Yuvaraj Govindarajulu

About

Head of Research - AIShield, Bosch Global Software Technologies, Bengaluru (India)

Focus Topics: AI Security, AI RedTeaming, AI Trustworthiness.

Master of Science (INFOTECH - Embedded Systems), University of Stuttgart, Stuttgart (Germany), with focus on Artificial Intelligence.

2020-2021, Embedded Software Engineer (HMI & Secure Bootloader), Lorch Schweisstechnik, Auenwald (Germany)
2019-2020, Tutor, Deep Learning - Master Laboratory course, Institute of Signals and System Theory, University of Stuttgart (Germany)
2017-2019, Working Student - Embedded Prototyping for Automotive ECUs, Robert Bosch GmbH, Schwieberdingen (Germany)
2012-2017, Senior Software Engineer - Complex Drivers Development - Fuel Injection Software for Automotive ECUs, Robert Bosch, Bengaluru (India)

Core Author, OWASP AI Exchange (AI Red Teaming), Since May 2024
Member, Technical Committee - Securing AI, European Telecom Standards Institute (ETSI), Since Aug 2022
Member, WG4 - Security Controls and Services Working Group, ISO/IEC/JTC1 SC27.
Panel Member, Panel P6 - For inputs on JTC 1/SC 27 Documents Panel, LITD 17 (Information Systems Security and Privacy), Bureau of Indian Standards ((Mirroring Committee ISO)

Contact:

Title: Human Activity Recognition and Study of Dynamic Filter Networks for Position-aware detection

Aim: To build a generic framework for Human activity recognition using smartphone Inertial sensors, use of Dynamic Filter generic networks to build a single activity recognition model for multiple positions
Building Recurrent Neural Network models for multi-variate time series data.
Signal pre-processing steps: Design and use of filters for noise and gravitation component removal, data normalization, sensor time-synchronization and sliding window techniques.
Study of multi-input, multi-output neural network architectures using concepts of Dynamic Filter Networks.
Libraries used: Tensorflow, Keras, Matplotlib, Scikit, Pandas among others.
Use of Tensorflow profiling to visualize tasks between CPU and GPU to identify performance bottlenecks.
Deployment of trained model on an Android smartphone using Tensorflow lite.