• Hua Xu

• Tenured Associate Professor, Doctoral Supervisor

• Computer Science Department

• Joined Department: 2003

• Email: xuhua@mail.tsinghua.edu.cn

• Phone: 13810102923

Education background

Bachelor's Degree (Computer Science and Technology), Xi'an Jiaotong University, China, 1998;

Master's Degree (Computer Application Technology), Tsinghua University, China, 2000;

Ph.D. (Computer Application Technology), Tsinghua University, China, 2003.

Areas of Research Interests

Multimodal Intelligent Information Processing, Key Technologies for Intelligent Mobile Robots, Research on Intelligent Optimization Methods

Research Status

I have been engaged in research in the field of artificial intelligence, focusing on intelligent optimization scheduling and human-machine natural interaction, with a particular emphasis on multimodal intelligent information processing and the control of integrated robots. We conduct our research at research platforms such as the National Research Center of Information Science and Technology in Beijing. In recent years, we have completed three major national scientific and technological projects, four projects funded by the National Natural Science Foundation, two projects under the National 973 Program, five projects under the National 863 Program, and collaborated on thirteen projects with international Fortune 500 companies. We have been invited to serve as core experts for the Ministry of Industry and Information Technology's Major Science and Technology Project No. 03. Additionally, we have served as the leaders of the expert group for the acceptance of the National Key Research and Development Program "Technical Support for Economic Development 2020." We are advisory experts for the National Industrial Innovation Center of the National Development and Reform Commission and the Editors-in-Chief of the international journal "Intelligent Systems Applications" published by Elsevier and the Deputy Editors-in-Chief of "Expert Systems Applications" (first quartile, SCI impact factor = 8.665).We have received numerous awards and honors, including a second-class National Science and Technology Progress Award, a first-class Beijing Science and Technology Award, two first-class awards from industry associations for scientific and technological achievements, and three second and third-class awards at the provincial and ministerial levels. We have obtained 36 national invention patents, 26 software copyrights, and authored three textbooks and six monographs. Our contributions have been cited in top journals such as ACM Computing Surveys and referenced by six academicians from both domestic and international institutions, as well as over 30 IEEE fellows. In the field of intelligent robot optimization scheduling, we have proposed intelligent evolutionary theories for multi-objective optimization scheduling and flexible job shop scheduling (FJS) methods for multi-robot collaboration. In the area of robot natural interaction, we have researched and established methods and open platforms for emotion analysis and intent understanding in natural interaction, which have become effective approaches in the field of intelligent robotic systems for perceiving emotional states and understanding dialogue intents.

Achievement Links

1) Research Team Home Page: https://thuiar.github.io/

2) Achievements of Sharing Website: https://github.com/thuiar

Honors And Awards

China Simulation Society Scientific and Technological Natural Science Second Prize (2023)

China Federation of Logistics and Purchasing Science and Technology Invention Award First Prize (2015)

Beijing Science and Technology Award Second Prize (2014)

Beijing Science and Technology Award Third Prize (2014)

China Federation of Logistics and Purchasing Science and Technology Progress Award First Prize (2013)

Chongqing Science and Technology Award Third Prize (2011)

PAKDD 2011 Best Paper Award (2011)

National Science and Technology Progress Award Second Prize (2009)

Beijing Science and Technology Award First Prize (2008)

Academic Achievements

Multimodal Emotion Analysis

[1]Mao H, Zhang B, Xu H, et al. Robust-MSA: Understanding the impact of modality noise on multimodal sentiment analysis[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2023, 37(13): 16458-16460.

[2]Yuan Z, Liu Y, Xu H, et al. Noise Imitation Based Adversarial Training for Robust Multimodal Sentiment Analysis[J]. IEEE Transactions on Multimedia, 2023.

[3]Xu H, Yuan Z, Zhao K, et al. Gar-net: A graph attention reasoning network for conversation understanding[J]. Knowledge-Based Systems, 2022, 240: 108055.

[4]Yu W, Xu H. Co-attentive multi-task convolutional neural network for facial expression recognition[J]. Pattern Recognition, 2022, 123: 108401.

[5]Mao H, Yuan Z, Xu H, et al. M-sena: An integrated platform for multimodal sentiment analysis[J]. arXiv preprint arXiv:2203.12441, 2022.

[6]Mao H, Zhang B, Xu H, et al. An End-to-End Traditional Chinese Medicine Constitution Assessment System Based on Multimodal Clinical Feature Representation and Fusion[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(11): 13200-13202.

