An Nguyen
Dr.-Ing. An Nguyen
Postdoc
Carl-Thiersch-Straße 2b
91052 Erlangen
Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Maschinelles Lernen und Datenanalytik
Carl-Thiersch-Straße 2b
91052 Erlangen
- Email: an.nguyen@fau.de
- Website: https://www.mad.tf.fau.de/person/an-nguyen/
| 03/2024 – present | Freelancer (part-time)
AI Engineer/Consultant |
| 01/2023 – present | Postdoc (part-time)
Machine Learning and Data Analytics Lab, Germany Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) |
| 10/2022 – present | Senior Data Scientist and Product Owner at Siemens Healthineers |
| 03/2022 – 06/2022 | Visiting Researcher at University of California Irvine
BaCaTeC project with Stephan Mandt |
| 10/2018 – 09/2022 | Data Scientist and Researcher at Siemens Healthineers |
| 10/2018 – 09/2022 | 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 |
My main research interest lies in the analysis of time series data. 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 range 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.
- Machine Learning For Predictive Analytics
- Holistic customer-oriented service optimization for fleet availability
- Multimodal Machine Learning for Decision Support Systems
2024
- Dietz S., Altstidl TR., Zanca D., Eskofier B., Nguyen A.:
How Intermodal Interaction Affects the Performance of Deep Multimodal Fusion for Mixed-Type Time Series
International Joint Conference on Neural Networks (IJCNN) (Yokohama, 30. June 2024 - 5. July 2024)
DOI: 10.1109/IJCNN60899.2024.10650421
URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10650421
BibTeX: Download - Roider J., Nguyen A., Zanca D., Eskofier B.:
Assessing the Performance of Remaining Time Prediction Methods for Business Processes
In: IEEE Access (2024), p. 1-19
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2024.3459648
URL: https://ieeexplore.ieee.org/abstract/document/10679137
BibTeX: Download - Schlieper P., Dombrowski M., Nguyen A., Zanca D., Eskofier B.:
Data-Centric Benchmarking of Neural Network Architectures for the Univariate Time Series Forecasting Task
In: Forecasting 6 (2024), p. 718-747
ISSN: 2571-9394
DOI: 10.3390/forecast6030037
BibTeX: Download
2023
- Altstidl TR., Nguyen A., Schwinn L., Köferl F., Mutschler C., Eskofier B., Zanca D.:
Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks
2023 International Joint Conference on Neural Networks (IJCNN) (Gold Coast, Australia, 18. June 2023 - 23. June 2023)
In: Proc. Intl. Joint Conf. Neural Netw. (IJCNN) 2023
DOI: 10.1109/IJCNN54540.2023.10191724
BibTeX: Download - Schwinn L., Raab R., Nguyen A., Zanca D., Eskofier B.:
Exploring misclassifications of robust neural networks to enhance adversarial attacks
In: Applied Intelligence (2023)
ISSN: 0924-669X
DOI: 10.1007/s10489-023-04532-5
BibTeX: Download
2022
- Ede S., Baghdadlian S., Weber L., Nguyen A., Zanca D., Samek W., Lapuschkin S.:
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI
6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2022, held in conjunction with the 17th International Conference on Availability, Reliability and Security, ARES 2022 (Vienna, 23. August 2022 - 26. August 2022)
In: Andreas Holzinger, Andreas Holzinger, Andreas Holzinger, Peter Kieseberg, A Min Tjoa, Edgar Weippl, Edgar Weippl (ed.): Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2022
DOI: 10.1007/978-3-031-14463-9_1
BibTeX: Download - Schwinn L., Bungert L., Nguyen A., Raab R., Pulsmeyer F., Precup D., Eskofier B., Zanca D.:
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
Proceedings of the 39th International Conference on Machine Learning (Baltimore, USA, 17. July 2022 - 23. July 2022)
In: PMLR (ed.): 162 2022
