为响应国家对高等教育改革的号召、促进后疫情时代的统计学教育适应社会发展的最新需求，基于2019年“第一届统计学教学改革研讨会”的讨论，清华大学统计学研究中心将于2021年6月25-26日举办“第二届统计学教学改革研讨会”，同从事一线教学的各位统计学者立足教学理念、教学体系、培养模式、教学方式四个维度，进行进一步深化探讨，欢迎各位老师报名参加！
【会议地点】：清华大学校内或附近
【会议时间】：2021/06/26 (周六)
【报到时间】：2021/06/25（周五）
【报名日期】：即日起至2021/06/15
【联系人】：
王江典：wangjiangdian@tsinghua.edu.cn
报名可发送“姓名、单位、教学课程、联系方式”至王江典老师邮箱。
自2016年来，北京大学、清华大学两校优秀的统计学师生济济一堂，发挥两校的学科优势，互通有无，着力推动中国优秀统计青年人才的成长，助力中国统计学学科的发展。为传承兄弟院校间的团结协作和友好交流，两校将于2021年6月10日举办第五届北大-清华统计论坛。
会议时间：2021年6月10日 下午1:00-5:00
会议地点：北京大学镜春园82号院甲乙丙楼报告厅（二层东侧）
主办方：北京大学统计科学中心 清华大学统计学研究中心
会议报名：
报名时间：即日起至2021年5月15日
报名链接：https://docs.qq.com/form/page/DWmhOWHRCZVdxQUli#/fill
特邀报告
北京大学 耿直教授
清华大学 许宪春教授
会议议程：
| 时间 | 议程 |
| 13:00-13:30 | 报到 |
| 13:30-13:40 | 开幕式 |
| 13:40-14:40 | 大会报告一（耿直教授） |
| 14:40-15:00 | 茶歇&合影 |
| 15:00-16:00 | 大会报告二（许宪春教授） |
Keywords: International view Broad-minded Practical need Leadership
Q: Can you talk about your childhood?
A: In my childhood, my family and I were faced with general tough life conditions, but eventually we went through the hardship and became stronger. I am also very lucky that I always have good teachers. One of the teachers that I remember particularly once told us a story about Hua Luogeng when I was very young. That was the time Hua Luogeng returned to China. My teacher told me how he became famous after studying, and that actually made a quite impression on me. I think it’s because of his story that I decided to study Mathematics. I moved to England in 1961 when my father worked there. The secondary school I attended was not a top school, but was very comprehensive. Under the support of the school and headmaster, I picked up English quickly. I was the only boy from that school who went to a university.
Q: In that period, did you encounter any challenge in your study or life?
A: Yes. First of all, I need to get used to the English way of schooling, for example moving across different classrooms to have lessons, and different dietary habit on campus. But luckily the students around were all very nice, and we became good friends. Because of this experience, I was able to know different culture and their way of speaking. It’s quite a big challenge to adapt from the Hong Kong school system to a working class environment in London at that time.
Q: Is there any particular reason that you choose Statistics as your major?
A: Well as I said I decided to study Mathematics, and I graduated in Mathematics in Manchester. We had good statistics teachers and received many statistics courses, which was unusual at that time in England. I also got a chance to listen to a lecture about probability theory given by an eminent probability researcher. I was impressed by his lecture and became interested in probability theory. Because of my family, I decided to suspend the post-graduate study and took a job. During that time, I started to read some papers, and I came across one paper about time series by my former teacher in Manchester. That’s how I became interested in time series. I wrote to him and went back to Manchester. Due to some reasons, I accidentally became a university teacher teaching statistics instead of a post-graduate student. I was very lucky.
Q: How did you finally decide to become a statistics researcher?
A: Once I returned to Manchester, I became quite clear that statistics is the career I want to pursue. Thanks to my school, I had an opportunity to meet with other scientists and technologists, and became interested in control engineering and stochastic control. So, time series became quite a natural subject for me. My early career was mainly oriented in frequency domain, and I changed to time domain later on when I met Akaike. He visited us in Manchester for half a year, working on multivariate control system using multivariate linear AR model, as well as some aspect of AIC(Akaike Information Criterion). We became very good friends, and I wanted to learn more from him, so I applied a Royal Society Japan Fellowship, and went to japan for 6 months. During that visit, I read a number of papers he collected, and learned a lot from not only the papers, but also the marks and personal notes he made. It was very valuable for me. By talking to him, I learned the background of why he did certain research. He did research not in front of the desk, but went out and met other scientists. He did not publish many papers in the first ten years of his career, but did a lot of great works later. He spent time cultivating friendship with engineers and other people. Because of this, he was asked to solve a problem of selecting a suitable model from a number of models in the field of predicting. That’s the original problem behind AIC. So, I got a deep understanding of the whole idea of his research besides reading papers.
