Prof. Dr. Stefan Lessmann
Prof. Lessmann is the head of the chair Information Systems at the Humboldt University of Berlin. In cooperation with his team, he initiated this website in oder to present, inform and discuss latest research results. For his more extensive bio click here. In research, he is most active in:
- Artificial neural networks, deep learning
- Big Data Analytics
- Credit risk modeling
- Ensemble models and forecast combination
- Marketing and E-Commerce analytics
- Non-standard paradigms to learn from data
- Sentiment and social network analysis
- Time series forecasting
Annika Baumann, M.Sc.
Annika Baumann, M.Sc., studied the Master in Information Systems at the Humboldt University in Berlin. Since June 2013 she works as a researcher at the Institute of information systems at the Humboldt University in Berlin. As part of her master’s thesis she investigated the robustness of the Internet graph based on a current dataset. In the course of her PhD she investigates the Internet topology, the readability of privacy policies as well as the social news aggregator Reddit. Fore a full bio click here. Moreove she is mostly interested in:
- Internet topology
- Graph Theory
- Customer behavior in Ecommerce
- Analysis of social networks
Johannes Haupt, M.Sc.
Since April 2016, Mr. Haupt has been working as a researcher at the Chair of Information Systems of the Business and Economics department. He implemented a machine learning framework to detect tracking objects in e-mails as part of his master’s thesis. More information about his person can be found here. In the course of his PhD, he researches applications for machine learning in e-commerce and unstructured data. More precisely he invests his time and energy in the follwing areas:
- Customer behavior in e-Commerce
- Recommendation Engines
- Text analytics
- Image analytics
- Deep learning
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Class of Winter Term 17/18
The students of the Seminar Information Systems contribute to this website to great extend. Groups of Master students from Humboldt University with backgrounds in Business, Economics, Statistics or Computer Science worked on analytical topics and created blog posts, presenting exciting insights into the following topis:
- Neural Networks and Convolutional Neural Networks
- Image Analysis
- Text Mining
- Time Series Analysis with LSTMs
- Survial Analysis
- Image Captioning
- Deep Learning and Recommender Systems
Class of Winter Term 18/19
The students of the Seminar Information Systems contribute to this website to great extend. Groups of Master students from Humboldt University with backgrounds in Business, Economics, Statistics or Computer Science worked on analytical topics and created blog posts, presenting exciting insights into the following topis:
- Unsupervised Neural Networks: GANs and Variational Autoencoders
- DL for sequential data: GRU, LSTM, and beyond
- DL for sequential data: Temporal convolutional neural networks
- Embed, encode, attend, predict: state-of-the-art in text analysis
- Hierarchical Attention for text classification & sentiment analysis
- Bayesian text analysis and topic modeling
- Uncertainty and Bayesian Deep Learning
- Opening the black-box: state-of-the-art in explaining opaque ML/DL models