Runlong Yu

About Me

I am an Assistant Professor in the Department of Computer Science at The University of Alabama and an ALAAI Faculty Fellow for Frontier AI. Previously, I was a Postdoctoral Associate in Computer Science at the University of Pittsburgh, working with Prof. Xiaowei Jia. I earned my Ph.D. and B.Eng. in Computer Science from the University of Science and Technology of China.

My research focuses on AI for Open-World Scientific Discovery in complex natural systems. I seek to move AI for Science beyond predefined prediction tasks toward discovering what is not yet known: identifying consequential scientific questions, learning representations that capture the essential structure of complex systems, recognizing when critical evidence is missing, and actively seeking new evidence through observation, intervention, and counterfactual testing. A central theme of my work is learning from data, reasoning with domain knowledge, and uncovering structures and mechanisms across mathematics, physics, environmental science, and geospatial science. My goal is to derive interpretable, falsifiable, and transferable scientific insights from complex, limited, and evolving observations, and to turn local discoveries into principles that generalize across scales, environments, and systems. My work has been recognized by the IEEE ICDM Best BlueSky Paper Award (First Prize). More details are available in my Curriculum Vitae.

Open Positions

Our lab is ALWAYS recruiting for fully funded Ph.D. and master's positions in data science and machine learning. Undergraduate RAs and remote interns are also welcome.

Please email your CV and research interests to ryu5@ua.edu with the subject [Prospective Student] Name - Position - Start Date.

Recent News

• [2026] Named an ALAAI Faculty Fellow for Frontier AI.
• [2026] NASA award 80NSSC26K0971 was funded for an end-user-driven inland water quality monitoring and decision-support system.
• [2026] Two papers were accepted to the ICDM 2026 Research Track.
• [2026] Two papers were accepted to EMNLP 2026, including one in the Main Conference and one in Findings of the ACL.
• [2026] Two papers were accepted to KDD 2026, including one in the Blue Sky Ideas Track and one in the AI4Sciences Track.
• [2026] A survey paper was accepted to the IJCAI 2026 Survey Track.
• [2026] Three papers were accepted to SDM 2026.
• [2026] Teaching Artificial Intelligence for undergraduate, graduate, and online students.

Research Areas & Interests

• AI for Science & Scientific Machine Learning: physics-guided machine learning, physics-informed neural networks, neural operators, PDE learning, scientific data mining, and process-based modeling.
• Foundation Models & Generative AI: large language and multimodal models, diffusion models, flow matching, retrieval-augmented generation, agents, and knowledge-grounded reasoning.
• GeoAI & Environmental Intelligence: geospatial reasoning, remote sensing, multiscale prediction, Earth system and aquatic modeling, wildfire response, and scientific decision-support systems.
• Optimization & Recommender Systems (Earlier Work): evolutionary computation, feature selection and interaction modeling, recommendation, click-through rate prediction, and learning-to-rank.

Teaching

I teach CS 465-001 / CS 565-001 / CS 565-920: Artificial Intelligence in Fall 2026.

• Lectures: Tuesdays & Thursdays, 11:00 AM–12:15 PM.
• Location: Lloyd Hall 132.
• The weekly schedule and office hours: Blackboard Ultra Course Page.

I taught CS 691-002: Physics-Guided Machine Learning in Spring 2026.

• Lectures: Tuesdays & Thursdays, 2:00–3:15 PM.
• Location: Hardaway Hall 251.
• The weekly schedule and office hours: Blackboard Ultra Course Page.

I taught CS 2756: Principles of Data Mining in Spring 2025.

• Lectures: Mondays & Wednesdays, 6:00–7:15 PM.
• Location: SENSQ 6110.
• The weekly schedule and office hours: Canvas Course Page.

I taught CS 1656 / CS 2056: Introduction to Data Science in Fall 2024.

• Lectures: Tuesdays & Thursdays, 1:00–2:15 PM.
• Location: IS 305.
• Recitations: Fridays, 2:00–2:50 PM (SENSQ 5129) or 3:00–3:50 PM (SENSQ 6110).
• The weekly schedule and office hours: Canvas Course Page.

