An Nguyen, M. Sc.
|Since 10/2018||Researcher and Ph.D. Student
Department of Computer Science, Friedrich-Alexander-University Erlangen-Nuernberg (FAU)
|05/2017 – 08/2017||Vistiting Student and Research Assistant|
|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 Mathmatics
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.
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
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
- An Nguyen, Srijeet Chatterjee, Sven Weinzierl, Leo Schwinn, Martin Matzner, Bjoern Eskofier. 2020. “Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring.” arXiv preprint arXiv:2010.00889
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
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.
- 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 2020/21||Machine Learning and Data Analytics for industry 4.0|
|Summer 2020||Machine Learning and Data Analytics for industry 4.0|
|Winter 2019/20||Machine Learning and Data Analytics for industry 4.0|
|Summer 2019||Machine Learning and Data Analytics for industry 4.0|
|2020/21||Andrey Kurzyukov||Benchmarking time-aware (R)NNs for irregularly sampled time series
|2020/21||Dominik Nitschmann||Benchmarking of Out-of-Distribution Detection Algorithms for Time Series
|2020/21||Johannes Roider||Modeling Mixed-Type Time Series Data With Neural Networks for Predictive Maintenance
|2020||Johannes Jablonski||Application of data and process analysis techniques for the evaluation of agile university projects
|2019/20||Wenyu Zhang||Conformance Checking for a Medical Training Process Using Petri net Simulation and Sequence Alignment
|2019/20||Srijeet Chatterjee||Enhancing Customer Experience – Deep Learning for Predictive Business Process Monitoring
|2019/20||Johannes Roider||Deep Learning for industrial time series anomaly detection
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 (focus on fundamentals)||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 efficiently.||Deep Learning, Machine Learning, Time Series Analysis|
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