Gurvinder Dahiya is a seasoned technology leader and Co-Founder/CTO based in Trondheim with 16 years of experience building data-driven systems, machine learning solutions, and secure network software. He has led AI and data teams in financial crime prevention at TietoEVRY and architected large-scale log and network monitoring platforms at UNINETT, combining production-grade ML with deep systems and security expertise. An active open-source contributor, he improved transformer generation and core stream-reassembly features in notable projects like x-transformers and Suricata, reflecting both ML and low-level networking fluency. Gurvinder’s work often draws on analogies between human thinking and computation to design future-facing solutions, and he routinely turns research insights into operational tools. Comfortable across the stack, he blends hands-on engineering, team leadership, and product-oriented experimentation to solve hard data and security problems. Colleagues describe him as initiative-driven and idea-rich, able to translate complex requirements into pragmatic, auditable systems.
16 years of coding experience
15 years of employment as a software developer
Bachelor of Technology, Computer Engineering, Bachelor of Technology, Computer Engineering at Kurukshetra University
Master in security and mobile computing, Mobile Computing, Master in security and mobile computing, Mobile Computing at Teknillinen korkeakoulu-Tekniska högskolan
Suricata is a network Intrusion Detection System, Intrusion Prevention System and Network Security Monitoring engine developed by the OISF and the Suricata community.
Role in this project:
Back-end Developer
Contributions:162 commits in 1 year 6 months
Contributions summary:Gurvinder's commits primarily focus on enhancing the Suricata network intrusion detection system. The user implemented the "Target Based Stream Reassembly" feature, including adding comments to the code. The commits involve modifications to `stream-tcp-reassemble.c` and `stream-tcp.c` files, indicating work on the core stream processing logic. Unit tests were also added to test the added features.
A concise but complete full-attention transformer with a set of promising experimental features from various papers
Role in this project:
ML Engineer
Contributions:8 commits, 6 PRs in 7 months
Contributions summary:Gurvinder contributed to the `x-transformers` repository, which focuses on transformer architectures for deep learning. Their contributions included implementing the "top_a" sampling method within the autoregressive wrapper, fixing a bug related to the use of L2 normalization, and modifying the code to avoid saving intermediate values unless needed. These changes indicate a focus on improving model generation and optimization within the transformer framework. The user also addressed minor bugs related to variable names and code structure.
pytorchnlptransformersexperimental-featuresbert
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