Honors and Awards

• [May 2026] ALAAI Faculty Fellow for Frontier AI, Alabama Center for the Advancement of Artificial Intelligence (ALAAI)
• [Nov. 2025] IEEE ICDM Best BlueSky Paper Award (First Prize)
• [Nov. 2025] IEEE ICDM Best Paper Award Candidate
• [Sep. 2022] Outstanding Student Leadership Award
• [May 2022] Outstanding Student Award of “Six Good” College Students
• [Nov. 2021] Gold Medal of the 7th Anhui Province “Internet+” College Student Innovation Competition
• [Sep. 2021] USTC Postgraduate First-Class Scholarship
• [Jul. 2021] China Scholarship Council Scholarship
• [Jan. 2021] Champion of the CCF BDCI Fighting Epidemics Big Data Charity Challenge [Anhui Daily] [USTC News]
• [Jan. 2021] USTC Special Scholarship
• [Sep. 2020] USTC Postgraduate First-Class Scholarship
• [Dec. 2019] China National Scholarship
• [Sep. 2019] USTC Postgraduate First-Class Scholarship
• [Aug. 2019] KDD CUP Regular ML Track PaddlePaddle Special Award
• [Dec. 2018] The Third Runner-up of China (Hefei) Big Data and Artificial Intelligence Innovation Application Competition
• [Dec. 2018] Suzhou Industrial Park Scholarship
• [Oct. 2018] GDC Technology Scholarship
• [Sep. 2018] USTC Postgraduate First-Class Scholarship
• [Sep. 2017] USTC Postgraduate First-Class Scholarship
• [Oct. 2012] First Prize of Chinese Physics Olympiad

Selected Publications

Publications Since Joining UA (2025–Present)

^† denotes corresponding author.

Journal Articles

• Runlong Yu, Yiqun Xie, Xiaowei Jia. Environmental Computing as a Branch of Science, Communications of the ACM (CACM), v 68, n 7, pages 92–94, 2025.

