Research

Justin N. Kreikemeyer

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I am currently a PhD student and research associate with the Modeling and Simuation Group at the University of Rostock, Germany. I have a passion for research, especially regarding the shared boundary of simulation, statistical methods, and machine learning. My current interests are:

Automatic generation (“learning”) of mechanistic simulation models
Simulation optimization and calibration, especially by gradient-descent
Differentiable Programming
Applications of statistical (and) machine-learning methods in simulation

The code, data and other artifacts associated with my publications can usually be found on GitHub or Zenodo and are also linked below.

Chronological Publication List

2026

Advances in the Inference of Chemical Reaction Networks from Time Series Data: A Systematic Survey.
Justin N. Kreikemeyer and Adelinde M. Uhrmacher. 2026. Preprint (03 2026).
preprint | pdf| bib

2025

Self-Adaptive Simulation Models: A Case Study in Cell Biology.
Pia Wilsdorf, Philipp Henning, Justin N. Kreikemeyer, Marcel Kliefoth, Simone Baltrusch, Adelinde M. Uhrmacher. In 29th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), September 17-19, Prague, Czech Republic. IEEE, 1-8.
conference paper | doi| bib| artifacts

Combining Natural Language and Time Series to Infer Reaction Networks.
Justin N. Kreikemeyer, Adelinde M. Uhrmacher. In 23rd International Conference on Computational Methods in Systems Biology (CMSB 2025), 10-12, Lyon, France. Eprint, not in proceedings.
conference poster | pdf| bib

Using (Not-so) Large Language Models to Generate Simulation Models in a Formal DSL: A Study on Reaction Networks.
Justin N. Kreikemeyer, Miłosz Jankowski, Pia Wilsdorf, Adelinde M. Uhrmacher. 2025. ACM Transactions on Modeling and Computer Simulation 35, 4 (September 2025), 1-27.
journal article | pdf | doi| bib| artifacts| github

Synopsis: Using (Not-so) Large Language Models to Generate Simulation Models in a Formal DSL: A Study on Reaction Networks.
Justin N. Kreikemeyer, Miłosz Jankowski, Pia Wilsdorf, Adelinde M. Uhrmacher. In SIGSIM-PADS '25: 39th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, June 23-26, Santa Fe, NM, USA. Association for Computing Machinery, 56-57.
extended abstract | pdf | doi| bib

Learning surrogate equations for the analysis of an agent-based cancer model.
Kevin Burrage, Pamela M. Burrage, Justin N. Kreikemeyer, Adelinde M. Uhrmacher, Hasitha N. Weerasinghe. 2025. Frontiers in Applied Mathematics and Statistics 11 (May 2025).
journal article | pdf | doi| bib| artifacts| github

2024

Challenges and promises of self-adaptive simulation models.
Adelinde M. Uhrmacher, Pia Wilsdorf, and Justin N. Kreikemeyer. 2024. SIMULATION 100, 12 (December 2024), 1281-1295.
journal article | doi| bib

Discovering Biochemical Reaction Models by Evolving Libraries.
[Received Best Paper Award]
J. N. Kreikemeyer, K. Burrage, A. M. Uhrmacher. In 22nd Conference on Computational Methods in Systems Biology (CMSB '24). Lecture Notes in Computer Science, vol. 14971, September 17 - 19, Pisa, Italy. Springer, Cham, 117-136.
conference paper | pdf | doi| bib| artifacts| github

Learning Reaction Networks by Gradient Descent.
Justin N. Kreikemeyer, Philipp Andelfinger, Adelinde M. Uhrmacher. In 22nd International Conference on Computational Methods in System Biology (CMSB 2024), 16-18, Pisa, Italy. Eprint, not in proceedings.
conference poster | pdf| bib

Automatic Gradient Estimation for Calibrating Crowd Models with Discrete Decision Making.
Philipp Andelfinger, Justin N. Kreikemeyer. In Computational Science – ICCS 2024. Lecture Notes in Computer Science, vol. 14836, July 2 - 4, Malaga, Spain. Springer, Cham, 227-241.
conference paper | pdf | doi| bib| github

Towards Learning Stochastic Population Models by Gradient Descent.
Justin N. Kreikemeyer, Philipp Andelfinger, Adelinde M. Uhrmacher. In Proceedings of the 38th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS '24), June 24-26, Atlanta, GA, USA. Association for Computing Machinery, New York, NY, USA, 88-92.
conference paper | pdf | doi| bib

2023

Smoothing Methods for Automatic Differentiation Across Conditional Branches.
Justin N. Kreikemeyer, Philipp Andelfinger. 2023. IEEE Access 11, 143190-143211.
journal article | pdf | doi| bib| artifacts| github

Tensor-Based Smooth Execution of Stochastic Agent-Based Simulations.
[Received INFO.RO award for best master thesis 2022/2023]
Justin N. Kreikemeyer. Master's thesis.
thesis | bib

2021

Inferring Dependency Graphs for Agent-Based Models Using Aspect-Oriented Programming.
Justin N. Kreikemeyer, Till Köster, Adelinde M. Uhrmacher, Tom Warnke. In 2021 Winter Simulation Conference (WSC), December 12-15, Phoenix, AZ, USA. IEEE, 1-12.
conference paper | doi| bib| github

Inferring Dependency Graphs for Agent-Based Models using Aspect-Oriented Programming.
[Received INFO.RO award for best bachelor thesis 2020/2021]
Justin N. Kreikemeyer. Bachelor's thesis.
thesis | bib| github

2020

Probing the Performance of the Edinburgh Bike Sharing System using SSTL.
Justin N. Kreikemeyer, Jane Hillston, Adelinde Uhrmacher. In Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (SIGSIM PADS '20), June 15-17, Miami, FL, USA. ACM, New York, NY, USA, 141-152.
conference paper | pdf | doi| bib| artifacts| github