Farid Aliniaeifard is a mathematician specializing in algebraic combinatorics, with a focus on developing algorithmic and pattern-recognition methods using representation theory.
Email: farid@sdu.edu.cn
Address:
Office E422, Research Center for Mathematics and Interdisciplinary Sciences, 72 Binhai Road, Jimo District, Qingdao, Shandong Province, China Post Code：266237
Address in Chinese:
山东省青岛市即墨滨海路72号 山东大学华岗苑东楼E422
Last lecture of Thesis Writing and Academic Ethics(Picture with the students 1 - Picture with the students 2) (December 2025).
I present a virtual talk with the title Can AI Show Creativity in Mathematics - Or Is It Just a Tool? at Beijing Instituite of Technology School of Mathematics on December 12, 2025. (.pdf Slides) and (.ppt Slides)
I wrote the note Can AI Show Creativity in Mathematics? Discussing this question may be as profound as whether the machines can ever become conscious. You can find the note here https://github.com/AIMath-Lab/CreativeAI (December 2025).
The paper, How to Use Deep Learning to Identify Sufficient Conditions: A Case Study on Stanley's e-Positivity, with Shu Xiao Li is now available at https://arxiv.org/abs/2511.20019 (November 26, 2025).
I wrote the note WHY, HOW, and WHAT of Writing a Research Paper in Mathematics presenting an approach to mathematical writing to help prevent that the main results from being overlooked by readers. You can find it here https://github.com/AIMath-Lab/MathematicalWriting (November 2025).
The paper, The extra basis in noncommuting variables, with Stephanie van Willigenburg, is the fourth Most Downloaded (in the past 90 days) paper in Advances in Applied Mathematics (August 2025).
The paper, The Peak Algebra in Noncommuting Variables, with Shu Xiao Li, is now available at https://arxiv.org/abs/2506.12868 (June 17, 2025)
I present a talk on how AI can help solving problems in combinatorics, especially combinatorial representation theory, at the Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, June 19, 2025.
The paper, Chromatic Quasisymmetric Functions of the Path Graph, with Shamil Asgarli, Maria Esipova, Ethan Shelburne, Stephanie van Willigenburg and Tamsen Whitehead McGinley was published in Annals of Combinatorics, May 26, 2025.
Farid Aliniaeifard (farid@sdu.edu.cn) and Shu Xiao Li (lishuxiao@sdu.edu.cn)
Abstract. In a study, published in Nature, researchers from DeepMind and mathematicians demonstrated a general framework using machine learning to make conjectures in pure mathematics. Their work uses neural networks and attribution techniques to guide human intuition towards making provable conjectures. Here, we build upon this framework to develop a method for identifying sufficient conditions that imply a given mathematical statement. Our approach trains neural networks with a custom loss function that prioritizes high precision. Then uses attribution techniques and exploratory data analysis to make conjectures. As a demonstration, we apply this process to Stanley’s problem of e-positivity of graphs–a problem that has been at the center of algebraic combinatorics for the past three decades. Guided by AI, we rediscover that one sufficient condition for a graph to be e-positive is that it is co-triangle-free, and that the number of claws is the most important factor for e-positivity. Based on the most important factors in Saliency Map analysis of neural networks, we suggest that the classification of e-positive graphs is more related to continuous graph invariants rather than the discrete ones. Furthermore, using neural networks and exploratory data analysis, we show that the claw-free and claw-contractible-free graphs with 10 or 11 vertices are e-positive, resolving a conjecture by Dahlberg, Foley, and van Willigenburg.