Conference Articles

• Yilong Dai, Yiming Sun, Yiheng Chen, Ziyi Wang, Shengyu Chen, Xiaowei Jia, Runlong Yu^†. Physics-Preserving Latent Compression for Zero-Shot Resolution Transfer in 3D Turbulence. IEEE International Conference on Data Mining (ICDM), Research Track, accepted, to be published, 2026.
• Chonghao Qiu, Shiyuan Luo, Feng Zhu, Runlong Yu, Yiqun Xie, Xiaowei Jia. ISO-UNet: A Climate-State Conditioned Regional Expert Network for Water Isotope Field Regression. IEEE International Conference on Data Mining (ICDM), Research Track, accepted, to be published, 2026.
• Yilong Dai, Shengyu Chen, Xiaowei Jia, Runlong Yu^†. Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Blue Sky Ideas Track, pages 13164–13169, 2026.
• Qi Cheng, Licheng Liu, Yixuan Chen, Qing Zhu, Runlong Yu, Zhenong Jin, Yiqun Xie, Xiaowei Jia. LLM-based Evaluation Policy Extraction for Ecological Modeling. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), AI4Sciences Track, pages 10704–10715, 2026.
• Yilong Dai, Shengyu Chen, Ziyi Wang, Xiaowei Jia, Yiqun Xie, Vipin Kumar, Runlong Yu^†. Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators. International Joint Conference on Artificial Intelligence (IJCAI), Survey Track, accepted, to be published, 2026.
• Yiming Sun, Qi Cheng, Licheng Liu, Runlong Yu, Yiqun Xie, Xiaowei Jia. Retrieval-Augmented Multi-scale Framework for County-Level Crop Yield Prediction Across Large Regions. SIAM International Conference on Data Mining (SDM), accepted, to be published, 2026.
• Yiheng Chen, Zihui Ma, Peishi Jiang, Yilong Dai, Qikai Hu, Xinyue Ye, Lingyao Li, Rita Leal Sousa, Runlong Yu^†. When Earth Foundation Models Meet Diffusion: An Application to Land Surface Temperature Super-Resolution. SIAM International Conference on Data Mining (SDM), accepted, to be published, 2026.
• Yilong Dai, Yiming Sun, Yiheng Chen, Shengyu Chen, Xiaowei Jia, Runlong Yu^†. FlowRefiner: Flow Matching-Based Iterative Refinement for 3D Turbulent Flow Simulation. SIAM International Conference on Data Mining (SDM), accepted, to be published, 2026.
• Lingyao Li^*†, Runlong Yu^*†, Qikai Hu, Bowei Li, Min Deng, Yang Zhou, Xiaowei Jia. From Pixels to Places: A Systematic Benchmark for Evaluating Image Geolocalization Ability in Large Language Models. Conference on Empirical Methods in Natural Language Processing (EMNLP), Main Conference, accepted, to be published, 2026. ^* Equal contribution; Lingyao Li and Runlong Yu are co-first authors and co-corresponding authors.
• Yiheng Chen, Lingyao Li, Zihui Ma, Qikai Hu, Yilun Zhu, Min Deng, Runlong Yu^†. Empowering LLM Agents with Geospatial Awareness: Toward Grounded Reasoning for Wildfire Response. Conference on Empirical Methods in Natural Language Processing (EMNLP), Findings of the ACL, accepted, to be published, 2026.
• Naiyi Li, Zihui Ma, Runlong Yu^†, Lingyao Li^†. LSDTs: LLM-Augmented Semantic Digital Twins for Adaptive Knowledge-Intensive Infrastructure Planning. AAAI Conference on Artificial Intelligence (AAAI), pages 38871–38879, 2026.
• Shiyuan Luo, Chonghao Qiu, Runlong Yu, Yiqun Xie, Xiaowei Jia. GREAT: Generalizable Representation Enhancement via Auxiliary Transformations for Zero-Shot Environmental Prediction. AAAI Conference on Artificial Intelligence (AAAI), pages 38998–39006, 2026.
• Runlong Yu, Xiaowei Jia. Truth Without Comprehension: A BlueSky Agenda for Steering the Fourth Mathematical Crisis. IEEE International Conference on Data Mining Workshops (ICDMW), BlueSky Track, pages 2472–2476, 2025. Best BlueSky Paper Award (First Prize).
• Shiyuan Luo^*, Runlong Yu^*, Chonghao Qiu, Rahul Ghosh, Robert Ladwig, Paul C. Hanson, Yiqun Xie, Xiaowei Jia. Learning to Retrieve for Environmental Knowledge Discovery: An Augmentation-Adaptive Self-Supervised Learning Framework. IEEE International Conference on Data Mining (ICDM), pages 527–536, 2025. ^* Equal contribution.
• Shiyuan Luo, Runlong Yu, Shengyu Chen, Yingda Fan, Yiqun Xie, Yanhua Li, Xiaowei Jia. Geo-Aware Models for Stream Temperature Prediction across Different Spatial Regions and Scales. ACM International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL), pages 124–136, 2025.
• Lingyao Li, Dawei Li, Zhenhui Ou, Xiaoran Xu, Jingxiao Liu, Zihui Ma, Runlong Yu, Min Deng. LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessment. Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3078–3096, 2025.