[7]Liu Y, Yuan Z, Mao H, et al. Make Acoustic and Visual Cues Matter: CH-SIMS v2. 0 Dataset and AV-Mixup Consistent Module[C]//Proceedings of the 2022 International Conference on Multimodal Interaction. 2022: 247-258.

[8]Yuan Z, Li W, Xu H, et al. Transformer-based feature reconstruction network for robust multimodal sentiment analysis[C]//Proceedings of the 29th ACM International Conference on Multimedia. 2021: 4400-4407.

[9]Yu W, Xu H, Yuan Z, et al. Learning modality-specific representations with self-supervised multi-task learning for multimodal sentiment analysis[C]//Proceedings of the AAAI conference on artificial intelligence. 2021, 35(12): 10790-10797.

[10]Li H, Xu H. Deep reinforcement learning for robust emotional classification in facial expression recognition[J]. Knowledge-Based Systems, 2020, 204: 106172.

[11]Yang K, Xu H, Gao K. Cm-bert: Cross-modal bert for text-audio sentiment analysis[C]//Proceedings of the 28th ACM international conference on multimedia. 2020: 521-528.

[12]Yu W, Xu H, Meng F, et al. Ch-sims: A chinese multimodal sentiment analysis dataset with fine-grained annotation of modality[C]//Proceedings of the 58th annual meeting of the association for computational linguistics. 2020: 3718-3727.

[13]Xu Y, Xu H, Zou J. Hgfm: A hierarchical grained and feature model for acoustic emotion recognition[C]//ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020: 6499-6503.

[14]Cao Y, Xu H. Satnet: Symmetric adversarial transfer network based on two-level alignment strategy towards cross-domain sentiment classification (student abstract)[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(10): 13763-13764.

[15]Wang X, Xu H, Sun X, et al. Combining fine-tuning with a feature-based approach for aspect extraction on reviews (student abstract)[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(10): 13951-13952.

Multimodal Intent Understanding

[1]Zhou Q, Xu H, Li H, et al. Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition[J]. arXiv preprint arXiv:2312.14667, 2023.

[2]Zhang H, Xu H, Wang X, et al. A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery[J]. IEEE Transactions on Knowledge and Data Engineering, 2023.

[3]Zhang H, Xu H, Zhao S, et al. Learning discriminative representations and decision boundaries for open intent detection[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2023.

[4]Zhang H, Xu H, Wang X, et al. Mintrec: A new dataset for multimodal intent recognition[C]//Proceedings of the 30th ACM International Conference on Multimedia. 2022: 1688-1697.

[5]Zhang H, Xu H, Lin T E, et al. Discovering new intents with deep aligned clustering[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(16): 14365-14373.

[6]Zhang H, Xu H, Lin T E. Deep open intent classification with adaptive decision boundary[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(16): 14374-14382.

[7]Zhang H, Li X, Xu H, et al. TEXTOIR: An integrated and visualized platform for text open intent recognition[J]. arXiv preprint arXiv:2110.15063, 2021.

[8]Lin T E, Xu H, Zhang H. Discovering new intents via constrained deep adaptive clustering with cluster refinement[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(05): 8360-8367.

[9]Lin T E, Xu H, Zhang H. Constrained self-supervised clustering for discovering new intents (student abstract)[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(10): 13863-13864.

[10]Lin T E, Xu H. A post-processing method for detecting unknown intent of dialogue system via pre-trained deep neural network classifier[J]. Knowledge-Based Systems, 2019, 186: 104979.

[11]Lin T E, Xu H. Deep unknown intent detection with margin loss[J]. arXiv preprint arXiv:1906.00434, 2019.

Reading Comprehension Related to Question Answering

[1]Wu Z, Xu H. Trustworthy machine reading comprehension with conditional adversarial calibration[J]. Applied Intelligence, 2023, 53(11): 14298-14315.

[2]Wu Z, Fang J, Xu H, et al. An In-depth Interactive and Visualized Platform for Evaluating and Analyzing MRC Models[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022: 5044-5048.

[3]Wu Z, Xu H, Fang J, et al. Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation[J]. arXiv preprint arXiv:2208.05217, 2022.

[4]Wu Z, Xu H. Improving the robustness of machine reading comprehension model with hierarchical knowledge and auxiliary unanswerability prediction[J]. Knowledge-Based Systems, 2020, 203: 106075.

[5]Wu Z, Xu H. A Multi-Task Learning Machine Reading Comprehension Model for Noisy Document (Student Abstract)[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(10): 13963-13964.

