Projects and Mentors

What types of projects will I be working on?

Projects range from exploring the application of existing methodologies in novel application areas to developing or testing new methodologies designed to address challenges unique to the data sets at hand. In many cases, in addition to developing algorithms that produce state-of-the-art results, scaling algorithms to operate on extremely large data sets generated by modern sensors and computational facilities must also be considered. In almost all cases there will be a strong interdisciplinary focus madepossible by the unique collection of leading scientists and engineers working on problems of national importance. See Research and Applications page for more details of the projects in the carousel above. 

A few examples of previous summer projects include:

  • Comparing networks using hyperbolic space embeddings (Nguyen, et al., 2017) 
  • Building networks from interaction data (Cadena, et al., 2017)
  • Linac Coherent Light Source data triaging
  • Large scale data mining for predictive medicine
  • Functional data analysis for uncertainty quantification
  • Expert finding in social media corpora
  • Drug discovery using HPC simulations
  • Spark machine learning tools
  • Video data summarization and classification
  • Energy efficiency analysis using HPC
  • Dynamic network structure inference
  • Automated video tracking algorithms
  • Deep learning applications
  • Classification and forward modeling of hyper-spectral data

Who will be my mentor and how will we work together?

Mentors are LLNL Staff Scientists. They will have put forth a research project for the DSSI program. A mentor has the responsibility to define a project that will be significant yet manageable in the course of two to three months; some of these projects are designed to lead to follow on work for students that would like to return in subsequent years. In a typical working arrangement a student may expect to see their mentor on a basis ranging from daily to a couple of times per week depending upon the research plan. However, as a student you should never hesitate to get in touch with your mentor if you have a question as your mentor would prefer to help you solve a difficult technical problem instead of watch you struggle.