Christoph Weisser

Bio and publications

Christoph joined the Chair of International Economic Policy and the Chair of Statistics as a doctoral student in 2019. He completed the PhD Programme in Applied Statistics and Empirical Methods. Christoph completed his PhD in May 2022.

He completed two master degrees at the University of St. Andrews and the University of Oxford as a scholar of the Studienstiftung des deutschen Volkes. Subsequently, he worked in the financial industry in London.

For his research on spatial statistics, please see also Mapping Coronavirus For China.

Publications can be found in Google Scholar

Publications

Thielmann, A, Weisser, C., Kneib, T., Säfken, B., Coherence based Document Clustering, Proceedings of the IEEE 17th International Conference on Semantic Computing (accepted for publication).
Tillmann, A., Kqiku, L., Reinhardt, D., Weisser, C., Säfken, B., Kneib, T., Privacy Estimation on Twitter: Modelling the Effect of Latent Topics on Privacy by Integrating XGBoost, Topic and Generalized Additive Models, Proceedings of the 8th IEEE International Conference on Privacy Computing (accepted for publication).
Seufert, J., Python, A., Weisser, C., Cisneros, E., Kis-Katos, K., Kneib, T. (2022), Mapping ex-ante spatial risks of COVID-19 in Indonesia using a Bayesian geostatistical model on airport network data, Journal of the Royal Statistical Society: Series A , http://doi.org/10.1111/rssa.12866.
Weisser, C., Gerloff, C., Thielmann, A., Python, A., Reuter, A., Kneib, T., Säfken, B. (2022), Pseudo-Document Simulation for Comparing LDA, GSDMM and GPM Topic Models on Short and Sparse Text using Twitter Data , Computational Statistics, https://doi.org/10.1007/s00180-022-01246-z.
Kant, G., Weisser, C., Kneib, T., Säfken, B. (2022), Topic Model—Machine Learning Classifier Integrations on Geocoded Twitter Data. In: Phuong, N.H., Kreinovich, V. (Eds), Biomedical and Other Applications of Soft Computing. Springer Studies in Computational Intelligence, https://doi.org/10.1007/978-3-031-08580-2_11.
Kant, G., Wiebelt, L., Weisser, C., Kis-Katos, K., Luber, M., Säfken, B. (2022), An iterative topic model filtering framework for short and noisy user-generated data: Analyzing conspiracy theories on twitter, International Journal of Data Science and Analytics, https://10.1007/s41060-022-00321-4.
Buchmüller, A., Kant, G., Weisser, C., Säfken, B., Kneib, T., Kis-Katos, K. (2021), Twitmo: Twitter Topic Modeling and Visualization for R. R package version 0.1.2., https://cran.r-project.org/package=Twitmo.
Weisser, C., Lenel, F., Lu, Y., Kis-Katos, K., Kneib, T. (2021), Using solar panels for business purposes: Evidence based on high-frequency power usage data, Development Engineering, https://doi.org/10.1016/j.deveng.2021.100074.
Tillmann, A., Thielmann, A, Kant, G., Weisser, C., Säfken, B., Silbersdorff, A., Kneib, T. (2021), AuDoLab Automatic document labelling and classification for extremely unbalanced data, Journal of Open Source Software, 6 (66), https://doi.org/10.21105/joss.03719.
Luber M., Weisser C., Säfken B., Silbersdorff A., Kneib T., Kis-Katos K. (2021), Identifying Topical Shifts in Twitter Streams: An Integration of Non-negative Matrix Factorisation, Sentiment Analysis and Structural Break Models for Large Scale Data, in Bright J., Giachanou A., Spaiser V., Spezzano F., George A., Pavliuc A. (Eds), Disinformation in Open Online Media. MISDOOM 2021. Oxford Internet Institute. Springer Lecture Notes in Computer Science, vol 12887, Springer. https://doi.org/10.1007/978-3-030-87031-7_3.
Kruse, R., Säfken, B., Silbersdorff, A., Weisser, C., (Eds.) (2021), Learning Deep Textwork: Perspectives on Natural Language Processing and Artificial Intelligence, Göttingen University Press, pages. 1-181, http://dx.doi.org/10.17875/gup2021-1608.
Thielmann, A., Weisser, C., Krenz, A. and Säfken, B. (2021), Unsupervised Document Classification integrating Web- Mining, One-Class SVM and LDA Topic Modeling, Journal of Applied Statistics (Special Issue: Statistical Approaches for Big Data and Machine Learning), https://www.tandfonline.com/doi/pdf/10.1080/02664763.2021.1919063.
Thormann, M., Farchmin, J., Weisser, C., Rene-Marcel Kruse, Säfken, B. and Silbersdorff, A. (2021), Stock Price Predictions with LSTM Neural Networks and Twitter Sentiment, Statistics, Optimization & Information Computing, https://doi.org/10.19139/soic-2310-5070-1202.
Thielmann, A., Weisser, C. and Krenz, A. (2021), One-Class Support Vector Machine and LDA Topic Model Integration - Evidence for AI Patents, in Phuong, N. H., Kreinovich, V. (Eds.), Soft Computing for Biomedical and Related Applications, Springer Studies in Computational Intelligence, Springer, https://doi.org/10.1007/978-3-030-49536-7.
Aydin, M., Weisser, C., Rué, O., Mariadassou, M., Maaß, S. Behrendt, A., Jaszczyszyn, Y., Heilker, T., Spaeth, M., Vogel, S., Lutz, S., Ahmad-Nejad, P., Graf, V., Bellm, A., Weisser, C., Naumova, E., Arnold, W., Ehrhardt, A., Meyer- Bahlburg, A., Becher, D., Postberg, J., Ghebremedhin, B., Wirth, S. (2021), The rhinobiome of exacerbated preschool wheezers & asthmatics: insights from a German pediatric exacerbation network, Frontiers in Allergy, https://doi.org/10.3389/falgy.2021.667562.
Säfken, B. Silbersdorff, A. and Weisser, C. (Eds.) (2020), Learning Deep - Perspectives on Deep Learning Algorithms and Artificial Intelligence, Göttingen University Press, pages. 1-146, https://doi.org/10.17875/gup2020-1338.
Kant, G., Weisser, C. and Säfken, B. (2020), TTLocVis: A Twitter Topic Location Visualization Package, Journal of Open Source Software, 5 (54), https://doi.org/10.21105/joss.02507.

