Welcome

Education

My name is Yunan Liu (刘雨楠). I am currently a principal research scientist at Amazon. Before this, I was a professor in the Industrial and Systems Engineering Department at NC State University from 2011 to 2024. Currently, I am holding an adjunct professor position at ISE. I received my B.E. in Electrical Engineering from Tsinghua University in 2002; my M.S. in Industrial Engineering and Operations Research (IEOR) from Columbia University in 2008; and my Ph.D., also in IEOR from Columbia University, in 2011. The title of my dissertation is: “Many-server queues with time-varying arrivals, customer abandonment and non-exponential distributions”. My Ph.D. adviser is Professor Ward Whitt.

Research and Teaching

My research interests are below:
– Methodologies: stochastic modeling, applied probability, computer simulation, queueing theory, queueing economics, optimal control, and reinforcement learning;
– Applications: customer contact centers, health care,  manufacturing systems, blockchain systems, food service systems, and transportation.

The classes I regularly teach include
– “ISE 362: Stochastic Models in IE”,
An introductory level Operations Research course for undergraduate students;
– “ISE 760: Applied Stochastic Modeling”,
A Ph.D. core course;
– “ISE 761: Advanced Stochastic Modeling and Queues”,
A sequel course to ISE 760 that covers various types of stochastic processes, such as Brownian motions, martingales, quasi birth-and-death processes, and fundamental queueing models;
– “ISE 762: Stochastic Simulation Techniques“,
Another sequel course to ISE 760 that introduces techniques to design effective simulation algorithms for the modeling and decision making of problems in OR and IE;
– “ISE 789: Dynamic Stochastic Optimization and Reinforcement Learning”
This new course introduces reinforcement learning (RL) methodologies to solve relevant problems in OR and IE. This course will cover a rigorous ground on formulation and solution techniques of Markov decision process (foundation of RL), introduce modern RL methods (Monte-Carlo, temporal-difference, function approximations, etc.), and connect to some state-of-the-art research on RL. The very unique feature of this class is that various IE/OR related problems (e.g., inventory management, queueing control, revenue management, platform delivery, sales management, gambling, financial engineering, etc.) will be used to demonstrate how RL methods may apply. Prerequisits of this course include ISE760, 762 and 505.

About Me

I was born and grew up in Beijing, China. I went to the No.4 high school. I like running, rock climbing, hiking, basket ball and badminton. When I am not in my office or my classrooms, I am probably running in Umstead Park or rock climbing at Triangle Rock Club.