Mark Wilber

Background

About Mark Wilber

Data Science: • feature engineering • algorithm development • deep dives/root cause analysis Skills: • Machine Learning: gradient boosted trees, random forest, logistic regression, SVM, K-means, PCA, collaborative filtering, matrix factorization, isolation forest, NLP. • Software: Python (numpy, pandas, scipy, scikit-learn, nltk); Scope/Cosmos big data platform; MATLAB/Octave, Interactive Data Language (IDL); scripting in bash, Perl, csh; VBA, SQL; administration of linux-based work-stations; Keras-TensorFlow; familiarity with AWS, Caffe, C#, R, Spark, Hadoop, Ruby, MapReduce, Scala. • Collaboration: worked cooperatively with colleagues on most of research projects. • Communication: able to convey complex ideas to audiences spanning wide ranges of expertise: presented high-level results from research at many international conferences; also, as course instructor and teacher, conveyed principles of physics at basic levels to college and high school students. • Supervision/Mentoring: supervised undergraduate students, provided guidance for post-docs and junior research staff • Work Review: conducted peer review for journals and granting agencies; rigorously assessed claims made, and identified important, unstated issues Research Experience: • 15 years data analysis of spacecraft plasma and field measurements from several NASA and ESA missions • Scientific Modeling: developed computer simulations (particle-in-cell and magnetohydrodynamics codes) to test physical models against data. • Writing: authored/co-authored 49 articles published in international, peer-reviewed journals, as well as several successful grant proposals and grant extensions. Teaching Experience: • As graduate student, taught undergraduate laboratories, and problem review sections • Taught 10th grade physics for two years