Intelligent Information Processing and Advanced Control Software Technology Research Team

State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University

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Main Research Direction

What we are doing

Multi-modal Intelligent Information Processing

Emotion recognition, sentiment analysis and intention recognition based on multi-modal information such as text, audio, video (picture).

Key Technologies of Intelligent Mobile Robots

Human-machine dialogue technology, intelligent mobile robot control technology and scene applications (business intelligent service robot, dual-mode intelligent disinfection robot).

Research on Intelligent Optimization Method

Evolution-based optimization theory, (high-dimensional, sparse, expensive) multi-objective optimization problem.

Team Members

Who are doing the work

Supervisor

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Hua Xu

Tenured Associate Professor, Associate Editor of Expert Systems with Application, Ph.D Supervisor

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Junhui Deng

Professor, Master Supervisor

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Xiaomin Sun

Associate Professor, Master Supervisor

Ph.D Student

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Ziqi Yuan

Ph.D Student

Multimodal Machine Learning

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Hanlei Zhang

Ph.D Student

Artificial Intelligence, Natrual Language Processing, Multimodal Language Understanding

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Qianrui Zhou

Ph.D Student

Artificial Intelligence, Natrual Language Processing, Multimodal Language Understanding

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Huigen Ye

Ph.D Student

Artificial Intelligence, Single-objective Optimization, Large-scale Integer Programming

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Tianxing Yang

Ph.D Student

Machine Learning for Combinatorial Optimization, Data Generation and Synthesis

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Yuetian Zou

Ph.D Student

Artificial Intelligence, Natrual Language Processing, Multimodal Language Understanding

Postgraduate Student

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Runmin Cao

Postgraduate Student

Multimodal Machine Learning, Multitask Learning

Visting Postgraduate Student

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Jianhua Su

Visting Postgraduate Student

Artificial Intelligence, Multimodal Learning, Intent understanding, Natrual Language Processing

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Weilong Liu

Visting Postgraduate Student

Artificial Intelligence, Natrual Language Processing, Multimodal Learning, Representation Learning

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Songze Li

Visting Postgraduate Student

Artificial Intelligence, Natrual Language Processing, Multimodal Language Understanding

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Shihao Zhou

Visting Postgraduate Student

Artificial Intelligence, Natrual Language Processing, Multimodal Language Understanding

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Yifan Wang

Visting Postgraduate Student

Artificial Intelligence, Natrual Language Processing, Multimodal Learning

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Xiaohan Zhang

Visting Postgraduate Student

Artificial Intelligence, Natrual Language Processing, Multimodal video understanding

Graduated

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Xin Wang

Visting Postgraduate Student

Multimoadal Learning, Facial Expression Recognition, Multi-task learning, Active Learning

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JingLiang Fang

Visting Postgraduate Student

Artificial Intelligence, Facial Expression Recognition, Machine Reading Comprehension, Natrual Language Processing

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Baozheng Zhang

Visting Postgraduate Student

Multimoadal Learning, Facial Expression Recognition, Multi-task learning, Active Learning

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Lunsong Huang

Postgraduate Student

Artificial Intelligence, Natrual Language Processing, Calibration

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Roberto Evans

Postgraduate Student

Multimodal Intent Recognition

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Huisheng Mao

Postgraduate Student

Multimoadal Learning, Facial Expression Recognition, Multi-task learning, Active Learning

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Xiaofei Chen

Visting Postgraduate Student

Artificial Intelligence, Multimodal Sentiment Analysis

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Yihe Liu

Visting Postgraduate Student

Artificial Intelligence, Natrual Language Processing, Multimodal Learning, Representation Learning

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Shaojie Zhao

Visting Postgraduate Student

Artificial Intelligence, Natrual Language Processing, Dialogue Intention Detection

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WeiYa Wang

Postgraduate Student

Artificial Intelligence, Multimodal Dialogue Intention Understanding

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Zhijing Wu

Ph.D Student

Artificial Intelligence, Natrual Intelligence, Intelligent Question-answering, Machine Reading Comprehension

