Dhawa Sang Dong is an Associate Professor in the Department of Electronics and Communication Engineering at Kathmandu Engineering College, Nepal, and a PhD researcher at Kathmandu University, School of Engineering, Dhulikhel. His doctoral research focuses on applying Large Language Models (LLMs) to mRNA design and optimization, with an emphasis on developing AI-driven approaches for computational biology and healthcare.

He has extensive teaching experience in Artificial Intelligence, Machine Learning, Python Programming for Data Science, Neural Networks, Deep Learning, and Generative AI across several academic institutions in Nepal. He has also worked as an AI/ML Engineer at Nanil Health Inc., Canada, focusing on computational drug design and discovery using Large Language Models.

His research interests include LLMs for drug design, machine learning, deep learning, biomedical signal and image processing, and advanced wireless communication systems. His publications span biomedical applications of machine learning, cancer diagnosis, network coding, and wireless communications. His recent research explores evolutionary approaches to designing stable, highly expressible, and low-immunogenicity therapeutic mRNA sequences.

Through his academic teaching, interdisciplinary research, and industry experience, Dhawa Sang Dong aims to advance AI-driven scientific discovery, contribute to computational biology and precision healthcare, and mentor the next generation of researchers in artificial intelligence and biomedical innovation.

TitleLarge Language Models for Biological Sequence Modeling and Protein-to-mRNA Design

AbstractThis talk explores the application of large language models (LLMs) to biological sequence modeling and protein-to-mRNA design. It examines how transformer-based models can learn sequence patterns and generate protein-specific mRNA coding sequences, with the goal of improving protein expression in human cells. The talk highlights the potential of LLM-driven sequence design to advance mRNA therapeutics and precision biologics.