Dinesh Atchuthan

Dinesh Atchuthan's work experience began in 2013 with a software development internship at Alcatel-Lucent, where they analyzed and corrected programs, added new features, and updated the group's wiki and website. In 2014, they interned at the Institute of Biomedical Engineering, working on medical imaging software development using MATLAB.

From 2015 to 2018, Dinesh worked at LAAS-CNRS as a PhD student and later as an intern. Dinesh contributed to the development of a C++ library using factor graphs for solving localization problems, specifically for the HRP2 humanoid robot. Additionally, they investigated person localization and tracking using a Kinect sensor, implementing machine learning methods for classification and estimation.

In 2018, Dinesh became a postdoctoral researcher at LAAS-CNRS, where they focused on visual-inertial state estimation using fiducial markers. Dinesh supervised a PhD student and implemented a fiducial marker-based SLAM method in C++.

Dinesh's most recent role was as an R&D Perception Engineer at EasyMile starting in 2019. Dinesh'swork involved contributing to the perception stack by implementing pointcloud filtering methods, validating potential obstacle-free regions, and supporting feedback via data analysis. Dinesh also assisted in thesis supervision on harsh weather phenomena and contributed to paper writing.

Overall, Dinesh Atchuthan's work experience spans research, development, and implementation in robotics, perception, and medical imaging.

Dinesh Atchuthan obtained a Master's Degree in Ingénierie et Science physique du Vivant from Télécom Physique Strasbourg, where they studied from 2012 to 2015. Prior to this, they completed their Classe préparatoire in Physique-Chimie at Lycée Lavoisier from 2010 to 2012. Dinesh completed their high school education at Lycée Louis Le Grand from 2006 to 2010. Additionally, they have obtained various certifications, including Decentralized Finance (DeFi) Deep Dive, Decentralized Finance (DeFi) Infrastructure, Decentralized Finance (DeFi) Opportunities and Risks, Decentralized Finance (DeFi) Primitives, and Decentralized Finance (DeFi): The Future of Finance from Duke University in December 2022. Dinesh also obtained certifications in Convolutional Neural Networks, Machine Learning Data Lifecycle in Production, Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization, Structuring Machine Learning Projects, Neural Networks and Deep Learning from DeepLearning.AI. Dinesh has also completed an Introduction to Machine Learning in Production certification, but the month and year of completion are not provided.