AI & Digital Assistants · Siemens Healthineers

Bridging AI research and enterprise AI — from strategy and architecture to production.

I help organizations turn AI research into production systems — from strategy and architecture to deployment and adoption.

My work spans agentic AI, large language models, retrieval-augmented generation, multimodal AI, knowledge graphs, and production platforms across Azure, Databricks, and on-premises environments. I turn research ideas into deployed products, build AI communities, and lead global cross-functional teams.

Alongside my industry role, I am a postdoctoral researcher at Friedrich-Alexander-Universität Erlangen-Nürnberg and a freelance AI engineer, with research interests in robust and explainable ML, probabilistic modelling, time-series analysis, clinical AI, and responsible AI.

10+years in AI/ML research & industry
10+peer-reviewed publications
500+AI community members
1U.S. patent
View experience Download resume

Experience

Industry, research, and technical leadership

Jun 2024 — Present

Digital Focus Topic Lead — AI & Digital Assistants

Siemens Healthineers

Define enterprise AI strategy, platform architecture, governance, and reusable AI capabilities while leading cross-functional delivery of agentic AI, LLM, multimodal, and semantic AI solutions.

AI strategyAgentic AIEnterprise architectureResponsible AI
Mar 2024 — Present

Freelance AI Engineer & Consultant

Independent

Advise startups on enterprise AI architecture, production AI systems, LLM applications, and agentic AI.

AI consultingAI engineeringEnterprise AILLM applications
Jan 2023 — Present

Postdoctoral Researcher

FAU Erlangen–Nürnberg · Part-time

Conduct research in machine learning for healthcare and enterprise applications, supervise Master's and Ph.D. students, and contribute to international collaborations.

ResearchHealthcare AIMentorshipAcademic leadership
Oct 2022 — May 2024

Senior Data Scientist & Product Owner

Siemens Healthineers

Designed and deployed production ML systems for near-real-time medical-device and operational data, including monitoring, predictive analytics, and service-ticket automation.

Product ownershipProduction MLIoT analytics
Mar 2022 — Jun 2022

Visiting Researcher

University of California, Irvine · USA

BaCaTeC collaborative project with Prof. Stephan Mandt on modelling and simulation of irregularly sampled and mixed-type time-series data.

Probabilistic MLTime seriesInternational research
Oct 2018 — Sep 2022

Data Scientist & Product Owner

Siemens Healthineers · Part-time during Ph.D.

Developed explainable ML solutions using IoT, enterprise, and product-lifecycle data and invented a U.S. patent for AI-based customer-satisfaction prediction.

Explainable AIEnterprise dataPatent
Oct 2018 — Sep 2022

Doctoral Researcher

FAU Erlangen–Nürnberg · Ph.D. in Computer Science · Magna Cum Laude

Researched machine learning for healthcare and business analytics, with applications in gait analysis, process mining, and customer service AI. Conducted in collaboration with Siemens Healthineers. Advisor: Prof. Dr. Björn Eskofier.

Healthcare AI Time Series Explainable AI Process Mining
May 2017 — Aug 2017

Visiting Researcher

University of Michigan, Ann Arbor · USA

Developed novel signal processing and machine-learning algorithms for Atrial Fibrillation detection from ECG signals at the Frankel Cardiovascular Center and Biomedical & Clinical Informatics Lab.

Clinical AIECG analysisSignal processing

Research

Machine learning grounded in real-world problems

My research connects methodological ML work with clinical and industrial applications.

  • Robust & explainable MLDistribution shift, adversarial robustness, interpretable decision support.
  • Healthcare AIClinical data, ECG analysis, wearable sensing, and medical devices.
  • Probabilistic modellingIrregular, mixed-type and multimodal time-series data.
  • Research leadershipStudent supervision, peer review, and international collaboration.

Selected publications

Research highlights

ICML 2022

Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification

Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer, Doina Precup, Björn Eskofier, and Dario Zanca.

Robust machine learning · Distribution shift
IEEE Access 2021

System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data

An Nguyen, Stefan Foerstel, Thomas Kittler, Andrey Kurzyukov, Leo Schwinn, Dario Zanca, Tobias Hipp, Sun Da Jun, Michael Schrapp, Eva Rothgang, and Bjoern Eskofier.

Explainable industrial AI · Enterprise and IoT data
JNRE 2019

Development and Clinical Validation of Inertial Sensor-Based Gait-Clustering Methods in Parkinson's Disease

An Nguyen, Nils Roth, Nooshin Haji Ghassemi, Julius Hannink, Thomas Seel, Jochen Klucken, Heiko Gassner, and Bjoern Eskofier.

Clinical validation · Wearable sensing · Healthcare AI
View all publications on Google Scholar →

Leadership & recognition

Building teams, communities, and research impact

01

Community leadership

Co-founder of the Siemens Healthineers Data Science Community reaching more than 500 members.

02

Research leadership

Supervisor of Master's and Ph.D. students, reviewer for NeurIPS, IJCAI, ECML, and IEEE BHI, and contributor to international collaborations.

03

Innovation

Co-inventor of a U.S. patent for AI-based prediction of customer satisfaction with medical devices.

04

Awards

Outstanding Master's Degree Award (VDI), Fulbright Travel Grant, Rosa Luxemburg Foundation Scholarship, and TU Berlin Study Award.

Teaching & Mentorship

Courses, supervision, and community

4university courses taught or created
25+students supervised
6+years of teaching & mentorship

Courses created & taught · FAU Erlangen–Nürnberg

  • ML and Data Analytics for Industry 4.0Creator & instructor of a multi-semester seminar for engineers. 2019–2022.
  • Machine Learning for Time SeriesIndependent instructor, project course. 2020–2022.
  • Project Machine Learning and Data AnalyticsOrganizer & instructor. 2020-2022.
  • ML for Industry Seminar DesignDesigned course content for an industry-facing ML seminar. FAU MaD Lab, 2018.

Research group leadership

Commissarial head of the Applied Machine Learning Workgroup at FAU MaD Lab (Jul 2019 – Oct 2020) — led a group of PhD students working on industry-funded research projects during a period between senior postdocs.

Student supervision · 2019–present

Supervising PhD and Master's students as postdoctoral researcher. During the PhD, co-supervised and led 20+ Master's theses, Master's projects, Bachelor's theses, and research internships on topics spanning time-series ML, process mining, explainable AI, computer vision, and healthcare applications.

A collaborative mentor and connector who bridges academic research, teaching, and industry practice.

Peer review

NeurIPS · IJCAI · ECML · IEEE BHI

Education

Academic background

Dr.-Ing.

Computer Science

FAU Erlangen–Nürnberg · Magna Cum Laude · 2023

M.Sc.

Electrical Engineering

Technical University of Berlin · Grade 1.0/1.0 · 2018

M.Sc.Eng.

Electrical & Computer Engineering

University of Michigan · GPA 4.0/4.0 · 2017

Study Abroad

Electrical Engineering

KTH Royal Institute of Technology, Stockholm · 2 Semester ERASMUS+ Programme · 2014-2015

B.Sc.

Electrical Engineering

Technical University of Berlin · Grade 1.4/1.0 Top 1% of class · 2016

Contact

Let's build trustworthy enterprise AI that delivers real business value.

For AI leadership, technical advisory, research collaboration, or freelance engineering inquiries.

an.nguyen@live.de