北京交大INSIS团队2025年度科研与人才培养进展

发布时间：2026年2月13日

       2025 年，北京交通大学计算机科学与技术学院网络科学与智能系统研究所（INSIS）聚焦前沿科技领域开展深度研究，在时空智能、时序挖掘、强化学习与多智能体等多个方向取得系列突破性成果，学术影响力持续提升；同时稳步推进成果转化与行业落地，在多个新领域实现技术应用突破，人才培养工作成效斐然，师资建设、学生培养与就业质量均迈上新台阶。

      一、学术论文成果再创佳绩，全年发表CCF A类论文32篇

      2025 年，INSIS 师生凝心聚力开展前沿研究，学术创新能力持续提升，全年共在 CCF A 类国际期刊与国际会议发表论文 32 篇，另有其他高级别期刊论文 16 篇。其中 CCF A 类期刊发表 9 篇，含 TKDE 5 篇、TMC 1 篇、TVCG 1 篇、TSE 1 篇；CCF A 类国际会议发表 23 篇，含 AAAI 6 篇、NeurIPS 5 篇、IJCAI 3 篇、ACM MM 3 篇、ICCV 2 篇，ICLR、ICML、KDD、ACL、OOPSLA 各 1 篇，研究成果覆盖多领域关键技术，学术影响力显著增强。

      (一)时空智能方向学术成果

      本年度，INSIS 在时空智能与时空数据挖掘方向，围绕智能交通系统等领域中时空数据建模的可靠性与泛化性关键问题开展系统性研究，针对传统确定性方法难以刻画交通系统随机波动、模型跨任务跨城市适配能力不足等挑战，创新探索不确定性感知建模与泛化推理方法，形成多维度技术解决方案。

      在可靠性建模上，聚焦路径级行程时间估计与路口级交通状态预测，提出 DutyTTE与 Proactive-XLight等模型，实现交通预测结果不确定性的精准刻画，为决策提供科学支撑；在泛化推理上，面向预测与插补统一建模及跨城市迁移需求，提出 STD-PLM与 STBaT等方法，大幅提升模型在多任务及零样本、少样本场景下的泛化能力。此外，围绕轨迹表示学习与轨迹语义建模，针对泛化性、效率、稀疏数据处理等问题，借助预训练语言模型能力，提出 TransferTraj、TrajMamba 等 6 大模型，构建覆盖多场景的技术体系，同时在时空知识图谱和图对齐领域取得多项重要成果。

