Thesis topics

Master’s and Bachelor’s Thesis Projects

Please contact the co-supervisor if you are interested in a thesis.


Metabolic Network Reconstruction (and Artificial Data Generation)

Motivation:

Inferring and manipulating the metabolic network of single-cell organisms holds excellent promise for synthesizing novel chemical substances. Together with the Fraunhofer-Institut für Grenzflächen- und Bioverfahrenstechnik IGB we want to contribute to methodologies of inferring the metabolic network (and proposing possible interventions) of organisms in a bioreactor. As a first step, we want to develop and solve toy problems close to real-world data.

Challenges: formalization of the problem setting together with domain experts, proposing an algorithm, efficient implementation

Prerequisites: ideally, some background in probability theory and combinatorial optimization, potentially deep learning

Co-Supervisor: Gerrit Großmann (with support of Jonathan Fabarius)

Related Work: Unsupervised Relational Inference Using Masked Reconstruction and Genome-scale reconstruction and system level investigation of the metabolic network of Methylobacterium extorquensAM1


Diffusion Modes for Graph and Molecule Generation

Motivation: Diffusion models have shown great promise in the generation of discrete structures like graphs and molecules, but many problems still remain (see our seminar’s paper list for more information.) We offer several topics in this area, for instance:

  • latent space (or multi-resolution) graph generation
  • exploring novel types of forward (and the corresponding backward) processes and formalisms (e.g., based on CTMCs)
  • investigating different guiding algorithms for the forward and backward process (e.g., discriminative or rejection-based methods)

Challenges: derivation of the corresponding equations; efficient implementation

Prerequisites: background in probability theory and deep learning, PyTorch

Co-Supervisor: Gerrit Großmann

Related Work: See Deep Generative Diffusion Models Seminar website.


Complex System Interaction Inference through Deep Symbolic Regression

Motivation: Inferring the underlying (i.e., hidden) interaction structure of complex systems from time-series data presents a significant challenge. Recent advancements in deep learning techniques have demonstrated remarkable progress in identifying these interaction structures by jointly learning the structure and a prediction model based on the time-series data. In this research, the student aims to improve this process by replacing the conventional prediction model with a symbolic regression layer.

Deep symbolic regression leverages discrete optimization to derive mathematical equations that accurately represent the given observations. By integrating a symbolic regression layer into the deep learning model, the proposed approach seeks to uncover more interpretable and generalizable representations of the hidden interaction structures within complex systems. This enhanced method will not only increase the transparency of the learning process but also facilitate a deeper understanding of the underlying dynamics governing the system.

Challenges: exploring suitable methods of integrating symbolic regression in network inference methods; efficient implementation, evaluation

Prerequisites: background in probability theory and deep learning

Co-Supervisor: Gerrit Großmann

Related Work: See Unsupervised Relational Inference Using Masked Reconstruction and Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws


Information Spreading in Networks

Motivation: Modeling the stochastic dynamics of diffusion processes in complex networks has many applications, e.g. modeling, understanding, predicting, and controlling the outbreak of epidemics, the spread of rumors and memes, and the interactions of interconnected neurons.
We offer several topics in this area, for instance:

  • Efficient stochastic simulation of spreading processes
  • Developing vaccination strategies for epidemics on large networks
  • Inferring the underlying network structure given time-series data
  • Deriving differential equations describing the mean behavior of the stochastic dynamics, taking community-structure into account

Challenges: derivation of the corresponding equations; efficient implementation

Prerequisites: background in probability theory and programming

Co-Supervisor: Gerrit Großmann