REU Site: Summer Undergraduate Research Experience (SURE) at Illinois Tech

伊利诺伊理工学院的夏季本科生研究体验（SURE）计划每年邀请10名本科生，3名教师导师和3名研究生参加为期10周的计划，以研究计算数学和机器学习的研究课题。SURE学生嵌入现有的，活跃的研究小组，他们获得技术以及沟通和团队合作技能。SURE通过针对女性和代表性不足的少数民族学生以及研究机会有限的学术机构的学生，共同努力使研究环境多样化。该计划旨在为学生准备在数学，统计学或相关领域的研究生学习，为学术界或工业界的研究生涯做准备。SURE计划促进STEM学科的高级培训。以学生为中心的学习研究单元提高了参与者的数学和统计知识，并培养了他们进行独立研究的能力。SURE学生探索现实世界的研究挑战，并学习提出问题，收集初步调查结果，根据需要修改他们的问题，并寻求答案。SURE学生学习有效的沟通，团队合作，专业精神和责任感。SURE从事广泛的计算数学和机器学习主题，包括i）构建和应用充满活力的变分推理算法，ii）为物理和生物系统中的模式形成模型开发代码，iii）评估模型，为金融中的环境，可持续性和政府风险因素打分，iv）确定新的抽样方法和蒙特卡洛模拟的停止标准。在夏季，SURE参与者每两周展示一次他们的工作，并从彼此以及他们的教师和研究生导师那里获得反馈。暑期项目以海报日结束，学生们将他们的研究成果展示给伊利诺伊理工学院社区。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

The Summer Undergraduate Research Experience (SURE) program at Illinois Tech engages ten undergraduates, three faculty mentors, and three graduate students for this ten week program for each of 3 years to study research topics in computational mathematics and machine learning. SURE students are embedded in existing, active research groups where they acquire both technical as well as communication and teamwork skills. SURE makes a concerted effort to diversify the research environment by targeting female and underrepresented minority students and students from academic institutions with limited research opportunities. The program is designed to prepare SURE students for graduate study in mathematics, statistics, or related fields in preparation for research careers in academia or industry. The SURE program promotes advanced training in STEM disciplines. The student-centered learning-research modules enhance participants’ knowledge of mathematics and statistics and develop their capabilities in conducting independent research. SURE students explore real-world research challenges and learn to pose questions, collect preliminary findings, modify their questions as necessary, and pursue answers. SURE students learn to be effective in communication, teamwork, professionalism, and responsibility. SURE engages in a wide range of computational mathematics and machine learning topics, including i) building and applying energetic variational inference algorithms, ii) developing code for pattern formation models in physical and biological systems, iii) assessing models for scoring environment, sustainability, and government risk factors in finance, and iv) identifying novel sampling methods and stopping criteria for Monte Carlo simulations. During the summer, SURE participants present their work bi-weekly and receive feedback from each other and their faculty and graduate student mentors. The summer program concludes with a poster day where students present their research to the Illinois Tech community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.