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Creators/Authors contains: "Dick, T."

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  1. ; ; ; (, ASEE PEER)
    The Angelo State Engineering Scholars (ASES) program is a track 2 S-STEM project that aims to increase the enrollment, graduation, and workforce participation of low-income engineering students. This presentation highlights the lessons learned from recruiting, mentoring, personal development, and career seminars in the program's first year. This presentation discusses issues encountered in the start-up year of this S-STEM program including, a late start date, problems with the use of Pell grant eligibility as a measure of low-income status, and the use of student essays in selection of scholarship recipients. Challenges in each of these areas are discussed and mitigations or changes made are presented. The presentation will be beneficial to similar programs in planning their recruitment efforts with a focus on retention and addressing the challenges associated with implementing an S-STEM program in the first year. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; (, STOC Annual Conference)
    null (Ed.)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; (, STOC annual conference)
    Full Text Available 
  4. ; ; ; ; ; (, STOC 2021: Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing)
    Algorithms often have tunable parameters that impact performance metrics such as runtime and solution quality. For many algorithms used in practice, no parameter settings admit meaningful worst-case bounds, so the parameters are made available for the user to tune. Alternatively, parameters may be tuned implicitly within the proof of a worst-case guarantee. Worst-case instances, however, may be rare or nonexistent in practice. A growing body of research has demonstrated that data-driven algorithm design can lead to significant improvements in performance. This approach uses a training set of problem instances sampled from an unknown, application-specific distribution and returns a parameter setting with strong average performance on the training set. 
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    Accepted Manuscript Full Text Available
  5. ; ; ; (, NeurIPS'19)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; (, not applicable - unpublished manuscript)
    Accepted Manuscript Full Text Available