Q: We know that you published many great papers in your early career, so what’s your secret for this fantastic achievements?
A: I remember the words of Mr. Yang Zhenning. He said do something that you are really passionate about. My father never interfered in my study, and I never interfered in my children’s career either. Let the person choose what he or she is really interested in. My mentor is a time series analyst, but he never pushed me, so I had the chance to choose my own area. The reason why I choose statistics is because I want to produce something new, so I am lucky to be in the right environment where there is no pressure. I am also very lucky to have a good wife taking care of my family, and lucky to have the chance meeting with other scientists. I am a good learner, and I am able to pick up the things I want to learn. I think passion is very important rather than any secret. Remember to be observant and passionate.
Q: Now let’s talk about one of your most important work in non-linear time series, the threshold model. Where did the idea come from?
A: When I was visiting Akaike, I learned the way he produced the spectral density estimate. So, I used the approach on the lynx data, which I was very interested in. There was a session in the Royal Statistic Society and I presented this paper. During that discussion, there was one gentleman who made a very, very important comment. He said that the data is cyclical, but the cycle is not symmetric. The lynx population would rise slowly but fall rapidly. If you use a linear Gaussian model, you would never be able to capture it. Also, he said that from the point view of dynamical system, the cycle should be considered limit cycle. So, if you can produce a model that leads to limit cycle, it would be ideal. And David Cox and Akaike also made some similar comments. However, it is very difficult and is a big challenge.So, I decided to work on the problem. But my entire education up to that time was all in linear. So, I need to teach myself nonlinear dynamical systems.
Then, one day I was in my garden and mowing the lawn. When you mow the lawn, you go strip by strip. Suddenly, the idea of piecewise linearity came into my mind. This is because I was subconsciously thinking of the problem all the time.
Then I started working on the idea and a student did programs. One day she brought me some results which were too perfectly periodic to be possible. Then I found that she forgot the noise. This was the first time I saw limit cycle. Then, I said we could also see whether this model can produce other nonlinear phenomena, such as subharmonics, higher harmonics, amplitude-frequency dependency and so on. And it turned out that the model could do that.
Q: Did you encounter difficult times with the model?
A: Yes. A lot of people discussed the paper but I could not say everybody liked it, maybe because the idea was so new. I also got one or two people attacking. The model was invented in 1980s but has remained fairly quiet for 10 years. It was in about the 1990s that the model attracted a lot of attention. So, the beginning was not easy.
Q: From your experience, how to find a good research problem?
A: First of all, you have to be social. To me, statisticians are toolmakers. What tool you want to invent must be dictated by practical needs from people on the ground. So, we should go out, interact and collaborate with other scientists. We should be members of scientific teams. Don’t follow fashion blindly. I never want to follow fashion. When I did nonlinear time series, almost none of the leaders in time series worked on that.
There are probably two types of research. One is the run-of-mill research, which means you have an incremental improvement. Those things do not take us long and you can publish these very quickly. The other one is the revolutionary research. Of course, in one’s lifetime, one would probably not have more than a couple of such revolutions. But you must always keep them in mind, work on them in any spare time.
Q: You have been Chair of Statistics at several universities. How can you do good jobs in both academic and management? What’s your secret?
A: I adopted the principle I learned from Lao Tzu (老子) and Sun Tzu’s “Art of War” (孙子兵法). I cannot micromanage, so if there is any big job I will identify a suitable person. Then I will give the person my full support. So if you use one person you need to trust him (用人不疑，疑人不用).
Q: Do you worry about the future of statistics given the competition from Machine Learning and AI?
