I am an associate professor of operations management at the Naveen Jindal School of Management of Unversity of Texas at Dallas. Before joining UTD, I am an assistant professor of information systems and operations management at the Warrington College of Business of University of Florida. I obtained my PhD in Machine Learning at Carnegie Mellon University, advised by Aarti Singh. Before coming to CMU, I was an undergraduate student at the Yao Class in Tsinghua University.

I am generally interested in machine learning and its applications in revenue management and information systems research. My main research focus is on the development and analysis of sequential decision making methods under uncertainty, with emphasis to revenue management applications such as assortment optimization and dynamic pricing. My research is also connected with bandit online learning and reinforcement learning in machine learning research.

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Education

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Experiences

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Research topics include combinatorial bandit and reinforcement learning.

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Research topics include deep neural network and natural language processing systems.

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Design and implementation of Project Harbinger, a system for enterprise level malicious attack prediction based on collaborative filtering.

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Research topics include small-variance asymptotic analysis for Bayesian nonparametric models and spectral learning for latent variable models.

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Courses: Inference and Information, Nonlinear Programming and Automatic Speech Recognition
Research advisors: Jingjing Liu and Cynthia Rudin
Research topics: semantic role labeling in spoken dialogue systems and discrete optimization for learning to rank applications.

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Development of natural language processing applications in the medical informatics domain.

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Selected papers

Full list of publications available here.

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(Revenue management, assortment optimization, robust statistics)
Robust Dynamic Assortment Optimization in the Presence of Outlier Customers.
By Xi Chen*, Akshay Krishnamurthy* and Yining Wang*. [journal link]
Operations Research, accepted for publication.

(Bandit optimization, non-stationary stochastic optimization)
On Adaptivity in Non-stationary Stochastic Optimization With Bandit Feedback.
By Yining Wang. [journal link]
Operations Research (tech. note), accepted for publication.

(Supply-chain management, joint pricing and inventory control, censored demands)
Optimal Policies for Dynamic Pricing and Inventory Control with Nonparametric Censored Demands.
By Beryl Boxiao Chen*, Yining Wang* and Yuan Zhou*. [slides] [journal link]
Management Science, accepted for publication.

(Revenue management, dynamic pricing, digital privacy)
Differential Privacy in Personalized Pricing with Nonparametric Demand Models.
By Xi Chen*, Sentao Miao* and Yining Wang*. [journal link] [slides]
Operations Research, 71(2):581-602, 2023.

(Revenue management, active learning)
Active Learning for Contextual Search with Binary Feedbacks.
By Xi Chen*, Quanquan Liu* and Yining Wang*. [journal link]
Management Science, 69(4):2165-2181, 2023.

(Revenue management, pricing with demand learning, robust statistics)
Robust Dynamic Pricing with Demand Learning in the Presence of Outlier Customers.
By Xi Chen* and Yining Wang*. [journal link]
Operations Research, 71(4):1362-1396, 2023.

(Revenue management, re-solving control, stochastic models)
Constant Regret Re-solving Heuristics for Price-based Revenue Management.
By Yining Wang and He Wang. [journal link]
Operations Research, accepted for publication.

(Supply-chain management, joint pricing and inventory control with demand learning)
Dynamic Pricing and Inventory Control with Fixed Ordering Cost and Incomplete Demand Information.
By Beryl Boxiao Chen*, David Simchi-Levi*, Yining Wang* and Yuan Zhou*. [journal link] [slides]
Management Science, accepted for publication.

(Revenue management, privacy-aware dynamic pricing)
Privacy-Preserving Dynamic Personalized Pricing with Demand Learning.
By Xi Chen*, David Simchi-Levi* and Yining Wang*. [journal link]
Management Science, 68(7):4878-4898, 2022.

(Revenue management, dynamic assortment)
Optimal Policy for Dynamic Assortment Planning Under Multinomial Logit Models.
By Xi Chen*, Yining Wang* and Yuan Zhou*. [journal link]
Mathematics of Operations Research, 46(4):1639-1657, 2021.
Preliminary version in NeurIPS 2018, Montreal, Canada.

(Revenue management, dynamic pricing with demand learning)
Multi-modal Dynamic Pricing.
By Yining Wang, Beryl Boxiao Chen and David Simchi-Levi. [journal link]
Management Science, 67(10):6136-6152, 2021.

(Revenue management, dynamic pricing with demand learning)
Data-Based Dynamic Pricing and Inventory Control with Censored Demand and Limited Price Changes.
By Beryl Boxiao Chen, Xiuli Chao and Yining Wang. [journal link]
Operations Research (tech. note), 68(5):1445-1456, 2020.

(Bandit optimization, non-stationary stochastic optimization)
Non-stationary Stochastic Optimization with Local Spatial and Temporal Changes.
By Xi Chen*, Yining Wang* and Yu-Xiang Wang*. [slides] [journal link]
Operations Research (tech. note), 67(6):1752-1765, 2020.

(Machine learning/Statistics, computational experimental design)
Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach.
By Zeyuan Allen-Zhu*, Yuanzhi Li*, Aarti Singh* and Yining Wang*. [code] [slides] [journal link]
Mathematical Programming (Series A), 186:439-478, 2021.
Preliminary version in ICML 2017, Sydney, Australia.

(Machine learning, sparse subspace clustering)
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data.
By Yining Wang, Yu-Xiang Wang and Aarti Singh. [journal link]
IEEE Transactions on Information Theory, 65(2):685-706, 2019.
Preliminary version in ICML 2015, Lille, France.

(Machine learning, active learning, matrix column subset selection)
Provably Correct Active Sampling Algorithms for Matrix Column Subset Selection with Missing Data.
By Yining Wang and Aarti Singh. [journal link]
Journal of Machine Learning Research, 18(156):1-42, 2018.
Preliminary version in AISTATS 2015, San Diego, USA.

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