Kim Hammar
Postdoctoral Researcher at University of Melbourne
Stockholm, Sweden
Summary
👤
Senior🎓
Top SchoolKim Hammar is a postdoctoral researcher with 11 years of industry and academic experience, currently affiliated with the University of Melbourne, KTH and Imperial College London after receiving a VR International Postdoctoral Fellowship. His work sits at the intersection of game and control theory, machine learning and large-scale systems, applied to networking and security problems. Kim combines rigorous academic training (MSc with top GPA and a PhD track at KTH) with hands-on engineering from roles at Hopsworks, Ericsson and Allstate, contributing to feature-store functionality and expanding data-type support in a widely used data-intensive AI platform. He teaches and supervises across networking and distributed systems courses, and his background as a former network operator and backend developer gives him practical insight into deployable, resilient system design. Notably, he blends theoretical modeling with production-focused contributions—bridging research prototypes and multi-tenant data platforms.
11 years of coding experience
2 years of employment as a software developer
KTH Royal Institute of TechnologyGymnasieexamen, Natural Sciences, Gymnasieexamen, Natural Sciences at Klara Norra Gymnasium
English, Spanish, Swedish
Github Skills (6)
feature-store10
feature-engineering10
feature-management10
python9
machine-learning9
front-end-development8
Programming languages (10)
Github contributions (5)
logicalclocks/hopsworks
Jan 2019 - Oct 2019
Hopsworks - Data-Intensive AI platform with a Feature Store
Role in this project:
Back-end Developer
Back-end DeveloperContributions:42 commits, 48 PRs, 74 comments in 9 months
Contributions summary:Kim's commits primarily focus on modifications to the Hopsworks frontend, particularly around feature store functionalities. They addressed issues related to undefined feature stores in the frontend and prevented the use of hyphens in feature group names. The user also made adjustments to training dataset creation and updating processes. Furthermore, they contributed by adding support for image and avro types within the feature store, expanding its data handling capabilities.
gcppythonserverlessai-platformhopsworks
Limmen/erl_pengine
May 2017 - Jun 2021
Contributions:4 releases, 67 commits, 28 pushes in 4 years 1 month
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