[{"id":48172,"date":"2026-10-24T08:30:28","date_gmt":"2026-10-24T13:30:28","guid":{"rendered":"https:\/\/www.pvamu.edu\/engineering\/?page_id=48172"},"modified":"2026-10-24T09:12:23","modified_gmt":"2026-10-24T14:12:23","slug":"research","status":"publish","type":"page","link":"https:\/\/www.pvamu.edu\/engineering\/credit\/lijun-qian\/research\/","title":{"rendered":"Research"},"content":{"rendered":"

Home<\/a>
\nResearch<\/a><\/div>\n<\/div><\/div><\/div><\/div><\/div>
\n

Publications<\/h2>\n
\n

Selected Recent Journal Papers<\/h3>\n
    \n
  1. C. Onyeke, L. Qian<\/strong>, P. Obiomon, X. Dong (2026). \u201cEnhancing DeepIRT Through Counterfactual Item-Invariance Regularization and Joint Learning.\u201d Frontiers in Artificial Intelligence<\/em>.<\/li>\n
  2. T. Akinola, X. Li, L. Qian<\/strong> (2026). \u201cImage Inpainting-Based Spectrum Occupancy Prediction in Cognitive Radio Networks Using Deep Learning.\u201d Telecom 2026, 7(5), 110<\/em>. DOI<\/a>.<\/li>\n
  3. C. Hsu, S. Xu, M. Lynch, Y. Wang, X. Liu, C. Williams, K. Field, X. Wang, L. Qian<\/strong>, and X. Qian (2026). \u201cINR-TEM: Robust Cavity Detection in Multi-Focus TEM Images via Implicit Neural Representations.\u201d APL Machine Learning, March 2026; 4(1): 016105<\/em>. DOI<\/a>.<\/li>\n
  4. O. Ihimekpen, X. Li, L. Qian<\/strong> (2026). \u201cModeling and Control of Spring-Mass Systems with Switched Dynamics using Reservoir Computing.\u201d IEEE Access, vol. 14, pp. 77825\u201377838, 2026<\/em>. DOI<\/a>.<\/li>\n
  5. D. Doe, X. Li, Y. Hu, Z. Han, L. Qian<\/strong> (2026). \u201cMulti-Tier Autonomous Ride Services: A Systematic Analysis of Fleet Providers\u2019 Pricing Strategies.\u201d IEEE Transactions on Intelligent Transportation Systems<\/em>.<\/li>\n
  6. O. Adegboye, T. Akinola, X. Li, L. Qian<\/strong> (2026). \u201cAI-Driven Inverse Antenna Design in UWB Band: Embedding Forward Consistency into Deep Learning Architectures.\u201d IEEE Transactions on Antennas and Propagation, vol. 74, no. 2, pp. 1356\u20131370, Feb. 2026<\/em>. DOI<\/a>.<\/li>\n
  7. M. Rana, R. Ray, X. Dong, L. Qian<\/strong> (2026). \u201cVegetation Health Prediction Using RGB Images from UAV with Deep Learning.\u201d IEEE Access, vol. 14, pp. 60752\u201360766, 2026<\/em>. DOI<\/a>.<\/li>\n
  8. H. Rajabu, L. Qian<\/strong>, and X. Dong (2026). \u201cMulti-task GINN-LP for Multi-target Symbolic Regression.\u201d Artificial Intelligence (AI)<\/em>.<\/li>\n
  9. M. Rahman, L. Qian<\/strong> (2026). \u201cComparative Analysis of Inference Performance of Pre-trained Convolutional Neural Networks in Analog Accelerators.\u201d IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 10, no. 2, pp. 1324\u20131339, April 2026<\/em>. DOI<\/a>.<\/li>\n
  10. I. Tommy, T. Akinola, X. Li, L. Qian<\/strong> (2025). \u201cSpatio-Temporal Beam-Level Traffic Forecasting in 5G Wireless Systems Using Multi-Task Learning.\u201d Frontiers in Communications and Networks, vol. 6<\/em>. DOI<\/a>.<\/li>\n
  11. M. Rahman, P. Roy, S. Frizell, L. Qian<\/strong> (2025). \u201cEvaluating Pretrained Deep Learning Models for Image Classification Against Individual and Ensemble Adversarial Attacks.\u201d IEEE Access, vol. 13, pp. 35230\u201335242, 2025<\/em>. DOI<\/a>.<\/li>\n
  12. J. Olamofe, R. Ray, X. Dong, L. Qian<\/strong> (2025). \u201cNormalized Difference Vegetation Index Prediction using Reservoir Computing and Pretrained Language Models.\u201d Artificial Intelligence in Agriculture, 15(1), 116\u2013129<\/em>.<\/li>\n
  13. C. H. Blow, L. Qian<\/strong>, X. Dong, P. Obiomon, C. Gibson (2025). \u201cData Augmentation via Diffusion Model to Enhance AI Fairness.\u201d Frontiers in Artificial Intelligence, 8:1530397<\/em>. DOI<\/a>.<\/li>\n
  14. J. Olamofe, L. Qian<\/strong>, K. Field (2025). \u201cPerformance Evaluation of Image Super-Resolution for Cavity Detection in Irradiated Materials.\u201d IEEE Access, vol. 13, pp. 68052\u201368065, 2025<\/em>. DOI<\/a>.<\/li>\n
  15. M. Kuo, X. Li, L. Qian<\/strong>, P. Obiomon, X. Dong (2025). \u201cTracing Student Learning Outcome at Historically Black Colleges and Universities via Deep Knowledge Tracing.\u201d IEEE Access, vol. 13, pp. 61340\u201361349, 2025<\/em>. DOI<\/a>.<\/li>\n
  16. B. Williams, L. Qian<\/strong> (2025). \u201cSemi-Supervised Learning for Intrusion Detection in Large Computer Networks.\u201d Applied Sciences, 15(11), 5930; Special Issue on Data Mining and Machine Learning in Cybersecurity<\/em>. DOI<\/a>.<\/li>\n
  17. M. Kuo, X. Li, P. Obiomon, L. Qian<\/strong> and X. Dong (2025). \u201cImproving Student Learning Outcome Tracing at HBCUs Using Tabular Generative AI and Deep Knowledge Tracing.\u201d IEEE Access<\/em>. DOI<\/a>.<\/li>\n<\/ol>\n