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Hongyan Wang

Ph.D Student

Artificial Intelligence, Black-box Optimization, Multi-objective Optimization, Bayesian Optimization

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Huadong Li

Master’s Degree

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Kang Zhao

Visting Postgraduate Student

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Xiaoteng Li

Visting Postgraduate Student

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Jiangong Yang

Visting Postgraduate Student

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Congfeng Yin

Master’s Degree

Jiadong Yang

Ph.D

Yuan Yuan

Ph.D

Wei Wan

Master’s Degree

Yun Wen

Master’s Degree

Bo Wang

Master’s Degree

Jia Li

Master’s Degree

Xingwei He

Master’s Degree

Jiyun Zou

Visting Postgraduate Student

Wenmeng Yu

Master’s Degree

Tingen Lin

Master’s Degree

Kaicheng Yang

Visting Postgraduate Student

Yuxiang Xie

Visiting Postgraduate Student

Tianqi Wu

Master’s Degree

Congcong Yang

Visiting Postgraduate Student

Patents

Patents we got

银行运维规章制度变更的合规性查验方法及装置
基于改进支持向量机的产品评论情感分类方法及装置
基于LDA主题模型的微博用户兴趣的挖掘方法及系统
银行后台任务跑批时间的相关性分析方法及装置
基于权重学习和多特征抽取的多情绪分类方法及装置
基于主题模型的自展式特征选择方法及系统
Biterm主题模型的采样加速方法
基于微博社交网络的话题自动推荐方法及其系统
基于编码-解码深度神经网络的文本摘要生成系统及方法
基于马尔科夫逻辑网络的中文微博情绪分类方法及系统
基于用户评论文本的上下文情感分类方法及分类系统
TPS交易量预测与阈值范围预测的可视化方法及系统
基于微博文本的微博用户兴趣发现方法及系统
银行后台TPS交易事件趋势预测方法及系统
基于微博文本的情绪分类和情绪成分分析方法及系统
See all

Courses

Courses we teach

Internet Product Design

Public Elective Courses of Tsinghua University
Jan 1, 2019 12:00 AM

Intelligent Mobile Robot: Design, Programming and Practice

Public Elective Courses of Tsinghua University
Jan 1, 2016 12:00 AM

Industrial Data Mining

Public Elective Courses of Tsinghua University
Jan 1, 2013 12:00 AM

Data Mining: Methods and Applications

Public Elective Courses of Tsinghua University
Jan 1, 2011 12:00 AM

Data Strcture

Tsinghua University Computer Department Undergraduate Professional Basic Course
Jan 1, 2001 12:00 AM — Jan 1, 2006 12:00 AM
Code Slides Video

Data Strcture

National Excellent Course, Public Elective Course of Tsinghua University
Jan 1, 2001 12:00 AM
Code Slides Video

Computational Geometry

Tsinghua University Computer Department Graduate Basic Theory Course
Jan 1, 1997 12:00 AM
Video

Textbooks

Textbooks that we published

Data Mining: Methodology and Applications(2nd edition)

Mainly based on the teaching practice and accumulation of the Data Mining Methods and Applications course set up by Tsinghua University, referring to the teaching system of relevant courses of famous foreign universities in recent years, systematically introducing the basic concepts and basic principles of data mining; combining some typical applications Examples show general patterns and ideas for solving problems with data mining thinking methods.
Mar 2, 2022
Data Mining: Methodology and Applications(2nd edition)

Data Mining: Methodology and Applications

Mainly based on the teaching practice and accumulation of the Data Mining Methods and Applications course set up by Tsinghua University, referring to the teaching system of relevant courses of famous foreign universities in recent years, systematically introducing the basic concepts and basic principles of data mining; combining some typical applications Examples show general patterns and ideas for solving problems with data mining thinking methods.
Sep 1, 2014
Data Mining: Methodology and Applications

