About

Debajyoti Halder is a final year undergraduate student pursuing a Bachelor + Master of Technology (Dual Degree) in Computer Science and Engineering from IIT Kharagpur. He is passionate about full-stack/web engineering and the applications of machine learning. Currently, he is exploring event-driven architecture like OpenAPI specification, AsyncAPI, etc. Debajyoti is a highly motivated individual who believes in a highly supportive, collaborative, and synergistic work culture. He is always looking forward to interesting opportunities to learn more and gain different perspectives in life. Currently, Debajyoti is a Member of Technical Steering Committee at AsyncAPI Initiative, where he is one of the maintainers of asyncapi/modelina. He is developing a support feature to generate valid JSON Schema models from input files. He has also shipped a core feature in asyncapi/modelina to support AsyncAPI model generation and processing for TypeScript files/projects. Additionally, he has added multiple usage examples, fixed website bugs, and developed automated documentation in asyncapi/diff to support TypeScript types used in the project. In the past, Debajyoti has interned as a Software Engineer at Freightwalla, where he worked on two projects. He designed and delivered carrier-specific extraction modules in Python to extract relevant unstructured data from data sheets into structured data. The automated updating system reduced errors significantly against the manual updating system. He also developed an engine model to extract active order statuses from multiple carrier-specific websites using web-scraping. He refactored the existing updating engine and integrated the model using frameworks like Node.js, cheerio & mocha, resulting in a 22% increase in automatic order updates. Debajyoti also documented the workflow of both systems for future reference of employees. Debajyoti has also interned as a Computer Science Intern at Xlerate Inc. where he developed a neural network model using the knowledge of semantic analysis and deep neural networks, primarily for sentence boundary detection. The model performs with an accuracy of 72%. Debajyoti's tech stack includes Typescript, Web, ML, Software Engineer, Javascript, NodeJS, Python, and Neural Networks. With relevant experience of 0.27 years, Debajyoti is an ambitious and talented individual who is always looking for new challenges to grow and develop.