Hong's Homepage

Word Cloud

I am a Physics PhD candidate at the University of Southern California, advised by Dr. Satish Kumar Thittamaranahalli (T. K. Satish Kumar) and Dr. Sven Koenig.

Email: hongx@usc.edu

I am currently on the academic job market. I am looking for a postdoc, computer scientist, or tenure-track faculty position.

Research Interests

My research interest is mainly on constraint programming (CP) and machine learning (ML), two subfields of artificial intelligence (AI). I am interested in continuing developing algorithms for foundations and real-world applications of AI. I believe that “intelligence” is the central concept of AI, and AI means “intelligence created by human”.

  • Intelligence is not only about perception-level tasks such as object and speech recognition. More importantly, it is about solving problems that require higher-level intellectual input. Search is one of the core frameworks for solving these types of problems and is the also one of the central techniques of artificial general intelligence, the original and most fundamental aspect of AI. In addition, due to the significant advancements of ML during the past decade, it is interesting to apply techniques from ML to search. Therefore, I am interested in using ML to facilitate search algorithms, especially on combinatorial problems, such as (weighted) constraint satisfaction problem (CSP) and Boolean satisfiability problem (SAT).
  • Intelligence and knowledge are synergistic: Intelligence creates knowledge from knowledge, and knowledge increases intelligence upon intelligence. In the Oxford English dictionary, intelligence is defined as “the ability to acquire and apply knowledge”. Therefore, knowledge representation is essential to AI. In addition, due to my experiences in CP, a mainstream approach for representing knowledge, I am interested in applying CP to knowledge representation.
  • Multiagent and parallel algorithms play essential roles in AI. Ultimately, to make AI ubiquitous, intelligent systems have to be distributed to preserve private values in individuals. In addition, the computation inside a single agent can be made more efficient with modern multi-threading hardware such as GPUs and multi-core CPUs. Therefore, I am interested in multiagent and parallel algorithms in AI.
  • The ultimate purpose of AI is to put it into real-world applications: A theoretically intelligent system is not intelligent without practical use. Therefore, I am motivated to work on AI applications. I believe that one important direction of application of AI is social good, including improving privacy protection, promoting free and open intellectual properties, reducing social bias, and so on. Another important direction of applications of AI is productivity improvement, including optimizing workflow, developing intelligent user interface, and so on.

Having said these, my interests are not limited to these. Feel free to contact me and there is a good chance to get me interested!

Student Mentoring

I have mentored three undergraduate/master students. All of them were successfully admitted into PhD programs in top universities and two of them received research awards from USC.

  • Kexuan Sun: Master student in Computer Science at the University of Southern California, achieved USC Viterbi School of Engineering Department of Computer Science best research award in April 2018, will join the University of Southern California as a PhD student in Fall 2018.
  • Cheng Cheng: Undergraduate student in Computer Science/Economics at the University of Southern California, achieved USC Viterbi School of Engineering computer science award for outstanding research in May 2018, will join Carnegie Mellon University as a PhD student in Fall 2018.
  • Zhi Wang: Undergraduate student in Computer Science at the University of Southern California, will join the University of California, San Diego as a PhD student in Fall 2018.
  • Dylan Johnke: Undergraduate student in Mathematics at Cornell University, Viterbi Summer Undergraduate Research Experience (SURE) Program.
  • Masaru Nakajima: Doctoral student in Physics at the University of Southern California, advised as a senior PhD student.
  • Ka Wa Yip: Doctoral student in Physics at the University of Southern California, advised as a senior PhD student.

Publications

Stars (*) next to names in the author lists indicate equal contribution.

