An Nguyen

An Nguyen, M. Sc.

Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Maschinelles Lernen und Datenanalytik

Room: Room 01.015
Carl-Thiersch-Straße 2b
91052 Erlangen

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Since 10/2018 Researcher and Ph.D. Student

Machine Learning and Data Analytics Lab, Germany

Department of Computer Science, Friedrich-Alexander-University Erlangen-Nuernberg (FAU)

05/2017 – 08/2017 Vistiting Student and Research Assistant

Frankel Cardiovascular Center in cooperation with the Biomedical & Clinical Informatics Lab

University of Michigan, USA

09/2016 –  04/2017 MSE Electrical and Computer Engineering

University of Michigan, USA

Project Lead at M-HEAL

04/2016 – 09/2018 MSc Electrical Engineering

Technical University of Berlin, Germany

Student researcher at the Control Systems Group

08/2014 – 06/2015 International Student

KTH Royal Institute of Technology, Sweden

10/2011 – 03/2016 BSc Electrical Engineering

Technical University of Berlin, Germany

Tutor at the Institute of Mathmatics

Student researcher at the High Voltage Engineering lab and Control Systems Group

Working student at Vattenfall Europe Netzservice Gmbh

My main research interest lies in the analysis of time series data using Deep Learning. Specifically Mixed-Type and Irregularly Sampled Time Series Analysis. In many real world applications and in the sciences it is not possible to get regularly spaced observations of the phenomena/system of interest. My applications reach from healthcare to predictive maintenance over predictive business process analytics. I am also interested in more fundamental properties of time series data and mechanisms for learning.




  • Nguyen, An, Nils Roth, Nooshin Haji Ghassemi, Julius Hannink, Thomas Seel, Jochen Klucken, Heiko Gassner, and Bjoern M. Eskofier. 2019. “Development and Clinical Validation of Inertial Sensor-Based Gait-Clustering Methods in Parkinson’s Disease.” Journal of NeuroEngineering and Rehabilitation 16 (1): 77.


  • Nguyen, A., S. Ansari, M. Hooshmand, K. Lin, H. Ghanbari, J. Gryak, and K. Najarian. “Comparative Study on Heart Rate Variability Analysis for Atrial Fibrillation Detection in Short Single-Lead ECG Recordings.” In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 526–29, 2018.

Year Name Title
2020/21 Simon  Dietz Multimodal machine learning for mixed-type time series analysis
(Research Internship)
2020/21 Andrey Kurzyukov Benchmarking time-aware (R)NNs for irregularly sampled time series
(Master’s Project)
2020/21 Dominik Nitschmann Benchmarking of Out-of-Distribution Detection Algorithms for Time Series
(Masters’s Thesis)
2020/21 Johannes Roider Modeling Mixed-Type Time Series Data With Neural Networks for Predictive Maintenance
(Master’s Thesis)
2020 Johannes Jablonski Application of data and process analysis techniques for the evaluation of agile university projects
(Bachelor’s Thesis)
2019/20 Wenyu Zhang Conformance Checking for a Medical Training Process Using Petri net Simulation and Sequence Alignment
(Research Internship)
2019/20 Srijeet Chatterjee Enhancing Customer Experience – Deep Learning for Predictive Business Process Monitoring
(Master’s Thesis)
2019/20 Johannes Roider Deep Learning for industrial time series anomaly detection
(Master’s Project)

If you are interested in any of the topics below, kindly send me your CV, Transcript of Records from meinCampus and brief description of experience and motivation.

The exact work packages and scope of the work can be defined on an individual basis.

I am also happy to discuss other topics with you and to supervise potential guest students.


[M] = More suited for a Master’s Thesis
[P] = More suited for a Project
[I] = Implementation Task (for Computer Science “Master Project” or Medical Engineering “Research Internship” (Forschungspraktikum))
[W] = Working Student/HiWi

2002 W Deep Learning/Machine Learning for Mixed-Type and Irregularly Sampled Time Series Analysis   Deep Learning, Machine Learning, Time Series Analysis
2023 I/P/M “Mixed-Type and Irregularly Sampled Time Series Analysis” – Such data includes for example electronic health records (EHR) or combined sensor and event log data. Tasks include the extension of RNN architectures to deal with such data effectively. Deep Learning, Machine Learning, Time Series Analysis
1915 I/P/M Deep Learning for Anomaly Detection Deep Learning,

Time Series Analysis

1916 I/P/M Deep Learning for Data Augmentation Deep Learning,

Time Series Analysis

1917 I/P/M Conformance Checking for Medical Processes Process Mining,

Data & Process Science

J1902 W (+ M) Working Student (potentially with follow up Master Thesis) at Siemens Healthineers – Computed Tomography (Data Analytics for non-image data) Research & Development
J1903 W (+ M) Working Student (potentially with follow up Master Thesis) at Siemens Healthineers – Predictive Maintenance (Computed Tomography + Customer Service) Research & Development
J1904 W Student researcher helping with topics in the field of Deep Learning or/and Process Mining Research




Partners & Funding Agencies