Samip Poudel (समिप पौडेल)
M. Tech. Student, Computer Science and Automation
Indian Institute of Science, Bangalore
Biography
I am Samip Poudel, a Computer Engineer with a strong interest in Theoretical Computer Science and Artificial Intelligence. I completed my bachelor’s degree in Computer Engineering from Pulchowk Campus, IOE, Nepal. Currently, I am pursuing my M.Tech. in Computer Science and Engineering at the Indian Institute of Science (IISc), Bangalore.
During my undergraduate years, I developed a passion for exploring the theoretical aspects of computer science. This interest led me to further my education at IISc, where I am honing my skills and deepening my knowledge in my chosen field. I am particularly fascinated by the intersection of Theoretical Computer Science and Artificial Intelligence.
With a solid academic foundation and unwavering dedication, my aim is to contribute to the advancement of computer science and AI. I aspire to push the boundaries of knowledge, develop innovative solutions, and make a positive impact in the field.
- Theoretical Computer Science
- Artificial Intelligence
- Computational Linguistics
M.Tech. in Computer Science and Engineering, 2025
Indian Institute of Science, Bangalore
B.E. in Computer Engineering, 2022
Tribhuvan University, Institute of Engineering, Pulchowk Campus
Experience
- With a strong understanding of React’s component-based architecture, I had effectively utilized its core concepts to create reusable code.
- Actively participated in company’s discussion over further plans and make it sustainable in the ultra competitive industry.
Accomplishments
Projects
Featured Publications
Billions of people across the globe have been using social media platforms in their local languages to voice their opinions about the various topics related to the COVID-19 pandemic. Several organizations, including the World Health Organization, have developed automated social media analysis tools that classify COVID-19-related tweets to various topics. However, these tools that help combat the pandemic are limited to very few languages, making several countries unable to take their benefit. While multi-lingual or low-resource language-specific tools are being developed, there is still a need to expand their coverage, such as for the Nepali language. In this paper, we identify the eight most common COVID-19 discussion topics among the Twitter community using the Nepali language, set up an online platform to automatically gather Nepali tweets containing the COVID-19-related keywords, classify the tweets into the eight topics, and visualize the results across the period in a web-based dashboard. We compare the performance of two state-of-the-art multi-lingual language models for Nepali tweet classification, one generic (mBERT) and the other Nepali language family-specific model (MuRIL). Our results show that the models’ relative performance depends on the data size, with MuRIL doing better for a larger dataset. The annotated data, models, and the web-based dashboard are open-sourced at https://github.com/naamiinepal/covid-tweet-classification.