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.
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.
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.
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.
Yaqiao Li, Pierre McKenzie,
Perspective on complexity measures targeting read-once branching programs,
Information and Computation (2024).
arXiv:2305.11276.
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.
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.
Fig b: A figure in our paper comparing different complexity measures.
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).
click for a Summary:
Is quantum computing more powerful than classical computing? Recently, many quantum computers built showed superior computational advantage.
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.
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.
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.
Fig b: An open question proposed in this work.
Information plays a central role in computation. I studied the information requirements for fundamental distributed computing tasks, such as determining set disjointness between two parties.
click for a Summary:
Information complexity, incorporating Shannon's information theory into communication models, is a continuous version of communication complexity. Initially, it was used to help study communication complexity, and later found many other applications such as in optimization and in circuit complexity, etc.
Fig a: Shannon, Yao, Braverman. Photo from wikipedia and amturing.acm.org.
This paper, as the 2nd part, completed the basic theory of the dependency of information complexity on the error allowed in computation. This in particular led to an an accurate formula of error-allowed information complexity of XOR function for a special distribution.
Fig b: An equality for information complexity of XOR function.Fig c: A snapshot of analysis of information complexity of AND function.
click for a Summary:
Information complexity, incorporating Shannon's information theory into communication models, is a continuous version of communication complexity. Initially, it was used to help study communication complexity, and later found many other applications such as in optimization and in circuit complexity, etc.
Fig a: Shannon, Yao, Braverman. Photo from wikipedia and amturing.acm.org.
This paper extended the optimal procol for two-party AND function in Braverman et. al. to multi-party, in doing so we had to invent various reductions and do meticulous calculation and computation.
Fig b: The (information cost) optimal protocol for computing k-party AND.Fig c: A condition we need to verify to verify to certify optimality of the protocol.
click for a Summary:
Information complexity, incorporating Shannon's information theory into communication models, is a continuous version of communication complexity. Initially, it was used to help study communication complexity, and later found many other applications such as in optimization and in circuit complexity, etc.
Fig a: Shannon, Yao, Braverman. Photo from wikipedia and amturing.acm.org.
This paper, as the 1st part, initiated the basic theory of the dependency of information complexity on the error allowed in computation. This led to a more accurate formula of the randomized communication complexity of the important disjointness function, and a solution of an open problem of Braverman.
Fig b: The formula for the randomized communication complexity of the disjointness function.Fig c: A communication protocol with economic information cost for the disjointness function.
Graph coloring is a fundamental mathematical topic with broad applications in computational resource allocation. My research investigates the lower and upper bounds of online graph coloring, and its connections to other online problems such as bin packing.
I'm currently exploring the intricate nature of online coloring 3-colorable graphs.
Yaqiao Li,
Denis Pankratov, Online Vector Bin Packing and Hypergraph Coloring Illuminated: Simpler Proofs and New Connections,
LAGOS 2023,
arXiv:2306.11241.
click for a Summary:
Bin packing is a classical NP-complete optimizaiton problem that is also of practical importance such as in loading cargo for shipping, creating file backups in media, etc.
Fig a: Illustration of 3-dimensional bin packing. Figure from the paper `Hybrid approach for solving real-world bin packing problem instances using quantum annealers' by Romero et. al.Fig b: A picture of high dimensional bin packing in AI's mind (AI generated image, credit to tongyi[通义]).
This paper introduced the online incidence matrix associated with the online hypergraph, which naturally led to a simple reduction from online hypergraph coloring to online vector bin packing, thus simplified a previous breakthrough by Azar et. al.. This paper also showed a 'diverse' multi-family has an interesting intersecting property, thus resolved a conjecture on online hypergraph coloring by Nagy-György and Imreh.