Publications Prior to Joining UA

Journal Articles

• Yin Gu, Kai Zhang, Qi Liu, Haojie Yuan, Runlong Yu. π-eLight: Learning Interpretable Programmatic Policies for Effective Traffic Signal Control. IEEE Transactions on Mobile Computing (IEEE TMC), v 25, n 1, pages 1122–1136, 2026.
• Xiaojie Li, Runlong Yu, Lei Chen, Shengjun Liu, Enhong Chen. Process Optimization of Multi-stage Continuous Production System Based on Feature Fusion Modeling. IEEE Transactions on Automation Science and Engineering (IEEE T-ASE), v 22, pages 21512–21524, 2025.
• Kai Zhang, Hongbo Gang, Feng Hu, Runlong Yu, Qi Liu. Adaptive ensemble learning for efficient keyphrase extraction: Diagnosis, aggregation, and distillation. Expert Systems with Applications (ESWA), v 278, Article 127236, 2025.
• Yuyang Ye, Hengshu Zhu, Tianyi Cui, Runlong Yu, Le Zhang, Hui Xiong. University Evaluation through Graduate Employment Prediction: An Influence based Graph Autoencoder Approach. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), v 36, n 11, pages 7255–7267, 2024.
• Yuyang Ye, Zheng Dong, Hengshu Zhu, Tong Xu, Xin Song, Runlong Yu, Hui Xiong. MANE: Organizational Network Embedding with Multiplex Attentive Neural Networks. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), v 35, n 4, pages 4047–4061, 2023.
• Runlong Yu, Qi Liu, Yuyang Ye, Mingyue Cheng, Enhong Chen, Jianhui Ma. Collaborative List-and-Pairwise Filtering from Implicit Feedback. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), v 34, n 6, pages 2667–2680, 2022.
• Huijie Liu, Han Wu, Le Zhang, Runlong Yu, Ye Liu, Chunli Liu, Minglei Li, Qi Liu, Enhong Chen. A Hierarchical Interactive Multi-channel Graph Neural Network for Technological Knowledge Flow Forecasting. Knowledge and Information Systems (KAIS), v 64, n 7, pages 1723–1757, 2022.
• Qixiang Shao, Runlong Yu, Hongke Zhao, Chunli Liu, Mengyi Zhang, Hongmei Song, Qi Liu. Toward Intelligent Financial Advisors for Identifying Potential Clients: A Multitask Perspective. Big Data Mining and Analytics (BDMA), v 5, n 1, pages 64–78, 2022.
• Runlong Yu, Hongke Zhao, Zhong Wang, Yuyang Ye, Peining Zhang, Qi Liu, Enhong Chen. Negatively Correlated Search with Asymmetry for Real-Parameter Optimization Problems. Journal of Computer Research and Development (J-CRAD), v 56, n 8, pages 1746–1757, 2019. (in Chinese)

