Google scholar: https://scholar.google.com/citationsuser=iD98H2sAAAAJ&hl=en

Conferences and workshops

1. 1. 1. Het Patel, Tiejin Chen, Hua Wei, Evangelos E Papalexakis, Jia Chen, "Are LLM Uncertainty and Correctness Encoded by the Same Features? a Functional Dissociation via Sparse Autoencoders", SIAM International Conference on Data Mining (SDM26), Salt Lake City, Utah, Novermeber, 2026.

      2. Tiejin Chen, Huaiyuan Yao, Jia Chen, Evangelos E. Papalexakis, Hua Wei, "Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition", The 64th Annual Meeting of the Association for Computational Linguistics (ACL), San Diego, California, June, 2026.

      3. Tiejin Chen, Xiaoou Liu, Longchao Da, Jia Chen, Evangelos E. Papalexakis, Hua Wei, "Uncertainty Quantification of Large Language Models through Multiple Uncertainty Sources", The 64th Annual Meeting of the Association for Computational Linguistics (ACL) Findings, San Diego, California, June, 2026.

      4. Dawon Ahn, Het Patel (co-1st author), Aemal Khattak, Jia Chen, Evangelos E. Papalexakis, "Extracting and Analyzing Rail Crossing Behavior Signatures from Videos using Tensor Methods", IEEE INNOVARail 2026, Champaign, Illinois, May, 2026.

      5. Paimon Goulart, Jordan Steinhauser (co-1st author), Kylene Shuler, Edward Korzus, Jia Chen and Evangelos Papalexakis, "Preliminary Use of Vision Language Model Driven Extraction of Mouse Behavior Towards Understanding Fear Expression", WSDM Day 2026.  Boise, Idaho, USA, February, 2026.

      6. Chansong Lim, Haz Sameen Shahgir, Yue Dong, Jia Chen and Evangelos E. Papalexakis, "A Real-Time System to Populate FRA Form 57 from News," The Nineteenth International Conference on Web Search and Data Mining (WSDM), Demonstrations Track. Boise, Idaho, USA, February, 2026.

      7. Kishor Kumar Bhaumik, Jia Chen, Evangelos E. Papalexakis ``DuLPA: Dual-Level Prototype Alignment for Unsupervised Domain Adaptation in Activity Recognition from Wearables," NeurIPS 2025 Workshop: Learning from Time Series for Health (TS4H), San Diego, CA, Dec. 6/7, 2025.

      8. Het Patel, Muzammil Allie, Qian Zhang, Jia Chen, Evangelos E. Papalexakis ``Robust Vision-Language Models via Tensor Decomposition: A Defense Against Adversarial Attacks," ICCV 2025 Workshop on Safe and Trustworthy Multimodal AI Systems (SafeMM-AI 2025), Honolulu, Hawaii, Oct. 19, 2025.

      9. Haz Sameen Shahgir, Chansong Lim, Jia Chen, Evangelos E. Papalexakis, Yue Dong ``ExpertGenQA: Open-ended QA Generation in Specialized Domains," Empirical Methods in Natural Language Processing (EMNLP), Findings, Suzhou, China, Nov. 2025.

      10. Yiran Luo, Het Patel (co-1st author), Yu Fu, Dawon Ahn, Jia Chen, Yue Dong, Evangelos E. Papalexakis ``TRAWL: Tensor Reduced and Approximated Weights for Large Language Models," Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) Special Sessions: Data Science: Foundations and Applications (DSFA), Sydney Australia, June 2025. 

      11. Jia Chen and Evangelos Papelexakis ``Project or Factorize? A case study of Multiview CCA and PARAFAC2 tensor factorization," Proc. of Asilomar Conf. on Signals, Systems, and Computers, Paper. Pacific Grove, CA, October 2024.

      12. Shaan Pakala, Bryce Graw, Dawon Ahn, Tam Dinh, Mehnaz Tabassum Mahin, Vassilis Tsotras, Jia Chen, and Evangelos E. Papalexakis ``Automating Data Science Pipelines with Tensor Completion,"  2024 IEEE International Conference on Big Data, Washington DC, Dec. 2024.

      13. G M Shahariar, Jia Chen, Jiachen Li, Yue Dong ``Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image Generation,” Empirical Methods in Natural Language Processing (EMNLP), Findings, Miami, Florida, Nov. 2024.

      14. Yu Fu, Wen Xiao, Jia Chen, Jiachen Li, Evangelos Papalexakis, Aichi Chien and Yue Dong ``Cross-Task Defense: Instruction-Tuning LLMs for Content Safety,” TrustNLP: Fourth Workshop on Trustworthy Natural Language Processing, Mexico City, Mexico, June 2024.

      15. E. Villalobos, C. Tarawneh, J. Chen, E. E. Papalexakis, and P. Xu ``Kernel Ridge Regression in Predicting Railway Crossing Accidents,” American Society of Mechanical Engineers (ASME) 2024 Joint Rail Conference (JRC2024), Columbia, SC, May 2024.