Working Paper

Thielman, A., Säfken, B., Weisser, C., Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class, https://arxiv.org/abs/2212.09422.
Buchmüller, A., Kant, G., Weisser, C., Säfken, B., Kneib, T., Kis-Katos, Twitmo: A Twitter Data Topic Modeling and Visualization Package for R, https://arxiv.org/abs/2207.11236.
Luber, M., Weisser, C., Thielmann, A., B. Säfken, Community-Detection via Hashtag-Graphs for Semi-Supervised NMF Topic Models, https://arxiv.org/abs/2111.10401.
Stemmler, H., Kis-Katos, K., Lenel, F., Weisser, C., Dealing with agricultural shocks: Income source diversification through solar panel home systems.
Svanidze, D., Python, A., Weisser, C., Säfken, B., Kneib, T., Fine-Scale Spatial Predictions of COVID-19 Cases in China using GIS Data and Deep Learning Algorithms.
Dreher, A., Kis-Katos, K., Moellerherm, R., Weisser, C., Terrorism in the media.
Alkhayer, T., Kivimaki, T., Kant, G., Weisser, C., Out-Groups and Threats as a Foundation for Terrorist Rhetoric: a Natural Language Processing based Sociopsychological Analysis of ISIS’s Al-Naba Magazine.

Teaching

Machine Learning and Artificial Intelligence (Summer School, Studienstiftung des deutschen Volkes, University of Cambridge)
Deep Learning Algorithmen (Master, University of Göttingen)
Spatial Statistics (Master, University of Göttingen)