Philipp Thomann
Managing Consultant at D ONE | Data Driven Value Creation
Zürich Metropolitan Area Switzerland
Summary
👤
Senior🎓
Top SchoolPhilipp Thomann is a managing consultant in data science based in Zürich with nine years of professional experience and a PhD in mathematics followed by a postdoc in machine learning. He blends deep theoretical expertise in probability and SVM research with practical software engineering—having built fast SVM tooling (liquidSVM bindings) and enterprise data solutions at D ONE. His background spans academia and industry, from teaching statistics and deep learning seminars to implementing web and numerical-analysis frameworks in Java. Philipp is comfortable translating complex mathematical models into production-ready systems and advising on data-driven value creation. A not-so-obvious strength is his long history of hands-on teaching and tool-building, which makes him effective at communicating advanced concepts to both technical and non-technical stakeholders.
9 years of coding experience
15 years of employment as a software developer
Doctor of Philosophy (PhD), Mathematics, Doctor of Philosophy (PhD), Mathematics at Universität Zürich
English, French, tschechisch, German, chinesisch (vereinfacht)
Stackoverflow
5reputation
2kreached
0answers
2questions
Github Skills (36)
pearson10
principal-component-analysis10
forge9
large-data9
quantile9
least-squares9
roc9
logistic-regression9
classification8
regression8
statsmodels8
svm8
scenarios8
conda7
kernel7
Programming languages (3)
Github contributions (5)
thomann/plotAR
Mar 2017 - Nov 2022
Contributions:165 commits, 182 pushes, 10 branches in 5 years 9 months
liquidSVM/liquidSVM
Apr 2017 - Sep 2019
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
Contributions:1 release, 44 commits, 9 pushes in 2 years 4 months
kernelprincipal-component-analysisrocselectionvector
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.