Data Mining: Methods and Applications - Application Cases

Mainly based on the teaching practice and accumulation of the Data Mining Methods and Applications course set up by Tsinghua University, referring to the teaching system of relevant courses of famous foreign universities in recent years, systematically introducing the basic concepts and basic principles of data mining; combining some typical applications Examples show general patterns and ideas for solving problems with data mining thinking methods.
Sep 1, 2017
Data Mining: Methods and Applications - Application Cases

Big Data Technology and Industry Applications

How to define big data? How to apply big data? What is big data thinking? How to learn big data? How to build a big data platform? How to apply big data in the industry? This series of problems are very confusing problems in the current era of big data boom. Big Data Technology and Industry Application faces these questions directly, answers the above questions from the perspective of practitioners, and hopes to provide some help to beginners in the big data industry.
Aug 1, 2016
Big Data Technology and Industry Applications

Computational Geometry: Algorithms and Applications

The first four chapters of “Computational Geometry: Algorithms and Applications (3rd Edition)” discuss geometric algorithms, including geometric intersection, triangulation, linear programming, etc. The random algorithm involved is also “Computational Geometry: Algorithms and Applications (Third Edition)” a distinctive feature. Chapters 5 to 10 introduce a variety of geometric structures. Chapters 11 to 16 continue to discuss several geometric algorithms and their data structures based on practical problems, they are also further deepening of the content of the first 10 chapters. “Computational Geometry: Algorithms and Applications (3rd Edition)” is not only comprehensive in content, but also closely related to practical applications, with prominent points. It has in-depth explanations, and each chapter has “notes and comments” and “exercises” for the convenience of readers A deeper understanding has been used as teaching materials by many universities around the world.
Feb 1, 2005
Computational Geometry: Algorithms and Applications

Data Structures and Algorithms (Java Description)

This book fully demonstrates the application of object-oriented technology in modern data structure theory, generally using abstraction, encapsulation, and inheritance technologies. This book not only introduces the basic data structure, but also introduces the application, implementation and analysis methods of the algorithm in combination with specific problems. The book also unifies various graph algorithms through the traversal algorithm framework and implements it based on the traversal algorithm template. Unique among similar textbooks.
Feb 1, 2006
Data Structures and Algorithms (Java Description)

Big Data Sentiment Analysis in Text

This book explains the text big data sentiment analysis technology from multiple dimensions. The content covers natural language processing and text emotion and sentiment methods, as well as sentiment analysis of Weibo texts and their incentives, position analysis of topic-oriented comments, texts Representation method and its application in emotion classification. The book is organized in a modular manner, with strong theoretical and clear organization. The author team implemented the main methods in the book with a serious and rigorous scientific attitude, and described the effects of various methods. This book can provide help for the study and scientific research work of college students in related majors (such as computer science and technology, software engineering, etc.). It also has a higher reference value for engineering and technical personnel engaged in text mining and natural language processing.
Oct 10, 2020
Big Data Sentiment Analysis in Text

Monographs

Intelligent Evolutionary Optimization

Intelligent Evolutionary Optimization introduces biologically-inspired intelligent optimization algorithms to address complex optimization problems and provide practical solutions for tackling combinatorial optimization problems. The book explores efficient search and optimization methods in high-dimensional spaces, particularly for high-dimensional multi-objective optimization problems, offering practical guidance and effective solutions across various domains. Providing practical solutions, methods, and tools to tackle complex optimization problems and enhance modern optimization techniques, this book will be a valuable resource for professionals seeking to enhance their understanding and proficiency in intelligent evolutionary optimization. The full codes are available for use at this link.
Intelligent Evolutionary Optimization

Multi-modal Sentiment Analysis

The natural interaction ability between human and machine mainly involves human-machine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability, and so on. To enable intelligent computers to have multi-modal sentiment analysis ability, it is necessary to equip them with a strong multi-modal sentiment analysis ability during the process of human-computer interaction. This is one of the key technologies for efficient and intelligent human-computer interaction. This book focuses on the research and practical applications of multi-modal sentiment analysis for human-computer natural interaction, particularly in the areas of multi-modal information feature representation, feature fusion, and sentiment classification. Multi-modal sentiment analysis for natural interaction is a comprehensive research field that involves the integration of natural language processing, computer vision, machine learning, pattern recognition, algorithm, robot intelligent system, human-computer interaction, etc. Currently, research on multi-modal sentiment analysis in natural interaction is developing rapidly. This book can be used as a professional textbook in the fields of natural interaction, intelligent question answering (customer service), natural language processing, human-computer interaction, etc. It can also serve as an important reference book for the development of systems and products in intelligent robots, natural language processing, human-computer interaction, and related fields. The full codes are available for use at this link.
Multi-modal Sentiment Analysis