2018

  • Hong Xu, Sven Koenig, and T. K. Satish Kumar. Towards effective deep learning for constraint satisfaction problems. In Proceedings of the 24th International Conference on Principles and Practice of Constraint Programming (CP). 2018.
    [abstract▼] [details] [full text] [BibTeX▼]
  • Hong Xu, Kexuan Sun, Sven Koenig, and T. K. Satish Kumar. A warning propagation-based linear-time-and-space algorithm for the minimum vertex cover problem on giant graphs. In Proceedings of the 15th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 567–584. 2018. doi:10.1007/978-3-319-93031-2_41.
    [abstract▼] [details] [full text] [slides] [BibTeX▼]
  • Hong Xu, Cheng Cheng, Sven Koenig, and T. K. Satish Kumar. Message passing algorithms for semiring-based and valued CSPs. In Proceedings of the 11th International Symposium on Combinatorial Search (SoCS). 2018.
    [abstract▼] [details] [full text] [BibTeX▼]
  • Masaru Nakajima*, Hong Xu*, Sven Koenig, and T. K. Satish Kumar. Towards understanding the min-sum message passing algorithm for the minimum weighted vertex cover problem: An analytical approach. In Proceedings of the 15th International Symposium on Artificial Intelligence and Mathematics (ISAIM). 2018. URL: http://isaim2018.cs.virginia.edu/papers/ISAIM2018_Nakajima_etal.pdf.
    [abstract▼] [details] [full text] [slides] [BibTeX▼]
  • Ferdinando Fioretto, Hong Xu, Sven Koenig, and T. K. Satish Kumar. Constraint composite graph-based lifted message passing for distributed constraint optimization problems. In Proceedings of the 15th International Symposium on Artificial Intelligence and Mathematics (ISAIM). 2018. URL: http://isaim2018.cs.virginia.edu/papers/ISAIM2018_Fioretto_etal.pdf.
    [abstract▼] [details] [full text] [slides] [BibTeX▼]
  • Hong Xu, Xin-Zeng Wu, Cheng Cheng, Sven Koenig, and T. K. Satish Kumar. The Buss reduction for the \(k\)-weighted vertex cover problem. In Proceedings of the 15th International Symposium on Artificial Intelligence and Mathematics (ISAIM). 2018. URL: http://isaim2018.cs.virginia.edu/papers/ISAIM2018_Xu_etal.pdf.
    [abstract▼] [details] [full text] [slides] [BibTeX▼]
  • T. K. Satish Kumar, Hong Xu, Zheng Tang, Anoop Kumar, Craig Milo Rogers, and Craig A. Knoblock. Alert generation in execution monitoring using resource envelopes. In Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference (FLAIRS), 38–43. 2018. URL: https://aaai.org/ocs/index.php/FLAIRS/FLAIRS18/paper/view/17615/16853.
    [abstract▼] [details] [full text] [slides] [BibTeX▼]

