宋哲，南京大学商学院教授，美国爱荷华大学（Universityof Iowa）工业工程博士、博士后。在美国留学期间参与多个被美国知名公司和机构资助的制造, 能源, 医疗等行业的系统, 决策优化项目, 这些公司和机构包括：Iowa Energy Center, John Deere, MidAmerican Energy, IAWIND, UIHC。在大数据分析建模和管理决策优化方向已经发表高影响因子国际期刊论文50多篇（均被SCI、EI索引，被引用3600多次；ESI近10年高被引论文一篇）, 获数十项中国美国发明专利。担任十多个国际一流期刊的审稿人, 如 IEEE Trans. Industrial Informatics，European Journal ofOperational Research、IEEE Trans.Industrial Electronics、IEEE Trans.Systems, Man, and Cybernetics等。1.国际知名期刊IEEE Transactions on Sustainable Energy （国际电子电气工程师协会会刊《可持续能源》，影响因子7.65，自引率7.6%）副主编；2.Journal of Intelligent Manufacturing（《智能制造》杂志，影响因子6.485）副主编；3..IEEE Power Engineering Society Letters,Industrial Engineering & Management编委成员；4.INFORMS协会会员（国际运筹与管理科学协会）5.IISE协会会员（国际工业与系统工程协会）6.IEEE协会会员（国际电子电气工程师协会）7.南京大学智慧商务与数据科学研究中心副主任8.中国能源研究会（智能微电网技术专业委员会）专家委员9.江苏省电机工程学会电力市场专委会委员

1.人工智能与图像处理建模、优化与监控应用2.智慧能源系统的建模、优化和激励机制设计3.电源侧风光新能源出力预测及优化控制，客户侧负荷预测与调控策略4.电化学储能经济分析及安全管理5.基于数据挖掘和机器学习的管理决策优化；复杂系统与不确定性管理；创新与创业管理

1.人工智能与图像处理2.基于数据挖掘和机器学习的管理决策优化和运营管理3.复杂系统与不确定性管理4.创新与创业管理

宋哲(指导教师), 2024.5, 第一届“中兴新云杯”智算大赛-金奖（特等奖）, 中兴新云-南京大学商学院；宋哲, 2020.9, 南京大学魅力导师奖, 南京大学；宋哲(指导教师),第三届(2019)工业大数据创新竞赛---“大型旋转机组转子部件脱落故障预测”，工业和信息化部指导、中国信息通信研究院主办，三等奖（排名4/1599）；宋哲(指导教师),首届(2017)中国工业大数据创新竞赛---“风机叶片结冰预测:基于物理原理+KNN分类的混合预测模型”,工业和信息化部指导、中国信息通信研究院主办，二等奖（排名2/1460）；宋哲(指导教师),首届(2017)中国工业大数据创新竞赛---“风机齿形带故障分类:回归+分类算法的混合预测模型”，工业和信息化部指导、中国信息通信研究院主办，二等奖（排名3/1460）;宋哲, 2016.9, 南京大学杜厦奖教金, 南京大学；徐小林，宋哲, 2016, OperationsManagement, 江苏省英文精品课程建设项目。

Kusiak Andrew,宋哲, 2016.8, 基本科学指标数据库ESI 高被引论文, Web of Science； 宋哲, 2016, 国家自然科学基金项目（#71001050）结题被评估为“优秀”, 国家自然科学基金委员会；宋哲, 2010.12, 科研新星奖, 南京大学商学院。

代表性科研项目：2024-1 to 2025-12，面向新型配电系统的数字化实训关键技术研究及应用，国网总部项目；2024-10 to 2025-12，电动自行车电池健康度评估和充电过程安全监控系统研发与示范应用，国网项目；2024-1 to 2024-12，复杂人工智能模型小型化与高效边端设备部署计算框架研究，国网项目;2024-11 to 2025-12，空调负荷预测与调控策略研究，国网项目；2023-11 to 2027-10, 基于多模态数据融合感知与识别的海上风电裝备监测、预警和高效运维，广东省基础与应用基础研究基金重点项目；2011-1 to 2013-12, 风电预测，并网调度与规划的决策优化模型, 国家自然科学基金。

著作：A. Kusiak and Z. Song, 2009, Optimization in the Energy Industry: Improving Combustion Performance by Online Learning, P.Pardalos eds., Springer, ISBN: 978-3-540-88964-9.