    Selected Recent Conference Papers<\/h3>\n
      \n
    1. M. Rahman, T. Oguntade, B. Feinberg, S. Agarwal, L. Qian<\/strong> (2027). \u201cStructured Weight-Space Robustness Analysis for Multi-Image Super-Resolution.\u201d Winter Conference on Applications of Computer Vision (WACV 2027)<\/em>.<\/li>\n
    2. P. Debnath, J. Osuorah, S. Ahmed, L. Gong, X. Li, A. Annamalai, L. Qian<\/strong> (2027). \u201cDesign and System Integration of a Mobile Edge Computing\u2013Assisted Microgrid Testbed for Real-Time Energy Management.\u201d 2027 IEEE Texas Power and Energy Conference<\/em>.<\/li>\n
    3. M. Rahman, T. Sharmin, M. Rana, M. Khan, A. Kulkarni, and L. Qian<\/strong> (2027). \u201cLeakage-Aware Uncertainty Estimation for Robust Deep Learning Side-Channel Analysis.\u201d IEEE International Symposium on Hardware Oriented Security and Trust (HOST), 2027<\/em>.<\/li>\n
    4. T. Oguntade, J. Olamofe, X. Li, L. Qian<\/strong> (2027). \u201cEvaluating Generalization in AI-Generated Image Detection: A Comparative Study of Pixel-Space, Latent-Space, and Reconstruction-Based Approaches.\u201d 9th International Conference on Image Processing and Machine Vision (IPMV 2027)<\/em>.<\/li>\n
    5. T. Sharmin, L. Qian<\/strong>, X. Li, X. Dong (2027). \u201cUncertainty-Aware Tensor4D for Dynamic Neural Rendering.\u201d 9th International Conference on Image Processing and Machine Vision (IPMV 2027)<\/em>.<\/li>\n
    6. M. Rahman, and L. Qian<\/strong> (2026). \u201cModeling Triplet Interactions Beyond Pairwise Attention.\u201d Findings of the Asian Conference on Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL\u2013IJCNLP), 2026<\/em>.<\/li>\n
    7. M. Rahman, M. Mahdi Mosleh Khan, and L. Qian<\/strong> (2026). \u201cDo Mathematical LLMs Survive Analog Inference?.\u201d NeurIPS 2026 Workshop MATH-AI<\/em>.<\/li>\n
    8. M. Rahman, and L. Qian<\/strong> (2026). \u201cReliability-Aware Metacognitive Routing for Cost-Efficient Mathematical Reasoning.\u201d NeurIPS 2026 Workshop MATH-AI<\/em>.<\/li>\n
    9. M. Rahman, J. Enouen, Y. Liu, and L. Qian<\/strong> (2026). \u201cTriplet-Aware Sparse Higher-Order Attention for Natural Language Understanding.\u201d NeurIPS 2026 Workshop LP4FM<\/em>.<\/li>\n
    10. S. Sarker, L. Qian<\/strong>, X. Dong (2026). \u201cIntegrating LLMs-based Data Augmentation and Transformer Circuit to Enhance Performance and Interpretability in Medication Identification and Events Classification.\u201d International Conference on Intelligent Biology and Medicine (ICIBM) 2026<\/em>.<\/li>\n
    11. T. Abdul-Quddoos, L. Qian<\/strong> (2026). \u201cCalibrated RF-Fingerprinting Under Interference With Heterogeneous Transmission Protocols.\u201d IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) 2026<\/em>.<\/li>\n
    12. O. Oshinuga, L. Gong and L. Qian<\/strong> (2026). \u201cBenchmarking Classical Machine Learning Models for Short-Circuit Fault Detection and Classification in Three-Phase Power Grids.\u201d 58th North American Power Symposium (NAPS) 2026<\/em>.<\/li>\n
    13. P. Debnath, L. Gong, X. Li and L. Qian<\/strong> (2026). \u201cDistributed Coordination of Utility-Scale Battery Energy Storage Systems Using Multi-Agent Reinforcement Learning.\u201d 58th North American Power Symposium (NAPS) 2026<\/em>.<\/li>\n
