项目内容涉及统计数据科学的沿革及其在公共卫生和生物医疗领域的应用，探索性分析、线性回归分析、时间序列回归分析，模型比较、评估与诊断等。学生将在项目中围绕空气污染与健康或COVID相关公共卫生或生物医学问题展开探究，使用Excel和R对样本公共卫生或生物医学数据集进行分析，在项目结束时，提交项目报告，进行成果展示。 个性化研究课题参考： 传染病预测预警 生物统计模型在PAHs致人群健康损害危险度评价中的应用研究 生物统计学在降血糖新药疗效评估中的应用 This project involves the evolution of statistical data science and its application in the fields of public health and biomedicine, exploratory analysis, linear regression analysis, time series regression analysis, model comparison, evaluation, and diagnosis. In this project, students will study with the public health problem related to air pollution or biomedical issues related to COVID, use Excel and R to analyze sample public health or biomedical data, submit their research report at the end of the project to show their results. Suggested Future Research Fields: Infectious disease prediction and warning Research on the application of biostatistics model in the evaluation of the risk of population health damage caused by PAHs Application of Biostatistics in Evaluating the Efficacy of New Drugs for Lowering Blood Sugar
R. Todd导师现任哥伦比亚大学终身正教授兼生物统计院副院长。R. Todd 导师的研究领域为生物统计方法论及其在各种领域的应用，被任命为神经领域的顶级科学家。 他目前正在与纽约州精神病学研究所的研究人员合作，研究各种统计建模问题，分析大脑成像研究的数据。 其他持续的兴趣包括功能数据分析、非参数回归、小波方法、统计建模、统计计算和统计教育。 担任国际顶级学术期刊Biometrics和 International Statistical Review的副主编。是国际统计研究所、美国统计协会成员，格伦达加维教学学院研究员。 Prof. R. Todd is a tenured full professor and associate dean of the Institute of Biostatistics at Columbia University. Prof. R. Todd's research field is biostatistics methodology and its applications in various fields, and he has been appointed as the top scientist in the neurological field. He is currently working with researchers at the New York State Institute of Psychiatry to study various statistical modeling issues and analyze data from brain imaging studies. Other continuing interests include functional data analysis, nonparametric regression, wavelet methods, statistical modeling, statistical computing, and statistical education. Served as the associate editor of the top international academic journals Biometrics and International Statistical Review. He is a member of the International Institute of Statistics, the American Statistical Association, and a researcher at Glenda Garvey Teaching College.
统计数据科学沿革：统计数据科学在公共卫生和生物医疗领域的应用 Background and development of statistical data science; applications in public and biomedicine.
探索性分析：为什么（分析目标）、是什么（分析任务列表）和怎么办（Excel和R统计分析） Exploratory analysis: why (the objectives of such analysis), what (the list of analysis tasks), and how (analysis using excel and R).
线性回归分析：为什么（目标）、是什么（统计概念、数学公式）和怎么办（估计和推断） Linear regression analysis: why (the objectives), what (statistical concept, mathematical formulation), and how (estimation and inference).
时间序列回归分析：为什么（目标）、是什么（统计概念、数学公式）和怎么办（估计和推断） Time series regression analysis: why (the objectives), what (statistical concept, mathematical formulation), and how (estimation and inference).
模型比较、评估与诊断 Model comparison, evaluation, and diagnostics.
项目回顾与成果展示 Program Review and Presentation
论文辅导 Project Deliverables Tutoring
工程
生命科学专题：癌症发生相关蛋白质研究与抗癌药物研制【大学组】
吴老师 上海交通大学生命科学技术学院 教授 博导 Memorial Sloan Kettering癌症中心/Howard Hughes医学研究所 博士后 哈佛大学医学院 博士后 普林斯顿大学 化学系 博士 上海生物物理学会理事 科技部国家重点研发计划合成生物学重点专项课题组长 研究方向： 物理化学合成生物学、冷冻电镜结构生物学 独立建立了上海交通大学第一个结构生物学实验室。在Science、Nature（2篇）、Molecular Cell（3篇）、PNAS、Nature Microbiology、Nature Communications（4篇）、Nature Chemical Biology、Cell Research（2篇）、Nucleic Acids Research（3篇）、Cell Discovery（3篇）、mBio（3篇）、Journal of Molecular Biology、Journal of Molecular Cell Biology、Molecular Microbiology（4篇）等发表论文，被引用2600余次。发表的APC-Asef复合物结构论文被英国皇家学会院士Mariann Bienz选入生物医药类论文权威数据库Faculty of 1000（Faculty Opinions数据库的前身），并在此基础上与同事合作开发抑制结肠癌细胞迁移的化合物（Nature Chemical Biology, 2017）。发表的TSCC复合物冷冻电镜结构论文被权威数据库Faculty Opinions录入。发表的Smac-XIAP复合物晶体结构被Novartis、Genentech等公司作为开发抗癌药物的基础。
人工智能
计算机网络与信息安全专题：机器学习在优化域名解析与分配、丢包及垃圾邮件等网络异常检测中的应用
Nick 导师现任芝加哥大学计算机科学讲席终身正教授，同时担任芝加哥大学数据与计算中心主任与网络运营与安全实验室主任，曾在普林斯顿大学担任计算机科学终身教授、信息技术政策中心副主任，也曾在佐治亚理工学院担任计算机学院教授。他的研究侧重于网络安全和性能。Nick 导师拥有麻省理工学院计算机科学博士学位、电气工程和计算机科学的硕士和学士学位。 Nick 导师是美国计算机协会院士（ACM Fellow），该协会为计算机科学领域大约1%的顶尖专业人士保留。 导师曾荣获NSF “美国青年科学家与工程师总统奖”（PECASE，美国青年科学家和工程师的最高荣誉）和“IBM优秀教师奖“，被《麻省理工学院技术评论》评为35岁以下最佳创新者之一。 Dr.Nick is Neubauer Professor of Computer Science and Director of the Center for Data and Computing at the University of Chicago. He also directs the Network Operations and Internet Security research lab, where we develop data-driven tools and systems to improve Internet security, performance, and adoption. He designs and deploys network protocols and systems that make the Internet work better. Prof.Nick uses empirical network measurement and machine learning to understand and improve network performance, security, and privacy. The results of his research often have policy implications. I regularly work with federal and municipal organizations, including the Federal Communications Commission (FCC) and the City of Chicago on equitable Internet access, security, and privacy.