Entity Recognition and Relation Extraction for Interactive Information

[1]Zhao K, Xu H, Yang J, et al. Consistent representation learning for continual relation extraction[J]. arXiv preprint arXiv:2203.02721, 2022.

[2]Zhao K, Xu H, Cheng Y, et al. Representation iterative fusion based on heterogeneous graph neural network for joint entity and relation extraction[J]. Knowledge-Based Systems, 2021, 219: 106888.

[3]Xie Y, Xu H, Li J, et al. Heterogeneous graph neural networks for noisy few-shot relation classification[J]. Knowledge-Based Systems, 2020, 194: 105548.

[4]Xie Y, Xu H, Yang C, et al. Multi-channel convolutional neural networks with adversarial training for few-shot relation classification (student abstract)[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(10): 13967-13968.

Intelligent Evolutionary Algorithms

[1]Yuan Y, Ong Y S, Gupta A, et al. Objective reduction in many-objective optimization: evolutionary multiobjective approaches and comprehensive analysis[J]. IEEE Transactions on Evolutionary Computation, 2017, 22(2): 189-210.

[2] Yuan Y, Xu H, Wang B, et al. A new dominance relation-based evolutionary algorithm for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2015, 20(1): 16-37.

[3] Yuan Y, Ong Y S, Gupta A, et al. Evolutionary multitasking in permutation-based combinatorial optimization problems: Realization with TSP, QAP, LOP, and JSP[C]//2016 IEEE Region 10 Conference (TENCON). IEEE, 2016: 3157-3164.

[4] Yuan Y, Xu H, Wang B, et al. Balancing convergence and diversity in decomposition-based many-objective optimizers[J]. IEEE Transactions on Evolutionary Computation, 2015, 20(2): 180-198.

[5] Yuan Y, Xu H, Wang B. An experimental investigation of variation operators in reference-point based many-objective optimization[C]//Proceedings of the 2015 annual conference on genetic and evolutionary computation. 2015: 775-782.

[6] Yuan Y, Xu H, Wang B. An improved NSGA-III procedure for evolutionary many-objective optimization[C]//Proceedings of the 2014 annual conference on genetic and evolutionary computation. 2014: 661-668.

[7] Yuan Y, Xu H, Wang B. Evolutionary many-objective optimization using ensemble fitness ranking[C]//Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation. 2014: 669-676.

Flexible Job Shop Scheduling Problem Algorithm

[1] Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms[J]. IEEE Transactions on Automation Science and Engineering.

[2] Yuan Y, Xu H, Yang J. A hybrid harmony search algorithm for the flexible job shop scheduling problem[J]. Applied soft computing, 2013, 13(7): 3259-3272.

[3] Yuan Y, Xu H. Flexible job shop scheduling using hybrid differential evolution algorithms[J]. Computers & Industrial Engineering, 2013, 65(2): 246-260.

[4] Yuan Y, Xu H. An integrated search heuristic for large-scale flexible job shop scheduling problems[J]. Computers & Operations Research, 2013, 40(12): 2864-2877.

[5] Yuan Y, Xu H. A memetic algorithm for the multi-objective flexible job shop scheduling problem[C]//Proceedings of the 15th annual conference on Genetic and evolutionary computation. 2013: 559-566.

[6] Yuan Y, Xu H. HHS/LNS: an integrated search method for flexible job shop scheduling[C]//2012 IEEE Congress on Evolutionary Computation. IEEE, 2012: 1-8.

Learning classifiers

[1] Yang J, Xu H, Jia P. Effective search for genetic-based machine learning systems via estimation of distribution algorithms and embedded feature reduction techniques[J]. Neurocomputing, 2013, 113: 105-121.

[2] Xu H, Yang J, Jia P, et al. Effective structure learning for estimation of distribution algorithms via L1-regularized Bayesian networks[J]. International Journal of Advanced Robotic Systems, 2013, 10(1): 17.

[3] Yang J, Xu H, Jia P. Effective search for Pittsburgh learning classifier systems via estimation of distribution algorithms[J]. Information Sciences, 2012, 198: 100-117.

[4] Xu H, Wen Y, Wang J. A fast-convergence distributed support vector machine in small-scale strongly connected networks[J]. Frontiers of Electrical and Electronic Engineering, 2012, 7: 216-223.

[5] Wen Y, Xu H, Yang J. A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system[J]. Information Sciences, 2011, 181(3): 567-581.

[6] Yang J, Xu H, Pan L, et al. Task scheduling using Bayesian optimization algorithm for heterogeneous computing environments[J]. Applied Soft Computing, 2011, 11(4): 3297-3310.

