An Nguyen
An Nguyen, M. Sc.
Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Maschinelles Lernen und Datenanalytik
Carl-Thiersch-Straße 2b
91052 Erlangen
- Phone number: +49 9131 85 20284
- Email: an.nguyen@fau.de
- Website: https://www.mad.tf.fau.de/person/an-nguyen/
Academic CV
| 03/2022 – 06/2022 | Visiting Researcher at University of California Irvine
Working with Stephan Mandt and Padhraic Smyth |
| Since 10/2018 | Data Scientist at Siemens Healthineers |
| Since 10/2018 | Researcher and Ph.D. Student
Machine Learning and Data Analytics Lab, Germany Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-University Erlangen-Nuernberg (FAU) |
| 05/2017 – 08/2017 | Visiting Student and Research Assistant
Frankel Cardiovascular Center in cooperation with the Biomedical & Clinical Informatics Lab |
| 09/2016 – 04/2017 | MSE Electrical and Computer Engineering
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 |
| 10/2011 – 03/2016 | BSc Electrical Engineering
Technical University of Berlin, Germany Tutor at the Institute of Mathematics Student researcher at the High Voltage Engineering lab and Control Systems Group Working student at Vattenfall Europe Netzservice Gmbh |
Research Projects
Machine Learning For Predictive Analytics
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.
Publications
2021
- An Nguyen, Stefan Foerstel, Thomas Kittler, Andrey Kurzyukov, Leo Schwinn, Dario Zanca, Tobias Hipp, Sun Da Jun, Michael Schrapp, Eva Rothgang, Bjoern Eskofier 2021. “System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data” IEEE Access
- An Nguyen, Srijeet Chatterjee, Sven Weinzierl, Leo Schwinn, Martin Matzner, Bjoern Eskofier. 2021. “Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring”, Process Mining Workshops: ICPM 2020 International Workshops
- Leo Schwinn, René Raab, An Nguyen, Dario Zanca, Bjoern Eskofier 2021. “Exploring Robust Misclassifications of Neural Networks to Enhance Adversarial Attacks“, arXiv preprint arXiv:2105.10304
- Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Bjoern Eskofier 2021. “Identifying untrustworthy predictions in neural networks by geometric gradient analysis“, arXiv preprint arXiv:2102.12196
2020
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Leo Schwinn, Daniel Tenbrinck, An Nguyen, René Raab, Martin Burger, Bjoern Eskofier. 2020. “Sampled Nonlocal Gradients for Stronger Adversarial Attacks.”arXiv preprint arXiv:2011.02707
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An Nguyen, Wenyu Zhang, Leo Schwinn, Bjoern Eskofier. 2020. “Conformance Checking for a Medical Training Process Using Petri net Simulation and Sequence Alignment.”
arXiv preprint arXiv:2010.11719 -
Sven Weinzierl, Sandra Zilker, Jens Brunk, Kate Revoredo, A Nguyen, Martin Matzner, Jörg Becker, Björn Eskofier. 2020. “An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs.” arXiv preprint arXiv:2005.01194
2019
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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.
2018
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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.
Awards
- 2019: Master’s Degree Award by the Association of German Engineers (VDI) Berlin-Brandenburg chapter
- 04/2011 – 09/2018: Scholarship from the Rosa Luxemburg Foundation
- 2016: Study award by the Faculty IV – Electrical Engineering and Computer Science (TU Berlin)
- 08/2016 – 04/2017: Travel grant from the Fulbright Program
Teaching
| Winter 2021/22 | Machine Learning and Data Analytics for industry 4.0, Machine Learning for Time Series, Project Machine Learning and Data Analytics |
| Summer 2021 | Machine Learning and Data Analytics for industry 4.0, Project Machine Learning and Data Analytics |
| Winter 2020/21 | Machine Learning and Data Analytics for industry 4.0, Project Machine Learning and Data Analytics, Machine Learning for Time Series Project |
| Summer 2020 | Machine Learning and Data Analytics for industry 4.0 |
| Winter 2019/20 | Machine Learning and Data Analytics for industry 4.0, Project Machine Learning and Data Analytics |
| Summer 2019 | Machine Learning and Data Analytics for industry 4.0 |
Student Projects
| Year | Name | Title |
| 2022 | Marc Windsheimer | Benchmarking time-aware (R)NNs for irregularly sampled time series (Master’s Project) |
| 2021/22 | Andrey Kurzyukov | Modeling Mixed-Type Time Series Data for Machinery Health Prognostics (Master’s Thesis) |
| 2021/22 | Simon Dietz | Machine Learning Methods for Mixed-Type Time Series Analysis (Master’s Thesis) |
| 2021 | Mischa Dombrowski | Systematic Analysis of the Transformer Architecture for Time Series Prediction Applications (Master’s Thesis, co-supervision) |
| 2021 | Jonas Utz | Unsupervised Modeling of Visual Attention (Master’s Project, co-supervision) |
| 2021 | Serop Baghdadlian | Overcoming Catastrophic Forgetting Using Neural Pruning Via Layer-Wise Relevance Propagation (Master’s Thesis, co-supervision) |
| 2021 | Dominik Prossel | Combining Kalman Filters and Neural Networks for Stride Trajectory Estimation (Master’s Thesis, co-supervision) |
| 2021 | Jonas Schauer | Benchmarking time-aware (R)NNs for irregularly sampled time series (Master’s Project) |
| 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 (Master’s Thesis, co-supervision) |
| 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, co-supervision) |
| 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) |
Partners & Funding Agencies