Intent Recognition for Human-Machine Interactions

The natural interaction ability between human and machine mainly involves humanmachine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability, and so on. In order to realize the efficient dialogue ability of intelligent computer, it is necessary to make the computer own strong user intention understanding ability in the process of human-computer interaction. This is one of the key technologies to realize efficient and intelligent human-computer dialogue. Currently, the understanding of the objects to be analyzed requires different levels of ability such as recognition, cognition, and reasoning. The current research on human-computer interaction intention understanding is still focused on the level of recognition. The research and application of human-computer natural interaction intention recognition mainly includes the following levels: intention classification, unknown intention detection, and open intention discovery. Intention understanding for natural interaction is a comprehensive research field involving the integration of natural language processing, machine learning, algorithms, human-computer interaction, and other aspects. In recent years, our research team from State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University has conducted a lot of pioneering research and applied work, which have been carried out in the field of intention understanding for natural interaction, especially in the field of intention classification, unknown intention detection and open intention discovery based on text information of human-machine dialogue based on deep learning models. Related achievements have also been published in the top academic international conferences in the field of artificial intelligence in recent years, such as ACL, AAAI, ACM MM, and well-known international journals, such as Pattern Recognition and Knowledge-based Systems. In order to systematically present the latest achievements in intention classification, unknown intention detection, and open intention discovery in academia in recent years, the relevant work achievements are systematically sorted out and presented to readers in the form of a complete systematic discussion. Currently, the research on intention understanding in natural interaction develops quickly. The author’s research team will timely sort out and summarize the latest achievements and share them with readers in the form of a series of books in the future. This book can not only be used as a professional textbook in the fields of natural interaction, intelligent question answering (customer service), natural language processing, human-computer interaction, etc., but also as an important reference book for the research and development of systems and products in intelligent robots, natural language processing, human-computer interaction, etc. As the natural interaction is a new and rapidly developing research field, limited by the author’s knowledge and cognitive scope, mistakes and shortcomings in the book are inevitable. We sincerely hope that you can give us valuable comments and suggestions for our book. Please contact ( xuhua@tsinghua.edu.cn) or a third party in the open source system platform https://thuiar.github.io/ to give us a message. All of the related source codes and datasets for this book have also been shared on the following websites https://github.com/thuiar/Books . The research work and writing of this book were supported by the National Natural Science Foundation of China (Project No. 62173195). We deeply appreciate the following student from State Key laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University for her hard preparing work: Xiaofei Chen. We also deeply appreciate the following students for the related research directions of cooperative innovation work: Ting-en Lin, Hanlei Zhang, Wenmeng Yu, and Xin Wang. Without the efforts of the members of our team, the book could not be presented in a structured form in front of every reader.

Intelligent Evolutionary Optimization (Chinese version)

Intelligent Evolutionary Optimization introduces biologically-inspired intelligent optimization algorithms to address complex optimization problems and provide practical solutions for tackling combinatorial optimization problems. The book explores efficient search and optimization methods in high-dimensional spaces, particularly for high-dimensional multi-objective optimization problems, offering practical guidance and effective solutions across various domains. Providing practical solutions, methods, and tools to tackle complex optimization problems and enhance modern optimization techniques, this book will be a valuable resource for professionals seeking to enhance their understanding and proficiency in intelligent evolutionary optimization.The full codes are available for use at this link.
Intelligent Evolutionary Optimization

Evolutionary Machine Learning

Mainly discusses the content of this book: it is a learning classifier and feature selection method, the key is to do both of the integration of research, will study the classification of the classifier model building process and feature selection of feature subset unified integration based on the genetic search process of machine learning framework, at the same time improve the prediction performance of classification algorithm and operation efficiency; Secondly, a learning classifier based on distribution estimation algorithm is introduced to improve the searching quality of rule space. This book can be used as a teaching material and reference book for big data and artificial intelligence majors.

