I am a fifth-year Ph.D. student at the MIT Operations Research Center, advised by Prof. Robert Freund. My current research interests are mainly in optimization, with twin interests in theoretical foundations and computational practice.

My research up to now has involved enhancing the scalability of optimization algorithms for solving large-scale linear and convex optimization -- both generically as well as on specific applications. I am delighted that some of my methods have already been implemented in the state-of-art commercial solvers (COPT in particular). My overarching aspiration is to extend the capability of modern optimization methods to effectively solve problems arising in operations research, data science, and machine learning/AI.

In addition to MIT, I have had research experience at JPMorgan Chase AI Research (summer 2023), and at Fudan University; at Fudan I worked on projects supervised by Prof. Yinyu Ye (Stanford) and Prof. Dongdong Ge (Shanghai Jiao Tong University). Please see below for details.

I am on the academic job market in AY2024-25.

Research

Papers Published or Under Review at Journals

• Using Taylor-Approximated Gradients to Improve the Frank-Wolfe Method for Empirical Risk Minimization
  Zikai Xiong and Robert M. Freund paper slides additional results and extensions
  SIAM Journal on Optimization
• The Role of Level-Set Geometry on the Performance of PDHG for Conic Linear Optimization
  Zikai Xiong and Robert M. Freund paper slides
  Under review at Mathematical Programming
  ⭐ George Nicholson Student Paper Competition Finalist
  ⭐ MIT ORC Best Student Paper Competition Honorable Mention
• Computational Guarantees for Restarted PDHG for LP based on "Limiting Error Ratios" and LP Sharpness
  Zikai Xiong and Robert M. Freund paper slides additional results
  Under revision for Mathematical Programming
  ⭐INFORMS Optimization Society Student Paper Prize Honorable Mention
• Accessible Theoretical Complexity of the Restarted Primal-Dual Hybrid Gradient Method for Linear Programs with Unique Optima
  Zikai Xiong paper slides
  Under review at Mathematics of Operations Research
• From an Interior Point to a Corner Point: Smart Crossover
  Dongdong Ge, Chengwenjian Wang, Zikai Xiong, and Yinyu Ye papercodesslides
  Minor revision at INFORMS Journal on Computing
• Low-rank Traffic Matrix Completion with Marginal Information
  Zikai Xiong, Renjie Xu, Yanwei Xu, and Yimin Wei paper
  Journal of Computational and Applied Mathematics

Papers Published or Under Review at Conferences

• Fair Wasserstein Coresets
  Zikai Xiong, Niccolò Dalmasso, Shubham Sharma, Freddy Lecue, Daniele Magazzeni, Vamsi K. Potluru, Tucker Balch, Manuela Veloso paper
  Preliminary version appeared in NeurIPS 2023 Workshop SyntheticData4ML (Oral Presentation)
  Neural Information Processing Systems (NeurIPS 2024)
• FairWASP: Fast and Optimal Fair Wasserstein Pre-processing
  Zikai Xiong, Niccolo Dalmasso, Alan Mishler, Vamsi Potluru, Tucker Balch, Manuela Veloso paper
  AAAI Conference on Artificial Intelligence (AAAI 2024)
• Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion
  Zhengqi Gao, Fan-Keng Sun, Mingran Yang, Sucheng Ren, Zikai Xiong, et al. paper codes
  European Conference on Computer Vision (ECCV 2022)
• Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem
  Dongdong Ge, Haoyue Wang, Zikai Xiong, and Yinyu Ye papercodesslides
  Neural Information Processing Systems (NeurIPS 2020)

Technical Reports

• Impact of the “Hidden City Ticketing” under the Presence of Risk-Conscious Customers
  Jiacheng Guo, Bo Jiang, Zikai Xiong, and Nanxi Zhang paper
  To be submitted, 2024
• On the Relation Between LP Sharpness and Limiting Error Ratio and Complexity Implications for Restarted PDHG
  Zikai Xiong and Robert M. Freund technical report

Work in Progress

• High-Probability Polynomial Complexity of Restarted PDHG for Linear Programming
  Zikai Xiong
  In preparation, 2024
• Advanced Diagonal Preconditioning for the First-Order Methods for LP
  Zikai Xiong, Wenzhi Gao and Yinyu Ye
  In preparation, 2024

Work Experience

• Research Associate Intern, J.P.Morgan Chase AI Research, Summer 2023
  Managers: Niccolò Dalmasso, Vamsi Potluru and Manuela Veloso
  Algorithmic fairness in machine learning and large-scale optimization.
• Research Assistant, MIT Sloan School of Management, 2020 - present
  Supervisor: Robert M. Freund
  Modern optimization methods for huge-scale operations research and machine learning problems.
• Research Assistant, SUFE Research Institute for Interdisciplinary Sciences, 2019 - 2020
  Supervisors: Yinyu Ye (Stanford), Dongdong Ge (Shanghai Jiao Tong University)
  Optimization for operations research and machine learning.
  Developed and improved algorithms for the state-of-art commercial solver COPT (Cardinal Optimizer).