[7] Wen Y, Xu H. A cooperative coevolution-based pittsburgh learning classifier system embedded with memetic feature selection[C]//2011 IEEE Congress of Evolutionary Computation (CEC). IEEE, 2011: 2415-2422.

[8] Wen Y, Xu H, Yang J. A heuristic-based hybrid genetic algorithm for heterogeneous multiprocessor scheduling[C]//Proceedings of the 12th annual conference on Genetic and evolutionary computation. 2010: 729-736.

[9] Yang J, Xu H, Cai Y, et al. Effective structure learning for EDA via L1-regularizedbayesian networks[C]//Proceedings of the 12th annual conference on Genetic and evolutionary computation. 2010: 327-334.

[10] Yang J, Xu H, Jia P. Task Scheduling for Heterogeneous Computing Based on Learning Classifier System[C]//2009 International Conference on Artificial Intelligence and Computational Intelligence. IEEE, 2009, 3: 370-374.

Bayesian Optimization

[1] Wang H, Xu H, Zhang Z. High-Dimensional Multi-Objective Bayesian Optimization With Block Coordinate Updates: Case Studies in Intelligent Transportation System[J]. IEEE Transactions on Intelligent Transportation Systems, 2023.

[2] Wang H, Xu H, Yuan Y. High-dimensional expensive multi-objective optimization via additive structure[J]. Intelligent Systems with Applications, 2022, 14: 200062.

[3] Wang H, Xu H, Yuan Y, et al. An adaptive batch Bayesian optimization approach for expensive multi-objective problems[J]. Information Sciences, 2022, 611: 446-463.

[4] Wang H, Xu H, Yuan Y, et al. Balancing exploration and exploitation in multiobjective batch bayesian optimization[C]//Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2019: 237-238.

[5] Wang H, Xu H, Yuan Y, et al. Noisy multiobjective black-box optimization using Bayesian optimization[C]//Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2019: 239-240.

Fast Large-Scale Optimization Algorithms

[1] Ye H, Xu H, Wang H, et al. GNN&GBDT-guided fast optimizing framework for large-scale integer programming[C]//International Conference on Machine Learning. PMLR, 2023: 39864-39878.

[2] Ye H, Wang H, Xu H, et al. Adaptive constraint partition based optimization framework for large-scale integer linear programming (student abstract)[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2023, 37(13): 16376-16377.

[3] Chen L, Xu H, Wang Z, et al. Self-paced learning based graph convolutional neural network for mixed integer programming (student abstract)[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2023, 37(13): 16188-16189.

[4] Chen L, Xu H. MFENAS: multifactorial evolution for neural architecture search[C]//Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2022: 631-634.

[5] Chen L, Xu H. CORAL-DMOEA: Correlation Alignment-Based Information Transfer for Dynamic Multi-Objective Optimization (Student Abstract)[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(10): 13765-13766.

Smart healthcare

[1] Xu H, Chen X, Qian P, et al. A two-stage segmentation of sublingual veins based on compact fully convolutional networks for Traditional Chinese Medicine images[J]. Health Information Science and Systems, 2023, 11(1): 19.

[2] Mao H, Zhang B, Xu H, et al. An End-to-End Traditional Chinese Medicine Constitution Assessment System Based on Multimodal Clinical Feature Representation and Fusion[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(11): 13200-13202.

[3] Chen X, Xu H, Qian P, et al. Multi-kernel Convolutional Neural Network for Wrist Pulse Signal Classification[C]//2022 32nd Conference of Open Innovations Association (FRUCT). IEEE, 2022: 75-86.

Demos

[1] Mao H, Zhang B, Xu H, et al. Robust-MSA: Understanding the impact of modality noise on multimodal sentiment analysis[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2023, 37(13): 16458-16460.

[2] Mao H, Yuan Z, Xu H, et al. M-sena: An integrated platform for multimodal sentiment analysis[J]. arXiv preprint arXiv:2203.12441, 2022.

[3] Mao H, Zhang B, Xu H, et al. An End-to-End Traditional Chinese Medicine Constitution Assessment System Based on Multimodal Clinical Feature Representation and Fusion[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(11): 13200-13202.

[4] Wu Z, Fang J, Xu H, et al. An In-depth Interactive and Visualized Platform for Evaluating and Analyzing MRC Models[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022: 5044-5048.

[5] Zhang H, Li X, Xu H, et al. TEXTOIR: An integrated and visualized platform for text open intent recognition[J]. arXiv preprint arXiv:2110.15063, 2021.