Xin Fei

Teaching

• UG: Business Analytics and Information Systems (BUST08032) (2021-present)
• PG: Principles of Data Analytics (CMSE11432) (2022)
• PG: Applied Decision Optimisation (CMSE11612) (2023-present)
• PG: Online Learning and Decision Making (CMSE11642) (2023-present)

Administrative Duties

• PGR Representative for Management Science and Business Economics Group (2022-present)

Professional Affiliations

• Fellowship of the Higher Education Academy
• The Operational Research Society
• The Institute for Operations Research and the Management Sciences

I am a University Lecturer at the University of Edinburgh Business School, where I have been serving since 2022. Prior to this appointment, I was a Lecturer in Business Analytics at the University of Bristol and held a fellowship at the Bristol Digital Futures Institute. I completed my PhD in Operations Research & Management Science at Warwick Business School under the supervision of Professor Juergen Branke and Professor Nalan Gulpinar.

My research stands at the intersection of business analytics, optimization, and decision science methodologies, with a particular emphasis on operational research applications and data-driven decision making. I specialize in developing novel methodological approaches within stochastic programming, simulation optimization, and reinforcement learning. My work focuses on creating computationally efficient methods for complex decision problems under uncertainty, including innovative approaches to scenario generation and information collection procedures.

These theoretical foundations have found practical applications across various domains, including transportation systems, industrial engineering, and pharmaceutical portfolio planning. The impact of this work has been recognized through competitive research grants and as part of a research team whose innovative work in air traffic management was honored with the Jane's Innovation Award at the 2019 World Air Traffic Management Congress in Madrid.

My approach to teaching centers on active engagement with students, particularly in the areas of predictive and prescriptive analytics. This commitment to teaching excellence has been recognized through multiple nominations for the EUSA Teaching Award in both 2023 and 2024, as well as receiving the Teaching Excellence Award from the Business School in 2024. 

I actively welcome opportunities to supervise students whose research interests align with my expertise.

Optimization and Decision Analytics

• Scholastic programming
• Reinforcement learning 
• Statistical ranking and selection

Applied Operations Research

• Transportation and logistics optimization
• Engineering systems design and efficiency
• Pharmaceutical portfolio optimization