苏喻，工学博士，硕士生导师，合肥师范学院副教授，合肥综合性国家科学中心人工智能研究院副研究员，中国计算机学会大数据专家委员会通讯委员，安徽省计算机学会青少年信息学教育专委会秘书长，研究方向为自然语言理解，数据挖掘与推荐系统。2011年7月-2022年2月就职于科大讯飞研究院，历任科大讯飞AI教育研究院副院长，AI研究院认知群教育条线负责人，学习机业务线业务总监，重点负责教育领域个性化学习业务，其研发的多项成果已经成功的应用到讯飞智学网、讯飞学习机等相关产品中，在全国32个省级行政区的16000余所学校推广，产品收益超过40亿元。于2018年获得讯飞首届华夏创新奖（讯飞个人最高奖项），获2020年吴文俊人工智能科学技术奖科技进步一等奖。同时，先后参与多项安徽省、部级等层面的重大项目科研工作，如国家自然科学基金重点项目“基于多模态数据的学习者认知诊断理论与关键技术研究”、科技部重大专项“面向分类用户个性化需求的科技大数据精准服务技术”等。其间获得多项发明专利，并在AAAI、KDD、IJCAI等国际知名学术会议与期刊发表文章近50余篇。

论文

[1] Su Y, Liu Q, Liu Q, et al. Exercise-enhanced sequential modeling for student performance prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2018, 32(1).，CCF-A类会议

[2] Su Y, Cheng Z, Luo P, et al. Time-and-Concept enhanced deep multidimensional item response theory for interpretable knowledge tracing[J]. Knowledge-Based Systems, 2021, 218: 106819.，SCI一区TOP

[3] Su Y, Cheng Z, Wu J, et al. Graph-based cognitive diagnosis for intelligent tutoring systems[J]. Knowledge-Based Systems, 2022, 253: 109547.，SCI一区TOP

[4] Su Y, Shen S, Zhu L, et al. Global and local neural cognitive modeling for student performance prediction[J]. Expert Systems with Applications, 2024, 237: 121637.，SCI一区TOP

[5] Su Y, Yang X, Lu J, et al. Multi-task Information Enhancement Recommendation model for educational Self-Directed Learning System[J]. Expert Systems with Applications, 2024, 252: 124073. SCI一区TOP

[6] Su Y, Han Z, Shen S, et al. Constructing a Confidence-guided Multigraph Model for cognitive diagnosis in personalized learning[J]. Expert Systems with Applications, 2024, 252: 124259. SCI一区TOP

[7] 苏喻，张丹，刘青文，张英杰，陈玉莹，丁宏强. 学生得分预测：一种基于知识图谱的卷积自编码器[J].中国科学技术大学学报 1(2019):21-30. 国内刊号：34-1054/N.

[8] 苏喻，汪成成，张丹，王士进.人工智能在教育试题检索中的应用与探索[J]. 中国新通讯，2020年 第3期：300.国内刊号：CN11-5402/TN.

[9] Shuanghong Shen, Zhenya Huang, Qi Liu, Yu Su, Shijin Wang, Enhong Chen, Assessing Student’s Dynamic Knowledge State by Exploring the Question Difficulty Effect, The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'2022), Virtual Conference, July 11-15 2022.

[10] Song Cheng, Qi Liu, Enhong Chen, Kai Zhang, Zhenya Huang, Yu Yin, Xiaoqing Huang, Yu Su, AdaptKT: A Domain Adaptable Method for Knowledge Tracing , The 15th ACM International Conference on Web Search and Data Mining (WSDM'2022) , 2022,Accepted.

[11] Jiatong Li, Fei Wang, Qi Liu, Mengxiao Zhu, Wei Huang, Zhenya Huang, Enhong Chen, Yu Su, Shijin Wang. HierCDF: A Bayesian Network-based Hierarchical Cognitive Diagnosis Framework . The 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'2022), 2022. accpted.