Jeffrey Camm et al.;耿修林，宋哲译, 2017年3月, 商业数据分析（Essentials of Business Analytics）, 机械工业出版社  数据科学、商务数量解析、商务智能系列教材.

代表性论文：1. Shen, Y., Wang, T., & Song, Z.(2024). Online performance and proactive maintenance assessment of data driven prediction models. Journal of Intelligent Manufacturing, 1-35.2. Liang, G., Su, Y., Wu, X., Ma, J., Long,H., & Song, Z. (2023). Abnormal data cleaning for wind turbines by image segmentation based on active shape model and class uncertainty. Renewable Energy, 216, 118965.3. Shen, Y., Song, Z., Kusiak, A., & Zhan, Z. (2022). Prognosis of rotor parts fly-off based on cascade classification and online prediction ability index. Measurement Science and Technology, 34(1), 015122.4. Shen, Y., Song, Z., & Kusiak, A.(2021). Enhancing the generalizability of predictive models with synergy ofdata and physics. Measurement Science and Technology, 33(3), 034002.5. Liang, G., Su, Y., Chen, F., Long, H.,Song, Z., & Gan, Y. (2020). Wind power curve data cleaning by image thresholding based on class uncertainty and shape dissimilarity. IEEE Transactions on Sustainable Energy, 12(2), 1383-1393.6. X. Liu, Z. Zhang and Z. Song,2020, Acomparative study of the data-driven day-ahead hourly provincial load forecasting methods: From classical data mining to deep learning, Renewableand Sustainable Energy Reviews, Vol. 119, 109632. SCI 一区, IF 12.7. J. Zhu, Y. Shen, Z. Song, D. Zhou, Z.Zhang, and A. Kusiak, Data-Driven Building Load Profiling and Energy Management, Sustainable Cities and Society, Vol. 49, 2019, pp. 1-15.8. Z. Song, Z. Zhang, Y. Jiang and J.Zhu,2018, Wind turbine health state monitoring based on a Bayesian data-driven approach, Renewable Energy, Vol. 125, pp.172-181.9. Y. Jiang, H. Long, Z. Zhang and Zhe Song,2017, Day-ahead Prediction of Bi-hourly Solar Radiance with a Markov Switch Approach, IEEE Transactions on Sustainable Energy, Vol. 8, No. 4,pp.1536-1547, SCI一区.10. L. Huan, Z. Zhang, Zhe Song, A.Kusiak,2017, Formulation and Analysis of Grid and Coordinate Models for Planning Wind Farm Layouts, IEEE Access, SCI, Vol. 5, pp.1810-1819.11. 蔡霞，宋哲等, 2017, 先发企业的崛起和后进企业的逆袭,南开管理评论, CSSCI，国家自然科学基金委管理科学A类重要期刊（在管理学学科类目期刊中荣获复合类、期刊综合类和人文社科影响因子三项指标第一，影响力指数第二）.12. Zhang Z. and Song Zhe, 2016, Mining SCADA Data Offers a New Roadmap of Wind Farm Operations and Management, Industrial Engineering & Management, Vol.5, No.2 邀请稿（社评）13. Zhe Song, Z. Zhang and X. Chen,2016,The decision model of 3-dimensional wind farm layout design, Renewable Energy, Vol.85 SCI，二区.14. Z. Zhang, Zhe Song and J. Xu,2015,Data-Driven Wind Turbine Power Generation Performance Monitoring, IEEE Transactions on Industrial Electronics, Vol. 62, No. 10 SCI, 一区, Impact factor, 6.498.15.H. Long, L. Wang, Z. Zhang, Z. Song andJ. Xu (2015.10). “Data-driven wind turbine power generation performance monitoring,” IEEE Transactions On Industrial Electronics. Vol. 62:6627-6635.16. Z Song, Y Jiang, Z Zhang,2014,Short-term wind speed forecasting with Markov-switching model, Applied Energy,  130SCI, 一区.17. Yu Jiang, Zhe Song, Andrew Kusiak,2013,Very short-term wind speed forecasting with Bayesian structural break model, Renewable Energy, Vol. 50, Pp. 637-647 SCI，二区.18. Z. Zhang, Andrew Kusiak, Zhe Song,2013,Scheduling electric power production at a wind farm, European Journalof Operational Research, Vol.224, pp. 227-238 SCI，二区.19. C. Xu, Z. Song, L.D. Chen and Y.Zheng,2011, Numerical investigation on porous media heat transfer in a solartower receiver, Renewable Energy, Vol. 36, No. 3, pp.  