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Artificial Intelligence Engineer | Robotics Enthusiast | Automation Expert
As an AI and robotics engineer, I am driven by a passion for leveraging cutting-edge technologies in machine learning, automation, and advanced robotics to revolutionize complex systems. My goal is to harness the power of artificial intelligence to design innovative solutions that not only enhance operational efficiency but also address real-world challenges across industries. With a commitment to continuous learning and a results-oriented mindset, I seek opportunities where I can deliver measurable impact by driving automation, improving decision-making processes, and developing intelligent systems that redefine possibilities. Through collaboration, leadership, and technical expertise, I aim to contribute to groundbreaking advancements in AI-driven solutions, paving the way for smarter, more sustainable technologies.
Master of Science in Artificial Intelligence, Expected Graduation: 2025
Bachelor of Engineering in Mechatronics, Graduated: 2022
2023 | Hyderabad, India
2018 | Chennai, India
TA Management Suite, a sophisticated platform tailored to enhance teaching assistant (TA) management within educational settings. This system aims to streamline TA applications, assignments, evaluations using advanced algorithms like TF-IDF and BM25 also it features advanced search engine through a unified, technology-driven approach. The suite will feature system with Data Breach Avoidance using a honeypot strategy. The anticipated benefits of this suite include significantly improved operational efficiency, an enhanced user experience for TAs, instructors, and administrative personnel, and a robust and secure data handling mechanism. This project addresses crucial needs in educational administration by automating and optimizing the management of TA resources, thus promising to markedly enhance both administrative operations and educational outcomes.
The proliferation of deepfake technology poses significant threats to the authenticity of vi sual media, making it increasingly challenging to distinguish manipulated images from genuine content. This project, DeepfakeDetect, aims to develop a robust image classification system uti lizing Vision Transformers (ViT) to accurately differentiate between real and deepfake images. By leveraging advanced techniques in deep learning and image processing, the system seeks to enhance detection accuracy and generalization. The proposed solution involves training a ViT model on a balanced dataset of real and fake images, incorporating data augmentation and oversampling to address class imbalance. The model achieved an accuracy of 99.35%, demonstrating the effectiveness of transformer-based architectures in identifying subtle incon sistencies characteristic of deepfakes. These findings contribute to enhancing digital media authenticity and mitigating the risks associated with deepfake technology
This project aims to automate the quality testing process for pails and lids, replacing manual operations with robotic automation to improve efficiency, accuracy, and data collection. The system integrates high-precision load cells, robot-mounted cameras, and pneumatic mechanisms for tasks such as weight measurement, dimensional inspection, water filling, lid positioning, handle testing, and drop testing. Critical data is logged into an HMI system for analysis. By streamlining these processes, the system reduces human involvement, enhances productivity, and ensures consistent quality in industrial manufacturing environments.