Teaching and Mentoring

Teaching

• Winter 2024: 15.S60 Computing in Optimization and Statistics
  - Instructor for the advanced optimization sessions of a computing course during MIT January Independent Activities Period. The duties are designing courses, creating teaching materials, and giving lectures.
  - Jupyter Notebook 1 Jupyter Notebook 2

• Fall 2022 and Fall 2023: 15.081 Introduction to Mathematical Programming (Teaching score: 6.8/7 Student feedback])
  - Head TA for the MIT’s doctoral introductory optimization course (for ORC and other MIT PhD programs)
  - Recitation Notes Recitation Solutions (Permission Required)
  ⭐ I was voted as the "Best TA" in the ORC Superlatives voting (photo)

• Spring 2022: 15.071 The Analytics Edge (Teaching score: 6.4/7 Student feedback])
  - TA for MBA course in business analytics at Sloan School of Management.

• Online Course in Data Science and Business Analytics
  - Designed course materials of a new online course in data science and business analytics.
  - Supervisor: Robert Freund

Mentoring

• Qiushi Han, University of Illinois Urbana-Champaign (UIUC)
  (Former SUFE undergraduate who transferred to UIUC and who asked me to continue to supervise his work)
  First-order methods for identifying optimal basic feasible solutions of linear programs, with applications to first-order integer programming
• Chenghan Xie, Stanford University
  (Incoming PhD student at Stanford who has asked me to collaborate with him on research)
  Computational guarantees and lower bound examples of PDHG for discounted Markov decision process (MDP)

Talks

• Level-Set Geometry and Improving the Performance of PDHG for Linear Optimization slides
  - OM Seminar, MIT, Cambridge, MA, September 2024
  - Cornell ORIE Young Researchers Workshop, Ithaca, NY, October 2024
  - INFORMS Annual Meeting, Seattle, WA, October 2024
  - INFORMS Annual Meeting (Nicholson Student Paper Competition), Seattle, WA, October 2024
• Theoretical Complexity of PDHG for Linear Programs with Unique Optima slides
  - Conference in honor of Yinyu Ye’s retirement, Shanghai, China, July 2024
  - International Symposium on Mathematical Programming (ISMP), Montréal, Canada, July 2024
  - Modeling and Optimization: Theory and Applications (MOPTA), Lehigh University, PA, August 2024
• Improving the Geometry of (Conic) Linear Optimization Problems for the Primal-Dual Hybrid Gradient Method (PDHG) slides
  - Workshop on Modern Continuous Optimization, Cambridge, MA, August 2023
  - ORC Student Seminar, MIT, Cambridge, MA, October 2023
  - INFORMS Annual Meeting, Phoenix, AZ, October 2023
  - Invited formal talk at Shanghai University of Finance and Economics, Shanghai, China, September 2023
  - LIDS Conference, MIT, Cambridge, MA, February, 2024
  - INFORMS Optimization Society Conference, Houston, TX, March 2024
  - Modeling and Optimization: Theory and Applications (MOPTA), Lehigh University, PA, August 2024
• Geometric Condition Measures for the Primal-Dual Hybrid Gradient Method for Linear Programming slides
  - SIAM Conference on Optimization (SIOPT), Seattle, WA, June 2023
  - ORC Student Seminar, MIT, Cambridge, MA, March 2023
• Using Taylor-Approximated Gradients to Improve the Frank-Wolfe Method for Empirical Risk Minimization slides
  - International Conference on Continuous Optimization (ICCOPT), Lehigh University, Bethlehem, PA, July 2022
  - ORC Student Seminar, MIT, Cambridge, MA, October 2022
  - INFORMS Annual Meeting, Indianapolis, IN, October 2022
• From an Interior Point to A Corner Point: Smart Crossover slides
  - INFORMS Annual Meeting, Indianapolis, IN, October 2022
• Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem slides
  - INFORMS Annual Meeting, Seattle, WA, October 2019
  - "Heart of the Machine" Seminar, Shanghai, China, October 2019
• Computing the Wasserstein Barycenter Efficiently: A Structured Linear System and Customized Algorithms
  - Seminar of New Advances in Theory and Application of ADMM, Shanghai, China, March 2019

Service

Reviewer

• Journal: SIAM Journal on Optimization (SIOPT), Mathematical Programming

• Conference: ICML, NeurIPS

Session (Co-)Chair

• INFORMS Annual Meeting, Seattle, WA, October 2024
• International Symposium on Mathematical Programming (ISMP), Montréal, Canada, July 2024
• INFORMS Optimization Society Conference, Houston, TX, March 2024
• INFORMS Annual Meeting, Indianapolis, IN, October 2022

Awards and Honors

• Finalist George Nicholson Student Paper Competition, 2024
• Honorable Mention, INFORMS Optimization Society Student Paper Prize, 2024
• Honorable Mention, MIT ORC Best Student Paper Competition, 2024
• First Prize, MIT ORC Common Experience Deep Learning Challenge, 2021
• INFORMS Optimization Society Conference Travel Award, 2024
• SIAM Travel Award, 2021
• MIT Graduate Student Council Travel Grant, 2022
• Fudan Graduation Star, 2020

My Photo Gallery

See here for some interesting photos from my travels and my life.