REU Site: Data Science of Risk and Human Activity

印第安纳州普渡大学印第安纳波利斯分校（IUPUI）的本科生研究经验（REU）计划将为来自美国各地的八名本科生提供对人类活动数据科学进行研究的机会。 学生将在夏季与IUPUI教师一起工作十周，研究与犯罪，冲突，政治不稳定和日常活动应用的预测建模和风险估计相关的项目。 学生们还将参加数据科学训练营，作为该计划的一部分，该计划将提供数据科学基础（统计，机器学习和软件开发）的培训。 数据科学是一个快速增长的领域，因为生成的数据数量和种类都在增加。 在数学，统计学，计算，数据分析和数据建模的交叉技能的毕业生在工业和学术界都有很高的需求。 REU网站将提高本科生的意识，准备和兴趣，在STEM领域攻读研究生学位，数据科学正在成为一个更大的焦点。 选择在毕业后从事工业职业的学生也将更好地准备迎接越来越多的公司的数据挑战，其中数据科学是一个高度优先事项。 REU项目解决了风险和人类活动数据科学的新挑战。 在问题领域1工作的学生将专注于学习对时空犯罪预测问题进行排名。 将开发根据风险对犯罪热点进行排名的机器学习模型，这些模型将针对犯罪学中出现的基于排名的损失函数进行定制。 第二个项目将侧重于对来自移动的传感器时间序列的活动类型进行分类。 学生们将比较现有技术与深度学习算法，该算法采用专门为来自加速度计和陀螺仪的3D数据开发的架构。 研究的第三个领域将集中在异质申诉数据的点过程模型。 特别是，学生将开发撒哈拉以南非洲地区冲突数据的时空模型，并结合同一时期的地理定位，相关推文。

This Research Experiences for Undergraduates (REU) program at Indiana University Purdue University Indianapolis (IUPUI) will provide eight undergraduate students from across the United States with the opportunity to conduct research on the data science of human activity. The students will spend ten weeks during the summer working with IUPUI faculty on projects related to predictive modeling and estimation of risk with applications to crime, conflict, political instability and daily routine activity. The students will also attend a data science bootcamp as part of the program that will provide training in the foundations of data science (statistics, machine learning, and software development). Data science is a rapidly growing field due to increases in the volume and variety of data being generated. Graduates with skill sets at the intersection of mathematics, statistics, computing, data analysis, and data modeling are in high demand, both in industry and academia. The REU site will increase undergraduate student awareness, preparation, and interest in pursuing graduate degrees in STEM fields where data science is becoming a larger focus. Students choosing to pursue a career in industry upon graduation will also be better prepared to meet the data challenges of the increasing number of companies where data science is a high priority. The REU projects address new challenges in the data science of risk and human activity. Students working in problem area 1 will focus on learning to rank problems for space-time crime prediction. Machine learning models for ranking crime hotspots according to risk will be developed that will be tailored to ranking based loss functions that arise in criminology. The second project will focus on classifying types of activity from mobile sensor time series. The students will compare existing techniques to deep learning algorithms employing architectures developed specifically for 3d data from the accelerometer and gyroscope. The third area of research will focus on point process models of heterogeneous grievance data. In particular, students will develop spatio-temporal models for conflict data in Sub-Saharan Africa coupled with geolocated, relevant Tweets from the same time period.