Vol. 36, No. 3, pp.1138-1144 SCI，二区.20. Z. Song, X. Geng, A. Kusiak, and C.Xu,2011, Mining Markov Chain Transition Matrix from Wind Speed Time Series Data, Expert Systems with Applications, Vol. 38, No. 8, pp.  Vol. 38, No.8, pp. 10229-10239 SCI, 二区.21. A. Kusiak, W. Li and Z. Song,2010,Dynamic Control of Wind Turbines, Renewable Energy, Vol.35, No.2,pp.456-463 SCI, 二区.22. Z. Song and A. Kusiak, 2010, Mining Pareto-Optimal Modules for Delayed Differentiation, European Journal ofOperational Research, Vol.201, No. 1, pp.123-128. SCI, 二区.23. Z. Song and A. Kusiak, 2010,Multi-objective Optimization of Temporal Processes, IEEE Trans. Systems,Man, and Cybernetics, Part B, Vol.40, No.3, pp.845-856. SCI, 一区.24. A. Kusiak and Z. Song,2010, Design of Wind Farm Layout for Maximum Wind Energy Capture, Renewable Energy,Vol.35, No. 3, pp. 685-694. SCI, 二区.25. A. Kusiak, H. Zheng and Z. Song,2010,Power optimization of wind turbines with data mining and evolutionary computation, Renewable Energy, Vol. 35, No. 3, pp. 695-702. SCI, 二区.26. Z. Song and A. Kusiak, 2009, Optimizing Product Configurations with a Data Mining Approach, International Journal ofProduction Research, Vol. 47, No. 7, pp. 1733-1751. SCI, 三区.27. A. Kusiak, H. Zheng and Z. Song, 2009,Wind Farm Power Prediction: A Data-Mining Approach, Wind Energy, Vol.12, No.3, pp. 275-293 SCI, 二区.28. A. Kusiak, H. Zheng and Z. Song, 2009,On-Line Monitoring of Power Curves, Renewable Energy, Vol. 34, No. 6,pp.1487-1493. SCI, 二区.29. A. Kusiak, H. Zheng and Z. Song, 2009,Models for Monitoring Wind Farm Power, Renewable Energy, Vol. 34, No. 3,pp.583-590. SCI, 二区.30. A. Kusiak, H. Zheng and Z. Song, 2009,Short-Term Prediction of Wind Farm Power: A Data Mining Approach, IEEE Transactions on Energy Conversion, Vol. 24, No. 1, pp. 125-136. SCI, 二区.31. A. Kusiak and Z. Song, 2009, Sensor Fault Detection in Power Plants, ASCE Journal of Energy Engineering,Vol.135, No.4,pp.127-137SCI, 三区.32. A. Kusiak, Z. Song and H. Zheng, 2009,Anticipatory Control of Wind Turbines with Data-Driven Predictive Models, IEEE Transactions on Energy Conversion, Vol. 24, No. 3, pp. 766-774. SCI, 二区.33. Z. Song and A. Kusiak, 2009,Optimization of Temporal Processes: A Model Predictive Control Approach,IEEE Transactions on Evolutionary Computation, Vol. 13, No. 1, pp. 169-179.SCI, 一区.34. A. Kusiak and Z. Song, 2008,Clustering-Based Performance Optimization of Boiler-Turbine System, IEEE Transactions on Energy Conversion, Vol.23, No. 2, pp. 651-658 SCI, 二区.35. A. Kusiak, M.R. Smith and Z. Song,2007, Planning Product Configurations Based on Sales Data, IEEE Transactionson Systems, Man and Cybernetics, Part C, Vol. 37, No. 4, pp. 602-609. SCI, 二区.36. Z. Song and A. Kusiak, 2007,Constraint-Based Control of Boiler Efficiency: A Data-Mining Approach, IEEE Transactions on Industrial Informatics, Vol. 3, No. 1, pp. 73-83.SCI, 一区.37. A. Kusiak and Z. Song, 2006, CombustionEfficiency Optimization and Virtual Testing: A Data-Mining Approach, IEEE Transactions on Industrial Informatics, Vol. 2, No. 3, pp. 176-184. SCI, 一区.