Summer Boot Camp

The Network Science group hosts a two-week boot camp right before the beginning of the academic year. Each year, our goal is to help participants increase their familiarity with various tools and concepts necessary for network science research.  We begin and end this crash-course with group discussions and panels addressing the value of these tools, and we provide opportunities for meet-and-greets with faculty who are leading the field.

We structure this boot camp as a series of hands-on classes and labs that introduce and refresh skills around programming, software, and data science. Topics include the following:

  • Computer Basics: from UNIX to Python scripting
  • Managing Large and Small Data Sets
  • Visualizing Data
  • Linear Algebra
  • Graph Algorithms
  • Dynamical Systems
  • Statistics
  • Machine Learning

We held our inaugural boot camp in 2014 with a group of 12 students, hailing mostly from Computer Science.  By 2015, participation grew by 400% and included students and researchers from a number of other departments, including Electrical Engineering, Mechanical Engineering, EEMB (Ecology, Evolution, and Marine Biology), Statistics, and more.  A great deal of this success is due to the leadership of Dr. Luca Foschini, who helped reshape the curriculum into a program that would better appeal to a broad audience.   

In 2016, we pared the group down to the incoming IGERT participants. We also added a project element to the boot camp, asking the students to collaborate with mentors on a short project related to network science. Students presented these projects on the last day of camp:

  • Modeling Disease Propagation over Networks
    Trainees: Isaac Mackey, Jacob Fisher, Taom Sakal
    Mentor: Shadi Mohagheghi

  • Is it What You Publish, or Who You Publish With?
    Trainees: Furkan Kocayusufoglu, Freddy Hopp, Xiaoming Duan
    Mentor: Yi Ding

  • Bikeshare Projects: A Bayesian Network Approach
    Trainees: Pedro Cisneros, Rachel Redberg, Devin Cornell
    Mentor: Minh Hoang

  • Ship Routing to Avoid Whale Strikes with Least Cost Routes
    Trainees: David Grimsman, Christina Awadalla
    Mentor: Ben Best

References:

Carl D. Meyer, Matrix Analysis and Applied Linear Algebra, SIAM, 2000.
Kermit Sigmon, Matlab Primer, 3rd Ed.
A paper on graph pattern mining by Prof. Xifeng Yan, UC Santa Barbara.
Material on unsupervised learning and community detection from Cornell University.
A paper on graph mining, from Chakrabarti & Faloutsos, Yahoo! and Carnegie Melon University
A paper on social networks, by Liben-Nowelly, M.I.T., and Kleinbergz, Cornell University