[12] Ruixin Li, Yu Yin, Le Dai, Shuanghong Shen, Xin Lin, Yu Su, Enhong Chen，PST: Measuring Skill Proficiency in Programming Exercise Process via Programming Skill Tracing. SIGIR 2022: 2601-2606

[13] 魏思, 沈双宏, 黄振亚，刘淇, 陈恩红, 苏喻等. 融合通用题目表征学习的神经知识追踪方法研究[J]. 中文信息学报, 2022, 36(4):10.

[14] Qi Liu, Zhenya Huang, Yu Yin, Enhong Chen*, Hui Xiong, Yu Su, Guoping Hu. EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction. IEEE Transactions on Knowledge and Data Engineering，2019.（ESI高被引）

[15] Zhenya Huang, Xin Lin, Hao Wang, Qi Liu, Enhong Chen, Jianhui Ma, Yu Su, Wei Tong, DisenQNet: Disentangled Representation Learning for Educational Questions, The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’2021), Virtual Conference, August 14-18 2021.

[16] Shuanghong Shen, Qi Liu, Enhong Chen, Zhenya Huang, Wei Huang, Yu Yin, Yu Su, Shijin Wang, Learning Process-consistent Knowledge Tracing, The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’2021), Virtual Conference, August 14-18 2021.

[17] Weibo Gao, Qi Liu*, Zhenya Huang, Yu Yin, Haoyang Bi, Mu Chun Wang, Jianhui Ma, Shijin Wang, Yu Su, RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems, The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'2021), Virtual Conference, July 11-15 2021.

[18] Wu R, Liu Q, Liu Y P, Chen E H, Su Y, Chen Z G, Hu G P. Cognitive modelling for predicting examinee performance[C]// The 24th International Joint Conference on Artificial Intelligence (IJCAI'2015), 2015:1017-1024.

[19] Qi Liu, Zai Huang, Zhenya Huang, Chuanren Liu, Enhong Chen*, Yu Su, Guoping Hu. Finding Similar Exercises in Online Education Systems. The 24nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'2018):1821-1830, 2018.

[20] Qi Liu, Runze Wu, Enhong Chen*, Guandong Xu, Yu Su, Zhigang Chen, Guoping Hu. Fuzzy Cognitive Diagnosis for Modelling Examinee Performance. ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2018, 9(4):39(1)-39(26).

[21] Huang Z Y, Liu Q, Chen E H, Zhao H K, Gao M Y, Wei S, Su Y, Hu G P. Question Difficulty Prediction for READING Problems in Standard Tests[C]// The 31st AAAI Conference on Artificial Intelligence.2017: 1352-1359.

[22] Yu Yin, Qi Liu, Zhenya Huang, Enhong Chen, Wei Tong, Shijin Wang and Yu Su. QuesNet: A Unified Representation for Heterogeneous Test Questions. The 25nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'2019), Anchorage, Alaska, USA, accepted, 2019.

[23] Shuanghong Shen, Qi Liu, Enhong Chen*, Han Wu, Zhenya Huang, Weihao Zhao, Yu Su, Haiping Ma and Shijin Wang, Convolutional Knowledge Tracing: Modeling Individualization in Student Learning Process , The 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’2020), Xi’an, China, July 25-30, 2020.

[24] Haoyang Bi, Haiping Ma, Zhenya Huang, Yu Yin, Qi Liu, Enhong Chen, Yu Su, and Shijin Wang, Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing , The 20th IEEE International Conference on Data Mining (ICDM'2020) , Sorrento, Italy, November 17-20 2020.

[25] Xiaoqing Huang, Qi Liu, Chao Wang, Haoyu Han, Jianhui Ma, Enhong Chen, Yu Su, Shijin Wang, Constructing Educational Concept Maps with Multiple Relationships from Multi-source Data, The 19th International Conference on Data Mining (ICDM'2019), Beijing, China, 2019.

[26] 王超，刘淇，陈恩红，黄振亚，朱天宇，苏喻，胡国平，面向大规模认知诊断的DINA模型快速计算方法研究[J]. 电子学报，2017，已接收.

[27] 朱天宇，黄振亚，陈恩红，刘淇，吴润泽，吴乐，苏喻，陈志刚，胡国平. 基于认知诊断的个性化试题推荐方法[J]. 计算机学报, 2017(1):176-191.

