Su Jia

Tepper School of Business, Carnegie Mellon University

sjia1 at andrew.cmu.edu

Tepper Quad 4135

Pittsburgh, PA, 15232

..............................................................

I am a 4th PhD candidate in the ACO program (Algorithms, Combinatorics and Optimization) at the Tepper School of Business, Carnegie Mellon University. I am fortunate to be advised by R.Ravi and Andrew Li.

My research lies on the interface between Operations Management, Operations Research, and Machine Learning. On the theory side, recently I focus on the interplay between online learning and optimization, especially problems motivated by pricing, supply chain management and online platforms. On the application side, I collaborate closely with industry to bridge the gap between theory and practice, and aim at maximizing the business impact of theoretical ideas. Currently, I am leading a joint project with Glance, India's largest mobile lock-screen content provider with more than 100 million daily active users, to improve their recommender system by incorporating ideas from online learning. I am also working with Bestar Bush, a leading North American furniture manufacturer, on modernizing their global supply chain, in particular, on improving the resilience and service level by leveraging their massive historical data.

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 lucky to work on geometric approximation algoriothms and computational gepmetry with Joseph Mitchell.

Research Interests:

Dynamic pricing, revenue management, approximation algorithms, online learning, optimization under uncertainty

Teaching

google scholar profile

Working Papers

Tight Regret Bounds for Short-Lived High-Volume Bandits. Su Jia, Andrew Li and R. Ravi. Poster (MIW2021)

Online Learning for Short-Lived High-Volume Content Recommendation: Evidence From A Field Experiment. Su Jia, Andrew Li, R. Ravi, Nishant Oli, Paul Duff, Ian Anderson.

Instance-Dependent Regret Bounds Markdown Pricing for Parametric Families. Su Jia, Andrew Li and R. Ravi.

Recent Publications and Papers Under Review

Approximation Algorithms for Adaptive Multiple Hypothesis Testing. Kyra Gan, Su Jia, Andrew Li. (Under Review)

Tight Regret Bounds for Markdown Pricing with Unknown Demand. Su Jia, Andrew Li and R.Ravi. (Under Review)

Effective Online Order Acceptance Policies for Omni-Channel Fulfilllment. Su Jia, Jeremry Karp, R. Ravi, Sridhar Tayur. (Forthcoming, Manufacturing and Service Operations Management)

Optimal Decision Tree with Noisy Outcomes. Su Jia, Fatemeh Navidi, Viswanath Nagarajan and R.Ravi. NeurIPS 2019 (Journal version under review)Poster full version

Earlier Papers

Geometric Tours to Visit and View Polygons Subject to Time Lower Bounds. 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. AAAI 2017

Competitive Analysis for Online Scheduling in Software-Defined Optical WAN. Su Jia, Xin Jin, Golnaz Ghasemiesfeh, Jiaxin Ding, and Jie Gao. IEEE INFOCOM 2017

Network Optimization on Partitioned Pairs of Points. with Esther Arkin, Aritra Banik, Paz Carmi, Gui Citovsky, Su Jia, Matthew Katz, Tyler Mayer and Joseph S. B. Mitchell. International Symposium on Algorithms and Computation 2017

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

Face Video Retrieval via Deep Learning of Binary Hash Representations. Zhen Dong, Su Jia, Tianfu Wu and Mingtao Pei. AAAI 2016

last update: May 2021