Natural Interaction for Tri-Co Robots (1) Human-machine Dialogue Intention Understanding

Inclusive robots can interact naturally with the work environment, humans, and other robots, adapt to complex dynamic environments autonomously, and work collaboratively. [Keen and considerate] natural interaction is one of the hot research issues of inclusive service robots. At present, there is an urgent need for robots and humans to have the ability to understand the intention of interactive dialogue. This book is based on the field of human-computer understanding based on deep learning methods. Starting from the knowledge of human-computer dialogue intentions, it systematically introduces intention recognition, unknown intention detection, and new intention discovery in human-computer dialogue. This book is the first domestic professional book to present interactive dialogue intention analysis in inclusive robot natural interaction. It can help readers master the key technologies and basic knowledge of human-machine dialogue intention analysis in inclusive robot research and track the development frontiers in this field. Provide meaningful learning and research references. The full codes are available for use at this link.
Natural Interaction for Tri-Co Robots (1) Human-machine Dialogue Intention Understanding

Natural Interaction for Tri-Co Robots (2) Sentiment Analysis of Multimodal Interaction Information

The natural interaction ability between human and machine mainly involves human-machine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability and so on. In order to realize the multi-modal sentiment analysis ability of intelligent computer, it is necessary to make the computer own strong multi-modal sentiment analysis ability in the process of human-computer interaction. This is one of the key technologies to realize efficient and intelligent human-computer interaction. The research and practical application of multi-modal sentiment analysis oriented to human-computer natural interaction, this book mainly discusses the following levels of hot research content: Multi-modal Information Feature Representation, Feature Fusion and Sentiment Classification. Multi-modal sentiment analysis oriented to natural interaction is a comprehensive research field involving the integration of natural language processing, computer vision, machine learning, pattern recognition, algorithm, robot intelligent system, human-computer interaction, etc. In recent years, our research team from State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science, Tsinghua University, has conducted a lot of pioneering research and applied work, which have been carried out in the field of multi-modal sentiment analysis for natural interaction, especially in the field of sentiment feature representation, feature fusion, robust sentiment analysis based on deep learning model. Related achievements have also been published in the top academic international conferences in the field of artificial intelligence in recent years, such as ACL, AAAI, ACM MM, COLING and well-known international journals, such as Pattern Recognition, Knowledge based Systems, IEEE Intelligent Systems and Expert Systems with Applications. In order to systematically present the latest achievements in multi-modal sentiment analysis in academia in recent years, the relevant work achievements are systematically sorted out and presented to readers in the form of a complete systematic discussion. Currently, the research on multi-modal sentiment analysis in natural interaction develops fastly. The author’s research team will timely sort out and summarize the latest achievements and share them with readers in the form of a series of books currenlty. This book can not only be used as a professional textbook in the fields of natural interaction, intelligent question answering (customer service), natural language processing, human-computer interaction, etc., but also as an important reference book for the research and development of systems and products in intelligent robots, natural language processing, human-computer interaction, etc. As the natural interaction is a new and rapidly developing research field, limited by the author’s knowledge and cognitive scope, mistakes and shortcomings in the book are inevitable. We sincerely hope that you can give us valuable comments and suggestions for our book. Please contact ( xuhua@tsinghua.edu.cn) or a third party in the open source system platform https://thuiar.github.io/ to give us a message. All of the related source codes and datasets for this book have also been shared on the following websites https://github.com/thuiar/Books . The research work and writing of this book were supported by the National Natural Science Foundation of China (Project No. 62173195). We deeply appreciate the following students from State Key laboratory of Intelligent Technology and Systems, Department of Computer Science, Tsinghua University for their hard preparing work: Xiaofei Chen, Yuanzhe Qiu and Jiayu Huang. We also deeply appreciate the following students for the related research directions of cooperative innovation work: Zhongwu Zhai, Wenmeng Yu, Kaicheng Yang, Jiyun Zou, Ziqi Yuan, Huisheng Mao, Wei Li，Baozheng Zhang and Yihe Liu . Without the efforts of the members of our team, the book could not be presented in a structured form in front of every reader. The full codes are available for use at this link.
Natural Interaction for Tri-Co Robots (2) Sentiment Analysis of Multimodal Interaction Information