    14. E. Karim, L. Gong, X. Li and L. Qian<\/strong> (2026). \u201cLarge Language Model-Generated MIP Warm Starts for Security-Constrained Unit Commitment.\u201d 58th North American Power Symposium (NAPS) 2026<\/em>.<\/li>\n
    15. B. Siwakoti, Y. Liu, X. Li, L. Qian<\/strong> (2026). \u201cClassical and Quantum Computing UCCSD Quantum Mechanical Study of the Hydrogen Bond between Nirmatrelvir and Gln-192 of SARS-CoV-2 Main Protease.\u201d The 1st International Symposium on Quantum Computing Systems and Applications (QCSA 2026), October 24, 2026, Paris, France<\/em>.<\/li>\n
    16. M. Rahman, and L. Qian<\/strong> (2026). \u201cAttention Under Attack: Analog Noise Effects and Mechanistic Vulnerabilities in Transformer Models.\u201d ACL (main conference)<\/em>.<\/li>\n
    17. M. Rahman and L. Qian<\/strong> (2026). \u201cUnderstanding Metacognition in Multi-Agent LLMs: Routing, Not Reasoning.\u201d ICLR 2026 AIWILD<\/em>.<\/li>\n
    18. J. Cai, M. Rahman, J. Enouen, Y. Liu, and L. Qian<\/strong> (2026). \u201cA Survey of Actionable Interpretability of Large Language Models.\u201d 35th International Joint Conference on Artificial Intelligence (IJCAI)<\/em>.<\/li>\n
    19. M. Rahman, and L. Qian<\/strong> (2026). \u201cMetacognitive Arbitration as Uncertainty Compression in Multi-Agent Language Models.\u201d Conference on Uncertainty in Artificial Intelligence (UAI)<\/em>.<\/li>\n
    20. T. Akinola, X. Li and L. Qian<\/strong> (2026). \u201cHybrid Quantum-Classical Complex-Valued Classifier: A Statevector-Based Approach.\u201d IEEE IPDPS 2nd Workshop on Advancing Scientific Computing through Quantum and HPC Synergies, May 25\u201329, 2026, New Orleans, LA, USA<\/em>.<\/li>\n
    21. J. Olamofe, X. Li, and L. Qian<\/strong> (2026). \u201cEnsemble-Aware YOLO Architectures For Robust Object Detection in Dense Urban Aerial Imagery.\u201d Computer Vision Conference, May 21\u201322, 2026, Amsterdam, Netherlands<\/em>.<\/li>\n<\/ol>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div><\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2106,"featured_media":0,"parent":48153,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"rs_blank_template":"","rs_page_bg_color":"","slide_template_v7":"","_content_title_display":"","footnotes":""},"class_list":["post-48172","page","type-page","status-publish","hentry"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/pages\/48172","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/users\/2106"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/comments?post=48172"}],"version-history":[{"count":6,"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/pages\/48172\/revisions"}],"predecessor-version":[{"id":48184,"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/pages\/48172\/revisions\/48184"}],"up":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/pages\/48153"}],"wp:attachment":[{"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/media?parent=48172"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":48153,"date":"2026-10-23T18:39:10","date_gmt":"2026-10-23T23:39:10","guid":{"rendered":"https:\/\/www.pvamu.edu\/engineering\/?page_id=48153"},"modified":"2026-10-24T09:09:08","modified_gmt":"2026-10-24T14:09:08","slug":"lijun-qian","status":"publish","type":"page","link":"https:\/\/www.pvamu.edu\/engineering\/credit\/lijun-qian\/","title":{"rendered":"lijun-qian"},"content":{"rendered":"

