Bio

I am an Assistant Research Professor at the Center for Data Science for Enterprise and Society (CDSES) in Cornell University. I am interested in data-driven decision-making for business problems, especially in marketing, such as advertising, recommendation, promotions and pricing. Specifically, my current research focuses on online learning and optimization problems in digital marketing. I have also worked on approximation algorithms for discrete optimization in the past.

A large bulk of of my work are motivated by collaboration with industry. For example, we collaborated with Glance, India's largest mobile lock-screen content platform, to improve their recommender system for short-lived contents, and showed the effectiveness of our policy in a large-scale field experiment.

Prior to joining Cornell, I obtained my PhD degree in the ACO program (Algorithms, Combinatorics and Optimization) at the Tepper School of Business, Carnegie Mellon University. I was fortunate to be able to work with R. Ravi and Andrew Li. I received my BS degree in Mathematics from Tsinghua University in 2014, and MS degree in Applied Mathematics and Statistics from the State University of New York at Stony Brook in 2017, where I was fortunate to work on computational geometry with Joseph Mitchell and Jie Gao.

I received the INFORMS Pierskalla Best Paper Award in Health Applications in 2021 and the INFORMS George B. Dantzig Dissertation Award Dissertation Award in Operations Research and Management Science in 2022. Here is my CV.

Selected Work

The full list of my papers can be found in a separate section.
Short-Lived High-Volume Bandits: Algorithms and Field Experiment

Su Jia, Nishant Oli, Andrew Li, R. Ravi, Paul Duff, Ian Anderson.

Platforms have been leveraging the scale of data to build recommenders for newly-generated contents. Drawing ideas from bandit theory, we propose policies for recommending a high-volume of short-lived contents, which we also show to be nearly best possible theoretically. More importantly, we collaborated with Glance, India's largest lockscreen content company and implmented a large-scale field experiment on their real system, where our policy significantly outperformed their current DNN-based recommender.



Active Sequential Hypothesis Testing

In clinical trials, tests are performed sequentially to identify a patient's unknown disease. In marketing, AB tests are performed sequentially to determine the best design of a website. We study the design of low-cost test procedures given a set of tests.

Unknown Outcomes: Optimal Decision Tree and Submodular Ranking with Noisy Outcomes. Su Jia, Fatemeh Navidi, Viswanath Nagarajan and R.Ravi. (NeurIPS'19)

Noisy Outcomes: Towards A Liquid Biopsy: Approximation Algorithms for Active Sequential Hypothesis Testing. Kyra Gan, Su Jia, Andrew Li, Sridhar Tayur. (NeurIPS'21) Winner, INFORMS Pierskalla Best Paper Award 2021



Dyanmic Pricing Under Monotonicity Constraint

While well-understood theoretically, bandit-based pricing policies are rarely deployed in the real world, largely for overlooking practical constraints. For example, oscillating prices may be unfavorable in many applications. We consider policies with non-increasing prices, and show optimal regret bounds that "separate" from the unconstrained setting.

Non-parametric model: Conservative Price Experiments: Markdown Pricing Under Unknown Demand. Su Jia, Andrew Li and R.Ravi. (Major Revision, Management Science)

Parametric model: Markdown Pricing Under Unknown Parametric Demand Models. Su Jia, Andrew Li, R. Ravi (NeurIPS'22)



Effective Online Order Acceptance Policies for Omni-Channel Fulfillment.

Su Jia, Jeremy Karp, R. Ravi, Sridhar Tayur. (Manufacturing and Service Operations Management)

Retailers nowadays may use in-store inventory to fulfill the demands from different channels. As opposed to the offline channel, the online channel is more flexible and its orders are fulfilled only periodically. The key problem then is how to jointly manage the orders from these two channels, with consideration of both profit and fulfillment level. We propose a gradient-based computational framework for finding the optimal threshold policy, and demonstrate its effectiveness using Onera's data.

Full List of Publications and Preprints

  • (Working Paper) Short-Lived High-Volume Bandit: Algorithms and Field Experiments.
    Su Jia, Nishant Oli, Andrew Li, R. Ravi, Paul Duff, Ian Anderson.
  • Markdown Pricing for Unknown Parametric Demand Models.
    Su Jia, Andrew Li and R. Ravi.
    Preliminary version appeared in the proceedings of Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS'22)
  • Toward A Liquid Biopsy: Approximation Algorithms for Adaptive Hypothesis Testing.
    Kyra Gan, Su Jia, Andrew Li, Sridhar Tayur.
    Preliminary version appeared in the proceedings of Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS'21)
    Winner, INFORMS Pierskalla Best Paper Award 2021
    Journal version submitted to Management Science
  • Conservative Price Experimentation: Markdown Pricing with Unknown Demand.
    Su Jia, Andrew Li and R. Ravi.
    Egon Balas Award for best CMU student operations research paper, 2020
    Major revision, Management Science
  • Optimal Decision Tree and Submodular Ranking with Noisy Outcomes.
    Su Jia, Fatemeh Navidi, Viswanath Nagarajan and R.Ravi.
    Preliminary version appeared in the proceedings of Thirty-third Conference on Neural Information Processing Systems (NeurIPS'19)
    Journal version submitted to Operations Research
  • Effective Online Order Acceptance Policies for Omni-Channel Fulfillment.
    Su Jia, Jeremy Karp, R. Ravi, Sridhar Tayur.
    Forthcoming, Manufacturing and Service Operations Management (M&SOM)
  • Competitive Analysis for Online Scheduling in Software-Defined Optical WAN.
    Su Jia, Xin Jin, Golnaz Ghasemiesfeh, Jiaxin Ding, and Jie Gao.
    IEEE International Conference on Computer Communications 2017 (INFOCOM’17)
  • Network Optimization on Partitioned Pairs of Points.
    Esther Arkin, Aritra Banik, Paz Carmi, Gui Citovsky, Su Jia, Matthew Katz, Tyler Mayer and Joseph S. B. Mitchell
    The 28th International Symposium on Algorithms and Computation (ISAAC'17)
  • Geometric Tours to Visit and View Polygons Subject to Time Lower Bounds. (Manuscript)
    Su Jia and Joseph S. B. Mitchell.
  • Deep Manifold Learning of Symmetric Positive Definite Matrices with Application to Face Recognition.
    Zhen Dong, Su Jia, Chi Zhang, Tianfu Wu, and Mingtao Pei.
    Thirty-First AAAI Conference on Artificial Intelligence (AAAI'17)
  • Exact and Approximation Algorithms for Time-Window TSP and Prize Collecting Problem.
    Su Jia, Jie Gao, Joseph S. B. Mitchell and Lu Zhao.
    International Workshop on the Algorithmic Foundations of Robotics 2016 (WAFR'16)
  • Face Video Retrieval via Deep Learning of Binary Hash Representations.
    Zhen Dong, Su Jia, Chi Zhang, Tianfu Wu, and Mingtao Pei.
    Thirtieth AAAI Conference on Artificial Intelligence (AAAI'16)
  • Contact

    Email: sjia1 at andrew dot cmu dot edu.

    Office: Tepper Quad 4201.