Guillermo Gallego

Guillermo Gallego is an American data scientist, academic and author. He is the Liu Family Emeritus professor at Columbia University, the Crown Worldwide Professor Emeritus at The Hong Kong University of Science and Technology and is the X.Q. Deng Presidential Chair Professorship at The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen). Since 2025, he has served as the Dean of School of Data Science at CUHK-Shenzhen.

Gallego is most known for his works on discrete choice models, dynamic pricing, pricing analytics, assortment optimization and dynamic programming. Among his authored works are his publications in academic journals, including Management Science, Operations Research, Mathematics of Operations Research, and MSOM, as well as a book titled Revenue Management and Pricing Analytics. He is the recipient of two best-papers awards from Management Science and Operations Research, the 2011 INFORMS Historical Award of the Revenue Management & Pricing Section, the 2012 INFORMS Practice Award, the 2016 INFORMS Impact Prize, the 2021 INFORMS Revenue Management and Pricing Section Prize among others.

Guillermo has been elected as an INFORMS fellow in the class of 2012 and an MSOM fellow in the class of 2013. He is a Hong Kong Institution of Engineers (HKIE) Fellow since 2016, and has been listed in Stanford's List of World’s top 2% scientists.

Education Gallego completed his B.A. in Mathematics from the University of California, San Diego in 1980. Later in 1987, he obtained his M.S. from Cornell University, followed by a PHD from the same institution in 1988. He currently serves as the Operations Research Area Coordinator in the School of Data Science at the Chinese University of Hong Kong, Shenzhen.

Research Gallego, in his early research, explored how companies could smoothly recover from production schedule disruptions, and on integrating inventory control and vehicle routing to optimize distribution systems. His 1993 collaborative work presented a new proof of Scarf's ordering rule for the newsboy problem and extended its analysis to scenarios in inventory management, including the recourse case, fixed ordering costs, random yields, and multi-item competition for scarce resources. In the following year, he proposed dynamic pricing strategies for inventory management in industries with time-constrained sales, stochastic and price-sensitive demand, using intensity control, obtaining structural monotonicity, closed-form solutions, and extensions to more complex scenarios. Moreover, in 1995, he addressed the optimization of pricing strategies for industries facing fixed stock and finite horizon challenges. The study determined the optimal timing of mark-ups or mark-downs based on stochastic demand and price sensitivity to maximize revenues.

Gallego's 1997 work proposed stochastic pricing strategies to optimize network revenue management problems, deriving asymptotic optimality results for simple policies. In 2001, he explored optimal inventory control policies, such as state-dependent (s, S) and base-stock policies, considering advance demand information, aiming to improve revenue, capacity use, and operational performance in stochastic inventory systems. In 2008, he explored the strategic use of callable products, analyzing their role in generating riskless additional revenue in the context of revenue management problems. Through this research, he also provided optimal pricing and booking strategies, with potential extensions to multifare structures, network models, and other industries. While investigating the assortment optimization problems under the nested logit model in 2014, he demonstrated that the problem is polynomial solvability for the standard model, and proposed heuristic strategies for NP-hard cases. Later in 2016, he introduced a Markov chain choice model for assortment planning, approximating discrete choice models, optimizing assortments, and offering insights into substitution behavior.

In 2019, Gallego authored a book titled Revenue Management and Pricing Analytics with H. Topaloglu. Through this book, he bridged the disparity between current research findings and well-established literature within the field. His more recent work has focused on personalized pricing and personalized assortment optimization.

Awards and honors 2005-2006 – IBM Faculty award 2011 – INFORMS Revenue Management and Pricing Historical Prize, INFORMS 2012 – Fellow, INFORMS 2017 – INFORMS, Best OM paper award in Management Science 2019 – INFORMS, Best OM paper in Operations Research 2021 – INFORMS Revenue Management and Pricing Section Prize, INFORMS