Transforming Potential into Promise: A Depth-First Approach

将潜力转化为承诺:深度优先的方法

基本信息

  • 批准号:
    1433220
  • 负责人:
  • 金额:
    $ 30万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2014
  • 资助国家:
    美国
  • 起止时间:
    2014-06-15 至 2018-08-31
  • 项目状态:
    已结题

项目摘要

The "depth-first" educational approach allows students with almost no background in a particular subject to study advanced topics in that subject in a relatively short time. The PI has successfully used this approach to train undergraduate students in computer science at Rutgers University-Camden and inspired them to pursue graduate studies. The program in Theoretical Computer Science (TCS for short) provides an opportunity to translate the potential of promising students into measurable impact. The program introduces undergraduate and high school students to TCS in a depth-first manner. New students join the program each summer, with almost no background in computer science and are introduced to topics in discrete mathematics and algorithms. The meetings with the students continue during the academic year. Students who return to the program in subsequent years study advanced topics in theoretical computer science and participate in research. Students participating in this program gain robust exposure to the field of computer science and are ready to study advanced topics early in college, which will enable them to do substantive research before they graduate. The program has been quite successful in attracting and retaining female participants.This project will continue to test the success of depth-first approach in training students to conduct research in TCS. Various research topics in optimization and approximation algorithms will be explored by high school and undergraduate students. In the end, a more diverse pool of students contribute to the research progress in TCS.
“深度优先”的教育方法允许几乎没有特定学科背景的学生在相对较短的时间内学习该学科的高级课题。PI已经成功地使用这种方法来培训罗格斯大学卡姆登分校的计算机科学本科生,并激励他们攻读研究生课程。 理论计算机科学(简称TCS)课程提供了一个机会,将有前途的学生的潜力转化为可衡量的影响。该计划以深度优先的方式向本科生和高中生介绍TCS。每年夏天,新学生加入该计划,几乎没有计算机科学背景,并介绍了离散数学和算法的主题。与学生的会议继续在学年。 谁在随后几年返回到程序的学生学习理论计算机科学的高级主题,并参与研究。 参加该计划的学生获得了计算机科学领域的强大曝光,并准备在大学早期学习高级主题,这将使他们能够在毕业前进行实质性研究。该方案在吸引和留住女性参与者方面相当成功,该项目将继续测试深度优先方法在培训学生进行TCS研究方面的成功。 高中生和本科生将探索优化和近似算法的各种研究课题。 最后,一个更加多样化的学生池有助于TCS的研究进展。

项目成果

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Rajiv Gandhi其他文献

Deep learning–based clustering for endotyping and post-arthroplasty response classification using knee osteoarthritis multiomic data
基于深度学习的聚类,用于使用膝关节骨关节炎多组学数据进行内型分析和关节置换术后反应分类
  • DOI:
    10.1016/j.ard.2025.01.012
  • 发表时间:
    2025-05-01
  • 期刊:
  • 影响因子:
    20.600
  • 作者:
    Jason S. Rockel;Divya Sharma;Osvaldo Espin-Garcia;Katrina Hueniken;Amit Sandhu;Chiara Pastrello;Kala Sundararajan;Pratibha Potla;Noah Fine;Starlee S. Lively;Kim Perry;Nizar N. Mahomed;Khalid Syed;Igor Jurisica;Anthony V. Perruccio;Y. Raja Rampersaud;Rajiv Gandhi;Mohit Kapoor
  • 通讯作者:
    Mohit Kapoor
AODV based adaptive distributed hybrid multipath routing for mobile AdHoc network
基于AODV的移动AdHoc网络自适应分布式混合多路径路由
Factors Associated with Return to Work Following Work-Related Injuries to the Lower Extremities
下肢因工受伤后重返工作岗位的相关因素
  • DOI:
    10.29011/2688-6413.100008
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Andrea Veljkovic;Rajiv Gandhi;P. Salat;K. Abbas;Khalid A Syed;J. Lau
  • 通讯作者:
    J. Lau
243 - TOTAL KNEE ARTHROPLASTY VERSUS EDUCATION AND EXERCISE: COMPARING PATIENT OUTCOMES USING PROPENSITY-SCORE MATCHED DATA
  • DOI:
    10.1016/j.joca.2024.02.255
  • 发表时间:
    2024-04-01
  • 期刊:
  • 影响因子:
  • 作者:
    James J. Young;Michael G. Zywiel;Søren T. Skou;Vinod Chandran;J Rod Davey;Rajiv Gandhi;Nizar N. Mahomed;Khalid Syed;Christian J. Veillette;Y. Raja Rampersaud;Anthony V. Perruccio
  • 通讯作者:
    Anthony V. Perruccio
Painful and Painless Diabetic Neuropathies: What Is the Difference?
  • DOI:
    10.1007/s11892-019-1150-5
  • 发表时间:
    2019-05-07
  • 期刊:
  • 影响因子:
    6.400
  • 作者:
    Pallai Shillo;Gordon Sloan;Marni Greig;Leanne Hunt;Dinesh Selvarajah;Jackie Elliott;Rajiv Gandhi;Iain D. Wilkinson;Solomon Tesfaye
  • 通讯作者:
    Solomon Tesfaye

Rajiv Gandhi的其他文献

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{{ truncateString('Rajiv Gandhi', 18)}}的其他基金

AF:RUI:Small:Approximation Problems with Tree Outputs Under Parameterized Constraints
AF:RUI:Small:参数化约束下树输出的近似问题
  • 批准号:
    1910565
  • 财政年份:
    2019
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
U.S.-India International Collaborative Research and Training for Computer Science Students
美印计算机科学专业学生国际合作研究与培训
  • 批准号:
    1050968
  • 财政年份:
    2010
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
EAGER: Computer Science Research and Enrichment Program
EAGER:计算机科学研究和强化计划
  • 批准号:
    1048606
  • 财政年份:
    2010
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
RUI: Approximation Algorithms for Scheduling Problems
RUI:调度问题的近似算法
  • 批准号:
    0830569
  • 财政年份:
    2008
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant

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