Zirou Qiu

Ph.D. Student in CS @ UVA; CV; Google Scholar

I am a Ph.D. student in Computer Science at the University of Virginia working with Madhav Marathe. I am also a research assistant at the Biocomplexity Institute, working with S. S. Ravi, Dan Rosenkrantz, and Dick Stearns.

I study the foundations of large complex systems (e.g., social systems, multi-agent systems, infrastructure systems) using machine learning, optimization, and computational modeling. I focus on developing formal computational models and techniques to simulate, analyze and optimize these large systems, with the goal of improving system performance and benefiting the social good.

Research Interests

  • Complex Systems

  • Machine Learning

  • Optimization

  • Computational Modeling

  • Computational Social Science & Epidemiology

  • Game Theory

Publications / Preprints

You may click the title of a paper to view its pdf file.

Learning Discrete Dynamical Systems under Classification Noise

In Submission

Zirou Qiu*, Zakaria Mehrab*, Abhijin Adiga, Madhav Marathe, S.S. Ravi, Daniel Rosenkrantz, Richard Stearns and Anil Vullikanti.

Summary. We proposed efficient noise-tolerant algorithms for learning the behaviors of large-scale systems in realistic noisy environments, with a provable high learning accuracy. Our algorithms are highly scalable to large networks, and the amount of training data required is provable near-optimal.

Keywords. PAC learning; learing under noise; discrete dynamical systems; sample complexity

Theoretical Foundations for Parent Divorcing Transformations in Bayesian Networks

In Submission

With Daniel Rosenkrantz, Madhav V. Marathe and S. S. Ravi

We estabilished a theoretical groundwork for dimensionality reduction in Bayesian inference. Further, we proposed efficient algorithms for dimensionality reduction with provable guarantees.

Keywords. Bayesian Networks; Parent divorcing transformation; Network embedding; Theoretical foundations

Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks

International Conference on Machine Learning (ICML-2024) [Acc.rate: 27.5%]

Zirou Qiu, Abhijin Adiga, Madhav Marathe, S.S. Ravi, Daniel Rosenkrantz, Richard Stearns and Anil Vullikanti.

We proposed the first efficient learning method for inferring the behaviors of large-scale multilayer social systems and multi-agent systems. Further, we lay a theoretical foundation of learning multilayer systems with an exact analysis of the model complexity and expressiveness

Keywords. PAC learning; multilayer graphs; discrete dynamical systems; sample complexity; machine learning

Learning the Topology and Behavior of Discrete Dynamical Systems

AAAI Conference on Artificial Intelligence (AAAI-2024) [Acc.rate: 23.8%]

Zirou Qiu, Abhijin Adiga, Madhav Marathe, S.S. Ravi, Daniel Rosenkrantz, Richard Stearns and Anil Vullikanti.

We investigate the core principles of learning both the behaviors and the topology of black-box social systems and multi-agent systems. Towards this end, we proposed several efficient learning algorithms with provable guarantees for realistic scenarios of learning large systems.

Keywords. PAC learning; discrete dynamical systems; sample complexity; machine learning

Assigning Agents to Increase Network-Based Neighborhood Diversity

Intl. Conf. Autonomous Agents and Multiagent Systems (AAMAS-2023) [Acc.rate: 23.3%, Oral]

Zirou Qiu, Andrew Yuan, Chen Chen, Madhav Marathe, S.S. Ravi, Daniel Rosenkrantz, Richard Stearns and Anil Vullikanti.

We developed a formal model for real-world spatial resources allocation problems, such as public housing assignment and urban spatial planning. Further, we presented rigorous methodologies for resource allocation with provable close-to-optimal guarantees, while achieving social diversity and fairness.

Keywords. Optimization; approximation algorithms; resource allocation; diversity

Networked Anti-Coordination Games Meet Graphical Dynamical Systems: Equilibria and Convergence

AAAI Conference on Artificial Intelligence (AAAI-2023) [Acc.rate: 19.6%, Oral]

Zirou Qiu, Chen Chen, Madhav Marathe, S.S. Ravi, Daniel Rosenkrantz, Richard Stearns, Anil Vullikanti.

We presented a theoretical groundwork for stability and convergence in real-word strategic situations such as resources sharing and competition within a society.

