44. Xiangwu Zuo and Anxiao Jiang, Symbolic Modeling for Financial Asset Pricing, in Journal of Finance and Data Science, January 2025. doi: https://doi.org/10.1016/j.jfds.2025.100150 

43.   Xiangwu Zuo and Anxiao Jiang, Enhancing Symbolic Regression with Side Information for Data Analysis, in Journal on Advances in Data Analysis and Classification, January 2025. DOI: https://doi.org/10.1007/s11634-024-00621-6     

42.   Anxiao Jiang, Analog Error-Correcting Codes: Designs and Analysis, in IEEE Transactions on Information Theory, vol. 70 no. 11, pp. 7740--7756, November 2024. DOI: 10.1109/TIT.2024.3454059 

41.   Wenyu Peng, Simeng Zheng, Michael Baluja, Tao Xie, Anxiao Jiang, and Paul H. Siegel, Generalizing Functional Error Correction for Language and Vision-Language Models, in Proc. IEEE International Conference on Machine Learning and Applications (ICMLA), December 2024. DOI: 10.1109/ICMLA61862.2024.00110 

40.   Anxiao (Andrew) Jiang, Error Correction and Detection for Analog AI Computing in Edge Systems, in Proc. ACM/IEEE International Conference on Computer-Aided Design (ICCAD), 2024. DOI: https://doi.org/10.1145/3676536.3697131 

39.   Xiangwu Zuo, Anxiao (Andrew) Jiang and Kaixiong Zhou, Reinforcement Prompting for Financial Synthetic Data Generation, in Journal of Finance and Data Science, vol. 10, no. 1, 100137, 2024. DOI : https://doi.org/10.1016/j.jfds.2024.100137 

38.   Chia-Yuan Chang, Yu-Neng Chuang, Zhimeng Jiang, Kwei-Herng Lai, Anxiao Jiang, and Na Zou, CODA: Temporal Domain Generalization via Concept Drift Simulator, in 2nd INFORMS Conference on Quality, Statistics, and Reliability (ICQSR), July 2024. (Best Paper Award at INFORMS Conference on Quality, Statistics and Reliability (ICQSR) in 2024.)

37.   Anxiao (Andrew) Jiang and Xiangwu Zuo, Analysis and Designs of Analog ECC, in Proc. 15th Annual Non-Volatile Memories Workshop (NVMW), La Jolla, CA, USA, March 2024.

36.   Xiangwu Zuo and Anxiao (Andrew) Jiang, Neural Decoder for Analog ECC, in Proc. 15th Annual Non-Volatile Memories Workshop (NVMW), La Jolla, CA, USA, March 2024.

35.   Xiangwu Zuo, Anxiao (Andrew) Jiang, Netanel Raviv and Paul H. Siegel, Side Information-Assisted Symbolic Regression for Data Storage, in Proc. 15th Annual Non-Volatile Memories Workshop (NVMW), La Jolla, CA, USA, March 2024.

34.   Anxiao (Andrew) Jiang and Erich F. Haratsch, Machine Learning: Enabling and Enabled by Advances in Storage and Memory Systems, in IEEE BITS The Information Theory Magazine, vol. 3, no. 4, pp. 86--97, December 2023. DOI: 10.1109/MBITS.2023.3314392.

33.   Xiangwu Zuo, Anxiao (Andrew) Jiang, Netanel Raviv, and Paul H. Siegel, Symbolic Regression for Data Storage with Side Information, in Proc. IEEE Information Theory Workshop (ITW), Mumbai, India, November 2022. (Paper topic: symbolic regression.)

32.   Kailun Zhang, Kiara Pankratz, Hau Duong, Jingwen Guan, Anxiao (Andrew) Jiang, Yiruo Lin, and Lanying Zeng, Interactions Between Viral Regulatory Proteins Ensure a Constant Probability of Host Outcome during Infection by Bacteriophage P1, in American Society for Microbiology (ASM) journal mBio, vol. 12, no. 5,  September 2021.

