COLORFUL ENGINEERING LABORATORY:
STATISTICAL ALGORITHMS FOR COMPUTATIONAL BIOLOGY
The laboratory specializes in the development of algorithms and statistical methods for analyzing big data from computational biology and mass spectrometry.
Key contributions and research interests:
Fast algorithms for operating on discrete distributions (the probabilistic convolution tree, ref: Serang 2014, PLOS ONE and fast numerical max-convolution, refs: Serang 2015, Journal of Computational Biology; Pfeuffer and Serang 2016, Journal of Machine Learning Research).
The fast p-norm rings approximation for solving generic combinatorics problems (for solving the all-pairs shortest paths problem, sorting x_i+y_j, and more, ref: Serang, in review 2015).
Dynamic programming for de novo analysis of glycoconjugates and small molecules (SweetSEQer, ref: Serang*, Froehlich* et al. Molecular & Cellular Proteomics 2013).
Population-level inference of polyploid genotypes (SuperMASSA, ref: Serang et al. 2012, PLOS ONE).
Nonparametric machine learning for evaluating discoveries using only empirical null samples (npCI, refs: Serang et al. 2012, Molecular & Cellular Proteomics; Serang et al. 2013, Journal of Proteome Research).
OPEN LECTURES ON THE ADVENTURE OF SCIENTIFIC COMPUTING
SCICOMP is a graduate course in scientific computing created and taught at Universität Bremen in 2014 and continued at Freie Universität Berlin in 2015 and 2016, which covers topics in computer science and statistics with applications from biology. The course is designed top-down, starting with a problem and then deriving a variety of solutions from scratch.
Topics include memoization, recurrence closed forms, string matching (sorting, hash tables, radix tries, and suffix tries), dynamic programming (e.g. Smith-Waterman and Needleman-Wunsch), Bayesian statistics (e.g. the envelope paradox), graphical models (HMMs, Viterbi, junction tree, belief propagation), FFT, and the probabilistic convolution tree.
ART ⋂ SCIENCE:
VISUAL ART AND FIKSCHöN
The ecstatic debut novel Stay Close, Little Ghost is an ambitious work of literature and mathematics, which connects mathematical optimization with sexual and social dynamics, and then thrusts these forces against the universal desire to be loved as we are. What emerges is an intimate and defiant treatise on the nature of love and human imperfection.
Critics have praised it as ``reminiscent of Murakami's finest moments'' (-The Next Best Book Blog), ``reminiscent of Camus' The Stranger or even Dostoyevsky's Crime and Punishment'' (-The Alternating Current Press), ``a love story of this generation'' (-The Lit Pub), and have written that ``the book pulses with the vibrant imagination of this unique outlook without ever becoming precious or pretentious. It's like a literary form of synaesthesia'' (-Top 500 reviewer on Amazon).