SIMOC Agent Based Model

SIMOC – an isolated, off-world human community, ASU SESE, 2017-current
SIMOC [see-mok] is a scalable, interactive model of an off-world community. The model is given foundation on published data derived from Environmental Control and Life Support Systems (ECLSS) and closed ecosystem research at NASA and universities world-wide. The goal is to design a habitat that sustains human life with a combination of physico-chemical ECLSS and bioregenerative (living plant) systems, and is scalable to accommodate a growing community.

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Machine Learning at LIGO:

  • Supernovae Candidate Detection, 2018-current
    With Dr. Marco Cavaglia, Dr. Sergio Gaudio, Dr. Marek Szczepanczyk, and Travis Hansen. Kai assists with the application of Genetic Programming (Karoo GP) to waveform analysis in single and dual detector cases, in search of supernova. Publication: Currently in review at LIGO.
     
  • Machine Learning Applied to Glitch Detection and Mechanical Couplings, 2017-2018
    With Dr. Marco Cavaglia, University of Mississippi, Kai guided the application of Genetic Programming (Karoo GP) to glitch classification and the follow-on discovery of mechanical couplings at LIGO. Funding provided (Award 1707668) by the National Science Foundation. Publication: Finding the origin of noise transients in LIGO data with machine learning
     
  • TensorFlow Enabled Genetic Programming, a comparative study, 2017
    A performance study of Genetic Programming (Karoo GP) applied to four, varied datasets, on both CPU and GPU computational architectures, using standard Python and TensorFlow computational libraries. Publication: TensorFlow Enabled Genetic Programming

 

Evolutionary Computation Applied to Neutrino Detection, OSU, 2016-current
In August 2016, at the Ohio State University, CCAPP, Columbus, Ohio, Kai was co-organiser and presenter at the first-ever Computing in High-Energy AstroParticle Research (CHEAPR) workshop in which Karoo GP (more below) was featured as a tool for improved understanding of complex data, as Evolutionary Computation applied to Astro-particle Physics.

Out of this workshop came the application of genetic algorithms to the evolutionary design of improved antennae for Antarctic neutrino detectors. With Dr. Amy Connolly and Dr. Carl Pfendner, The Ohio State University, Dr. Stephanie Ann Wissel, Calpoly, and a host of undergraduate and graduate students at both institutions, Kai provides adjunct guidance and review for this collaborative effort.

Publications: anticipated in 2020

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