generative AI, ESG reports, coding
As one of the main organizers of the AIM Hackathon "Sustainability meets LLMs" I had the privilege of working alongside an incredible team to create an event focused on impactful AI solutions. We challenged participants to design custom Retrieval-Augmented Generation (RAG) architectures that could utilize real-world ESG and sustainability reports to tackle pressing challenges. The teams came up with impressive solutions, including tools for detecting greenwashing in corporate reports and benchmarking company compliance with EU Green Deal goals. It was inspiring to see so much creativity and commitment to sustainability in action.
SOTA deep learning, PyTorch, bioinformatics, master thesis
Work in progress: When designing mRNA sequences, e.g. for vaccinations or gene therapy, it is essential to know how well it will be translated to protein in the human body. In this thesis, different deep learning architectures for predicting protein-to-mRNA ratios will be assessed and optimized using mRNA sequence and secondary structure information. The models being studied include MLPs, LSTMs, xLSTMs, Transformers, and the state-of-the-art SSM, Mamba. I am exploring how features such as sequence bases, predicted loop types, predicted structures, minimum free energy, and tissue types impact predictive performance. The sequences range from 1000 to 8000 bases, making efficient model selection and architecture design critical. By evaluating different architectures, the research aims to identify the most effective approach for protein-mRNA ratio prediction in diverse biological contexts. Finally, the most promising architecture should be further optimized using SOTA approaches like unsupervised pre-training and semi-supervised fine-tuning.
NLP, embeddings, t-SNE, interactive visualization, D3
This project presents an innovative interactive visualization tool designed to explore the historical objects of Vienna’s first district. Leveraging sentence transformers to embed Wien Geschichte Wiki articles, the tool organizes hundreds of historical objects into a meaningful 2D space using t-SNE. Enabling a broader audience to intuitively and playfully discover and analyze the relationships of Vienna's historical landscape.
deep learning, transformers, PyTorch, explainable AI, bioinformatics
The goal of this project is to utilize deep learning models to predict the likelihood of a base breaking in an mRNA sequence, as used in COVID-19 vaccines. Understanding and predicting mRNA stability is crucial for enhancing the efficacy and longevity of these vaccines. Additionally, the project aims to provide explanations for the model's predictions regarding base instability. This will be achieved by examining the attention weights of various models in conjunction with exploratory data analysis. These explanations can offer valuable insights into the underlying biological mechanisms and potentially guide the design of more stable mRNA sequences. The main insights suggest that the secondary structure of the mRNA significantly influences its degradation likelihood.
transfer learning, YOLOv5, object detection, distance estimation
The project aimed at developing an algorithm for detecting license plates in real-time footage from car cameras to estimate distances to other vehicles. Images of cars with standardized license plates were used for training, as well as their corresponding distances from the camera. The PyTorch based YOLOv5 architecture for object detection was utilized and fine-tuned for license plate detection with data augmentation. By applying triangle similarity calculations and angle correction refinements, distances could be accurately estimated. The best model achieved an 85% accuracy rate with a 5% tolerance in estimating distances to cars in front, showing potential for deployment in advanced driver-assistance systems.
generative AI, deep learning, diffusion
The overall goal was to generate entirely new images using the PFGM (Poisson Flow Generative Model) approach. It aims to outperform existing diffusion models by using Poisson's equation instead of the Wiener process. Employing a uniform prior, which is thought to be closer to the real data range. PFGM samples from a hemisphere for image generation and utilizes reverse dynamics with a U-Net. The replication of paper results was hindered by limited resources, though satisfactory results could be obtained. Finally, attempts were made to enhance the PFGM architecture by using a different prior.
transfer learning, image segmentation, satellite images
This project focuses on the detection of truck parking lots on satellite images for logistics optimization. Complex AI algorithms - or more specifically deep learning models for image segmentation - were trained to predict parking lot shapes. PSPNet initially showed promising results but lacked generalizability. Upon further testing, better precision and generalizability were demonstrated by the LinkNet architecture, achieving a mean intersection over union (mIoU) of up to 75%. The project exemplifies an innovative approach to leveraging satellite imagery and machine learning for cost-effective estimation of parking areas' shapes, offering potential benefits for transport operations optimization based on GPS signals.
D3, dashboard design, interactivity
Here, the task was to create a user-friendly interface displaying COVID-19 data in Africa. The solution involves building a static webpage using HTML, CSS, and JavaScript, and employing D3 for interactivity and cross-effects in the plots. The resulting dashboard consists of a map view of Africa showcasing total COVID-19 cases, a timeline of new cases and vaccinations, and a bubble chart demonstrating the correlation between socio-economic indicators and total cases. If a country is selected, it will be highlighted across all the plots for easy comparison.
NLP, BERTopic, sentiment
The goal of this analysis is to examine the emotions and perspectives expressed in Reddit comments about "climate change" over time. The data used consists of 4.6 million relevant comments from January 2010 to August 2022. The solution involves using BERT-based pre-trained models for emotion detection and BERTopic for topic detection. The results show that emotions remain relatively stable over the years, while topics are heavily influenced by major events such as elections, bushfires, mass shootings, and the COVID-19 pandemic. Many topics are specific to the western world, particularly the United States.
agent optimization, DRL, PPO
This project aims to enhance energy management within a microgrid through the implementation of a reinforcement learning model. By integrating solar, wind, gas turbine, battery storage, and grid energy trading, the goal is to minimize operational costs while ensuring a reliable power supply and maximizing renewable energy usage. Leveraging data from several hundred household profiles, including solar and wind generation, energy demand, and grid prices, a Deep Reinforcement Learning model using PPO was utilized. Through simulations, the model exhibited notable improvements in cost reduction and energy efficiency across various scenarios. Ultimately demonstrating the potential of reinforcement learning in optimizing microgrid energy systems for increased sustainability and economic benefits.
deep learning, privacy, neural networks, forecasts, PyTorch
The project aimed to predict and classify appliance energy consumption using federated learning. Daily energy consumption data of 50 households was processed. Recurrent neural networks (RNN) and long short-term memory (LSTM) models were built with PyTorch and trained decentralized to preserve privacy. By experimenting with hyperparameters and activation functions model performance could be optimized. The main results showed that LSTM outperformed RNN in prediction quality, though with higher training time. Federated learning was shown to be efficient in preserving data privacy and achieving comparable final model performance to traditional training methods in this implementation.