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 █████╗ ██████╗  ██████╗ ██╗   ██╗████████╗
██╔══██╗██╔══██╗██╔═══██╗██║   ██║╚══██╔══╝
███████║██████╔╝██║   ██║██║   ██║   ██║   
██╔══██║██╔══██╗██║   ██║██║   ██║   ██║   
██║  ██║██████╔╝╚██████╔╝╚██████╔╝   ██║   
╚═╝  ╚═╝╚═════╝  ╚═════╝  ╚═════╝    ╚═╝

Short summary, resume, full C.V.

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███████╗████████╗██╗   ██╗██╗     ███████╗     ██████╗ ██╗   ██╗██╗██████╗ ███████╗
██╔════╝╚══██╔══╝╚██╗ ██╔╝██║     ██╔════╝    ██╔════╝ ██║   ██║██║██╔══██╗██╔════╝
███████╗   ██║    ╚████╔╝ ██║     █████╗      ██║  ███╗██║   ██║██║██║  ██║█████╗  
╚════██║   ██║     ╚██╔╝  ██║     ██╔══╝      ██║   ██║██║   ██║██║██║  ██║██╔══╝  
███████║   ██║      ██║   ███████╗███████╗    ╚██████╔╝╚██████╔╝██║██████╔╝███████╗
╚══════╝   ╚═╝      ╚═╝   ╚══════╝╚══════╝     ╚═════╝  ╚═════╝ ╚═╝╚═════╝ ╚══════╝

 

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████████╗██╗  ██╗███████╗      ████╗██╗   ██╗███╗   ██╗██╗ ██████╗ ██████╗ 
╚══██╔══╝██║  ██║██╔════╝      ╚██╔╝██║   ██║████╗  ██║██║██╔═══██╗██╔══██╗
   ██║   ███████║█████╗         ██║ ██║   ██║██╔███╔██║██║██║   ██║██████╔╝
   ██║   ██╔══██║██╔══╝    ██╗  ██║ ██║   ██║██║╚██╝██║██║██║   ██║██╔══██╗
   ██║   ██║  ██║███████╗  ██████╔╝ ╚██████╔╝██║ ╚████║██║╚██████╔╝██║  ██║
   ╚═╝   ╚═╝  ╚═╝╚══════╝  ╚═════╝   ╚═════╝ ╚═╝  ╚═══╝╚═╝ ╚═════╝ ╚═╝  ╚═╝                    
 ██████╗ ██╗   ██╗██╗██████╗ ███████╗  ████████╗ ██████╗ 
██╔════╝ ██║   ██║██║██╔══██╗██╔════╝  ╚══██╔══╝██╔═══██╗
██║  ███╗██║   ██║██║██║  ██║█████╗       ██║   ██║   ██║
██║   ██║██║   ██║██║██║  ██║██╔══╝       ██║   ██║   ██║
╚██████╔╝╚██████╔╝██║██████╔╝███████╗     ██║   ╚██████╔╝
 ╚═════╝  ╚═════╝ ╚═╝╚═════╝ ╚══════╝     ╚═╝    ╚═════╝ 
 ██████╗ ██████╗ ███╗   ███╗██████╗ ██╗███╗   ██╗ ████╗ ████████╗ ██████╗ ██████╗ ██╗ ████╗ ██╗     
██╔════╝██╔═══██╗████╗ ████║██╔══██╗██║████╗  ██║██╔═██╗╚══██╔══╝██╔═══██╗██╔══██╗██║██╔═██╗██║     
██║     ██║   ██║██╔████╔██║██████╔╝██║██╔███╔██║██████║   ██║   ██║   ██║██████╔╝██║██████║██║     
██║     ██║   ██║██║╚██╔╝██║██╔══██╗██║██║╚██╝██║██║ ██║   ██║   ██║   ██║██╔══██╗██║██║ ██║██║     
╚██████╗╚██████╔╝██║ ╚═╝ ██║██████╔╝██║██║ ╚████║██║ ██║   ██║   ╚██████╔╝██║  ██║██║██║ ██║███████╗
 ╚═════╝ ╚═════╝ ╚═╝     ╚═╝╚═════╝ ╚═╝╚═╝  ╚═══╝╚═╝ ╚═╝   ╚═╝    ╚═════╝ ╚═╝  ╚═╝╚═╝╚═╝ ╚═╝╚══════╝
 ██████╗██╗  ██╗███████╗███╗   ███╗██╗███████╗████████╗██████╗ ██╗   ██╗
██╔════╝██║  ██║██╔════╝████╗ ████║██║██╔════╝╚══██╔══╝██╔══██╗╚██╗ ██╔╝
██║     ███████║█████╗  ██╔████╔██║██║███████╗   ██║   ██████╔╝ ╚████╔╝ 
██║     ██╔══██║██╔══╝  ██║╚██╔╝██║██║╚════██║   ██║   ██╔══██╗  ╚██╔╝  
╚██████╗██║  ██║███████╗██║ ╚═╝ ██║██║███████║   ██║   ██║  ██║   ██║
 ╚═════╝╚═╝  ╚═╝╚══════╝╚═╝     ╚═╝╚═╝╚══════╝   ╚═╝   ╚═╝  ╚═╝   ╚═╝