[28] Liu Y P, Liu Q, Wu R Z, Chen E H, Su Y, Chen Z G, Hu G P. Collaborative Learning Team Formation: A Cognitive Modeling Perspective[C]// The 21st International Conference on Database Systems for Advanced Applications (DASFAA'2016) , 2016: 383-400.

[29] Chen Y Y, Liu Q, Huang Z Y, Wu L, Chen E H, Wu R Z, Su Y, Hu G P. Tracking Knowledge Proficiency of Students with Educational Priors[C]// ACM, 2017:989-998.

[30] 黄振亚, 苏喻, 吴润泽, 刘玉苹，刘淇，陈志刚，胡国平. 一种面向教育评估的智能教育辅助平台[J]. 中国科学技术大学学报, 2015, 45(10):846-854.

[31] 刘淇，陈恩红，黄振亚，苏喻，胡国平. 面向个性化学习的学生认知能力分析. 中国计算机学会通讯 2017.

[32] 胡国平，张丹，苏喻，刘青文，李佳，王瑞。试题知识点预测：一种教研知识强化的卷积神经网络模型[J].中文信息学报，2018.

专利软著

[1] 苏喻; 刘淇; 朱林波; 丁军; 汤进; 吴震一; 一种编程学习场景的智能习题推荐方法、系统及储存介质, 2023-07-18, CN202310421039.X (专利)

[2] 苏喻; 刘淇; 沈双宏; 黄振亚; 韩泽; 杨雪洁; 一种联合全局和局部特征的学生表现预测方法, 2023-07-07, CN202310452623.1 (专利)

[3] 苏喻; 刘玉萍; 陈志刚; 胡国平; 胡郁; 刘庆峰; 基于在线题库的学情诊断方法及系统, 2020-6-2, ZL201510481726.6 (专利)

[4] 苏喻; 张丹; 刘青文; 邓晓栋; 陈志刚; 魏思; 胡郁; 试题高阶属性挖掘方法及系统, 2020-2-7, ZL201610425977.7 (专利)

[5] 苏喻; 陈志刚; 胡国平; 王影; 胡郁; 刘庆峰; 在线学习试题推荐方法及系统, 2020-2-7, ZL201510481754.8 (专利)

[6] 苏喻; 陈志刚; 胡国平; 胡郁; 刘庆峰; 一种文本字串匹配方法及系统, 2019-2-26, ZL201410577735.0 (专利)

[7] 软件著作权，青少年编程智能作业系统1.0

著作

出版学术专著1篇

[1] 《面向分类用户个性化需求的科技大数据精准服务技术》

项目

[2] 安徽省教育厅，2022 年高校协同创新项目，GXXT-2022-042，面向多模态学科数据的认知推理与智能应用，在研，主持。

[3] 认知智能国家重点实验室开放课题，Ied2022-002，面向课后服务的师范类大学生服务模型及成效研究，结项，主持。

[4] 安徽省哲学社会科学重点实验室开放基金项目，SYS2023A06，融合伴随式数据的青少年心理健康现状评估与预测，在研，主持。

[5] 合肥师范学院，高层次人才引进项目，2022rcjj57，基于深度学习的试题语意理解算法的研究与应用，在研，主持。

[6] 安徽省人力资源和社会保障厅，安徽省博士后基金，2020B444，基于人工智能的个性化学习关键技术及应用研究，结项，主持。

[7] 国家自然科学基金，U20A20229，基于多模态数据的学习者认知诊断理论与关键技术研究，在研，第二参与人。

[8] 科技部, 国家重点研发计划现代服务业共性关键技术研发及应用示范专项, 2018YFB1402605, 面向分类用户个性化需求的科技大数据精准服务技术, 结项，科大讯飞方负责人。

[9] 北师大出版社，国家新闻出版署出版融合发展重点实验室2020年度开放课题，BSDRHK2020-08,基于人工智能测试与推荐系统对学科能力影响的实证研究，结项，主持。