Gerrit Großmann

Dr. rer. nat., M. Sc., Postdoc at DFKI. Saarbrücken and Kaiserslautern. Germany.

Hello World! My name is Gerrit Großmann, welcome to my personal academic webpage! I work at the research department on Data Science and its Applications (DSA) (formerly Neuro-mechanistic Modeling) at the German Research Center for Artificial Intelligence (DFKI).

Research Interests:
🤖💡 My research revolves around the question: How can we integrate the distinct realms of discrete structures such as graphs and networks with the continuous nature of dynamic evolution, diffusion, and learning?

🎲🕸️ I am developing numerical methods to analyze stochastic dynamical processes on complex networks. This research aims to understand how networks shape collective phenomena like epidemics and rumors.

🧪🧠 Additionally, in collaboration with the NextAid project, my focus is on geometric deep learning for molecules. In this area, probabilistic flow models offer an innovative approach to integrating geometric deep learning with stochastic processes. My current projects include advancing neuro-symbolic guidance of diffusion models, implementing semi-supervised learning on metabolic networks, exploring the expressiveness of message-passing architectures, and the development of non-parametric methods for network reconstruction.

Overview:



Short CV

2006 - 2007
Attending Parkland Secondary School in British Columbia
2010 - 0000
High school degree in Lübeck at OzD
2010 - 2018
Bachelor's and Master's degree in computer science at Saarland University
2017 - 0000
Part-time deep learning engineer at iMAR Navigation GmbH
2012 - 2017
Part-time software development/data analysis at the Cognitive Psychology group
2019 - 2022
PhD (Dr. rer. nat) in computer science at the Modeling and Simulation group at Saarland University


Teaching


Tutorials, Talks, and Opinion Pieces


(Co-)Supervised Students


2024
Efficient Numerical Methods for Simulating Epidemic Spreading on Adaptive Networks
Désirée Wiltzius
Bachelor's thesis

2024
Simulation and Inference of Point Processes
Yehia Farghaly
Bachelor's thesis

2024
Computer-Aided Molecule Generation Using Optimal Transport and Variational Autoencoders
Magnus Cunow
Bachelor's thesis

2024
Small Molecule Generation and Optimization: A GNN-VAE Appraoch
Nesara Belakere Lingarajaiah
Master's thesis

2023
Learning the Function of Neural Networks in Deep Weight Spaces
Janine Lohse
Research Immersion Lab

2023
Network Reconstruction Using Deep Learning and Sensitivity Analysis
Joshgun Guliyev
Master's thesis

2022
Adapting the Approximate Master Equations for Realistic Epidemic and Network Dynamics
Yan Yan Lau
Bachelor's thesis

2021
Learning Dynamical Processes to Infer the Underlying Network Structure
Julian Zimmerlin
Bachelor's thesis

2020
Effects of Interventions on the COVID-19 Outbreak: A Network-based Approach
Lisa Heidmann
Bachelor's thesis

2020
Learning Vaccine Allocation Strategies to Control Epidemic Outbreaks on Networks
Jonas Klesen
Research Immersion Lab
You can find my How-to Thesis 📘 guide here.


Theses


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Stochastic Spreading on Complex Networks

PhD Dissertation Thesis

PDF     GitHub

2022
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Lumping the Approximate Master Equation for Stochastic Processes on Complex Networks

Master’s Thesis

Avaliable upon request or at Campus-Bibliothek.

2018
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Efficient Computation of Likelihoods in Large Markov Models

Bachelor’s Thesis

Avaliable upon request or at Campus-Bibliothek.

2015


Publications


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Peculiarities of Counterfactual Point Process Generation

G. Großmann, S. Mukherjee, S. Vollmer

Paper at STCausal, 2024

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2024
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GRIP: Physics-Informed Neural Network for Gradient Retention Time Prediction in Liquid Chromatography

K. George, F.P. J. Haeckl, G. Großmann, A. Gurevich, A. Tagirdzhanov

Preprint, 2024

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Enhancing GNNs with Architecture-Agnostic Graph Transformations: A Systematic Analysis

Z. Li, G. Großmann, V. Wolf

Paper at Complex Networks Conference, 2024

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icon: Fast Simulation of Epidemics on Coevolving Networks