      16. E. Villalobos, H. Lugo, B. Cheng, M. Gutierrez, C. Tarawneh, P. Xu, J. Chen, and E. E. Papalexakis , ``Spectral Clustering in Railway Crossing Accidents Analysis,” American Society of Mechanical Engineers (ASME) 2024 Joint Rail Conference (JRC2024), Columbia, SC, May 2024.

      17. R. Gurav, H. Patel, Z. Shang, E. Scudiero, J. Chen, A. Eldawy, and E. E. Papalexakis, ``Can SAM recognize crops? Quantifying the zero-shot performance of a semantic segmentation foundation model on generating crop-maps using satellite imagery for precision agriculture,” NeurIPS 2023 AI4Science Workshop, New Orleans, December 2023.

      18. Y. Wu, U. Saini, J. Chen and E. E. Papalexakis, ``TENALIGN: Joint Tensor Alignment and Coupled Factorization,” 22nd IEEE International Conference on Data Mining (ICDM), Orlando, FL, November 2022.

      19. D. Xu [Ayala High School], W. Shiao, J. Chen and E. E. Papalexakis, ``SV-Learn: Learning Matrix Singular Values with Neural Networks,” ICDM OEDM Workshop (Optimization Based Techniques for Emerging Data Mining Problems), Orlando, FL, November 2022.

      20. J. Chen, D. Orozco, L. Figueroa and E. E. Papalexakis, ``Unsupervised Multiview Embedding of Node Embeddings,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, paper, Pacific Grove, CA, November 2022.

      21. M. Duarte, E. E. Papalexakis, and J. Chen, ``Graph-Assisted Tensor Disaggregation,” 17th International Workshop on Mining and Learning with Graphs (MLG), paper, August 2022.

      22. M. Kaish, M. Hossain, E. E. Papalexakis, and J. Chen, ``COVID-19 or Flu? Discriminative Knowledge Discovery of COVID-19 Symptoms from Google Trends Data,” 4th epiDAMIK ACM SIGKDD International Workshop on Epidemiology meets Data Mining and Knowledge Discovery, August 2021.

      23. J. Chen and E. E. Papalexakis, ``Ensemble Node Embeddings using Tensor Decomposition: A Case-Study on DeepWalk,” 1st Workshop on Multi-Source Data Mining, November 2020.

      24. J. Chen, G. Wang, and G. B. Giannakis, ``Multiview Canonical Correlation Analysis over Graphs,” Proc. of Intl. Conf. on Acoustics, Speech, and Signal Processing, Brighton, UK, May 12-17, 2019.

      25. J. Chen, G. Wang, and G. B. Giannakis, ``Nonlinear Discriminative Dimensionality Reduction of Multiple Datasets,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 28-31, 2018.

      26. J. Chen, G. Wang, Y. Shen, and G. B. Giannakis, ``Canonical Correlation Analysis with Common Graph Priors,” Proc. of Statistical Signal Processing Workshop, Freiburg, Germany, Jun. 10-13, 2018, pp. 488 - 492.

      27. G. Wang, J. Chen, and G. B. Giannakis, ``DPCA: Dimensionality Reduction for Discriminative Analytics of Multiple Large-Scale Datasets,” Proc. of Intl. Conf. on Acoustics, Speech, and Signal Processing, Calgary, Canada, Apr. 15-20, 2018, pp. 2211 – 2215.

      28. J. Chen and I. D. Schizas, ``Distributed Efficient Multimodal Data Clustering,” Proc. of the European Signal Processing Conference, Kos, Greece, Aug. 28-Sep. 2, 2017, pp. 2304 - 2308.

      29. J. Chen, A. Malhotra, and I. D. Schizas, ``Information-Based Clustering and Filtering for Field Reconstruction,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 8-11, 2015, pp. 576 - 580.

      30. J. Chen and I. D. Schizas, ``Regularized Canonical Correlations for Sensor Data Information Clustering,” Proc. of Intl. Conf. on Acoustics, Speech, and Signal Processing, Brisbane, Australia, Apr. 19-25, 2015, pp. 3601 - 3605.

      31. J. Chen and I. D. Schizas, ``Adaptive Regularized Canonical Correlations in Clustering Sensor Data,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 2-5, 2014, pp. 1611 - 1615.

      32. J. Chen and I. D. Schizas, ``Distributed Sparse Canonical Correlation Analysis in Clustering Sensor Data,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 3-6, 2013, pp. 639 - 643.

      33. J. Chen and Y. Zhang, ``Study on Synchronization Algorithm of Maximum Likelihood Based on Cyclic Prefix in the OFDM System,” 13th Annual Meeting of China Association for Science and Technology, Tianjing, China, Sep. 2011.