31.   Xiaojing Yu and Anxiao (Andrew) Jiang, Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates, in Proc. 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pp. 3202-3212, Kyiv, Ukraine, April 2021.

30.   Netanel Raviv, Siddharth Jain, Pulakesh Upadhyaya, Jehoshua Bruck and Anxiao (Andrew) Jiang, CodNN – Robust Neural Networks From Coded Classification, in Proc. IEEE International Symposium on Information Theory (ISIT), Los Angeles, CA, June 2020. 

29.   Kunping Huang, Paul H. Siegel and Anxiao (Andrew) Jiang, Functional Error Correction for Reliable Neural Networks, in Proc. IEEE International Symposium on Information Theory (ISIT), Los Angeles, CA, June 2020.

28.   Xiaojing Yu, Tianlong Chen, Zhengjie Yu, Huiyu Li, Yang Yang, Xiaoqian Jiang and Anxiao (Andrew) Jiang, Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing, in Proc. 12th International Conference on Language Resources and Evaluation (LREC), Marseille, France, May 2020.

27.   Kunping Huang, Paul H. Siegel and Anxiao (Andrew) Jiang, Functional Error Correction for Robust Neural Networks, in IEEE Journal on Selected Areas in Information Theory (JSAIT), vol. 1, no. 1, pp. 267-276, May 2020.

26.   Netanel Raviv, Pulakesh Upadhyaya, Siddharth Jain, Jehoshua Bruck and Anxiao (Andrew) Jiang, Coded Deep Neural Networks for Robust Neural Computation, in Proc. Non-Volatile Memories Workshop (NVMW), La Jolla, CA, March 2020.

25.   Kunping Huang, Paul H. Siegel and Anxiao (Andrew) Jiang, Performance Oriented Error Correction for Robust Neural Networks, in Proc. Non-Volatile Memories Workshop (NVMW), La Jolla, CA, March 2020.

24.   John Mathai Reji, Xiaoqian Jiang, Robert Murphy, Luyao Chen and Anxiao (Andrew) Jiang, Deep Learning Approach for Severe Sepsis Prediction, in 13th ACM International Conference on Web Search and Data Mining (WSDM), Healthcare Day Workshop, Houston, TX, February 2020.

23.   Huang, K., Raviv, N., Jain, S., Upadhyaya, P., Bruck, J., Siegel, P. H. and Jiang, A., Improve Robustness of Deep Neural Networks by Coding, in Proc. Information Theory and Its Applications (ITA) Workshop, San Diego, CA, February 2020.

22.   Pulakesh Upadhyaya and Anxiao (Andrew) Jiang, Representation-Oblivious Error Correction by Natural Redundancy, in Proc. IEEE International Conference on Communications (ICC), Shanghai, China, May 2019.

21.   Ramakrishna Prabhu, Xiaojing Yu, Zhangyang Wang, Ding Liu, and Anxiao (Andrew) Jiang, U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting, in book The Springer Series on Challenges in Machine Learning, April 2019. (This work was the winner of the 2nd-place award in the ECCV 2018 Chalearn LAP Inpainting Competition, Track 3: Fingerprint Denoising and Inpainting.)

20.   Pulakesh Upadhyaya and Anxiao (Andrew) Jiang, File Type Recognition and Error Correction for NVMs with Deep Learning, in Proc. Non-Volatile Memories Workshop (NVMW), San Diego, CA, March 2019.

19.   P. Upadhyaya, X. Yu, J. Mink, J. Cordero, P. Parmar and A. Jiang, Error Correction for Hardware-Implemented Deep Neural Networks, in Proc. Non-Volatile Memories Workshop (NVMW), San Diego, CA, March 2019.

18.   P. Upadhyaya, X. Yu, J. Mink, J. Cordero, P. Parmar and A. Jiang, Error Correction for Noisy Neural Networks, in Proc. Information Theory and Applications (ITA) Workshop, San Diego, CA, February 2019.

17.   P. Upadhyaya and A. Jiang, Representation-Oblivious Error Correction by Natural Redundancy, in arXiv, November 2018.