Natural Interaction for Tri-Co Robots (3) Named Entity Recognition and Relationship Extraction

Cognitive robots are robots capable of natural interaction with their operating environment, humans, and other robots, adapting autonomously to complex dynamic environments and collaborating in tasks. 'Sensitive and considerate' natural interaction is one of the key research topics in the field of cognitive service robots. There is currently an urgent need for cognitive robots to possess the ability to understand complex semantic information. This book is rooted in the field of information and knowledge extraction based on deep learning methods, starting from text representation learning. It systematically introduces methods for extracting named entities and entity relationships from knowledge information in the real world and further explores how to achieve robust entity relationship analysis in open domains. This book is the first domestic systematic introduction to deep learning-based named entity recognition and relationship extraction in the field of natural interaction for cognitive robots. It provides readers with key technologies and foundational knowledge in the field of information and knowledge extraction in the cognitive robotics research domain, offering important learning and research references to keep up with the cutting edge of this field. The full codes are available for use at this link.
Natural Interaction for Tri-Co Robots (1) Human-machine Dialogue Intention Understanding

Multi objective Bayesian Optimization

It is the first academic monograph in China to systematically elaborate the solutions of expensive multi-objective Bayesian optimization problems. The Gauss process proxy model and acquisition function strategy are used to solve the problems of low dimensional parallel evaluation and high dimensional disaster, and improve the optimization efficiency. Features of this book: In depth multi-level research: comprehensively explore the practical application of various representative multi-objective intelligent optimization methods in various fields, provide practical guidance and solutions for professionals, and help with problem solving and intelligent decision-making. Algorithms inspired by nature: learn from the wisdom of biological systems, adopt bionic intelligent optimization algorithms, break through traditional mathematical analysis methods, solve expensive multi-objective optimization problems, and provide unique optimization solutions. Focus on high-dimensional multi-objective Bayesian Optimization: focus on the challenges in high-dimensional target space, introduce advanced algorithms and technologies, including the auxiliary optimization of target dimensionality reduction agent model, and provide key methods and tools for dealing with complex intelligent decision-making problems in the real world.
Multi objective Bayesian Optimization.png

Multi-modal Sentiment Analysis (Traditional Chinese Version)

This book systematically explores several hot research topics from shallow to deep, including feature representation of multi-modal emotional information, feature fusion, and emotional classification of multi-modal interaction information. The analysis of emotional information of natural interaction multi-modalities involves a comprehensive research field integrating natural language processing, computer vision, machine learning, pattern recognition, algorithms, robot intelligent systems, human-machine interaction, and other aspects. In recent years, the research team of the State Key Laboratory of Intelligent Technology and Systems in the Department of Computer Science and Technology at Tsinghua University, where the author is located, has carried out a considerable amount of innovative research and application work on the natural interaction of co-robots' multi-modal information and emotional analysis, especially in areas such as deep learning-based facial emotion feature recognition, learning representation of multi-modal emotional information, fusion of multi-modal emotional features, and the robustness of multi-modal emotional analysis under modal information missing conditions. Related achievements have also been published in top international conferences in the field of artificial intelligence in recent years, such as ACL, AAAI, ACM MM, and well-known international journals such as Pattern Recognition, Knowledge Based Systems, and Expert Systems with Applications. In order to systematically present the latest achievements of the academic community and the author's team in the field of multi-modal emotional analysis of co-robot natural interaction in recent years, this book systematically organizes the content of relevant work achievements and presents them to readers in a complete and systematic manner.
Intelligent Evolutionary Optimization

