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About Me

My name is Nan Jiang (姜楠). I’m a PhD candidate at the Department of Computer Science, at Purdue University. I am fortunate to be supervised by Professor Yexiang Xue. My PhD research focuses on Integrating Automated Reasoning with Machine Learning for Structured Prediction and Scientific Discovery.

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Research Highlights

Reasoning + Learning to accelerate AI-driven Scientific Discovery

• Integrating scientific approach-inspired reasoning, my work accelerates the discovery of physical knowledge from experimental data. My approach significantly extended the capabilities of existing methods in solving datasets with multiple independent variables.
• My approach successfully discovers ground-truth scientific expressions involving up to 50 variables, whereas previous approaches struggle with equations of just three variables.

Reasoning + Learning to ensure constraint satisfaction in machine learning

• By embedding AR solvers as differentiable layers into neural network-based ML models, my work ensures constraint satisfaction of the predicted output when solving a variety of structural learning problems across operations research, combinatorial optimization, and natural language processing.
• Notably, in a data-driven vehicle dispatching task, our approach generates routes that 100% satisfy constraints while previous approaches produce <1% valid routes.

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Publications

2025

1. Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait Sketching.
   Nan Jiang, Md Nasim, Yexiang Xue.
   AAAI 2025.
   (poster) (code)

2. An Exact Solver for Satisfiability Modulo Counting with Probabilistic Circuits.
   Jinzhao Li, Nan Jiang, Yexiang Xue.
   Arxiv.

3. Neuro-Symbolic Action Anticipation from a Single Image with Learned Probabilistic Rules.
   Muyang Yan, Maxwell J. Jacobson, Nan Jiang, Yaqi Xie, Simon Stepputtis, Katia Sycara, Yexiang Xue.
   Under Review.

4. Enhancing Deep Symbolic Regression with Expression Rewriting Module.
   Nan Jiang, Ziyi Wang, Yexiang Xue.
   Under Review.

2024

1. A Tighter Convergence Proof of Reverse Experience Replay.
   Nan Jiang, Jinzhao Li, Yexiang Xue.
   The first Reinforcement Learning Conference (RLC) 2024.
   (poster) (slides) (code)

2. Vertical Symbolic Regression via Deep Policy Gradient.
   Nan Jiang, Md Nasim, Yexiang Xue.
   IJCAI 2024.
   (poster) (slides) (code)

3. Solving Satisfiability Modulo Counting for Symbolic and Statistical AI Integration with Provable Guarantees.
   Jinzhao Li, Nan Jiang and Yexiang Xue.
   AAAI 2024.
   (poster) (code)

4. Racing Control Variable Genetic Programming for Symbolic Regression.
   Nan Jiang, Yexiang Xue.
   AAAI 2024.
   (poster) (slides) (video) (code)

2023

1. Symbolic Regression via Control Variable Genetic Programming.
   Nan Jiang, Yexiang Xue.
   ECML-PKDD 2023.
   (poster) (slides) (code)

2. Learning Markov Random Fields for Combinatorial Structures via Sampling through Lovász Local Lemma.
   Nan Jiang*, Yi Gu*, Yexiang Xue.
   AAAI 2023.
   (poster) (slides) (code) (CPML@AAAI2025)

2022

1. Constraint Reasoning Embedded Structured Prediction.
   Nan Jiang, Maosen Zhang, Willem-Jan van Hoeve, Yexiang Xue.
   JMLR 2022.
   (poster) (code)

2. Massive Text Normalization via an Efficient Randomized Algorithm.
   Nan Jiang, Chen Luo, Vihan Lakshman, Yesh Dattatreya, Yexiang Xue.
   TheWebConf 2022.
   (slides) (video) (code)

2021

1. PALM: Probabilistic Area Loss Minimization for Protein Sequence Alignment.
   Nan Jiang*, Fan Ding*, Jianzhu Ma, Jian Peng, Jinbo Xu, and Yexiang Xue.
   UAI 2021.
   (poster) (slides) (code)

2020

1. Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach.
   Maosen Zhang, Nan Jiang, Lei Li, and Yexiang Xue.
   Finding in EMNLP 2020.
   (poster) (code)

2. A dual channel class hierarchy-based recurrent language modeling.
   Libin Shi, Wenge Rong, Shijie Zhou, Nan Jiang, Zhang Xiong.
   Neurocomputing 2020.

Older Papers

1. LSDSCC: A Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics.
   Zhen Xu, Nan Jiang and et. al.
   NAACL-HLT 2018.

2. Exploration of Tree-based Hierarchical Softmax for Recurrent Language Models.
   Nan Jiang, Wenge Rong, Min Gao, Yikang Shen, Zhang Xiong.
   IJCAI 2017. (code)

3. Biological Event Trigger Identification with Noise Contrastive Estimation.
   Nan Jiang, Wenge Rong, Yifan Nie, Yikang Shen, and Zhang Xiong.
   IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017.
   (code)

4. An Empirical Analysis of Different Sparse Penalties for Autoencoder in Unsupervised Feature Learning.
   Nan Jiang, Wenge Rong, Baolin Peng, Yifan Nie, Zhang Xiong.
   IJCNN, 2015. (code)