16.   Ramakrishna Prabhu, Xiaojing Yu, Zhangyang Wang, Ding Liu, and Anxiao (Andrew) Jiang, U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting, European Conference on Computer Vision (ECCV) Chalearn LAP Workshop, Munich, Germany, September 2018.

This paper was the winner of the 2nd-place award in the ECCV 2018 Chalearn LAP Inpainting Competition (Track 3: Fingerprint Denoising and Inpainting).

15.   A. Jiang, Elimination of Cyclic Stopping Sets for Enhanced Decoding of LDPC Codes, in Proc. IEEE International Symposium on Information Theory (ISIT), Vail, Colorado, June 2018.

14.   A. Jiang, P. Upadhyaya, Y. Wang, K. R. Narayanan, H. Zhou, J. Sima and J. Bruck,  Efficient Assistance to LDPC Code-based Erasure Recovery in NVM Storage, in Proc. Non-Volatile Memories Workshop (NVMW), San Diego, CA, March 2018.

13.   A. Jiang, Machine Learning and Algorithmic Techniques for Error Correction, in Proc. Information Theory and Applications (ITA) Workshop, San Diego, CA, February 2018.

12.   A. Jiang, P. Upadhyaya, Y. Wang, K. R. Narayanan, H. Zhou, J. Sima and J. Bruck, Stopping Set Elimination for LDPC Codes, in Proc. 55th Annual Allerton Conference on Communication, Control and Computing (Allerton), Monticello, IL, October 2017.

11.   P. Upadhyaya and A. Jiang, On LDPC Decoding with Natural Redundancy, in Proc. 55th Annual Allerton Conference on Communication, Control and Computing (Allerton), Monticello, IL, October 2017.

10.   Y. Wang, K. R. Narayanan and A. Jiang, Exploiting Source Redundancy to Improve the Rate of Polar Codes, in Proc. IEEE International Symposium on Information Theory (ISIT), pp. 864--868, Aachen, Germany, June 2017.

9.   A. Jiang, P. Upadhyaya, E. F. Haratsch and J. Bruck, Error Correction by Natural Redundancy for Long Term Storage, in Proc. Non-Volatile Memories Workshop (NVMW), La Jolla, CA, March 2017.

8.   P. Upadhyaya and A. Jiang, LDPC Decoding with Natural Redundancy, in Proc. Non- Volatile Memories Workshop (NVMW), La Jolla, CA, March 2017.

7.   Y. Wang, A. Jiang and K. R. Narayanan, Modeling and Analysis of Joint Decoding of Language- based Sources with Polar Codes, in Proc. Non-Volatile Memories Workshop (NVMW), La Jolla, CA, March 2017.

6.    A. Jiang, P. Upadhyaya, E. F. Haratsch and J. Bruck, Correcting Errors by Natural Redundancy, in Proc. Information Theory and Applications (ITA) Workshop, San Diego, CA, February 2017.

5.    Y. Wang, M. Qin, K. R. Narayanan, A. Jiang and Z. Bandic, Joint Source-Channel Decoding of Polar Codes for Language-Based Source, in Proc. IEEE Global Communications Conference (Globecom), Washington D.C., December 2016.

4.    A. Jiang, Y. Li and J. Bruck, Error Correction through Language Processing, in Proc. IEEE Information Theory Workshop (ITW), Jerusalem, Israel, April to May 2015.

3.    A. Jiang, Y. Li and J. Bruck, Enhanced Error Correction via Language Processing, in Proc. Non- Volatile Memories Workshop (NVMW), La Jolla, CA, March 2015.

2.    A. Jiang and J. Bruck, Is There A New Way to Correct Errors, in Proc. Information Theory and Applications (ITA) Workshop, San Diego, CA, February 2015.

1. Y. Li, Y. Wang, A. Jiang and J. Bruck, Content-assisted File Decoding for Nonvolatile Memories, in Proc. 46th Asilomar Conference on Signals, Systems and Computers, pp. 937–941, Pacific Grove, CA, November 2012.