An Nguyen

Dr.-Ing. An Nguyen

Postdoc

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

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

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

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 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.

 

 

 

 

2024

2023

2022

2021

2020

2019

2018

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