Tanja Hagemann

Datenlabor

In various projects in cooperation with Deutsche Telekom and T-Labs, respectively, I research and develop methods of machine learning for network analysis. An essential part of my work is data engineering where I use (big data) frameworks such as SQL databases, SPARK, Python and Tensorflow. Furthermore, an adequate and appealing data visualization as well as the development of prototypes is part of my personal work style.

As a research assistant and PhD candidate in the SNET research group at TU Berlin, I am mainly engaged in applied research in the fields of machine learning and statistics. In particular, I am interested in the detection and prediction of anomalies in high-dimensional complex data structures . In my work I mostly consider the entire data pipeline: data preprocessing, feature engineering, model training and testing. My main experiences include manifold learning, deep learning, and time series analysis.

Through my participation in this summer school, I have expanded my expertise on both the theory of deep neural networks, on related questions such as generalization, expressivity, or explainability, as well as on applications of deep neural networks (e.g. to PDEs, inverse problems, or specific real-world problems). 

Furthermore, I attended practical coding sessions (Tensorflow).

Lehrtätigkeit als Tutorin der Mathematik, Korrektur von Übungsblättern und Leitung von Tutorien

Teilnahme an der Summer School zum Thema ,,Weltraumwetter" am Standort Neustrelitz und der University of Alabama in Huntsville, Projektarbeit im Bereich der numerischen Modellierung

Wissenschaftliche Projektarbeit in der Forschungsgruppe „Energie, Verkehrs- und Klimapolitik” zum Thema ,,fahrradfreundliche Wohnungswirtschaft", Internet- und Literaturrecherchen, Konzeption und Erstellung von Fragebögen, statistische Datenauswertungen mit SPSS, Erstellung von Ergebnispräsentationen

Codierung von Fragebögen in SPSS, statistische Datenauswertungen mit SPSS, Erstellen von Ergebnispräsentationen

Datensichtung und Datenanalyse im Bereich ,,Barclay's Premier League"

Machine Learning, Data Science, Time Series Analysis, Anomaly Detection, Deep Learning, Manifold Learning, Industry 4.0, IoT, Predictive Maintenance.

Multidimensional complex analysis, functional analysis, stochastic differential equations, probability theory, measurement and integration theory

Complex analysis, applied statistics, stochastics, linear algebra, number theory, topology, numerical modeling, foundations of computer science and programming in C and Matlab