Hai Xia

I am a PhD student in the Doctoral College Logics for Computer Science at TU Wien (LogiCS@TUWien) that is co-funded by the EC H2020 Marie Skłodowska-Curie COFUND, within the Algorithms and Complexity group under the supervision of Prof. Stefan Szeider. My research interests are constraint satisfaction solving/reasoning, algorithm selection, parameterized complexity and evolutionary optimization.

 

Publications

Here is my DBLP entry. (*: the corresponding author)

Hai Xia, Carla P. Gomes, Bart Selman, Stefan Szeider, Agentic Neurosymbolic Collaboration for Mathematical Discovery: A Case Study in Combinatorial Design. [pre-print under review]

Hai Xia, Vaidyanathan Peruvemba Ramaswamy, Stefan Szeider, LLM-Guided Graph Generation for Structure-Based Local Improvement Methods. [under review]

Hai Xia, Stefan Szeider, Carlos Ansótegui, Synthesizing Feature Extractors: An Agentic Approach for Algorithm Selection. [under review]

Markus Kirchweger, Hai Xia*, Tomás Peitl, Stefan Szeider, Smart Cubing for Graph Search: A Comparative Study. [pre-print under review]

Carlos Ansótegui, Vaidyanathan Peruvemba Ramaswamy, Stefan Szeider, Hai Xia*, Uncovering and Verifying Optimal Community Structure in Complex Networks: A MaxSAT Approach, International Conference on Computational Science (ICCS), 2025. [oral, supplementary file]

Vaidyanathan Peruvemba Ramaswamy, Stefan Szeider, Hai Xia*, The Power of Collaboration: Learning Large Bayesian Networks at Scale, IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2024. [oral, supplementary file]

Vaidyanathan Peruvemba Ramaswamy, Stefan Szeider, Hai Xia*, Enhancing MaxSAT-Based Bayesian Network Learning with Real-Time Tuning, AAAI Conference on Artificial Intelligence Workshop on Learnable Optimization (AAAI Workshop on LEANOPT), 2024. [poster]

Hai Xia, Stefan Szeider, SAT-Based Tree Decomposition with Iterative Cascading Policy Selection, AAAI Conference on Artificial Intelligence (AAAI), 2024. [oral, poster, supplementary file]

Hai Xia, Changhe Li, Qingshan Tan, Sanyou Zeng, Shengxiang Yang, Learning to Search Promising Regions by Space Partitioning for Evolutionary Methods, Swarm and Evolutionary Computation (SWEVO), 2024. [source code, supplementary file]

Hai Xia, Changhe Li, Sanyou Zeng, Qingshan Tan, Shengxiang Yang, Learning to Search Promising Regions by a Monte-Carlo Tree Model, IEEE Congress on Evolutionary Computation (CEC), 2022. [oral, source code]

Hai Xia, Changhe Li, Sanyou Zeng, Qingshan Tan, Shengxiang Yang, A Reinforcement-Learning-Based Evolutionary Algorithm using Solution Space Clustering for Multimodal Optimization Problems, IEEE Congress on Evolutionary Computation (CEC), 2021. [oral, source code]

Qingshan Tan, Changhe Li, Hai Xia, Sanyou Zeng,  Shengxiang Yang, A Novel Scalable Framework for Constructing Dynamic Multi-objective Optimization Problems, IEEE Congress on Evolutionary Computation (CEC), 2021. [oral, source code]

Competitions

2nd Place with Mai Peng and Changhe Li in “Competition on Seeking Multiple Optima in Dynamic Environment” of IEEE WCCI 2022, Jul. 2022