A: As Lao Tzu has said, behind every good fortune there is a misfortune, and misfortune leads to good fortune (祸兮福之所倚，福兮祸之所伏). I think the two aspects are certainly true for what challenge statistics is facing in the domain of data science. But if we sensibly steer our ship of statistics, we can benefit. Machine learning is certainly a powerful tool, but some of the ideas are not unknown or uncommon in statistics. Because in statistics, the basic training is how to handle randomness, and for anything that requires that, statistics has advantages. But on the other hand, we have to be fully prepared and liberate our minds. Some of the old ideas may be too restrictive. We used to deal with small data set in days of Fisher, but now we have to deal with large data sets. To defeat the new challenge, we have to adopt the attitude in Chinese culture: when foreigners come, we absorb them.
So, I don’t worry. As long as we are broad-minded and ready to adapt, we can survive and grow.
2020年12月27-29日，“世界华人数学家联盟年会”在安徽合肥举行，清华大学统计学研究中心邓柯副教授作为第一作者的学术论文“On the unsupervised analysis of domain-specific Chinese texts”荣获“2020世界华人数学家联盟最佳论文奖-银奖”。该论文是邓柯副教授与美国哈佛大学Peter Bol教授、哈佛大学刘军教授和萨福克大学李佳漪副教授共同完成，论文发表于美国科学院院刊PNAS杂志。
文章提出运用统计学模型和原理进行无指导中文文本分析的新方法-TopWORDS，可对特定领域中文文本进行词语发现和中文分词。此方法还可以结合其他文本分析工具，如词嵌入、主题模型、关联规则挖掘等，可提取文本中的主要特征和信息，是中文文本挖掘领域的重要突破。
近日，“清华大学第九届青年教师教学大赛”（简称“青教赛”）落下帷幕，我中心邓婉璐老师荣获“清华大学第九届青年教师教学大赛”（理科、医科组）一等奖；周在莹老师荣获“清华大学第九届青年教师教学大赛”（理科、医科组）二等奖。
据悉，全校共有47个院系的140名青年教师参加了清华大学第九届青年教师教学大赛的培训和比赛。赛前进行了2个月的教学培训，从教学内容、教学方法、教案写作等全方位对青年教师进行了教学培训，之后组织了10次教学工作坊进行交流。
全国第五届青年教师教学大赛暨清华大学第九届青年教师教学大赛总结交流座谈会在上周召开，副校长郑力，校务委员会副主任、工会主席王岩出席座谈会，副教务长、教务处处长曾嵘主持会议。
郑力向在国赛和校赛上取得优异成绩的青年教师表示祝贺。郑力回顾了青教赛的发展历程，对指导教师团队的工作表示肯定。他指出，青教赛是一个学习的盛会，在备赛、参赛的过程中，青年教师通过指导教师的辅导、选手之间的交流提高了教学能力和水平；青教赛是一个创作的盛会，参赛选手不断突破自我、追求卓越，提升了专业素养；青教赛是一个传承的盛会，青年教师在指导教师的言传身教下迅速成长，将学习到的教学技能应用到课堂上，将学校教书育人的优良传统和经验一代代传承下去。
图文|清华新闻网
11月27日，清华大学举行抗击新冠肺炎疫情表彰大会。地球系统科学系宫鹏教授、徐冰教授领衔的“流行病学传播预测与对策”科技抗疫突击队荣获“清华大学抗击新冠肺炎疫情先进集体”荣誉称号。我中心执行主任邓柯副教授、侯琳副教授及中心博士生刘朝阳、沈翀、王掣、宋爽、余博作为突击队骨干成员，共同出席表彰大会接受表彰。
