Lin An

PhD Candidate in Algorithms, Combinatorics, and Optimization (ACO)
Tepper School of Business
Carnegie Mellon University
linan@andrew.cmu.edu

Biography

I am currently a fourth year PhD candidate in the interdisciplinary Algorithms, Combinatorics, and Optimization (ACO) doctoral program at the Tepper School of Business, Carnegie Mellon University. I am fortunate to be advised by Prof. Andrew A. Li and Prof. Benjamin Moseley.

My research focuses on leveraging AI to enhance decision-making processes, particularly through the integration of optimization and machine learning techniques. I explore a range of models, including stochastic, online, and prediction-based frameworks, aimed at addressing key challenges in operations management. My primary areas of application include resource allocation, recommendation systems, and inventory management, where I seek to develop innovative solutions that improve efficiency and performance in dynamic, data-driven environments.

Before coming to CMU, I earned my Masters's degree in Operations Research from Columbia IEOR in 2021, where I worked with Prof. Yuri Faenza. I obtained my Bachelor's degree in Math with a minor in Economics from Boston College in 2020.

Awards and Honors

Journal Articles

  1. The Nonstationary Newsvendor with (and without) Predictions.
        Lin An, Andrew A. Li, Benjamin Moseley, and R. Ravi. 2023.
        Minor revision, Manufacturing & Service Operations Management (M&SOM).

  2. Online Resource Allocation with Predictions under Unknown Arrival Model.
        Lin An, Andrew A. Li, Benjamin Moseley, and Gabriel Visotsky. 2024.
        Major revision, Management Science.
        A preliminary version appeared in the refereed venue of the 2024 Informs Optimization Society Conference (IOS 2024).

Conference Proceedings

  1. Timeliness Through Telephones: Approximating Information Freshness in Vector Clock Models.
        Da Qi Chen, Lin An, Aidin Niaparast, R. Ravi, and Oleksandr Rudenko. 2022.
        ACM-SIAM Symposium on Discrete Algorithms (SODA 2023).

Preprints under Review/Works in Preparation

  1. Variety Drives Engagement: Insights from Glance.
         Lin An, Joy Lu, R. Ravi, and Michael Zlatin. 2024.
         In Preparation.

  2. Real-Time Personalization with Simple Transformers.
         Lin An, Andrew A. Li, and Gabriel Visotsky. 2024.
         In Preparation.

Teaching

  • Mathematical Models for Consulting (Undergraduate). Instructor, 2024 Fall, Carnegie Mellon University.
    This is a semester-long undergraduate elective course for bussiness majors (syllabus). The course had 19 students.

    My course evaluation was 4.92 out of 5. As a comparison, the average course evaluation across the school was 4.22. Here is the complete course evaluation form. Below are some student comments:

    "Thank you for being a wonderful professor this semester! You did great for your first time teaching a course, and I hope you continue to have success on your academic journey. You did wonderfully at explaining different concepts and how they can be applied to real world examples, something that other classes dont really highlight."

    "Truly amazing course. Lin listens to his students and only assigns work that truly helps to reinforce our learning. I love how he thoroughly explains topics in class, then we immediately have a homework where we get to implement those learnings in real-world scenarios. Hes very realistic in his teachings and helps us prepare for optimization in the real world where we may not have all the data we want, so finding ways to deal with that and still get a relatively optimal solution."

    "For being a PhD student, I thought Lin did an excellent job with the course. He was engaged with students, provided good instructions, and cared about student success. I believe Lin has a future in academia if that is something he wishes to pursue."

    Personal comments: Teaching this course has been an incredibly rewarding and fulfilling experience, especially seeing students actually apply course materials to real-world problems. One particularly memorable moment was during the final project. A group of students were the founders of Capital Grains, a student-run restaurant at CMU. Using Gurobi and scheduling models from the course, they optimized employee schedules, which improved operational efficiency and saved $1,000 per month. Moments like these are why I love teaching. They remind me that education is not just about sharing knowledge but about empowering students to make a difference in their communities and beyond. Helping students connect theory to practice, witnessing their growth, and celebrating their successes make teaching a truly special and gratifying endeavor.

  • Operations Strategy (MBA). Teaching Assistant, 2023 Fall, Carnegie Mellon University.
  • Optimization (Undergraduate). Teaching Assistant, 2023 Fall, Carnegie Mellon University.
  • Advanced Graph Theory (PhD). Teaching Assistant, 2022 Fall, Carnegie Mellon University.

Invited Talks

  • Online Resource Allocation with Predictions
    INFORMS Annual Meeting, October 2024.
    NYU Stern MOILS Seminar, September 2024.
    INFORMS Revenue Management and Pricing Section (RM&P) Coference, July 2024.
    M&SOM Coference, July 2024.
    INFORMS Optimization Society (IOS) Coference, April 2024.
    INFORMS Annual Meeting, October 2023.

  • The Nonstationary Newsvendor with (and without) Predictions
    INFORMS Annual Meeting, October 2022.

Service

Personal

  • I like playing poker (all kinds, mainly Texas hold 'em). Here and here are my WSOP Player Profiles. Here is my Hendon Mob Profile. Here are some photos of me playing poker at WSOP and at CMU (where I eliminate Xuan Liu to get this nice photo). It would be nice to win a bracelet someday!

    Update: I won a WSOP Circuit Ring on August 02, 2024.

  • I am a big fan of Tottenham Hotspur, a soccer team in the English Premier League. We have a Pittsburgh Spurs Supporters' Club, where we go to a bar and watch games together on matchdays (if we win, we take a photo). If you would like to join, contact me!
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