Open Access: https://proceedings.mlr.press/v162/schwinn22a.html
URL: https://proceedings.mlr.press/v162/schwinn22a.html
BibTeX: Download
2021
- Nguyen A., Foerstel S., Kittler T., Kurzyukov A., Schwinn L., Zanca D., Hipp T., Jun SD., Schrapp M., Rothgang E., Eskofier B.:
System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data
In: IEEE Access (2021)
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2021.3106791
BibTeX: Download - Schwinn L., Nguyen A., Raab R., Bungert L., Tenbrinck D., Zanca D., Burger M., Eskofier B.:
Identifying untrustworthy predictions in neural networks by geometric gradient analysis
Conference on Uncertainty in Artificial Intelligence (UAI) (Online, 27. July 2021 - 30. July 2021)
URL: https://arxiv.org/abs/2102.12196
BibTeX: Download - Schwinn L., Nguyen A., Raab R., Zanca D., Eskofier B., Tenbrinck D., Burger M.:
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks
International Joint Conference on Neural Networks (IJCNN) (Online, 18. July 2021 - 22. July 2021)
DOI: 10.1109/ijcnn52387.2021.9534190
BibTeX: Download - Schwinn L., Raab R., Nguyen A., Zanca D., Eskofier B.:
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks
URL: https://arxiv.org/abs/2105.10304
BibTeX: Download
2020
- Nguyen A., Chatterjee S., Weinzierl S., Schwinn L., Matzner M., Eskofier B.:
Time matters: Time-aware LSTMs for predictive business process monitoring
International Conference on Process Mining (Padua, 4. October 2020 - 9. October 2020)
In: Proceedings of the ICPM 2020 International Workshops 2020
DOI: 10.1007/978-3-030-72693-5_9
BibTeX: Download
2019
- Nguyen A., Roth N., Haji Ghassemi N., Hannink J., Seel T., Klucken J., Gaßner H., Eskofier B.:
Correction to: Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson's disease (J Neuroeng Rehabil (2019) 16:77 DOI: 10.1186/s12984-019-0548-2)
In: Journal of neuroEngineering and rehabilitation 16 (2019), Article No.: 98
ISSN: 1743-0003
DOI: 10.1186/s12984-019-0567-z
BibTeX: Download - Nguyen A., Roth N., Haji Ghassemi N., Hannink J., Seel T., Klucken J., Gaßner H., Eskofier B.:
Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson's disease
In: Journal of neuroEngineering and rehabilitation 16 (2019)
ISSN: 1743-0003
DOI: 10.1186/s12984-019-0548-2
BibTeX: Download
2018
- Nguyen A., Ansari S., Hooshmand M., Lin K., Ghanbari H., Gryak J., Najarian K.:
Comparative Study on Heart Rate Variability Analysis for Atrial Fibrillation Detection in Short Single-Lead ECG Recordings
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (Honolulu, HI, 18. July 2018 - 21. July 2018)
In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2018
DOI: 10.1109/EMBC.2018.8512345
BibTeX: Download
- 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
| 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 |
| Year | Name | Title |
| 2023 | Dimitrii Maksimov | Personalized Real-Time Anomaly Detection on Event Sequences (Master’s Project, co-supervision) |
| 2022/23 | Hung Nguyen | Neural Temporal Point Processes for Predictive Business Process Monitoring. (Master’s Thesis, co-supervision) |
| 2022/23 | Weixin Wang | Evaluating Remaining Time Prediction of Business Processes with Event Log Imperfections Using Synthetic Event Log Data. (Master’s Thesis, co-supervision) |
| 2022 | Maximilian Vogel | A compressed Deep Learning Model for Human Activity Recognition (HAR) Using Hearing Aid Integrated Inertial Sensors. (Master’s Thesis, co-supervision) |
| 2022 | Marc Windsheimer | Benchmarking time-aware (R)NNs for irregularly sampled time series (Master’s Project) |
| 2021/22 | Weixin Wang | Predictive CT Examinations (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
