Bio

I am a 5th year PhD candidate in the ACO program (Algorithms, Combinatorics and Optimization) at the Tepper School of Business, Carnegie Mellon University. I am fortunate to be able to work with R. Ravi, Andrew Li and Sridhar Tayur.

Prior to joining CMU, 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 am currently on the job market, here is my resume. I have also uploaded my 20-minute INFORMS talk on Youtube, titled Conservative Price Experimentation: Markdown Pricing Under Unknown Demand .

Research Interest

My research interest lies in the operations-marketing interface. More precisely, my work focuses on operations efficiency with customer satisfaction, with applications in (1) transient content recommendation for content-aggregation platforms, (2) conjoint analysis in service and product design, and (3) dynamic pricing and inventory control in omnichannel retailing.

Many 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. Currently, I am collaborating with Bestar&Bush, a North American furniture manufacturer, on modernizing their inventory management policy for better service level on a third-party retailer.

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.



Markdown Pricing Under Unknown Parametric Demand Models

Su Jia, Andrew Li, R. Ravi

Our previous work showed the first separation between markdown and unconstrained pricing, under minimal assumptions. However, in practice, demand functions are usually assumed to have certain functional forms, which can potentially be utilized for designing better policies. We introduce a notion called "markdown dimension" that measures the difficulty of performing markdown pricing on a family, and provide a complete settlement of the problem by proving a tight regret bound for each markdown dimension.



Towards A Liquid Biopsy: Approximation Algorithms for Active Hypothesis Testing.

Kyra Gan, Su Jia, Andrew Li, Sridhar Tayur. (NeurIPS'21) Winner, INFORMS Pierskalla Best Paper Award 2021

Liquid biopsy is an emerging approach for early cancer detection, which aims at detecting mutations from the free-floating DNA in the blood. We consider the Active Hypothesis Testing problem, where the goal is to identify the unknown cancer type using minimal number of tests. In addition to theoretical guarantees, we also demonstrated the practical efficacy of our policies on real DNA mutation data.



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.



Conservative Price Experimentation: Markdown Pricing with Unknown Demand.

Su Jia, Andrew Li and R.Ravi. (Major Revision, Management Science), Egon Balas Award for best CMU student OR paper

Dynamic pricing with unknown demand has been extensively studied and often formulated as a bandit problem. While well-understood theoretically, bandit-based policies are rarely deployed in the real world, largely due to overlooking practical constraints. For example, the price may oscillate, which is unfavorable in practice. We consider markdown policies, i.e. policies with non-increasing prices, and show a tight regret bound that "separates" markdown and unconstrained pricing under unknown demand.



Optimal Decision Tree and Submodular Ranking with Noisy Outcomes.

Su Jia, Fatemeh Navidi, Viswanath Nagarajan and R.Ravi. (NeurIPS'19)

In medical diagnosis, a sequence of medical tests is performed to identify the patient's disease. The problem of finding the lowest cost testing procedure (i.e. decision tree) for a given set of tests is a classical problem in discrete optimization. As opposed to prior work where the test outcomes are assumed to be known, we consider the setting where some tests may have unknown outcomes, and provide the first approximation algorithms.

Full List of Publications and Preprints

  1. (Working Paper) The True Cost of Amazon Prime: Managing Inventory Under Fulfillment-Dependent Demands
    Su Jia and Sridhar Tayur.
  2. (Working Paper) Short-Lived High-Volume Bandit: Algorithms and Field Experiments.
    Su Jia, Nishant Oli, Andrew Li, R. Ravi, Paul Duff, Ian Anderson.
  3. Markdown Pricing for Unknown Parametric Demand Models.
    Su Jia, Andrew Li and R. Ravi.
    Journal version submitted to Management Science
  4. 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 Operations Research
  5. 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
  6. 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
  7. 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)
  8. 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)
  9. 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)
  10. Geometric Tours to Visit and View Polygons Subject to Time Lower Bounds. (Manuscript)
    Su Jia and Joseph S. B. Mitchell.
  11. 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)
  12. 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)
  13. 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.