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. I will be giving a talk titled Conservative Price Experimentation: Markdown Pricing Under Unknown Demand in the INFORMS meeting at 7:45-9:15 AM (PST) on Oct 26, in CC - Room 201A (in person) or Virtual Room 26 (virtual). You may also find my 20-minute video on Youtube. Detailed INFORMS schedule can be found here.

Research Interest

Below is a 7-minute video about my recent research:

My research interest is prescriptive analytics and revenue management, mainly on sequential decision-making, statistical learning theory, and approximation algorithms for computationally challenging operations problems.

On the practical side, I am fortunate to be able to collaborate closely with industry to bridge the gap between theory and practice, while exploring the aspects that theoreticians tend to overlook. Currently, I am leading a joint project with Glance, India's largest mobile lock-screen content platform, to improve their recommender system by incorporating ideas from bandits theory. I am also working with Bestar&Bush, a North American furniture manufacturer, on modernizing their global supply chain by leveraging their massive data.

Selected Work

The full list of my papers can be found in a separate section.


Markdown Pricing For 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. Is there still a separation if we add more assumptions, for example assuming the demand model has certain functional form? We introduce a complexity index that measures the complexity of a family, and provide a complete settlement of the problem under this framework by proving tight regret bounds for each regime. (Click here to watch my informs talk video)



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

Kyra Gan, Su Jia, Andrew Li, Sridhar Tayur. (NeurIPS'21) Winner, INFORMS Pierskalla 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 an Active Hypothesis Testing problem with the goal of identifying the cancer type using minimal number of tests. Apart from theoretical guarantees, we also demonstrated the 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. (Forthcoming, Manufacturing and Service Operations Management)

Retailers nowadays may use in-store inventory to fulfil the demands from different channels. Different from offline orders, online orders may be fulfilled periodically, and thus the key decision is the number of online orders to accept. We propose a gradient-based computational framework and demonstrated its effectiveness on Onera's data.



Conservative Price Experimentation: Markdown Pricing with Unknown Demand.

Su Jia, Andrew Li and R.Ravi.

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, since many of them overlooked 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.



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. We consider the problem of finding the lowest-cost decision tree. As opposed to prior work where the outcome of each test is assumed to be known, here 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) Beyond Holding Costs: How Overstocking Backfires in the Era of E-Commerce
    Su Jia, Sagnik Das and Sridhar Tayur
  2. (Working Paper) Short-Lived High-Volume Bandits: Algorithms and Field Experiments.
    Su Jia, Andrew Li, R. Ravi, Nishant Oli, 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. Towards 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 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
    Journal version submitted to 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.
    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.