      Home<\/a>
      \n      Research<\/a><\/div>\n<\/div><\/div><\/div><\/div><\/div>
      <\/span><\/div><\/div><\/div>

      Academic Profile<\/h2><\/div>
      \n\n\n\n\n\n\n\n\n\n
      Name<\/strong><\/td>\nLijun Qian<\/td>\n<\/tr>\n
      Title<\/strong><\/td>\nRegents Professor, AT&T Endowed Professor & Director (CREDIT)<\/td>\n<\/tr>\n
      Affiliation<\/strong><\/td>\nElectrical and Computer Engineering,
      \n
      \nPrairie View A&M University
      \n<\/a><\/td>\n<\/tr>\n
      Office<\/strong><\/td>\n[Office number]<\/td>\n<\/tr>\n
      Address<\/strong><\/td>\nPrairie View A&M University,
      \nPrairie View, TX 77446<\/td>\n<\/tr>\n
      Email<\/strong><\/td>\nliqian@pvamu.edu<\/td>\n<\/tr>\n
      Phone<\/strong><\/td>\n+1-832-859-9279<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>

      Biography<\/h2><\/h2><\/div>
      \n

      Dr. Lijun Qian is the AT&T Endowed Professor of Electrical and Computer Engineering at Prairie View A&M University<\/a>. He received his B.S. degree from Tsinghua University, China<\/a>, his M.S. degree from Technion\u2013Israel Institute of Technology<\/a>, and his Ph.D. degree from Rutgers University<\/a>.<\/p>\n

      Before joining Prairie View A&M University, he worked at Bell Labs Research<\/a> and served as a visiting professor at Aalto University, Finland<\/a>. He has played an important role in developing and expanding the Ph.D. program in Electrical Engineering<\/a> at Prairie View A&M University. He has supervised ten Ph.D. students, including the program’s first and second graduates. He is also the Principal Investigator of the NSF HBCU-RISE<\/a> grant.<\/p>\n

      Dr. Qian is the founder and director of the Center of Excellence in Research and Education for Big Military Data Intelligence (CREDIT Center)<\/a>. His research projects have received support from multiple government agencies and industry partners. His research interests include big data processing, wireless communications and mobile networks, network security, and computational systems biology. As a dedicated educator and research mentor, he has engaged numerous students in cutting-edge research.<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>

      \u00a0<\/p>\n<\/div><\/div><\/div><\/div><\/div>

      \u00a0<\/p>\n<\/div><\/div><\/div><\/div><\/div>

      \u00a0<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2106,"featured_media":0,"parent":40188,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"rs_blank_template":"","rs_page_bg_color":"","slide_template_v7":"","_content_title_display":"","footnotes":""},"class_list":["post-48153","page","type-page","status-publish","hentry"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/pages\/48153","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/users\/2106"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/comments?post=48153"}],"version-history":[{"count":20,"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/pages\/48153\/revisions"}],"predecessor-version":[{"id":48183,"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/pages\/48153\/revisions\/48183"}],"up":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/pages\/40188"}],"wp:attachment":[{"href":"https:\/\/www.pvamu.edu\/engineering\/wp-json\/wp\/v2\/media?parent=48153"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":47991,"date":"2026-10-01T05:23:20","date_gmt":"2026-10-01T10:23:20","guid":{"rendered":"https:\/\/www.pvamu.edu\/engineering\/?page_id=47991"},"modified":"2026-10-02T12:03:55","modified_gmt":"2026-10-02T17:03:55","slug":"form","status":"publish","type":"page","link":"https:\/\/www.pvamu.edu\/engineering\/microcredentials\/partners\/talent\/form\/","title":{"rendered":"Talent Development Interest Form"},"content":{"rendered":"