Keywords. Anti-coordination games; dynamical systems; Nash equilibrium, convergence time

Airborne Disease Transmission During Indoor Gatherings over Multiple Time Scales: Modeling Framework and Policy Implications

Proceedings of the National Academy of Sciences (PNAS-2023)

Avinash Dixit, Baltazar Espinoza, Zirou Qiu, Anil Vullikanti, and Madhav Marathe

We introduced a principled modeling framework that couples the fast dynamics of viral loads in the local scale and the slow dynamics of disease progression at the population level. Further, we derived policy guidelines to lessen the negative impact of epidemics.

Keywords. Group gathering; multiple time scales; large-scale modeling; pandemic science;

Understanding the Coevolution of Mask Wearing and Epidemics:
A Network Perspective

Proceedings of the National Academy of Sciences (PNAS-2022)

Zirou Qiu, Baltazar Espinoza, Vítor V. Vasconcelos, Chen Chen, Sara M. Constantino, Stefani A. Crabtree, Luojun Yang, Anil Vullikanti, Jiangzhuo Chen, Jörgen Weibull, Kaushik Basu, Avinash Dixit, Simon Levin, Madhav Marathe.

We presented a fundamental model for dueling dynamics of social and biological contagions both cooperating and completing in a population. Through our modeling, we uncovered a collection of previously unknown phenomena in contagion dynamics. We derived corresponding policy guidelines to suppress epidemics and prevent their resurgence.

Keywords. Dueling dynamics, multilayer networks, non-pharmaceutical interventions, pandemic science

Finding Nontrivial Minimum Fixed Points in Discrete Dynamical Systems: Complexity, Special Case Algorithms and Heuristics

AAAI Conference on Artificial Intelligence (AAAI-2022) [Acc.rate: 15%, Oral: 4.8%]

Zirou Qiu, Chen Chen, Madhav Marathe, S.S. Ravi, Daniel Rosenkrantz, Richard Stearns, Anil Vullikanti.

We established the principles of finding equilibrium points in contagion diffusion within a population, with the goal of minimizing the overall scale of infection in a society.

Keywords. Optimization; dynamical systems; fixed point; contagion diffusion

Efficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries

International Conference on Machine Learning (ICML-2022) [Acc.rate: 22%, Spotlight]

Daniel Rosenkrantz,(α-β)Abhijin Adiga, Madhav Marathe, Zirou Qiu, S.S. Ravi, Richard Stearns, Anil Vullikanti

We presented the first collection of active learning based methods for inferring both the behaviors and the topology of black-box social systems and multi-agent systems. Further, we conducted an extensive experimental study on the empirical performs on the proposed algorithms on real-world networks over various learning scenarios.

Keywords. Active learning; query model; discrete dynamical systems; query complexity

ELRUNA: Elimination Rule-based Network Alignment

ACM Journal of Experimental Algorithmics (ACM-JEA-2021)

Zirou Qiu, Ruslan Shaydulin, Xiaoyuan Liu, Yuri Alexeev, Christopher S. Henry, Ilya Safro

Summary. We propose novel algorithms for the network alignment problem that outperforms the state-of-the-art methods, with applications in computational biology and social network analysis.

Keywords. Optimization; network alignment; social network analysis

Research Experience

Biocomplexity Institute

Graduate Research Assistant; Advisor: Madhav Marathe

  • Investigated the foundations of complex systems.

  • Developed large-scale frameworks for socio-technical system modeling.

  • Led four research projects, all resulted in multiple publications.

Fall 2020 - Present

Clemson University

Graduate Research Assistant; Advisor: Ilya Safro

  • Studied computational problems in quantum computing and biology.

  • Built a pipeline for microbiome modeling. Discovered novel microbiome migration pattern.

Jan 2019 - May 2020

Argonne National Laboratory

Research Intern; Host: Yuri Alexeev & Chris Henry

  • Performed comprehensive analysis of microbiome data.

  • Investigated the network alignment problem with applications in computational biology.

  • One first-author publication.

Summer 2019

Other Stuff

What is a discrete dynamical system?

My research focuses on the foundation of complex systems, although one won't often find the term "complex systems" in my papers. Instead, you may note that my work involves things called discrete dynamical systems.

In essence, the discrete dynamical system is a mathematical framework that models complex systems. Importantly, discrete dynamical systems provide a theoretical foundation to formally address questions about complex systems. Such questions include (1) convergence and stability of system dynamics; (2) inference of black-box systems; (3) resource allocation and fariness; (4) large-scale (agent-based) simulation.

Overall, I investigate complex systems through the structured framework of discrete dynamical systems. Desipte this connection, the problems, techniques, and results obtained in my work apply to much border realms such as social science, multi-agent system, and economics.