Yaqiao Li 李雅樵

I am an Assistant Professor in SUAT (深圳理工大学) in Shenzhen, China.
I got my Master in pure math from Peking Univeristy, PhD in computer science from McGill Univeristy, Postdoc in University of Montreal and Concordia Univeristy.
I was fortunately mentored by Jinpeng An, Hamed Hatami, Pierre McKenzie, Denis Pankratov and Lata Narayanan.

Postdocs, PhD/Master students, research visitors positions are available. Send your CV and RS to apply.

Email: liyaqiao@suat-sz.edu.cn

News in Research(Google Scholar):

  • 2025: Our paper "Diversity-seeking swap games in networks" is accepted by AAMAS 2025. Topic: game theory.
  • 2025: Our paper "Renting servers for multi-parameter jobs in the cloud" won ICDCN 2025 best paper award! Topic: online computation.
  • 2024: Our paper "Perspective on complexity measures targeting read-once branching programs" is accepted by Information and Computation. Topic: computational complexity.
  • 2024: Our paper "On the Online Weighted Non-Crossing Matching Problem" is accepted by SWAT 2024. Topic: online computation.
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My research focuses on understanding resource-constrained computing and developing the mathematics that underpin it. Interestingly, seemingly disparate resource constraints share deep connections, see the figure. Explore more about specific resource types below.

I'm currently investigating branching programs and online graph coloring.
For the related math, I'm exploring a new version of Kakeya sets.

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Time and space are fundamental computational resources analyzed within various models, including query complexity in decision trees, time-space tradeoffs in branching programs, and quantum computing models.

I am currently investigating the challenge of establishing lower bounds for branching programs.

  1. Yaqiao Li, Pierre McKenzie, Perspective on complexity measures targeting read-once branching programs, Information and Computation (2024). arXiv:2305.11276.
  2. click for a Summary: Branching program (aka, binary decision diagram) is a more flexible counterpart of decision tree that represents/computes Boolean functions. It is: (i) a basic model for studying computational space, (ii) closely related to pseudorandomness, (iii) a fundamental data structure, (iv) useful to synthesize circuits and in formal verification, etc.
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    Fig a: A decision tree (left) and a branching program (right). Picture from wikipedia.
    This paper studied the minimum size of read-once branching programs from the perspective of proposing lower bound complexity measures, and demonstrated their strengths as well as limitations, such as on the Tseitin formula (which is applicable in proof complexity), and on the Tree Evaluation Problem (which is studied for understanding a fundamental open problem in complexity: Logspace vs Polytime), etc.
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    Fig b: A figure in our paper comparing different complexity measures.

  3. Shenggen Zheng, Yaqiao Li, Minghua Pan, Jozef Gruska, Lvzhou Li, Lifting query complexity to time-space complexity for two-way finite automata, Journal of Computer and System Sciences (2024).
  4. click for a Summary: Is quantum computing more powerful than classical computing? Recently, many quantum computers built showed superior computational advantage.
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    Fig a: Quantum computer Zuchongzhi, photo credit from Pan Jianwei's team, see their work and a news report.
    This paper, using the lifting technique in communication complexity, proved that in the two-way finite automata model, quantum computing is more time-space efficent than classical computing.
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    Fig b: A snapshot of a proof in our paper.

  5. Yaqiao Li, Conflict complexity is lower bounded by block sensitivity, Theoretical Computer Science 856 (2021) 169–172. arXiv:1810:08873.
  6. click for a Summary: If you use a decision tree to compute a composition of two Boolean functions f and g, how does this relate to a decision tree computing f and a decision tree computing g? The measure of conflict complexity was proposed to study (a randomized version of) this problem. Block sensitivity is another complexity measure that tells how a function's value is affected by changing a block of its input.
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    Fig a: A decision tree computing a Boolean function. Picture from wikipedia.
    This paper showed block sensitivity is no greater than conflict complexity, and posed an open problem concerning conflict complexity and certificate complexity, solution of which would have interesting consequences.
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    Fig b: An open question proposed in this work.