Fig c: Illustration of the online incidence matrix that we defined in this work.Fig d: Illustration of proving a conjecture on online hypergraph coloring by Nagy-György and Imreh.
click for a Summary:
Many large networks are constructed online, such as social networks and internet. Such graphs can be modelled by online graph, where at each time step a vertex appears together with its set of edges to some of existing vertices. A fundamental question is: suppose an online graph has completed its construction and turns out to be k-colorable, then, how many colors do we need if we have to irrevocably color every vertex as soon as it appears? A line of work including Lovász et. al. and Gutowski et. al. gives satisfactory understanding for bipartite graphs. For 3-colorable graphs nontrivial progress has also been made, but far from satisfactory.
Fig a: Social networks could facilitate and enrich our lives. Picture from baidu.Fig b: Social networks connect people in universe (AI generated image, credit to tongyi[通义]).
Motivated to explore the strength and weakness of existing techniques, this paper introduced the bounded components model. One one hand, we discovered that the efficiency for coloring online bipartite graph is accurately determined by the number of connected components during the online construction. In comparison, we showed that bounded components is not a serious constraint for 3-colorable graphs.
Fig c: A snapshot of the analysis for online coloring bipartite graphs.Fig d: Illustration of showing bounded components does not seem to be a serious constraint for 3-colorable graphs.
Math is at the core of theoretical computer science study.
I am currently investigating a new version of Kakeya sets to analyze problems in branching programs.
Yaqiao Li,
Ali Mohammad Lavasani,
Mehran Shakerinava,
Newman's theorem via Carathéodory,
arXiv:2406.08500.
click for a Summary:
Randomness is useful, sometimes necessary, e.g., you do not want to play a simple deterministic strategy in rock paper scissors. Randomness could help save communication cost as well. Now, if you can share randomness with your communicating partner, versus that you each have private randomness, is there a difference? Newman says they do differ, but not much.
Fig a: Rock paper scissors: a worldwide popular game orignated from China. Second picture from wikipedia.Fig b: Friends play rock paper scissors, with fire.
This paper gave a new short proof of Newman's theorem in communication complexity by applying the classical and the approximate Carathéodory's theorems in convex geometry.
click for a Summary:
This paper, extending the idea in Impagliazzo et.al., gave a short information theoretic proof for Chang's lemma via Pinsker's inequality.
click for a Summary:
Random graph is an important model in mathematics, physics, computer science, and in studying epidemics such as COVID-19. The Erdős–Rényi random graph has its edges generated independently with some probability p. A graph is quasi-random if it has similar statistics of subgraph-counts with the Erdős–Rényi random graph, e.g., the number of triangles should be close to n^3 p^3.
Fig a: Various random networks, picture from 'Random Networks and their Applications to the COVID-19 Pandemic' by Joseph Sheely.
Although graphs seem combinatorial in nature, this paper used an analytic tool called Walsh expansion to understand the structure of functions satisfying certain integration property, the structure discovered led to resolving some of the conjectures on quasi-random graphs by Janson and Sós.
Fig b: The structure theorem.Fig c: A snapshot of the proof.
Yaqiao Li,
The winning property of mixed badly approximable numbers,
Moscow J. Comb. Number Theory, vol 3 issue 1 (2013).
arXiv:1212.6584.
click for a Summary:
This paper improved the winning property of mixed badly approximable numbers to the best possible, thus answered in affirmative a question in a survey of N. Moshchevitin.
Fig a: Badly approximable numbers.
A full list of my publications, organized by main topic:
Computational Complexity
Yaqiao Li, Pierre McKenzie,
Perspective on complexity measures targeting read-once branching programs,
Information and Computation (2024).
arXiv:2305.11276.
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.
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.
Fig b: A figure in our paper comparing different complexity measures.
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).
click for a Summary:
Is quantum computing more powerful than classical computing? Recently, many quantum computers built showed superior computational advantage.
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.
click for a Summary:
Information complexity, incorporating Shannon's information theory into communication models, is a continuous version of communication complexity. Initially, it was used to help study communication complexity, and later found many other applications such as in optimization and in circuit complexity, etc.
Fig a: Shannon, Yao, Braverman. Photo from wikipedia and amturing.acm.org.
