Joerg Kiegeland
R&D GEOVIA Senior Software Developer at Dassault Systèmes
Queensland, Australia
Summary
👤
Senior🎓
Top SchoolJoerg Kiegeland is an experienced R&D senior software developer with 18 years building domain-focused systems across automotive, construction (BIM), and healthcare, now contributing to Dassault Systèmes GEOVIA from Queensland. He works across the full Java stack with a strong emphasis on model-driven technologies and standards such as IFC and FHIR, having consulted on national eHealth and BIM projects and implemented HL7/CDA solutions. Joerg is an open-source-minded engineer who has improved core functionality in notable projects like the engine-agnostic Deep Java Library (djl), addressing batch handling and multi-engine indexing challenges. His background in distributed systems and mathematical optimization (Diplom-Informatiker) informs a pragmatic approach to complex integration and traceability problems, and he often bridges research, standards, and production software.
19 years of coding experience
7 years of employment as a software developer
Diplom-Informatiker, Distributed Systems, Mathematical Optimization, Very Good, Diplom-Informatiker, Distributed Systems, Mathematical Optimization, Very Good at Computer Science at Technical University of Braunschweig
English, German
Github Skills (13)
neural-network10
javas10
mxnet10
pytorch10
machine-learning10
tensorflow10
deep-learning10
java10
ndarray9
ai9
autograd7
dgl7
onnxruntime5
Programming languages (4)
Github contributions (5)
deepjavalibrary/djl
Apr 2022 - Nov 2022
An Engine-Agnostic Deep Learning Framework in Java
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 reviews, 8 commits, 16 PRs in 6 months
Contributions summary:Joerg made multiple contributions related to enhancing the core functionality of the DJL framework. Their work focused on improving the handling of batch data, particularly allowing for empty batches. They addressed resource management issues within the TensorFlow engine and improved indexing capabilities across multiple engines. Furthermore, the user worked on providing a LinearCollection block.
deep-learningjavaneural-networkaimxnet
patins1/djl
Apr 2022 - Sep 2024
An Engine-Agnostic Deep Learning Framework in Java
Contributions:6 PRs, 47 pushes, 12 branches in 2 years 5 months
caffe2deep-learningagnosticmachine-learningneural-network
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.