Galib Hassan Khan

1417 White Avenue

Knoxville, TN 37996

Born and raised in Bangladesh, I am currently pursuing my Ph.D. in chemical engineering at the University of Tennessee, Knoxville, which I started in January 2023. I obtained my M.S. in chemical engineering from the same university in December 2024, focusing on computational biology and metabolic engineering.

My research interests lie at the intersection of machine learning, systems biology, and metabolic engineering. I work on developing computational tools for metabolic network modeling, modular cell design, and microbial community design. My work aims to leverage artificial intelligence and optimization techniques to advance our understanding of biological systems and enable rational design of engineered microorganisms.

Before starting my graduate studies, I completed my B.Sc. in chemical engineering from Bangladesh University of Engineering and Technology (BUET) in April 2019. The same year, I co-founded an edtech startup named “Bohubrihi” with the mission of providing professional technical education in Bengali, where I served as Co-Founder and Chairman. Following its successful acquisition in late 2021, I joined the International Centre for Diarrhoeal Disease Research, Bangladesh (iccdr,b) as a Research Officer, where I worked on reducing air pollution in the brick manufacturing industry in Bangladesh.

I am passionate about using computational approaches to solve complex biological and engineering problems, with a particular focus on metabolic network modeling, machine learning applications in biology, and omics data analysis.

selected publications

Smart culture medium optimization for recombinant protein production: Experimental, modeling, and AI/ML-driven strategies

Galib Khan, C. Sanford, and Cong T. Trinh

Biotechnology Advances, 2025

Abs DOI

This comprehensive review explores the integration of experimental approaches, mathematical modeling, and artificial intelligence/machine learning strategies for optimizing culture medium in recombinant protein production. The paper presents state-of-the-art methodologies that combine traditional bioprocess engineering with modern computational techniques to enhance protein yield and quality in biomanufacturing systems.
A Multi-Objective Optimization Framework for Scalable Biomanufacturing through Modular Cell Design and Resource Allocation

Galib Hassan Khan, and Cong T. Trinh

In 2025 AIChE Annual Meeting, 2025

Abs

This work presents a novel multi-objective optimization framework for scalable biomanufacturing that leverages modular cell design principles and optimal resource allocation strategies. The framework addresses the challenge of designing efficient microbial cell factories by balancing multiple competing objectives including productivity, yield, and robustness. The approach enables systematic optimization of metabolic pathways and cellular resources for industrial-scale bioprocessing applications.