I am working on Machine Learning and Neural Network models to process text, images, audio, and other sensor data. The ultimate goal is to bring multiple of these data types together to build multimodal models for various applications.
In the BigData@Geo project, we work with climate data, so time series and geospatial data. Our goal is to help the geography department to improve climate modeling using Machine Learning.
- 2015–2017: M.Sc. Computer Science at the University of Hamburg
- 2012–2015: B.Sc. Computer Science at the University of Hamurg
(2020) “Improving Sentiment Analysis with Biofeedback Data”, in Proceedings Of The Workshop On People In Language, Vision And The Mind (Onion), available: https://downloads.hci.informatik.uni-wuerzburg.de/2018-ieeevr-lugrin-vr-teacher-training/2020-onion-sentiment-eeg-preprint.pdf.
(2020) “OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning”, Atmospheric Environment, 233, 117535, available: http://www.sciencedirect.com/science/article/pii/S1352231020302703.
(2020) “MapLUR: Exploring a new Paradigm for Estimating Air Pollution using Deep Learning on Map Images”, available: https://arxiv.org/abs/2002.07493.
(2020) “Anomaly Detection in Beehives using Deep Recurrent Autoencoders”, in Proceedings Of The 9Th International Conference On Sensor Networks (Sensornets 2020), SCITEPRESS – Science and Technology Publications, Lda., 142-149.
(2020) “SimLoss: Class Similarities in Cross Entropy”, available: http://arxiv.org/abs/2003.03182.
(2020) “Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels”, ACM Transactions on Social Computing.