Petri Net: Theory and Applications

Chapter 12: Timed Hierarchical Object-oriented Petri Net, I-Tech Education and Publishing, Vienna, Austria, 2008, pp.253-280, ISNN:978-3-902613-12-7 (Hua Hu participated in the writing, published in February 2008)
Petri Net: Theory and Applications

Sentiment Analysis and Ontology Engineering

Chapter 10: Chinese Micro-Blog Emotion Classification by Exploiting Linguistic Features and SVMperf ), Springer International Publishing, 2016, pp. 221-236, ISNN:978-3-319-30317-8 (Hua Hu participated in the writing, published in February 2016)
Sentiment Analysis and Ontology Engineering

Recent Advances in Multi-robot Systems

Chapter 13: A Novel Modeling Method for Cooperative Multi-robot Systems Using Fuzzy Timed Agent Based Petri Nets ), I-Tech Education and Publishing, Vienna, Austria, 2008, pp.249-262, ISNN:978-3-902613-24-0 (Hua Hu participated in the writing, published in February 2008)
Recent Advances in Multi-robot Systems

Quality Software Management (Volume 1)-System Thinking

Tsinghua University Press (Jun. 2004) ISBN: 7-302-08298-7 （Original Work：Gerald M. Weinberg, Quality Software Management:Systems Thinking ,Dorset House (Sep. 1991), ISBN: 0-932-63322-6.）
Feb 1, 2004
Quality Software Management (Volume 1)-System Thinking

The Psychology of Computer Programming

Tsinghua University Press (Sep. 2003) ISBN: 7-302-07026-1 （Original Work：Gerald M. Weinberg, The Psychology of Computer Programming: Silver Anniversary Edition, Dorset House (Sep. 1998), ISBN: 0-932-63342-0.）
Feb 1, 2003
The Psychology of Computer Programming

Accomplishments

Awards that we got

Chinese Association for Simulation Science and Technology Award

Beijing Simulation Society Aug 2023
Intelligent Simulation Optimization Theory and Methods Based on Knowledge Mining and Collaborative Learning

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

China Federation of Logistics and Purchasing Oct 2015
Logistics Multi-Objective Optimal Scheduling Technology and Application Based on Mobile Internet O2O Mode

Beijing Science and Technology Award Second Prize

Beijing Municipal Science and Technology Commission Dec 2014
Award-winning project: Magnetron sputtering equipment research and development and industrialization, completed by: Beijing North Microelectronics Base Equipment Technology Research Center Co., Ltd., Institute of Microelectronics, Chinese Academy of Sciences, Tsinghua University, Fudan University

Beijing Science and Technology Award Third Prize

Beijing Municipal Science and Technology Commission Dec 2014
Integrated Circuit Manufacturing Equipment Standardized Cluster Control and Complete Machine Testing Platform

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

China Federation of Logistics and Purchasing Oct 2013
Intelligent public service platform for international transportation information

Chongqing Science and Technology Award Third Prize

Chongqing Municipal People’s Government May 2011
Research on Key Technologies of Etching Process Control for Very Large Scale Integrated Circuits

PAKDD 2011 Best Paper

Pacific Asia Conference on Knowledge Discovery and Data Mining May 2011
Constrained LDA for Grouping Product Features in Opinion Mining

National Science and Technology Progress Award Second Prize

The State Concil of People’s Republic of China Dec 2009
Award-winning Project: 100nm High-density Plasma Etching Machine Research and Development and Industrialization (Winning Number: J-237-2-02), Grade: national level, Completion Unit: Beijing North Microelectronics Technology Research Center Co., Ltd., Chinese Academy of Sciences Microelectronics Research Institute, Tsinghua University, Peking University (collective award; ranked 1st in Tsinghua University)

Beijing Science and Technology Award First Prize

Beijing Municipal Science and Technology Commission Dec 2008
100nm High-density Plasma Etching Machine Research and Development and Industrialization

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