Teaching

We help our students in their first research steps through student-level project and master thesis supervision. Interested in proposing a student project or interested in working on a master thesis with us? Please feel free to reach out, we will be glad to discuss. magda.gregorova@thws.de

Exploration of Brain-Inspired Networks on Neuromorphic Hardware

M.Sc. Thesis

Student: Aniket Dattatraya Kulkarni

This thesis presents an end-to-end framework for deploying Spiking Neural Networks (SNNs) on the THOR neuromorphic processor. The proposed pipeline transforms quantization-aware trained models into hardware-compatible representations, maps them using five strategies including the novel THOR-Native Adaptive Mapping Algorithm (TAMA 2.0) and evaluates them through cycle-accurate simulation.

Manuscript

Loss Functions in Diffusion Models: A Comparative Study

M.Sc. Thesis

Student: Dibyanshu Kumar

This thesis comprehensively explores various loss functions in diffusion models, unifying them under the variational lower bound objective framework. Through theoretical analysis and empirical studies, it examines how different objectives affect performance under various conditions and investigates.

GitHub

Sugar-beet Stress Detection using Satellite Image Time Series

M.Sc. Thesis

Student: Bhumika Sadbhave

The thesis investigates stress detection in sugarbeet crops using Sentinel-2 image time series and various machine learning and deep learning models, with a focus on autoencoder-based architectures. The primary objective is to build a stress detection pipeline that works with minimal supervision.

GitHub

Determination of Drug Efficacy on Pancreatic Tumor 3D Spheroidal Tissues

M.Sc. Thesis

Student: Bibin Babu

This thesis addresses pancreatic tumor treatment challenges by ranking drug combinations and concentrations based on their efficacy. Using SimCLR self-supervised learning, it extracts features from bright-field microscopy images of 3D tumor tissue models to characterize drug-induced alterations over time.

GitHub

Electrical Engine Efficiency Prediction Bypassing Finite Element Analysis

M.Sc. Thesis

Student: Lilly Abraham

This thesis develops surrogate models to predict Key Performance Indicators for Permanent Magnet Synchronous Machine Electric Motors using geometric and physical parameters. The author creates a tabular representation of motor designs, applies Multi Linear Perceptron models.

GitHub

Weakly Supervised Semantic Segmentation of Multispectral Satellite Images for Land Cover Mapping

M.Sc. Thesis

Student: Esther Ademola

This thesis evaluates deep learning models for deforestation detection using weakly annotated Sentinel-2 satellite imagery. U-Net and DeepLabV3+ outperform traditional methods but face precision challenges. Results highlight the complexities of multispectral remote sensing and the need for improved annotation quality.

GitHub

Cross Topology Modeling and Optimization of Electrical Drives Using Machine Learning

M.Sc. Thesis

Student: Marius Benkert

This paper addresses challenges in electrical machine simulation by enhancing deep learning approaches that bypass resource-intensive finite element analysis. Building on previous work (Parekh et al., 2022; 2023), it refines a variational autoencoder architecture that maps diverse motor topologies into a unified latent space, improving both training efficiency and optimization robustness compared to earlier models.

Springer

Step Detection

M.Sc. Thesis

Student: Muhammad Sarwar

This thesis explores neural network approaches for step detection using accelerometer and gyroscope data. It compares two methods: CNN models that extract spatial features from time-series data with improved accuracy and speed over traditional LSTM models, and a YOLO-like architecture that predicts step start-end pairs with minimal post-processing. The research evaluates models using both traditional metrics and mean absolute error to assess temporal prediction accuracy.

GitHub

Transfer Learning in Crop Remote Sensing

M.Sc. Thesis

Student: Ana Muñoz Gutiérrez

This research addresses the challenge of estimating global wheat cultivation before harvest seasons, which is difficult due to limited quality data despite wheat's importance for global food security. The study explores deep learning techniques with a focus on transfer learning methods, using satellite imagery combined with US agricultural statistics to predict winter and spring wheat production.

GitHub