Wanxiaogeng

  Wan Xiaogeng is currently a lecturer in the Mathematics Department of the College of Science and Engineering, Beijing University of Chemical Technology. She graduated from the Department of Mathematics and Mechanics, Beijing University of Science and Technology in 2010, obtained her master's degree from the Department of Mathematics, Durham University in 2012, where she was awarded a Distinction Honor for her Math MSc (specializing in Differential Geometry), and graduated with a PhD from the Department of Mathematics, Imperial College London in 2015 (specializing in Complex Networks and Causal Inference). From 2015 to 2017, she conducted postdoctoral research in the Department of Mathematical Sciences, Tsinghua University, focusing on biological complex networks and bioinformatics.

   Xiaogeng Wan primarily teaches courses such as Linear Algebra, Probability and Mathematical Statistics, English for Science and Technology, and Academic English Reading and Writing for Graduate Students. She has led a teaching reform project that integrates chemical engineering with Linear Algebra. She has participated in the training of mathematical modeling competitions and the evaluation of statistical modeling competitions. Under her guidance, students have achieved F-award (special award nomination), first prize, and second prize in the American Mathematical Modeling Competition, as well as first prize in the Asia-Pacific Cup Mathematical Modeling Competition. She has been awarded the title of Excellent Supervisor for Undergraduate Graduation Design, and has guided multiple undergraduate students to achieve excellence in their graduation designs at the university level. Additionally, she has guided one undergraduate student to publish a Chinese core paper as the first author.

   Xiaogeng Wan primarily engages in research on time series causal algorithms, deep causal algorithms and large causal models, causal game theory, complex biological networks, geometric deep learning, topological data analysis, geometric topological representation learning, geometric topological structure modeling of biomolecules, bioinformatics, artificial intelligence algorithms, as well as related fields such as causal geometry, information geometry, medical imaging, integrated information decomposition, causal emergence, network topology, mutual information estimation, and complex systems. She has led one project funded by the Fundamental Research Funds for the Central Universities (Research on Geometric Modeling of Biomolecules), and participated as a main contributor in one national key research and development program. As the first author and corresponding author, she has published over ten SCI, EI, and core academic papers, including co-authoring two English monographs in the fields of complexity science and bioinformatics: Edward Elgar Handbook of Complexity Sciences (Edward Elgar Publishing, UK) and Bioinformatics--Recent Advances (IntechOpen Publishing, UK); he independently wrote a chapter for the latter book. She has attended over ten domestic and international academic conferences, and presented conference reports and poster presentations at multiple international conferences.

   Xiaogeng Wan serves as a reviewer for the journal Computational Biology and Bioinformatics, a panel member for the Knowledge Processing and Representation track at the 2025 Second Summit Forum on Information Processing and Intelligent Perception. She has been invited to serve as a reviewer by SCI journals such as PLOS ONE, BMC Bioinformatics, EURASIP Journal on Advances in Signal Processing, International Journal of Biomathematics, International Journal of Data Science and Analytics, Pattern Analysis and Applications (Springer Nature), as well as the International Symposium on Automation, Information and Computing academic conference.

   Xiaogeng Wan primarily focuses on time series causality measures, deep causal algorithms and large causal models, causal game theory, complex biological networks, bioinformatics, geometric deep learning, topological data analysis, geometric topological representation, network topology, and artificial intelligence algorithms. She has a strong research interest in interdisciplinary research areas such as causal inference based on artificial intelligence algorithms, geometric deep learning, topological data analysis, geometric topological representation learning algorithms, as well as biomolecular structure modeling, network topology, causal geometry, and information geometry. She also dabbles in cross-disciplinary research in AI for Science, including medical imaging, integrated information decomposition, causal emergence, network topology, complex systems, and medical imaging.

   Xiaogeng Wan warmly welcomes collaborative research across disciplines!

Xiaogeng Wan primarily focuses on deep causal algorithms and large causal models, causal game theory, complex biological networks, bioinformatics, geometric deep learning, topological data analysis, geometric topological representation, network topology, and artificial intelligence algorithms. He has a strong research interest in interdisciplinary research areas such as causal inference based on artificial intelligence algorithms, geometric deep learning, topological data analysis, geometric topological representation learning algorithms, as well as biomolecular structure modeling, network topology, causal geometry, and information geometry. He also delves into cross-disciplinary research in AI for Science, including medical imaging, integrated information decomposition, causal emergence, network topology, complex systems, and medical imaging.

Xiaogeng Wan serves as a reviewer for the journal Computational Biology and Bioinformatics, an evaluation expert at the National Research Evaluation Center, a panel member for the Knowledge Processing and Representation track at the 2nd Summit Forum on Information Processing and Intelligent Perception 2025, and an expert in the national pool of experts for spot-checking and reviewing undergraduate thesis (design). He has been invited to serve as a reviewer by SCI journals such as PLOS ONE, BMC Bioinformatics, EURASIP Journal on Advances in Signal Processing, International Journal of Biomathematics, International Journal of Data Science and Analytics, Pattern Analysis and Applications (Springer Nature), Discover Applied Science, as well as by the Qieos academic platform and the International Symposium on Automation, Information and Computing academic conference.

Xiaogeng Wan warmly welcomes interdisciplinary research collaborations!

1. Probability Theory and Mathematical Statistics (48 periods)

2. Specialty English in Mathematics (32 periods)

3. Linear Algebra (56 periods)