[/ˈkwɒntɪnɪər/]: A person that extracts insights and answers from data using quantitative engineering skills.
I am a pragmatic quantitive modeling expert. Currently I am modeling the COVID-19 pandemic in Denmark and researching new methods to describe and predict epidemiological outbreaks.
In addition to this, I have experience in computational neuroscience, modeling networks and neural signal processing, digital communication networks as well as financial derivatives and risk models.
My weapon of choice is R, but I occasionally work with c++, Python and SQL when called for.
I hold a MSc (2010) and PhD (2018) in Statistics from Aarhus University and the University of Copenhagen.
J Østergaard, A Rahbek, S Ditlevsen. Oscillating systems with cointegrated phase processes. Journal of mathematical biology 75 (4), 845-883 (2017)
J Østergaard, MA Kramer, UT Eden. Capturing spike variability in noisy Izhikevich neurons using point process generalized linear models. Neural computation 30 (1), 125-148 (2018)
J Stærk-Østergaard, RW Berg, UT Eden, S Ditlevsen. A state space model for characterizing bursting in neurons. In Review (2020)
J Stærk-Østergaard, A Rahbek, S Ditlevsen. Rank testing in high dimensional cointegrated systems and estimation under symmetry restrictions. To be submitted. (2021)
R Engelhardt, VF Hendricks, J Stærk-Østergaard. The Wisdom and Persuadability of Threads. arXiv preprint arXiv:2008.05203 (2020)
L Jahn, J Stærk-Østergaard J, RK Rendsvig. Bot Detecting Jury Selection Procedures to Counter Nefarious Vote Inflation on Social Media. Submitted (2021)
G Cavaliere, Y Lu, A Rahbek, Stærk-Østergaard J. Bootstrap inference for point process models, with applications to Hawkes processes in social media data. Working Paper. (2021)
Animal Welfare and Disease Control
Department of Veterinary and Animal Science
University of Copenhagen
contact (at) jacobostergaard.com