Hello there! I'm Ashwanth Kuppusamy, a passionate and curious software engineer, researcher, and problem-solver. This GitHub repository is a window into my world of coding, experimentation, and discovery. Whether you're a fellow developer, a potential employer, or just a tech enthusiast, you'll find a portfolio of projects that showcase my skills, interests, and dedication to the field of computer science and software engineering.

• 🎓 I'm currently a graduate student at Oregon State University, Corvallis, OR, focusing on improving my combined expertise in AI and Cybersecurity.
• 💼 I'm a Graduate Researcher at Oregon State University since Feb. 2024. I have been working on AI based extraction of specifcations for real time systems and LLM based conversion of natural language specifications to logic.
• 💼 I was an undergraduate student researcher at Amrita Vishwa Vidyapeetham, Coimbatore, where I focused on the fascinating domain of multi-modal deep learning applications at the edge.
• 🔭 My research work includes a significant contribution to the field of deep learning, particularly in edge computing, as demonstrated in my paper presented at the 2023 International Conference on Computing Communication and Networking Technologies (ICCCNT).

• 🖥️ Deep Learning & Edge Computing: Computer Vision, Natural Language Processing, Optimizing Deep Learning workloads for multiple accelerators.
• 📊 Data Analysis & Forecasting: Expertise in regression analysis, correlation analysis, and leveraging machine learning for predictive insights.
• 💻 Full Stack Development: Building dynamic and responsive web applications using the latest technologies and frameworks.

• Languages: Python, C, C++, Java, JavaScript, HTML, CSS, Haskell, Scala
• Frameworks & Libraries: React.js, Node.js, Express.js, Flutter, OpenCV, pandas, NumPy, matplotlib, Scikit-learn, Seaborn
• Databases & Tools: Firebase, SQL, MongoDB, XML, JSON, OpenGL, GLSL, Cisco packet tracer, Git
• Management Skills: Agile methodologies, Scrum, Problem-solving, Communication, Teamwork

1. Brain Tumor Segmentation (Mar. 2024): Trained 2D and 3D UNet based models to compare performance on multi-class brain tumor segmentation. Implemented 2D and 3D versions of UNet, ResUNet, and Attention ResUNet architectures using PyTorch. Created a novel Mixture of Experts architecture for 2D Attention ResUNet and improved IOU score by 6%.
2. Library Management System (Jan. 2022 - Apr. 2022): Led a team to develop a comprehensive system for managing library operations, utilizing React.js, Node.js, and Firebase.
3. Intelligent Crop Recommendation System (Jul. 2021 - Nov. 2021): Implemented various machine learning models for soil dataset analysis, achieving 95% accuracy with the Naïve Bayes classifier.

• Inference at the Edge for Complex Deep Learning Applications: Discussed innovative approaches to manage inference load in edge computing using multiple models and accelerators.

Feel free to browse through my repositories to get a better understanding of my work. I'm always open to collaborate on interesting projects, learn new technologies, and exchange ideas. Let's connect!

🔗 LinkedIn | 🔗 Personal Website

Thank you for stopping by, and I hope you find something that piques your interest!