      相关成果可广泛应用于智能交通、水流域治理、智慧城市、新能源等涉及时空数据的应用领域，为行业智能化升级提供核心技术支撑。

      主要学术论文：

1. [TMC 2025] Yang Jiang, Shengnan Guo, Hanyang Chen, Xiaowei Mao, Youfang Lin, Huaiyu Wan. Proactive-XLight: Proactive Traffic Signal Control with Pluggable and Reliable Traffic Prediction. IEEE Transactions on Mobile Computing (TMC), 2025. (CCF A)
2. [TKDE 2025] Yan Lin, Jilin Hu, Shengnan Guo, Bin Yang, Christian S. Jensen, Youfang Lin, Huaiyu Wan. UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2025. (CCF A)
3. [TKDE 2025] Wei Chen, Haoyu Huang, Zhiyu Zhang, Tianyi Wang, Youfang Lin, Liang Chang, Huaiyu Wan. Next-POI Recommendation via Spatial-Temporal Knowledge Graph Contrastive Learning and Trajectory Prompt. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2025. (CCF A)
4. [TKDE 2025] Letian Gong, Shengnan Guo, Yan Lin, Yichen Liu, Erwen Zheng, Yiwei Shuang, Youfang Lin, Jilin Hu, Huaiyu Wan. STCDM: Spatio-Temporal Contrastive Diffusion Model for Check-in Sequence Generation. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2025, (CCF A)
5. [TKDE 2025] Yan Lin, Zeyu Zhou, Yichen Liu, Haochen Lv, Haomin Wen, Tianyi Li, Yushuai Li, Christian S Jensen, Shengnan Guo, Youfang Lin, Huaiyu Wan. UniTE: A Survey and Unified Pipeline for Pre-training ST Trajectory Embeddings. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2025. (CCF A)
6. [TKDE 2025] Songyang Chen，Yu Liu，Lei Zou，Zexuan Wang，Youfang Lin. CombAlign: Enhancing Model Expressiveness in Unsupervised Graph Alignment. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2025. (CCF A)
7. [NeurIPS 2025] Yichen Liu, Yan Lin, Shengnan Guo, Zeyu Zhou, Youfang Lin, Huaiyu Wan. TrajMamba: An Efficient and Semantic-rich Vehicle Trajectory Pre-training Model. The 39th Conference on Neural Information Processing Systems (NeurIPS), 2025. (CCF A)
8. [NeurIPS 2025] Tonglong Wei, Yan Lin, Zeyu Zhou, Haomin Wen, Jilin Hu, Shengnan Guo, Youfang Lin, Gao Cong, Huaiyu Wan. TransferTraj: A Vehicle Trajectory Learning Model for Region and Task Transferability. The 39th Conference on Neural Information Processing Systems (NeurIPS), 2025. (CCF A)
9. [NeurIPS 2025] Tonglong Wei, Yan Lin, Youfang Lin, Shengnan Guo, Jilin Hu, Haitao Yuan, Gao Cong, Huaiyu Wan. PLMTrajRec: A Scalable and Generalizable Trajectory Recovery Method with Pre-Trained Language Models. The 39th Conference on Neural Information Processing Systems (NeurIPS), 2025. (CCF A)
10. [ICLR 2025] Mengqi Liao, Wei Chen, Junfeng Shen, Shengnan Guo, Huaiyu Wan. HMoRA: Making LLMs More Effective with Hierarchical Mixture of LoRA Experts. The 13th International Conference on Learning Representations (ICLR), 2025. (CCF A)
11. [EMNLP 2025] Mengqi Liao, Xiangyu Xi, Ruinian Chen, Leng Jia, Yangen Hu, Ke Zeng, Shuai Liu, Huaiyu Wan. Enhancing Efficiency and Exploration in Reinforcement Learning for LLMs. The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), Main Conference, 2025. (清华A)
12. [ACL 2025] Zhiyu Zhang, Wei Chen, Youfang Lin, Huaiyu Wan. A Generative Adaptive Replay Continual Learning Model for Temporal Knowledge Graph Reasoning. The 63rd Annual Meeting of the Association for Computational Linguistics (ACL), 2025. (CCF A)
13. [IJCAI 2025] Xinyan Hao, Huaiyu Wan, Shengnan Guo, Youfang Lin. Balancing Imbalance: Data-Scarce Urban Flow Prediction via Spatio-Temporal Balanced Transfer Learning. The 34th International Joint Conference on Artificial Intelligence (IJCAI), 2025. (CCF A)
14. [IJCAI 2025] Zeyu Zhou, Yan Lin, Haomin Wen, Shengnan Guo, Jilin Hu, Youfang Lin, Huaiyu Wan. TrajCogn: Leveraging LLMs for Cognizing Movement Patterns and Travel Purposes from Trajectories. The 34th International Joint Conference on Artificial Intelligence (IJCAI), 2025. (CCF A)
15. [AAAI 2025] Yiheng Huang, Xiaowei Mao, Shengnan Guo, Yubin Chen, Junfeng Shen, Tiankuo Li, Youfang Lin, Huaiyu Wan. STD-PLM: Understanding Both Spatial and Temporal Properties of Spatial-Temporal Data with PLM. The 39th AAAI Conference on Artificial Intelligence (AAAI), 2025. (CCF A)
16. [AAAI 2025] Wei Chen, Yuting Wu, Shuhan Wu, Zhiyu Zhang, Mengqi Liao, Youfang Lin, Huaiyu Wan. CognTKE: A Cognitive Temporal Knowledge Extrapolation Framework. The 39th AAAI Conference on Artificial Intelligence (AAAI), 2025. (CCF A)
17. [AAAI 2025] Shuyuan Zhao, Wei Chen, Boyan Shi, Liyong Zhou, Shuohao Lin, Huaiyu Wan. Spatial-Temporal Knowledge Distillation for Takeaway Recommendation. The 39th AAAI Conference on Artificial Intelligence (AAAI), 2025. (CCF A)
18. [AAAI 2025] Xiaowei Mao, Yan Lin, Shengnan Guo, Yubin Chen, Xingyu Xian, Haomin Wen, Qisen Xu, Youfang Lin, Huaiyu Wan. DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation. The 39th AAAI Conference on Artificial Intelligence (AAAI), 2025. (CCF A)
19. [DASFAA 2025] Zekai Shen, Haitao Yuan, Xiaowei Mao, Congkang Lv, Shengnan Guo, Youfang Lin, Huaiyu Wan. Towards an Efficient and Effective En Route Travel Time Estimation Framework. The 30th International Conference on Database Systems for Advanced Applications (DASFAA), 2025. (CCF B)
20. [WWW 2025] Songyang Chen, Youfang Lin，Yu Liu，Yuwei Ouyang，Zongshen Guo, Lei Zou. Enhancing Robust Semi-Supervised Graph Alignment via Adaptive Optimal Transport. World Wide Web (WWW), 2025. (CCF B)
21. [KBS 2025] Wei Chen, Yuting Wu, Shengnan Guo, Shuhan Wu, Zhishu Jiang, Youfang Lin, Huaiyu Wan. Dual-view Temporal Knowledge Graph Reasoning. Knowledge-Based Systems (KBS), 2025. (CCF C)

