Viswa Ravichandran
Software Engineer
Passionate about High-performance computing, Computer Vision, and Real-time Rendering
Work Experience
Computer Vision Researcher Student
Carl Zeiss AG
Oct 2023 - Oct 2024
Research and development on Radiance Field based 3D geometry reconstruction and physically based material reconstruction pipeline through differentiable rendering using Pytorch3D and CUDA C++
Computer Vision Research Assistant
University of Siegen
Apr 2023 - Mar 2024
Research and Development of real-time 3D reconstruction algorithms using NeRF and Gaussian Splatting with CUDA C++
Unreal Engine C++ Developer
TWT Science & Innovation GmbH
Aug 2022 - Sep 2023
Developed components for autonomous driving simulation and 3D reconstruction in Unreal Engine C++
Senior Software Developer
Magilhub India Private Limited
Jun 2020 - Sep 2021
Led the development of high-performance customer-facing web and mobile applications using React, React Native, TypeScript, and Redux.
Recent Posts
MultiProcessing in Python to Speed up your Data Science
When dealing with large datasets in Python, a common issue arises - processing takes too long. This can significantly slow down your data analysis workflow and hinder productivity. To optimize your code running time and speed up the process you’ll eventually consider Parallelization as one of the methods. In this article, we’ll explore how to use parallelization in Python to accelerate your data science.
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Memory Latency Hiding in CUDA using Streams (CUDA Adventures - 1)
I’m starting a new CUDA project to deepen my understanding of GPU acceleration. I’ll begin with simple tasks like vector addition and move on to more involved projects, including image processing and language model optimizations. While this series won’t be a step-by-step tutorial, I’ll share the interesting parts of my implementations, highlighting the challenges I faced and the reasoning behind my decisions. For the complete code, feel free to check out the project repository.
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