项目内容包括无监督学习和监督学习等机器学习入门概念，以及如何将以上概念应用于网络的实践活动。学生将完成导师布置的课堂作业，以及将机器学习应用于网络的项目，在项目结束时，提交项目报告，进行成果展示。 This course will provide students with a background in machine learning concepts, as well as how they apply to concepts in computer networking, ranging from network performance to network security. The course will include a primer in a range of machine learning concepts in both unsupervised and supervised learning, as well as hands-on activities that apply these machine learning concepts to network applications and data. Students will complete several hands-on lab assignments as well as a course project that involves the application of machine learning to networking. 个性化研究课题参考 Suggested Research Field： 利用机器学习算法进行网络安全风险预测中的收敛性控制 Machine learning-based convergence control of network security risk prediction Android恶意软件检测方法性能比较 Review of performance comparison of Android malware detection methods 基于朴素贝叶斯分类器的恶意网站自动分类 An automatic classifier of Malicious Websites based on Naive Bayes
工程
工程项目管理专题：建筑工程项目安全风险控制和管理决策分析【大学组】
Paolo导师现任伊利诺伊大学香槟分校（UIUC）土木与环境工程学院讲席终身正教授、杰出研究学者；社会风险和危害缓解项目联合主任，工程风险研究&控制中心主任、美国三大建筑工程研究中心MAE中心主任；美国土木工程学会(ASCE)的工程力学学会(EMI)、国际土木工程风险与可靠性协会(CERRA)的成员；国际期刊《可靠性工程与系统安全》的主编，国际期刊《可持续与弹性基础设施》的创始人。Professor Paolo is a tenured full professor and distinguished research scholar in the School of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign. Co-director of the Social Risk and Hazard Mitigation Program, Director of the Center for Engineering Risk Research & Control, and Director of the MAE Center, one of the three major architectural engineering research centers in the United States; Member of the Engineering Mechanics Society (EMI) of the American Society for Civil Engineering (ASCE) and the International Society for Risk and Reliability in Civil Engineering (CERRA); Editor in chief of the international journal Reliability Engineering and Systems Safety and founder of the international journal Sustainable and Resilient Infrastructure.
Paolo导师科研生涯成果颇丰：曾获得美国土木工程师学会颁发的“土木基础设施风险分析和管理Alfredo Ang奖”，共发表200多篇参考期刊论文、并发表了超过60次全体会议和主题演讲。Paolo has achieved a lot in his scientific research career: he was awarded the Alfredo Ang Award for Risk Analysis and Management of Civil Infrastructure by the American Society of Civil Engineers, published more than 200 refereed journal papers, and delivered more than 60 plenary meetings and keynote speeches.
Paolo导师还曾担任英国拉夫堡大学、武汉江汉大学任职教授。目前他的研究领域为建筑可靠性、建筑工程风险和建筑生命周期分析、智能建造与土木建筑机器学习工程不确定因素、多危害分析建筑风险评估等。Paolo has also served as a professor in Loughborough University and Wuhan Jianghan University. His current research interests include building reliability, building engineering risk and building life cycle analysis, intelligent construction and civil construction machine learning engineering uncertainties, multi-hazard analysis and building risk assessment.
本项目关于建筑工程与人工智能的交叉领域，我们将从高层建筑的结构系统入手，探究高层结构的安全与设计原理，并着重分析房屋结构的设计流程、设计时应考虑的安全因素、环保的设计理念。随后，导师将带领学生一起分析建筑中的结构系统和荷载问题，最后，导师将带领我们探究人工智能在建筑安全风险方面的应用，如何将安全、环保的理念融入房屋设计。通过本项目的学习，学生将进一步了解和掌握大楼结构的设计原理，并实现对现实生活中房屋建筑的设计分析，在项目结束时提交报告，进行成果展示。个性化研究课题参考：智能建造在土木工程施工中的应用综述、机器学习LGBM回归算法预测混凝土强度、机器学习的结构缺陷检测研究
This project is about the intersection of architectural engineering and artificial intelligence, we will start from the structural system of high-rise buildings, explore the safety and design principles of high-rise structures, and focus on the analysis of the design process of housing structure, the design should consider safety factors, environmental protection design concept. Then, the tutor will lead the students to analyze the structural system and load problems in the building. Finally, the tutor will lead us to explore the application of artificial intelligence in the building safety risk and how to integrate the concept of safety and environmental protection into the house design. Through the study of this project, students will further understand and master the design principle of building structure, and realize the design analysis of real life housing construction. At the end of the project, the report will be submitted and the results will be displayed. Personalized research topic reference: Overview of the application of intelligent construction in civil engineering construction, prediction of concrete strength by LGBM regression algorithm based on machine learning, structural defect detection based on machine learning