      (二)时序挖掘方向

      2025 年度，INSIS 在时序数据分析挖掘方向，聚焦时序数据不完整、模态不匹配、标签静态化、模型泛化能力不足等问题，通过提出创新算法框架、构建特色数据集推动领域技术进步，形成从理论创新到落地应用的完整技术链路。

      针对时序数据解释与不完整分类问题，提出基于信息保留原则的 ORTE 框架与层次条件信息瓶颈方法 HCIB，显著提升解释完整性与缺失数据下的分类性能；针对医学时序解码，提出知识感知进化学习框架 InDiGO，系统融合临床指标优化提示构建；在自监督对比学习中改进 SimCLR 框架，有效缓解假负样本与类别不平衡问题。在情感计算领域，构建首个实时动态标注的 EEG-fNIRS 情绪数据集 REFED，提出 MoCERNet 模型解决模态不匹配下的情感识别难题，设计 DHGRNN 网络实现多标签多模态情绪的鲁棒建模。

      相关研究成果已成功落地实际场景：InDiGO 模型为基于 LLM 的数据中心大型服务集群智能运维系统提供时序 - 语义对齐新思路；ORTE 框架与 HCIB 方法在睡眠智能评估中协同发力，增强模型决策可解释性、解决监测信号缺失问题，提升睡眠评估的可靠性与实用性，持续推动时序分析技术在跨模态、不完整数据与可信解释方向的深度发展。

      主要学术论文：

1. [ICML 2025] Jinghang Yue, Jing Wang, Lu Zhang, Shuo Zhang, Da Li, Zhaoyang Ma, Youfang Lin, Optimal Information Retention for Time-Series Explanations. International Conference on Machine Learning (ICML), 2025. (CCF A)
2. [NeurIPS 2025] Xiaojun Ning, Jing Wang，Zhiyang Feng, Tianzuo Xin，Shuo Zhang, Shaoqi Zhang, Zheng Lian, Yi Ding, Youfang Lin，Ziyu Jia. REFED: A Subject Real-time Dynamic Labeled EEG-fNIRS Synchronized Recorded Emotion Dataset. The 39th Conference on Neural Information Processing Systems.(NeurIPS), 2025. (CCF A)
3. [NeurIPS 2025] Xiyuan Jin, Jing Wang，Ziwei Lin，Qianru Jia, Yuqing Huang, Xiaojun Ning, Zhonghua Shi，Youfang Lin. From Indicators to Insights: Diversity-Optimized for Medical Series-Text Decoding via LLMs. The 39th Conference on Neural Information Processing Systems (NeurIPS), 2025. (CCF A)
4. [KDD 2025] Shuo Zhang, Jing Wang, Shiqin Nie, Jinghang Yue, Weikang Zhu, Youfang Lin, Loss or Gain: Hierarchical Conditional Information Bottleneck Approach for Incomplete Time Series Classification. The 31th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025. (CCF A)
5. [ACM MM 2025] Tianzuo Xin, Jing Wang, Xiyuan Jin, Xiaojun Ning, Zhiyang Feng, Youfang Lin. MoCERNet: A Modality-Complete Modeling Framework for Emotion Recognition in Physiological Signals under Imperfect Modal Matching. The 33rd ACM International Conference on Multimedia. (ACM MM), 2025. (CCF A)
6. [TNNLS 2025] Xiyuan Jin, Jing Wang, Xiaoyu Ou, Lei Liu, Youfang Lin, Time-Series Contrastive Learning against False Negatives and Class Imbalance. IEEE Transactions on Neural Networks and learning systems (IEEE TNNLS) , 2025. (CCF B)
7. [TAC 2025] Jing Wang, Zhiyang Feng, Xiaojun Ning, Youfang Lin, Badong Chen, Ziyu Jia, Two-stream Dynamic Heterogeneous Graph Recurrent Neural Network for Multi-Label Multi-modal Emotion Recognition. IEEE Transactions on Affective Computing (IEEE TAC), 2025. (CCF B)
8. [NN 2025] Xiyuan Jin, Jing Wang, Huaiyu Qin, Xiaojun Ning, Tianzuo Xin, Youfang Lin, Group-wise Relation Mining for Weakly-supervised Fine-grained Multimodal Emotion Recognition, Neural Networks (NN), 2025. (CCF B)
9. [NN 2025] Jinghang Yue, Jing Wang, Shuo Zhang, Zhaoyang Ma, Yuxing Shi, Youfang Lin, TV-Net: Temporal-Variable feature harmonizing Network for multivariate time series classification and interpretation, Neural Networks (NN) 2025. (CCF B)