 

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████████╗██╗  ██╗███████╗███████╗███████╗███████╗
╚══██╔══╝██║  ██║██╔════╝██╔════╝██╔════╝██╔════╝
   ██║   ███████║█████╗  ███████╗█████╗  ███████╗
   ██║   ██╔══██║██╔══╝  ╚════██║██╔══╝  ╚════██║
   ██║   ██║  ██║███████╗███████║███████╗███████║
   ╚═╝   ╚═╝  ╚═╝╚══════╝╚══════╝╚══════╝╚══════╝
KYLE LUCKE, MASTERS (COMPUTER SCIENCE, 2021)
Kyle's thesis upends the usual metrics used to evaluate mass spectrometry protein inference, moving to a superior ensemble approach. In the absence of strong protein-specific priors, it also uses symmetry to establish a space of measures for scoring peptide assignments. Using a moment-based format for the protein-peptide graph, this approach is used to motivate a convolutional neural network model for protein inference.

JAKE PENNINGTON, MASTERS (COMPUTER SCIENCE, 2021)
Jake's (Rooster's) thesis derives an optimal point on the continuum of orderedness for some algorithms, such as computing the most abundant isotopes in a chemical compound. It also includes a demonstration of a novel method for estimating big-theta runtimes when master theorem and Akra-Bazzi cannot be applied.

PATRICK KREITZBERG, MASTERS (COMPUTER SCIENCE, 2019)
Patrick's thesis is a phenomenal reference on using LP, QP, relaxations of those, and fast algorithms for de novo mass spectrometry inference.

JULIANUS PFEUFFER, (VISITING STUDENT, 2016)
This work from first author Julianus proposes a numeric approximation reminiscent of the fast multipole algorithm for approximating the semiring (\mathbb{R},\max,\times) [or (\mathbb{R},\max,+)] with rings on (\mathbb{R},+,\times) [or (\mathbb{R},\max,+)]. This is relevant to max-product inference, to graph problems such as APSP, and to softmax functions in neural networks.

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██████╗ ██╗██╗  ██╗███████╗███████╗███████╗██╗
██╔══██╗██║╚██╗██╔╝██╔════╝██╔════╝██╔════╝██║
██║  ██║██║ ╚███╔╝ ███████╗█████╗  █████╗  ██║
██║  ██║██║ ██╔██╗ ╚════██║██╔══╝  ██╔══╝  ╚═╝
██████╔╝██║██╔╝ ██╗███████║███████╗███████╗██╗
╚═════╝ ╚═╝╚═╝  ╚═╝╚══════╝╚══════╝╚══════╝╚═╝

 

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