    • Thesis: Overlapping Schwarz Domain Decomposition Methods in Python with Applications in Structural Mechanics
    • Supervisors: Prof. Dr. Oliver Rheinbach, Dr. Stephan Köhler, Institute of Numerical Mathematics and Optimization, TU Bergakademie Freiberg
  • Bachelor of Engineering in Mechanical Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, India (Aug 2015 – Jul 2019)
    • Grade: 8.6/10
    • Focus: Structural Mechanics, Computer-Aided Design and Manufacturing, Machine Design, Dynamics of Machines, Fluid Mechanics, Thermodynamics, Heat Transfer

Skills and Expertise

  • Core Competencies: GPU-Accelerated Computing, AI Model Optimisation, Finite Element Analysis, High-Performance Computing, Numerical Modelling, Distributed & Parallel Computing
  • GPU & Accelerated Computing: CUDA, Numba, kernel optimisation, performance profiling
  • Parallel & Distributed Computing: MPI, OpenMP, PETSc, SLURM, PBS
  • Machine Learning & AI: PyTorch, TensorFlow, scikit-learn, NumPy, SciPy
  • Programming Languages: Python, MATLAB, C++, Fortran, Rust
  • Numerical Methods: PDE discretisation, finite element methods, numerical linear algebra, Krylov methods (CG, PCG, GMRES), preconditioning
  • Simulation & Analysis Tools: Abaqus, GMsh, COMSOL Multiphysics, LS-DYNA, ANSYS Workbench
  • Software Development Practices: Git, PyTest, CMake, Docker, GoogleTest, Linux
  • Documentation & Communication: LaTeX, Sphinx, Doxygen
  • Soft Skills: Problem solver, Adaptable, Research-oriented, Technical Documentation, Presentation

Professional Experience

  • Technical Intern — AI Model Optimisation Aientasar Software Solutions, Hyderabad, India (Oct 2024 – Apr 2025)

    • Analysed numerical performance and model evaluation pipelines for computer-vision anomaly detection using Python and TensorFlow.
    • Owned data preprocessing, validation, and robustness analysis for camera-feed image data.
    • Improved model deployment readiness in collaboration with the engineering team.

  • Research Assistant (HiWi) & Master’s Thesis Institute of Numerical Mathematics and Optimization, TU Bergakademie Freiberg (Aug 2023 – Sep 2024)

    • Formulated and implemented a nonlinear FEM solver in Python for a von Mises plasticity model under small-strain assumptions.
    • Applied numerical methods for discretised PDE systems, including Newton-Raphson, Krylov solvers (CG, PCG, GMRES), and UMFPACK.
    • Implemented overlapping Additive Schwarz preconditioning and benchmarked the solver against conventional Newton and Abaqus reference simulations, achieving an 8× runtime reduction (160s → 20s per step).
    • Maintained code reproducibility and modularity through version control (Git) and structured testing (PyTest unit and integration tests).

  • Summer Intern — PRACE Summer of HPC 2021 MdlS – Maison de la Simulation (CEA/CNRS), France (Jun 2021 – Aug 2021)

    • Selected as one of 66 participants for the PRACE Summer of HPC programme.
    • Studied an existing parallel FEM solver codebase, written in C/C++ with OpenMP and MPI support, across several use cases and configurations.
    • Collaborated with an international team to extend the parallel FEM codebase for heat transfer through diffusion and radiation.
    • Received practical training in MPI, OpenMP, GPU programming with CUDA and Numba, and batch scheduling on a computing cluster using SLURM.

Certifications & Achievements

  • PyTorch for Deep Learning Professional Certificate — DeepLearning.AI (July 2026) (Credentials)
  • Fundamentals of Accelerated Computing with CUDA Python — NVIDIA Deep Learning Institute (Credentials)
  • Fortran for Scientific Computing — PRACE & Vlaams Supercomputer Centre (Credentials)
  • Deutsch: Kommunikation in Studium und Beruf — International University Center (IUZ), TU Bergakademie Freiberg

I’m passionate about open-source software and computational science, with a focus on the numerical simulations, and AI fueled engineering solutions. I’m actively exploring PhD opportunities and industry roles in high-performance computing, scientific ML, and GPU engineering.

For a comprehensive overview of my qualifications, you can refer to my detailed CV available in English and German.