      (三)强化学习与多智能体方向

      2025 年度，INSIS 在强化学习与多智能体方向，围绕复杂环境智能决策中的表征学习、安全决策、群体协同与持续学习等关键科学问题取得系列创新性进展，面向复杂现实环境的智能决策需求，推进技术在多场景的应用探索，为工业无人化应用储备核心技术。

      团队构建时域相关性表征预训练与双向转移一致性建模方法，提升复杂视觉场景下策略学习的效率与泛化能力；提出安全约束分布式强化学习与轨迹约束扩散规划方法，大幅降低智能交互过程中的安全风险；创新设计意图 — 时效双对齐协同机制，显著提升弱通信条件下多智能体协同的稳定性；构建关系模式驱动的持续多智能体强化学习框架，有效缓解多任务学习中的灾难性遗忘问题。

      相关技术可提升复杂视觉环境下的决策稳定性，为高风险作业提供安全决策支撑，增强通信受限环境下多智能体的协同可靠性，支持跨区域、多任务场景的经验积累与迁移，技术落地价值显著。

      主要学术论文：

1. [IJCAI 2025] Chang Yao, Youfang Lin, Shoucheng Song, Hao Wu, Yuqing Ma, Sheng Han, Kai Lv, From General Relation Patterns to Task-Specific Decision-Making in Continual Multi-Agent Coordination. Thirty-Fourth International Joint Conference on Artificial Intelligence (IJCAI), 2025. (CCF A)
2. [ACM MM] Jinwen Wang, Youfang Lin, Xiaobo Hu, Siyu Yang, Sheng Han, Shuo Wang, Kai Lv. From Pixels to Temporal Correlations: Learning Informative Representations for Reinforcement Learning Pre-training. The 33rd ACM International Conference on Multimedia (ACM MM), 2025. (CCF A)
3. [AAAI 2025] Shoucheng Song, Youfang Lin, Kai Lv, Sheng Han, Chang Yao, Hao Wu, Shuo Wang, CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timeliness. The 39th AAAI Conference on Artificial Intelligence (AAAI), 2025. (CCF A)
4. [NN 2025] Xiaobo Hu, Youfang Lin, Jinwen Wang, Yue Liu, Shuo Wang, Hehe Fan, Kai Lv. Bidirectional Transition Consistency between Multi-Domain Observations for Visual Reinforcement Learning Generalization. Neural Networks (NN), 2025. (CCF B)
5. [TOMM 2025] Xiaobo Hu, Youfang Lin, Jinwen Wang, Yue Liu, Shuo Wang, Hehe Fan, Kai Lv. Learning Robust Representations via Bidirectional Transition for Visual Reinforcement Learning. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)， 2025. (CCF B)
6. [TSMC 2025] Hengrui Zhang, Youfang Lin, Sheng Han, Shuo Wang, Kai Lv. Off-policy Conservative Distributional Reinforcement Learning with Safety Constraints. IEEE Transactions on Systems Man Cybernetics Systems (TSMC)， 2025. (CCF B)
7. [AAMAS 2025] Hengrui Zhang, Youfang Lin, Shuo Shen, Hanfeng Lin, Peng Cheng, Sheng Han, Kai Lv, Enhancing Offline Safe Reinforcement Learning with Trajectory-Constrained Diffusion Planning. The 24th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2025. (CCF B)

