<about />
Fiddling around with relativistic emission processes to describe gamma-ray bursts (GRBs) was my first passion. Now I apply the same rigorous statistical thinking to geospatial computer vision and machine learning.
Proper statistical inference isn't just about MCMC sampling or "Big Data." It's about proper model specification, correct implementation of statistical concepts, and quantifying uncertainty. Everything else follows.
Whether analyzing distant cosmic explosions or extracting property intelligence from satellite imagery, the principles remain the same: proper model specification, robust inference, and embracing... uncertainty.
<current />
Currently Assistant Director - Machine Learning Engineer at Moody's (Cape Analytics).
Developing geospatial computer vision systems to analyze property characteristics from aerial imagery. Extracting property intelligence for insurance, real estate, and risk assessment.
location = "Munich, Germany"
phone = "+49 173.604.6869"
focus = ["geospatial CV", "property intelligence", "ML infrastructure"]
data_sources = ["aerial imagery", "satellite data", "property records"]
mission = "bringing certainty to property data"
<journey />
Moody's (Cape Analytics)
Assistant Director - Machine Learning Engineer. Building ML infrastructure for property intelligence using geospatial computer vision and satellite imagery analysis.
hema.to (AI Cytometry)
Senior AI Research Scientist (Oct 2023 - Apr 2025). Munich, Germany.
Developed AI cytometry solutions for automated cell population classification and clinical flow cytometry analysis. Built ML-powered workflows for reproducible, secure, and fast cytometry analysis with privacy-first, GDPR-validated solutions.
Max-Planck-Institut für extraterrestrische Physik (MPE)
Humboldt Research Fellow (2017 - Oct 2023). Member of Dr. Jochen Greiner's research group studying GRB emission physics via data from optical to high-energy gamma-ray instruments. Coordinated group projects, educated students in analysis and software design. Implemented automated burst alert and localization system including coded mask imaging.
KTH Royal Institute of Technology
Oskar Klein Research Fellow (2014-2017). Investigated GRB physics, developed Bayesian analysis tools for Fermi data, and explored spectral evolution. Designed novel scheme to fit Type Ia SNe cosmology data. Assisted graduate students in thesis studies.
University of Alabama in Huntsville
Ph.D. in Physics (2011-2013). Discerning Physical Properties of Gamma-Ray Bursts via Time-Resolved Analysis with Physical Spectral Models. Also worked with Fermi GBM Team (2009-2013) on daily satellite operations, data monitoring, and GRB trigger distribution. Developed multinational collaborations on various research projects.
<tools />
Lead developer of 3ML - The Multi-Mission Maximum Likelihood framework for multi-mission astrophysics.
Other open-source projects include astromodels, popsynth, pynchrotron, cosmogrb, and various tools for GRB analysis and data simulation.
Now applying same rigorous statistical methods and inference techniques to geospatial computer vision and property intelligence at CAPE Analytics.
core_stack = ["Python", "TensorFlow", "Bayesian inference"]
domains = ["CV", "ML", "statistical modeling"]
<projects />
Open-source tools for GRB analysis, population synthesis, and Bayesian inference. Built on 3ML, Stan, and plain Python.
<honors />
Alexander von Humboldt Fellowship (2017)
Royal Swedish Academy of Sciences Research Scholarship (2015)
Oskar Klein Postdoctoral Fellowship (2014)
AAS Chamblis Award (2012)
<talks />
Recorded talks and presentations from conferences and workshops.
X-ray Spectral Fitting Workshop (2019)
Ioffe Workshop on GRBs (2019)
Recent invited talks: Spectroscopy of GRBs (Rome, 2021), 3ML Framework (Berkeley, 2021), Synchrotron (Nanjing, 2019), Nazgul GRB triangulation (Sardenga, 2021)
<publications />
Selected publications in high-energy astrophysics and astrostatistics.
For complete list, check my CV.
total_publications = "50+"
citations = "thousands"
career_stage = "astrophysics_to_ML"
★
BALROG: Bayesian Location Reconstruction of GRBs
Burgess, J.M. et al.
ApJ 2017
arXiv
★
Time-resolved GRB Polarization with POLAR and GBM
Burgess, J.M., Kole, M. et al.
A&A 2019
arXiv
★
The Peculiar Physics of GRB 170817A
Begue, D., Burgess, J.M., Greiner, J.
ApJL 2017
arXiv
★
Spectral Width of Gamma-Ray Bursts
Burgess, J.M.
ApJ 2017
arXiv
Automatic Detection of Long-duration Transients in Fermi-GBM
Kunzweiler, F., Biltzinger, B., Greiner, J., Burgess, J.M.
A&A 2022
arXiv
3ML: The Multi-Mission Maximum Likelihood Framework
Vianello, G., Burgess, J.M. et al.
ApJS 2015
arXiv
<astrostats />
Resources for proper statistical treatment in astrophysical data analysis. We're good at mathematics and modeling, but sometimes lack skills from the statistical literature.
Essential Papers
Useful Tools
BXA - Bayesian X-ray Analysis by Johannes Buchner
Minimal Statistics Checklist
<skills />
# Astrophysics meets Geospatial CV
core_competencies = [
"statistical inference",
"machine learning",
"computer vision",
"geospatial analytics",
"Bayesian methods",
"uncertainty quantification"
]
From GRBs to Properties
The same statistical rigor used to analyze gamma-ray bursts now powers property intelligence systems. Different data, same science.
<fun_fact />
I play bass. There was a time when I was in the same article as a band that won a Grammy. It was not my band. The last band I did anything serious with was Oto Benga - a great bunch of dudes trying to say something in a nowhere town.
🎸🎵