PeopleofSuperintelligence

Researcher

Jiali Wang

Also known as 王佳莉

Jiali Wang is a machine learning researcher at Ant Group whose work develops fast optimization algorithms for large-scale Stackelberg prediction games. In 2019, she began master's studies at Fudan University's School of Data Science under advisor Rujun Jiang. With collaborators, she published a 2021 semidefinite and second-order cone programming method for least-squares Stackelberg prediction games that ran up to 20,000+ times faster than prior methods, followed in June 2022 by a spherically constrained least-squares reformulation solved with factorization-free Krylov subspace and Riemannian trust-region algorithms. Her 2022 paper won an ICML Outstanding Paper Award in July 2022, and by 2022 she had graduated from Fudan University and joined Ant Group.

Timeline

  1. 2019

    Master's studies at Fudan University

    In 2019, she enters the master's program at Fudan University's School of Data Science under advisor Rujun Jiang, focusing on optimization algorithms for large-scale machine learning problems.

  2. May 2021

    A single-SDP Stackelberg game reformulation

    With He Chen, Rujun Jiang, Xudong Li and Zihao Li, she is first author of Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss in May 2021. The paper reformulates the least-squares Stackelberg prediction game as a single semidefinite program of the same dimension as bisection subproblems and reduces it to a second-order cone program.

  3. 2021

    Second-order cone program speedups

    On synthetic and real-world datasets in 2021, numerical results indicate that her team's second-order cone programming method runs up to 20,000+ times faster than the state of the art.

  4. June 2022

    Spherically constrained least-squares reformulation

    In June 2022, Wang posts Solving Stackelberg Prediction Game with Least Squares Loss via Spherically Constrained Least Squares Reformulation as first author. To solve large-scale instances, the paper applies factorization-free Krylov subspace and Riemannian trust-region methods.

  5. July 2022

    An ICML Outstanding Paper Award

    At ICML in Baltimore in July 2022, her paper on the spherically constrained least-squares reformulation with Wen Huang, Rujun Jiang, Xudong Li and Alex L. Wang receives one of ten ICML 2022 Outstanding Paper Awards.

  6. By 2022

    Fudan graduation and Ant Group

    By 2022, she graduates with her master's degree from Fudan University's School of Data Science and works at Ant Group.

  1. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Fudan University. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  2. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Fudan University. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  3. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Fudan University. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  4. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. 复旦大学大数据学院硕士研究生王佳莉、副教授江如俊、青年研究员郦旭东获得ICML2022杰出论文奖 sds.fudan.edu.cn. Checked 22 October 2026.
  2. Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss arxiv.org. Checked 22 October 2026.
  3. Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss proceedings.mlr.press. Checked 22 October 2026.
  4. Solving Stackelberg Prediction Game with Least Squares Loss via Spherically Constrained Least Squares Reformulation arxiv.org. Checked 22 October 2026.
  5. Solving Stackelberg Prediction Game with Least Squares Loss via Spherically Constrained Least Squares Reformulation proceedings.mlr.press. Checked 22 October 2026.
  6. ICML 2022 Awards icml.cc. Checked 22 October 2026.
  7. Congratulations to the #ICML2022 outstanding paper award winners - AIhub aihub.org. Checked 22 October 2026.