Conference Articles

• Runlong Yu^*, Shengyu Chen^*, Yiqun Xie, Xiaowei Jia. A Survey of Foundation Models for Environmental Science. Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pages 39–57, 2025. ^* Equal contribution.
• Runlong Yu, Yiqun Xie, Xiaowei Jia. What We Talk About When We Talk About AI for Science. SIAM International Conference on Data Mining (SDM), pages 439–442, 2025.
• Runlong Yu, Chonghao Qiu, Robert Ladwig, Paul C. Hanson, Yiqun Xie, Xiaowei Jia. Physics-Guided Foundation Model for Scientific Discovery: An Application to Aquatic Science. AAAI Conference on Artificial Intelligence (AAAI), v 39, n 27, pages 28548–28556, 2025.
• Yiming Sun, Runlong Yu, Runxue Bao, Yiqun Xie, Ye Ye, Xiaowei Jia. Domain-Adaptive Continual Meta-Learning for Modeling Dynamical Systems: An Application in Environmental Ecosystems. SIAM International Conference on Data Mining (SDM), pages 297–306, 2025.
• Yingda Fan, Runlong Yu, Janet Rice Barclay, Alison P. Appling, Yiming Sun, Yiqun Xie, Xiaowei Jia. Multi-Scale Graph Learning for Anti-Sparse Downscaling. AAAI Conference on Artificial Intelligence (AAAI), v 39, n 27, pages 27969–27977, 2025.
• Yuyang Ye, Zhi Zheng, Yishan Shen, Tianshu Wang, Hengruo Zhang, Peijun Zhu, Runlong Yu, Kai Zhang, Hui Xiong. Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation. AAAI Conference on Artificial Intelligence (AAAI), v 39, n 12, pages 13069–13077, 2025.
• Xiang Xu, Hao Wang, Wei Guo, Luankang Zhang, Wanshan Yang, Runlong Yu, Yong Liu, Defu Lian, Enhong Chen. Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 2745–2755, 2025.
• Yin Gu, Kai Zhang, Qi Liu, Runlong Yu, Xin Lin, Xinjie Sun. ProCC: Programmatic Reinforcement Learning for Efficient and Transparent TCP Congestion Control. ACM International Conference on Web Search and Data Mining (WSDM), pages 963–972, 2025.
• Runlong Yu, Chonghao Qiu, Robert Ladwig, Paul C. Hanson, Yiqun Xie, Yanhua Li, Xiaowei Jia. Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations. IEEE International Conference on Data Mining (ICDM), pages 580–589, 2024.
• Runlong Yu, Robert Ladwig, Xiang Xu, Peijun Zhu, Paul C. Hanson, Yiqun Xie, Xiaowei Jia. Evolution-based Feature Selection for Predicting Dissolved Oxygen Concentrations in Lakes. International Conference on Parallel Problem Solving from Nature (PPSN), pages 398–415, 2024.
• Yuyang Ye, Lu-An Tang, Haoyu Wang, Runlong Yu, Wenchao Yu, Erhu He, Haifeng Chen, Hui Xiong. PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 6148–6157, 2024.
• Runlong Yu, Xiang Xu, Yuyang Ye, Qi Liu, Enhong Chen. Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 3151–3161, 2023.
• Runlong Yu, Qi Liu, Yuyang Ye, Mingyue Cheng, Enhong Chen, Jianhui Ma. Collaborative List-and-Pairwise Filtering from Implicit Feedback (Extended Abstract). IEEE International Conference on Data Engineering (ICDE), pages 3801–3802, 2023.
• Yujie Chen, Runlong Yu, Qi Liu, Enhong Chen, Zhenya Huang. Using Entropy for Group Sampling in Pairwise Ranking from Implicit Feedback. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pages 2496–2500, 2023.
• Xiaojie Li, Runlong Yu, Guiquan Liu, Lei Chen, Enhong Chen, Shengjun Liu. Research on Multi-objective Optimization Algorithm for Coal Blending. China National Conference on Big Data and Social Computing (BDSC), pages 37–60, 2023.
• Yang Yu, Qi Liu, Likang Wu, Runlong Yu, Lei Yu, Zaixi Zhang. Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense. AAAI Conference on Artificial Intelligence (AAAI), pages 4854–4863, 2023.
• Zhikang Mo, Qixiang Shao, Likang Wu, Runlong Yu, Jiexin Xu, Hongmei Song, Enhong Chen. TGNN: A GNN-Based Method with Multi-entity Node for Personal Banking Time Prediction. Intelligent Networked Things: 5th China Conference (CINT), pages 68–79, 2023.
• Yuren Zhang, Enhong Chen, Binbin Jin, Hao Wang, Min Hou, Wei Huang, Runlong Yu. Clustering based Behavior Sampling with Long Sequential Data for CTR Prediction. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pages 2195–2200, 2022.
• Zheng Gong, Shiwei Tong, Han Wu, Qi Liu, Hanqing Tao, Wei Huang, Runlong Yu. Tipster: A Topic-Guided Language Model for Topic-Aware Text Segmentation. International Conference on Database Systems for Advanced Applications (DASFAA), pages 213–221, 2022.
• Huijie Liu, Han Wu, Le Zhang, Runlong Yu, Ye Liu, Chunli Liu, Qi Liu, Enhong Chen. Technological Knowledge Flow Forecasting through A Hierarchical Interactive Graph Neural Network. IEEE International Conference on Data Mining (ICDM), pages 389–398, 2021.
• Mingyue Cheng, Fajie Yuan, Qi Liu, Shenyang Ge, Zhi Li, Runlong Yu, Defu Lian, Senchao Yuan, Enhong Chen. Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pages 575–584, 2021.
• Runlong Yu, Yuyang Ye, Qi Liu, Zihan Wang, Chunfeng Yang, Yucheng Hu, Enhong Chen. XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction. Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pages 436–447, 2021.
• Shiwei Tong, Qi Liu, Runlong Yu, Wei Huang, Zhenya Huang, Zachary A. Pardos, Weijie Jiang. Item Response Ranking for Cognitive Diagnosis. International Joint Conference on Artificial Intelligence (IJCAI), pages 1750–1756, 2021.
• Yuyang Ye, Hengshu Zhu, Tong Xu, Fuzhen Zhuang, Runlong Yu, Hui Xiong. Identifying High Potential Talent: A Neural Network based Dynamic Social Profiling Approach. IEEE International Conference on Data Mining (ICDM), pages 718–727, 2019.
• Mingyue Cheng, Runlong Yu, Qi Liu, Vincent W. Zheng, Hongke Zhao, Hefu Zhang, Enhong Chen. Alpha-Beta Sampling for Pairwise Ranking in One-Class Collaborative Filtering. IEEE International Conference on Data Mining (ICDM), pages 1000–1005, 2019.
• Han Wu, Kun Zhang, Guangyi Lv, Runlong Yu, Weihao Zhao, Enhong Chen, Jianhui Ma. Deep Technology Tracing for High-tech Companies. IEEE International Conference on Data Mining (ICDM), pages 1396–1401, 2019.
• Runlong Yu, Yunzhou Zhang, Yuyang Ye, Le Wu, Chao Wang, Qi Liu, Enhong Chen. Multiple Pairwise Ranking with Implicit Feedback. ACM Conference on Information and Knowledge Management (CIKM), pages 1727–1730, 2018.