Journals 

1. 1. 1. Jarvis Haupt, Qin Lu, Yanning Shen, Jia Chen, Yue Dong, Dan McCreary, Mehmet Akc¸akaya and Georgios B. Giannakis, ``Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities," IEEE Signal Processing Magazine, Special Issue on Artificial Intelligence for Education: A Signal Processing Perspective, DOI: 10.1109/MSP.2025.3600846, Volume: 43, Issue: 1, Jan. 2026.

      2. J. Chen and I. D. Schizas, ``Multimodal Correlations-Based Data Clustering,” Foundations of Data Science (AIMS, American Institute of Mathematical Sciences), DOI 10.3934/fods.2022011, Volume 4, Issue 3, Pages 395-422, Sep. 2022. 

      3. X. Zhang, J. Chen, Y. Wu, L. Tang, and X. Ling, ``Predicting the Maximum Seismic Response of the Soil-Pile-Superstructure System using Random Forests," Journal of Earthquake Engineering (Taylor & Francis), Oct. 2021.

      4. J. Chen, G. Wang, and G. B. Giannakis, ``Graph Multiview Canonical Correlation Analysis,” IEEE Transactions on Signal Processing, vol. 67, no. 11, pp. 2826-2838, June 2019. Matlab Code.

      5. J. Chen, G. Wang, and G. B. Giannakis, ``Nonlinear Dimensionality Reduction for Discriminative Analytics of Multiple Datasets,” IEEE Transactions on Signal Processing, vol. 67, no. 3, pp. 740-752, February 2019. 

      6. W. Yu, Y. Wen, S. Köse, and J. Chen, ``Exploiting Multi-Phase On-Chip Voltage Regulators as Strong PUF Primitives for Securing IoT,” Journal of Electronic Testing: Theory and Applications (Springer), vol. 34, no. 5, pp. 587-598, Oct. 2018.

      7. W. Yu and J. Chen, ``Deep Learning-Assisted and Combined Attack: A Novel Side-Channel Attack,” IET Electronics Letters (feature article), vol. 54, no. 19, pp. 1114-1116, Sep. 2018. 

      8. J. Chen, G. Wang, Y. Shen, and G. B. Giannakis, ``Canonical Correlation Analysis of Datasets with a Common Source Graph,” IEEE Transactions on Signal Processing, vol. 66, no. 16, pp. 4398-4408, Aug. 2018.

      9. W. Yu and J. Chen, ``Masked AES PUF: a New PUF Against Hybrid SCA/MLAs,” IET Electronics Letters (feature article), vol. 54, no. 10, pp. 618-620, May 2018. 

      10. J. Chen, A. Malhotra, and I. D. Schizas, ``Data-Driven Sensor Clustering and Filtering for Communication Efficient Field Reconstruction,” Elsevier Signal Processing, vol. 133, pp. 156-168, Apr. 2017.

      11. J. Chen and I. D. Schizas, ``Distributed Information-Based Clustering of Heterogeneous Sensor Data,” Elsevier Signal Processing, vol. 126, pp. 35--51, Sep. 2016. 

      12. J. Chen and I. D. Schizas, ``Online Distributed Sparsity-Aware Canonical Correlation Analysis,” IEEE Transactions on Signal Processing, vol. 64, no. 3, pp. 688-703, Mar. 2016. 

      13. Q. Zhao, P. Deng, and J. Chen, ``Fingerprinting Positioning Algorithm in WLAN Based on AP ID Filter,” Journal of Communications Technology, vol. 45, no. 10, pp. 61-63, 2012.

Posters

1. E. E. Papalexakis, R. Jiang, M. Gutierrez, D. Ahn, P. Xu, C. Tarawneh, and J. Chen, ``Tensor Mining of Public Railroad Accident Reports,” 13th International Heavy Haul Association (IHHA) Conference, Colorado Springs, CO, Nov. 2025.

2. B. Cheng, E. Papalexakis, and J. Chen, ``Towards Aligned Canonical Correlation Analysis: Preliminary Formulation and Proof-of-Concept Results," Southern California Data Science Day at 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 7, 2023.

3. A. Flores, R. Charak, I. Cano-Gonzalez, J. Chen, ``Use of Machine Learning in Estimating the Predictability of Proximal and Distal Risk and Protective Factors of Cyber Sexual Perpetration in Intimate Partner Relationships," 36th Annual Meeting of the International Society for Traumatic Stress Studies (ISTSS), Poster. November 2020.

Book chapter

1. J. Chen, H. Cao, A. Sadeghi, and G. Wang, `` Learning Shared and Discriminative Information from Multiview Data," Recent Advancements in Multi-View Data Analytics. Studies in Big Data (Springer, Cham), Editors: Witold Pedrycz, Shyi-Ming Chen, vol 106. DOI https://doi.org/10.1007/978-3-030-95239-6_9. May 2022.