Bibek Aryal, Ph.D., is an Applied Scientist II at Microsoft working on Natural Language Processing and Large Language Model alignment. He holds a doctorate in Computational Science from The University of Texas at El Paso (2022).

What are Bibek Aryal's research interests?

His research spans NLP, LLM alignment, deep learning, computer vision, and remote sensing. He has published at ICML 2025, NeurIPS workshops, and in Remote Sensing journal. His full publication list is available on Google Scholar.

Bibek Aryal, Ph.D. | Applied Scientist at Microsoft

Q: What does Bibek Aryal do at Microsoft?

He fine-tunes Large Language Models for Microsoft products like Outlook and Teams, builds scalable data pipelines and AI-powered knowledge assistants for enterprise content, and develops advanced AI alignment techniques for production systems.

Experience

Applied Scientist II, Microsoft Redmond, WA Nov 2022 – Present

Fine-tuning LLMs for Outlook and Teams, building scalable data pipelines and AI-powered knowledge assistants for enterprise content. Scaled tenant-specific Q&A models to Fortune 500 environments with advanced AI alignment techniques.

Research Associate, UTEP El Paso, TX Aug 2020 – Oct 2022

Deep learning for glacier segmentation and arctic shoreline mapping using satellite and airborne imagery. Published in Remote Sensing and NeurIPS workshops.

Research Programmer, KDC Kathmandu Aug 2016 – Jun 2018

Built AI/ML products including a Nepali language news classifier and personalized recommendation engine. Led R&D initiatives and trained interns in deep learning.

Applications Developer, Upveda Technology Kathmandu May 2015 – Aug 2016

Web applications using CodeIgniter for government clients including Nepal's Ministry of Home Affairs. Supervised development of the Personnel Information System.

Education

Ph.D. Computational Science, UTEP El Paso, TX Aug 2018 – Dec 2022

M.Sc. Computational Science, UTEP El Paso, TX Aug 2018 – Dec 2020

B.Sc. Computer Science and IT, TU Kathmandu Oct 2011 – Dec 2015

Selected Publications

Xu, Y., Chakraborty, T., Kıcıman, E., Aryal, B., Rodrigues, E., Sharma, S., et al. (2025). RLTHF: Targeted Human Feedback for LLM Alignment. ICML 2025.

Aryal, B., Miles, K. E., Zesati, S. A. V., & Fuentes, O. (2023). Boundary Aware U-Net for Glacier Segmentation. Proceedings of the Northern Lights Deep Learning Workshop.

Aryal, B., Escarzaga, S. M., Vargas Zesati, S. A., Velez-Reyes, M., Fuentes, O., & Tweedie, C. (2021). Semi-automated semantic segmentation of arctic shorelines using very high-resolution airborne imagery, spectral indices and weakly supervised machine learning approaches. Remote Sensing, 13(22), 4572.

Baraka, S., Akera, B., Aryal, B., Sherpa, T., Shresta, F., Ortiz, A., ... & Bengio, Y. (2020). Machine learning for glacier monitoring in the Hindu Kush Himalaya. NeurIPS 2020 Workshop.

View all on Google Scholar →