This paper, as the 2nd part, completed the basic theory of the dependency of information complexity on the error allowed in computation. This in particular led to an an accurate formula of error-allowed information complexity of XOR function for a special distribution.
Fig b: An equality for information complexity of XOR function.Fig c: A snapshot of analysis of information complexity of AND function.
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.
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.
click for a Summary:
Information complexity, incorporating Shannon's information theory into communication models, is a continuous version of communication complexity. Initially, it was used to help study communication complexity, and later found many other applications such as in optimization and in circuit complexity, etc.
Fig a: Shannon, Yao, Braverman. Photo from wikipedia and amturing.acm.org.
This paper extended the optimal procol for two-party AND function in Braverman et. al. to multi-party, in doing so we had to invent various reductions and do meticulous calculation and computation.
Fig b: The (information cost) optimal protocol for computing k-party AND.Fig c: A condition we need to verify to verify to certify optimality of the protocol.
click for a Summary:
Information complexity, incorporating Shannon's information theory into communication models, is a continuous version of communication complexity. Initially, it was used to help study communication complexity, and later found many other applications such as in optimization and in circuit complexity, etc.
Fig a: Shannon, Yao, Braverman. Photo from wikipedia and amturing.acm.org.
This paper, as the 1st part, initiated the basic theory of the dependency of information complexity on the error allowed in computation. This led to a more accurate formula of the randomized communication complexity of the important disjointness function, and a solution of an open problem of Braverman.
Fig b: The formula for the randomized communication complexity of the disjointness function.Fig c: A communication protocol with economic information cost for the disjointness function.
Online Computation
Yaqiao Li,
Ali Mohammad Lavasani,
Denis Pankratov, Online interval selection on a simple chain,
submitted.
click for a Summary:
Interval selection models many resource-optimizing scheduling and other conflict-resovling problems. This work shows that a natural revoking algorithm in fact performs worse than the greedy algorithm, when the input intervals form a simple chain and arrive randomly. The solution relies on a clever choice of a partition, and on calculating the resulting infinite series via solving differential equations. We also obtain other bounds, in particular, a first nearly tight bound for advice complexity in this model. A fascinating challenge (we have unsuccessful attempts) is to understand the performance of the revoking algorithm on general interval graphs.
click for a Summary:
Computing in cloud has many advantages that leads to overall better social welfare, e.g., some companies such as in gaming industry rent servers from public cloud companies for their computational tasks. A basic problem is how to schedule diverse requirements to minimize cost of running cloud servers, this is a dynamic vector bin packing problem called RSiC.
Fig a: Servers in the cloud (AI generated image, credit to tongyi[通义]).Fig b: A picture of high dimensional bin packing in AI's mind (AI generated image, credit to tongyi[通义]).
This paper, among other results, proposed a new and natural greedy-type algorithm that we call 'Greedy', and demonstrated in simulation that it outperforms other existing algorithms in the majority of cases. Thus, 'Greedy' could be useful for industrial applications.
Fig c: Simulation shows our 'Greedy' algorithm outperforms other algorithms.
click for a Summary:
Matching is a common task in nature, society, and mathematics. Avoiding crossing (of matching lines/curves) is a desired sometimes indispensable condition, such as for circuit design and understanding RNA structures, etc.
Fig a: A picture showing how RNA is related to k-noncrossing matching from
this paper.
This paper introduced the online and weighted version of the non-crossing matching problem, and gave optimal or competitive online algorithms and lower bounds in various models. In particular, we found a beautiful 1/3-competitive randomized algorithm, which uses a binary tree to guide its matching decisions.
Fig b: A figure illustrating a deterministic algorithm in our paper.
Yaqiao Li,
Denis Pankratov, Online Vector Bin Packing and Hypergraph Coloring Illuminated: Simpler Proofs and New Connections,
LAGOS 2023,
arXiv:2306.11241.
click for a Summary:
Bin packing is a classical NP-complete optimizaiton problem that is also of practical importance such as in loading cargo for shipping, creating file backups in media, etc.