      (四)计算机视觉方向

      2025 年度，INSIS 在计算机视觉领域深耕光场数据处理方向，创新探索出几何驱动与场景自适应的光场处理新范式，攻克多项技术瓶颈，构建形成从底层图像修复到高层语义理解的完整技术链路，显著提升光场视觉在复杂物理环境中的应用可靠性。

      针对光场结构固有特性，提出基于极线一致性的注意力聚合网络与语义分割框架，成功解决无监督场景下深度估计与结构识别的精度难题；针对现实工况中的极端挑战，研发面向极低光照增强、遮挡去除及非兰伯特表面（反射 / 透明体）的深度感知算法，填补了复杂环境下光场处理的技术空白。相关研究成果为光场数据处理的产业化应用奠定了坚实的技术基础。

      主要学术论文：

1. [ICCV 2025] Chen Gao, Youfang Lin and Shuo Zhang. Epipolar Consistent Attention Aggregation Network for Unsupervised Light Field Disparity Estimation. IEEE International Conference on Computer Vision(ICCV）, 2025. (CCF A)
2. [ICCV 2025] Shuo Zhang, Chen Gao and Youfang Lin. Exploring View Consistency for Scene-Adaptive Low-Light Light Field Image Enhancement. IEEE International Conference on Computer Vision (ICCV), 2025. (CCF A)
3. [ACM MM 2025] Chen Gao，Youfang Lin, Wenbin Wang and Shuo Zhang. Epipolar Consistency-based Network for Structure-Aware LF Semantic Segmentation. The 33rd ACM International Conference on Multimedia. (ACM MM), 2025. (CCF A)
4. [TVCG 2025] Shuo Zhang, Song Chang, Youfang Lin, Progressive Multi-Plane Images Construction for Light Field Occlusion Removal [J]. IEEE Transactions on Visualization and Computer Graphics (TVCG), 2025. (CCF A)
5. [AAAI 2025] Kai Lv, Yunlong Li, Zhuo Chen, Shuo Wang, Sheng Han, Youfang Lin. Infer the Whole from a Glimpse of a Part: Keypoint-based Knowledge Graph for Vehicle Re-identification. The 39th AAAI Conference on Artificial Intelligence (AAAI), 2025. (CCF A)
6. [TCSVT 2025] Shuo Zhang, Yanlin Xie, Jiaxin Chen, Youfang Lin, Decoupling and Aggregating: Dual-layer Light Field Depth Estimation with Reflective and Transparent Surfaces[J]. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2025. (CCF B)

      (五)软件质量缺陷检测方向

      2025 年，INSIS 围绕系统软件与编译器质量保障核心问题开展深度研究，以传统人工变异分析方法为基础，创新性引入大语言模型与自动化技术，提出一系列面向真实缺陷、可扩展、低成本的编译器测试与变异分析方法，为提升国产基础软件可靠性和安全性提供可落地的技术路径。

      相关方法可直接应用于编译器与基础软件的自动化测试、回归验证和质量评估场景，尤其适用于 C/C++ 等复杂语言编译器、工业级系统软件以及高可靠性基础设施软件。该方法已在大规模真实缺陷数据集上完成有效性验证，成功挖掘出 GCC、LLVM、rustc、gccrs、仓颉官编译器等系统软件的大批潜在缺陷，其中 7 个 GCC 缺陷隐藏时长达 22 年，40 个缺陷隐藏 5 年以上；1 个缺陷为 TensorFlow 出错的根本原因，1 个缺陷涉及语言设计核心问题，已被编译器开发者提交至 C++ ISO 标准委员会讨论。