2017

  • Hong Xu, T. K. Satish Kumar, and Sven Koenig. The Nemhauser-Trotter reduction and lifted message passing for the weighted CSP. In Proceedings of the 14th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming (CPAIOR), 387–402. 2017. doi:10.1007/978-3-319-59776-8_31.
    [abstract▼] [details] [full text] [slides] [BibTeX▼]
  • Hong Xu, Sven Koenig, and T. K. Satish Kumar. A constraint composite graph-based ILP encoding of the Boolean weighted CSP. In Proceedings of the 23rd International Conference on Principles and Practice of Constraint Programming (CP), 630–638. 2017. doi:10.1007/978-3-319-66158-2_40.
    [abstract▼] [details] [full text] [slides] [BibTeX▼]
  • T. K. Satish Kumar, Hong Xu, Zheng Tang, Anoop Kumar, Craig Milo Rogers, and Craig A. Knoblock. A distributed logical filter for connected row convex constraints. In Proceedings of the 29th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 96–101. 2017. doi:10.1109/ICTAI.2017.00026.
    [abstract▼] [details] [full text] [slides] [BibTeX▼]
  • Therese Anders, Hong Xu, Cheng Cheng, and T. K. Satish Kumar. Measuring territorial control in civil wars using hidden Markov models: A data informatics-based approach. In Proceedings of the NIPS 2017 Workshop on Machine Learning for the Developing World. 2017. arXiv:1711.06786.
    [abstract▼] [details] [full text] [poster] [BibTeX▼]
  • Hong Xu, T. K. Satish Kumar, and Sven Koenig. Min-max message passing and local consistency in constraint networks. In Proceedings of the 30th Australasian Joint Conference on Artificial Intelligence (AI), 340–352. 2017. doi:10.1007/978-3-319-63004-5_27.
    [abstract▼] [details] [full text] [slides] [BibTeX▼]
  • Hang Ma, Wolfgang Hönig, Liron Cohen, Tansel Uras, Hong Xu, T. K. Satish Kumar, Nora Ayanian, and Sven Koenig. Overview: a hierarchical framework for plan generation and execution in multi-robot systems. IEEE Intelligent Systems, 32(6):6–12, 2017. doi:10.1109/MIS.2017.4531217.
    [abstract▼] [details] [full text] [BibTeX▼]
  • Wolfgang Hönig, T. K. Satish Kumar, Liron Cohen, Hang Ma, Hong Xu, Nora Ayanian, and Sven Koenig. Summary: Multi-agent path finding with kinematic constraints. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 4869–4873. 2017. Sister Conference Best Paper Track. doi:10.24963/ijcai.2017/684.
    [abstract▼] [details] [full text] [BibTeX▼]
  • Hong Xu, T. K. Satish Kumar, and Sven Koenig. A linear-time and linear-space algorithm for the minimum vertex cover problem on giant graphs. In Proceedings of the 10th International Symposium on Combinatorial Search (SoCS), 173–174. 2017. URL: https://aaai.org/ocs/index.php/SOCS/SOCS17/paper/viewFile/15789/15080.
    [abstract▼] [details] [full text] [poster] [BibTeX▼]

2016

  • Hong Xu, T. K. Satish Kumar, and Sven Koenig. A new solver for the minimum weighted vertex cover problem. In Proceedings of the 13th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming (CPAIOR), 392–405. 2016. doi:10.1007/978-3-319-33954-2_28.
    [abstract▼] [details] [full text] [slides] [BibTeX▼]
  • Hong Xu, T. K. Satish Kumar, Dylan Johnke, Nora Ayanian, and Sven Koenig. SAGL: a new heuristic for multi-robot routing with complex tasks. In Proceedings of the 28th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 530–535. 2016. doi:10.1109/ICTAI.2016.0087.
    [abstract▼] [details] [full text] [slides] [BibTeX▼]
  • Liron Cohen, Tansel Uras, T. K. Satish Kumar, Hong Xu, Nora Ayanian, and Sven Koenig. Improved solvers for bounded-suboptimal multi-agent path finding. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), 3067–3074. 2016. URL: http://www.ijcai.org/Abstract/16/435.
    [abstract▼] [details] [full text] [slides] [BibTeX▼]
  • Wolfgang Hönig, T. K. Satish Kumar, Liron Cohen, Hang Ma, Hong Xu, Nora Ayanian, and Sven Koenig. Multi-agent path finding with kinematic contraints. In Proceedings of the 26th International Conference on Automated Planning and Scheduling (ICAPS), 477–485. 2016. Outstanding paper award in the robotics track. URL: https://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13183/12711.
    [abstract▼] [details] [full text] [video] [BibTeX▼]
  • Hang Ma, Sven Koenig, Nora Ayanian, Liron Cohen, Wolfgang Hoenig, T.K. Satish Kumar, Tansel Uras, Hong Xu, Craig Tovey, and Guni Sharon. Overview: generalizations of multi-agent path finding to real-world scenarios. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Multi-Agent Path Finding. 2016. URL: https://www.andrew.cmu.edu/user/gswagner/workshop/IJCAI_2016_WOMPF_paper_6.pdf.
    [abstract▼] [details] [full text] [BibTeX▼]