Fig a: Illustration of 3-dimensional bin packing. Figure from the paper `Hybrid approach for solving real-world bin packing problem instances using quantum annealers' by Romero et. al.Fig b: A picture of high dimensional bin packing in AI's mind (AI generated image, credit to tongyi[通义]).
This paper introduced the online incidence matrix associated with the online hypergraph, which naturally led to a simple reduction from online hypergraph coloring to online vector bin packing, thus simplified a previous breakthrough by Azar et. al.. This paper also showed a 'diverse' multi-family has an interesting intersecting property, thus resolved a conjecture on online hypergraph coloring by Nagy-György and Imreh.
Fig c: Illustration of the online incidence matrix that we defined in this work.Fig d: Illustration of proving a conjecture on online hypergraph coloring by Nagy-György and Imreh.
click for a Summary:
Many large networks are constructed online, such as social networks and internet. Such graphs can be modelled by online graph, where at each time step a vertex appears together with its set of edges to some of existing vertices. A fundamental question is: suppose an online graph has completed its construction and turns out to be k-colorable, then, how many colors do we need if we have to irrevocably color every vertex as soon as it appears? A line of work including Lovász et. al. and Gutowski et. al. gives satisfactory understanding for bipartite graphs. For 3-colorable graphs nontrivial progress has also been made, but far from satisfactory.
Fig a: Social networks could facilitate and enrich our lives. Picture from baidu.Fig b: Social networks connect people in universe (AI generated image, credit to tongyi[通义]).
Motivated to explore the strength and weakness of existing techniques, this paper introduced the bounded components model. One one hand, we discovered that the efficiency for coloring online bipartite graph is accurately determined by the number of connected components during the online construction. In comparison, we showed that bounded components is not a serious constraint for 3-colorable graphs.
Fig c: A snapshot of the analysis for online coloring bipartite graphs.Fig d: Illustration of showing bounded components does not seem to be a serious constraint for 3-colorable graphs.
(pure) Mathematics
Yaqiao Li,
Ali Mohammad Lavasani,
Mehran Shakerinava,
Newman's theorem via Carathéodory,
arXiv:2406.08500.
click for a Summary:
Randomness is useful, sometimes necessary, e.g., you do not want to play a simple deterministic strategy in rock paper scissors. Randomness could help save communication cost as well. Now, if you can share randomness with your communicating partner, versus that you each have private randomness, is there a difference? Newman says they do differ, but not much.
Fig a: Rock paper scissors: a worldwide popular game orignated from China. Second picture from wikipedia.Fig b: Friends play rock paper scissors, with fire.
This paper gave a new short proof of Newman's theorem in communication complexity by applying the classical and the approximate Carathéodory's theorems in convex geometry.
click for a Summary:
This paper, extending the idea in Impagliazzo et.al., gave a short information theoretic proof for Chang's lemma via Pinsker's inequality.
click for a Summary:
Random graph is an important model in mathematics, physics, computer science, and in studying epidemics such as COVID-19. The Erdős–Rényi random graph has its edges generated independently with some probability p. A graph is quasi-random if it has similar statistics of subgraph-counts with the Erdős–Rényi random graph, e.g., the number of triangles should be close to n^3 p^3.
Fig a: Various random networks, picture from 'Random Networks and their Applications to the COVID-19 Pandemic' by Joseph Sheely.
Although graphs seem combinatorial in nature, this paper used an analytic tool called Walsh expansion to understand the structure of functions satisfying certain integration property, the structure discovered led to resolving some of the conjectures on quasi-random graphs by Janson and Sós.
Fig b: The structure theorem.Fig c: A snapshot of the proof.