Gerrit Großmann, Sebastian Vollmer

Extended Abstract at Complex Networks Conference, 2024

PDF     GitHub

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Discriminator-Driven Diffusion Mechanisms for Molecular Graph Generation

Gerrit Großmann

Extended Abstract at ML4Molecules ELLIS Workshop, 2023

PDF     GitHub

2023
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Elucidating the Relationship Between Transformers and GNNs

J. Groß, G. Großmann, V. Wolf

Preprint, 2023

PDF

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Deep Sets Are Viable Graph Learners

Gerrit Großmann

Extended Abstract at Complex Networks Conference, 2023

PDF     GitHub

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Network Reconstruction via Sensitivity Analysis

Gerrit Großmann

Extended Abstract at Complex Networks Conference, 2023

PDF     GitHub

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TeachOpenCADD goes Deep Learning: Open-source Teaching Platform Exploring Molecular DL Applications

M. Backenköhler, P. L. Kramer, J. Groß, G. Großmann, R. Joeres, A. Tagirdzhanov, D. Sydow, H. Ibrahim, F. Odje, V. Wolf, A. Volkamer

Preprint, 2023

PDF     GitHub

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Unsupervised Relational Inference Using Masked Reconstruction

G. Großmann, J. Zimmerlin, M. Backenköhler, V. Wolf

Applied Network Science, 2023

PDF     GitHub

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Birth-Death Processes Reproduce the Epidemic Footprint

G. Großmann, M. Backenköhler

Extended Abstract at Complex Networks Conference, 2022

PDF     GitHub     Publication

2022
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Abstraction-Guided Truncations for Stationary Distributions of Markov Population Models

M. Backenköhler, L. Bortolussi, G. Großmann, V. Wolf

Quantitative Evaluation of Systems Conference (QEST), 2021

PDF     Publication

2021
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Heterogeneity Matters: Contact Structure and Individual Variation Shape Epidemic Dynamics

G. Großmann, M. Backenköhler, V. Wolf

PLOS ONE, 2021

PDF     GitHub

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Epidemic Overdispersion Strengthens the Effectiveness of Mobility Restrictions

G. Großmann, M. Backenköhler, V. Wolf

Poster Abstract, 24th International Conference on Hybrid Systems: Computation and Control (HSCC), 2021

PDF     GitHub     Publication

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Analysis of Markov Jump Processes under Terminal Constraints

M. Backenköhler, L. Bortolussi, G. Großmann, V. Wolf

Tools and Algorithms for the Construction and Analysis of Systems Conference (TACAS), 2021

PDF     Publication

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Efficient Simulation of non-Markovian Dynamics on Complex Networks

G. Großmann, L. Bortolussi, V. Wolf

PLOS ONE, 2021

PDF     GitHub

2020
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Learning Vaccine Allocation from Simulations

G. Großmann, M. Backenköhler, J. Klesen, V. Wolf

The 9th International Conference on Complex Networks and their Applications, 2020

PDF     GitHub     Publication

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Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study

G. Großmann, M. Backenköhler, V. Wolf

Quantitative Evaluation of Systems Conference (QEST), 2020

PDF     GitHub     Publication

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Rejection-Based Simulation of Non-Markovian Agents on Complex Networks

G. Großmann, L. Bortolussi, V. Wolf

The 8th International Conference on Complex Networks and their Applications, 2019

PDF     GitHub     Publication

2019
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Rejection-Based Simulation of Non-Markovian Agents on Complex Networks

G. Großmann, V. Wolf

6th International Workshop on Hybrid Systems Biology (HSB), 2019

PDF     GitHub     Publication

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Lumping of Degree-Based Mean Field and Pair Approximation Equations for Multi-State Contact Processes

C. Kyriakopoulos, G. Großmann, V. Wolf, L. Bortolussi

PHYSICAL REVIEW E, 2019

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Reducing Spreading Processes on Networks to Markov Population Models

G. Großmann, L. Bortolussi

Quantitative Evaluation of Systems Conference (QEST), 2019

PDF     GitHub

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Lumping the Approximate Master Equation for Multistate Processes on Complex Networks

G. Großmann, C. Kyriakopoulos, L. Bortolussi, V. Wolf

Quantitative Evaluation of Systems Conference (QEST), 2019

PDF     GitHub