Books & Chapters

• Tong Xu, Runlong Yu. Epidemic Prevention and Control Big Data Cloud Platform: Solutions for Major Public Health Emergencies. Annual Report on Development of Big Data Applications in China No. 4 (2020) (Blue Book of Big Data Applications), Social Sciences Academic Press, ISBN 978-7-5201-7651-4, 2020.

Research Funding

• Development of an End-User-Driven Inland Water Quality Monitoring and Decision-Support System Powered by NASA-IBM Prithvi-EO2 Foundation Model
  National Aeronautics and Space Administration (NASA), Award No. 80NSSC26K0971, $749,999, 2026–2029. Status: Funded.
  Role: Runlong Yu — Co-Investigator. PI: Hongxing Liu. Other Co-Investigators: Yuehan Lu and Christopher Impellitteri.

Invited Talks

• [Mar. 2026] “Advance Water Monitoring Using Machine Learning.” Environmental Data Initiative (EDI) Power Users Meeting, Sacramento, CA, USA.
• [Mar. 2026] “Geospatial Intelligence in Large Language Models.” OM–ST PhD Seminar, Department of Information Systems, Statistics, and Management Science, The University of Alabama, Tuscaloosa, AL, USA.
• [Nov. 2025] “Truth Without Comprehension: A BlueSky Agenda for Steering the Fourth Mathematical Crisis.” IEEE International Conference on Data Mining (ICDM 2025), Washington, DC, USA.
• [May 2025] “What We Talk About When We Talk About AI for Science.” SIAM International Conference on Data Mining (SDM 2025), Alexandria, VA, USA.
• [Aug. 2024] “Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations.” KGML 2024 Workshop, Minneapolis, MN, USA.
• [Aug. 2023] “Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction.” ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023), Long Beach, CA, USA.
• [May 2021] “XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction.” Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2021), Delhi, India.
• [Aug. 2019] “Negatively Correlated Search with Asymmetry for Real-Parameter Optimization Problems.” CCFAI 2019, Xuzhou, China.

Academic and Departmental Services

Conference Program Committee / Reviewing

AAAI 2027 (Main Conference & AI for Social Impact Track); KDD 2027 (AI4Sciences & Datasets and Benchmarks Tracks); NeurIPS 2026; ICML 2026; ACL ARR 2026; IJCAI 2026; KDD 2026 (AI4Sciences Track); AAAI 2026; WWW 2026; PAKDD 2026; AISTATS 2026; WWW 2025; AAAI 2025; IJCAI 2025; PAKDD 2025; ICML 2025; AISTATS 2025; ICLR 2025; NeurIPS 2024; ICLR 2024; ICML 2024; AAAI 2024; IJCAI 2024; NeurIPS 2023; ICML 2023; AAAI 2023; IJCAI 2023; NeurIPS 2022; ICML 2022; AAAI 2022; IJCAI 2022; AAAI 2021; IJCAI 2021.

Journal Reviewing

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI); IEEE Transactions on Knowledge and Data Engineering (TKDE); IEEE Transactions on Big Data; IEEE Transactions on Systems, Man, and Cybernetics: Systems; ACM Computing Surveys (CSUR); ACM Transactions on Knowledge Discovery from Data (TKDD); ACM Transactions on Recommender Systems; Communications of the ACM; Advanced Science; etc.

Departmental Service

• Member, CS Curriculum Committee, 2025–Present
  Undergraduate Curriculum, Department of Computer Science, The University of Alabama