Yaqiao Li,
The winning property of mixed badly approximable numbers,
Moscow J. Comb. Number Theory, vol 3 issue 1 (2013).
arXiv:1212.6584.
click for a Summary:
This paper improved the winning property of mixed badly approximable numbers to the best possible, thus answered in affirmative a question in a survey of N. Moshchevitin.
click for a Summary:
Diversity is observed to be beneficial in social and working environment, and UN calls biodiversity "our strongest natural defense against climate change". Darwin and Wallace already discovered that ecosystem diversity, via niche differentiation, supports higher productivity and better invasion resistance, than a monoculture. Schelling in his original study of segregation of residential neighborhoods (Dynamic models of segregation, 1971) has already, perhaps presciently, experimented diversity-seeking agents (of two types only), called "integrationist", and discovered "phenomena that are not present in the case of purely separatist demands", e.g., "integration requires more complex patterning than separation". Hayes (The Math of Segregation, 2013) observed that "the social fabric of segregation has begun to fray" and "the time has come for a model of racial reintegration".
Fig a: Picture from wikipedia, cite:"The diverse forest canopy on Barro Colorado Island, Panama, yielded this display of different fruit".Fig b: Different peoples live together on earth.
This paper, parallel to the famous Schelling game, modelled diversity-seeking agents and studied their swap games, whose equilibria are measured by several diversity measures we proposed. The theory and simulation both showed that segregation would be effectively removed if agents become diversity-seeking, though strong diversity measures such as evenness is still hard to achieve.
Fig c: Comparing diversity via simulation of divserity-seeking swap games on a (degree 4) grid, the result is measured by four diversity measures.Fig d: A topology certifying simultaneous low diversity for several diversity measures.
Dingyu Zhang,
Yaqiao Li,
Nadia Bhuiyan,
A Tale of Two Organizational Structures,
pdf.
click for a Summary:
An organization/team consists of diverse people often of distinct capabilities, this we call the team structure. A team makes its decision by aggregating individuals' decisions according to certain pre-speficied rules (e.g., polyarchy, hierarchy, committee, etc) , this we call the decision structure. If an organization has a fixed team, how should it chooses its decision structure? Similarly, if an organization has a fixed decision structure (e.g., cannot be easily changed), what types of people it should hire? More generally, if both sturctures are subject to design (e.g., for a new company), how does an organization systematically consider its various choices?
Fig a: A team with diverse members. Picture from the movie Inception, showing distinct/complementary capabilities are vital for the success of the team.Fig b: Illustration of polyarchy and hierarchy decision structures. Picture from `The architecture of economic systems: Hierarchies and polyarchies' by Sah and Stiglitz.
This paper, using university faculty hiring as a guiding example, modeled these two structures into one framework and studied carefully the interplay between the two, and how they together influence on an organization's performance. Some findings are surprising to the extent of counter-intuitive at the first sight.
Fig c: Illustration of the choice of team and decision structures in an organization.Fig d: Our model can help to identify an optimal choice of team and decision structures with respect to the change of the scenario.
click for a Summary:
Knowledge of how humans move can help intelligent robots in tasks involving an interactive human environment, such as navigating through a crowded street, or playing sports or tabletop games with humans.
Fig a: Robots walk along with people (AI generated image, credit to tongyi[通义]).
This paper, inspired by ideas in cognitive science, proposed a new and general approach to solve human motion understanding via pattern completion on a learned latent representation space. Our model outperforms current state-of-the-art in human motion prediction across a number of tasks, with no customization. The architecture uses a new and generic autoencoder based on sequence-to-sequence learning. See here for more introduction of this work by the first author Yi Tian Xu.
Fig b: A figure showing learned representation helps prediction.Fig c: The Autoencoder achitecture.
COMP 6651: Algorithm Design Techniques, Concordia, 2023/01 to 2023/04.
COMP/MATH 552 Combinatorial Optimization, McGill, 2018/01 to 2018/04.
Work
Assistant Professor, Faculty of Comp. Sci. and Control Eng., SUAT, 2024/5 to now.
Postdoc researcher, Department of Com. Sci. and Soft. Eng., Concordia, 2023/01 to 2024/01. Advised by professor Denis Pankratov and professor Lata Narayanan.
Senior Lecturer, Department of Electronic and Computer Engineering, SMBU, 2021/10 to 2022/09.
Postdoc researcher, Department of Computer Science, UdeM, 2019/11 to 2021/07. Advised by professor Pierre McKenzie.