蒋斐宇，清华大学统计学研究中心五年级博士生，指导老师为李东副教授。主要研究方向为非线性时间序列分析、金融计量学和变点检测等。目前已在Journal of Econometrics, Statistica Sinica等期刊发表多篇学术论文。
学术经历：
2018/09-2018/11：香港大学访问
2019/03-2019/08：香港大学访问
2019/08-2020/08：伊利诺伊大学厄巴纳-香槟分校（UIUC）访问
社会工作经历：
2018/09-2019/08：清华大学统计学研究中心学生会主席
2018/09-2019/01：清华大学统计咨询中心学生咨询师
日前，我中心执行主任邓柯副教授被清华大学教务处及研究生院共同聘请为“清华大学教学顾问组成员”，聘期3年。此前，由邓柯副教授带领的统计咨询中心团队曾受清华大学教学质量评估中心的委托，针对学校教学评估系统优化给出专业指导建议。团队高质量的咨询服务及专业化的优化方案受到学校教务处及教学质量评估中心的高度肯定，并将相关方案成功应用到教学评估系统的整体升级工作中。
项目背景：
教学评估是衡量教师教学质量的重要手段，可为教师的考核、奖励和晋升提供关键依据。清华大学从1998年秋开始“课堂教学质量学生问卷调查”工作，2004年开始实行网上评估，已经连续开展了22年。为了保证结果的科学性和有效性，教学评估系统经过多次升级，不断完善评价体系和方法。
从2019年5月开始，统计咨询中心接受清华大学教学质量评估中心的委托，对现有教学评估系统的计算方法和程序代码进行解析和优化，以提高评估系统的计算稳定性和计算效率。
解决方案：
咨询中心团队在深入分析研究当前算法、代码和评估结果的基础上，锁定了影响评估系统计算稳定性的关键因素，并基于统计学原理对原算法中部分不合理的模块进行了调整和重构。经实践验证，调整后的评估系统计算稳定性和计算效率均得到了大幅度提高，成功解决了长期困扰教学评估系统有效运转的关键问题。相关成果为清华教学评估工作提供了更为可靠的理论方法和计算框架，并为教学评估体系的进一步完善打下了坚实的基础。
日前，清华大学统计学研究中心邓婉璐、周在莹两位老师荣获“2019-2020学年度春季学期疫情防控期间在线教学优秀教师”称号。
我能有幸能得到这份肯定与鼓励，更多想说的是感谢。自年初疫情以来，其实我们作为一线教师一开始都比较茫然。要谢谢学校的果断决策，并迅速组织了各类支持小组，经过多次测试拟出了应对各种情形的方案，给了我们充分的培训，我们才能从容按时地开课。也特别谢谢系里和中心的全力而温暖的支持，无论是设备还是经验上都给我们提供了很多便利，我也经常在教学交流群中得到其他老师们的帮助。这些后盾让我有了应对可能的突发情况的底气。所以这份肯定应该属于我们整个集体，而我会带着这份鼓励继续前行。这学期又有了新的挑战，开启了融合课堂，相信我们一起努力，也可以顺利把课上好！
——邓婉璐
“Education is not the filling of a pail, but the lighting of a fire.”据说这是著名诗人William Butler Yeats的名言，它深深地影响了我。我热爱教学，每当学生反馈学有所得总令我无比欣慰。我也用心呵护学生，非常荣幸可以为他们的专业学习提供帮助，陪伴他们走过一小程人生。得这个奖实属侥幸，感谢信任我的各位同事、学生，感谢关心我的统计学研究中心和工业工程系的各位领导。借用前辈Howell Tong先生给我的留言，”In life, one needs first performance and then luck.” 与诸君共勉吧。当我们坚定信念、努力修炼，幸福总会来敲门。
——周在莹
自2020年3月开始，新冠肺炎国际疫情形势日益严峻，海外输入性风险给我国的疫情防控带来巨大挑战。海关总署卫生检疫司及时组织以清华大学统计学研究中心邓柯副教授和中国检验检疫科学研究院杨宇研究员为首的多学科专家团队，成立“境外新冠肺炎疫情流行趋势研判分析专家组”，为口岸实施针对性防控措施提供支持。
邓柯教授带领清华大学统计咨询中心团队，联合中国检科院杨宇研究员团队，在清华大学科技抗疫攻关“流行病学传播预测与对策突击队”宫鹏教授、徐冰教授团队的大力支持下，开展跨部门、多学科、多领域的风险研判分析工作。研究团队实时采集整理了世界各国新冠疫情发展、传播、防控方面的大量数据；综合运用多种统计学和流行病学方法建立新冠肺炎国际疫情风险评估和趋势预测模型，及时对全球各国的新冠疫情风险、未来发展趋势和对我国影响进行系统分析；定期撰写《境外新冠肺炎疫情流行趋势研究报告》30余期。
海关总署卫生检疫司于2020年8月11日为研究团队出具了《成果应用证明》，指出相关工作为监管部门及时掌握全球疫情动态和发展趋势，有针对性地指导全国口岸做好疫情防控工作，提供了关键技术支持；为实现科学精准的疫情防控做出了重要贡献。
清华大学统计学团队深受鼓舞，将以更加饱满的工作热情和更加严谨的科学态度，积极参与到关系国计民生的重大课题研究，运用数据科学技术保障人民健康。