      主要学术论文：

1. [OOPSLA 2025] Bo Wang, Chong Chen, Ming Deng, Youfang Lin, Junjie Chen, Xing Zhang, Dan Hao, Jun Su. Fuzzing C++ Compilers via Type-Driven Mutation. ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications(OOPSLA). 2025. (CCF A)
2. [TSE 2025] Bo Wang, Chong Chen, Junjie Chen, Bowen Xu, Chen Ye, Youfang Lin, Guoliang Dong, Jun Sun. A Comprehensive Study of OOP-Related Bugs in C++ Compilers. IEEE Transactions on Software Engineering (TSE), 2025. (CCF A)
3. [AUSE 2025] Bo Wang, Ming Deng, Mingda Chen, Youfang Lin, Jianyi Zhou, Jie Zhang. Assessing the Effectiveness of Recent Closed-source Large Language Models in Fault Localization and Automated Program Repair. Automated Software Engineering Journal (AUSE). 2025. (CCF B)
4. [STVR 2025] Bo Wang, Jinkang Wei, Mingda Chen, Chong Chen, Youfang Lin, Jie Zhang. A Systematic Exploration of Mutation-Based Fault Localization Formulae. Software Testing, Verification and Reliability (STVR). 2025. (CCF B)
5.  王博,陈冲,邓明,董震,林友芳,郝丹, 移动应用GUI测试自动生成技术综述. 软件学报. 2025.

      二、行业应用与成果转化取得多方面突破

      2025 年，INSIS 在持续深化与原有合作伙伴合作的基础上，积极开拓技术落地新领域，在时空智能分析与应用、时序数据挖掘、仿真与智能控制等方向持续发力，获得多项国家级、省部级科研项目立项支持，同时斩获交通、国防、医疗等多个行业十余项应用项目的研究经费支持，技术成果产业化落地速度持续加快。

      在新能源领域，团队研发的智慧安环辅控平台已在多个新能源电站完成部署并投入实际使用，后续将持续推进新能源场站告警数据智能体与智慧巡检软件产品的研发工作，助力新能源行业智能化、精细化管理。

      在轨道交通领域，针对铁路钢轨伸缩变形检测的行业难题，团队成功研发一系列高精度视觉感知算法，攻克复杂环境下钢轨状态实时监测与预警的关键技术；在系统集成层面，研发出铁路钢轨伸缩调节器监测系统，实现设备状态的多维度可视化呈现与异常状态的即时告警。目前，该系统已在内蒙古某铁路大桥完成落地部署与工程实施，有效提升铁路轨道运维的智能化水平与安全保障能力。

      三、人才培养成效斐然，师资与学生培养双丰收

      （一）师资队伍建设再上新台阶

      本年度，师资队伍职称结构进一步优化，王晶老师荣升教授，王博老师荣升副教授；教学能力持续精进，郭晟楠副教授新晋学校优秀主讲教师，至此团队已有 6 名教师（超50%）获评学校优秀主讲教师称号，团队中另有 1 名北京市教学名师，充分彰显了 INSIS 教师团队严谨负责的教学态度与过硬的教学能力。

      教学成果方面，万怀宇、林友芳老师牵头获2025 年北京交通大学研究生教学成果一等奖，另有多项教学项目与成果荣获校级及以上表彰；多名教师凭借突出的教学贡献，获评握奇奖教金、华为奖教金等荣誉。

      （二）学生培养质量稳步提升

      在研究生培养方面，INSIS 研究生凭借扎实的学术功底、出色的科研能力和综合素养，在各类奖学金与荣誉称号评选中表现突出。其中，靳希源、毛潇苇、韦统龙 3 位同学斩获博士国家奖学金（学院共 11 人）；黄奕恒、廖梦祈、张芷毓、辛天佐、张烁、王金文 6 位同学荣获硕士国家奖学金（学院共 22 人）。此外，2025 年共有 30 人次研究生获得一等奖学金、三好研究生、优秀研究生干部等校级荣誉称号，以及奇安信奖学金等专项奖励，学生科研与综合能力得到全面认可。

      （三）毕业生就业竞争力显著

      2025 年，INSIS 毕业生凭借扎实的专业基础、强悍的实践能力和良好的综合素养，就业去向广泛、就业质量优异。毕业生纷纷入职字节跳动、百度、华为、快手等头部科技企业，工商银行、交通银行、兴业银行等金融机构，四川省交通厅、科研院所等事业单位，以及中车长客、中船等大型国企，充分展现了 INSIS 培养的人才具备广泛的行业适应性与核心竞争力。

      2026 年，INSIS 将继续聚焦核心研究方向，深化前沿科研探索与人才培养工作，直面实际应用场景，强化技术成果转化，持续为行业智慧化发展提供技术与人才支撑！欢迎各界朋友、同行莅临北京交通大学 INSIS 交流指导、开展需求对接与合作研究！