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tekinertekin/README.md

Tekin Ertekin

Software Engineer — 12+ years building production systems. Currently working on low-precision numerics and machine-learning infrastructure in C.

Open-source contributions

Thirteen merged pull requests across four organisations. Click a logo to open that organisation's list of my merged pull requests.

NASA
NASA

F′ flight software · 3 merged
ROOT
ROOT

CERN · 5 merged
SciPy
SciPy

scipy & xsf · 3 merged
llama.cpp
llama.cpp

ggml-org · 2 merged

Mostly numerical correctness and performance work: premature overflow in Bessel functions, a flood fill that hung or leaked past its outline depending on the brush alpha, a socket timeout that was configured but never applied, generation settings an inference server accepted but never reported back, and a transposed convolution that silently computed only the first batch.

mantissa

mantissa — a low-precision numerics core for neural networks, written in C.

It stores weights and activations in narrow formats (fp8, fp4, and a block-scaled variant) and runs dense and convolutional layers over them with hand-written NEON kernels. Every performance claim in the repository is backed by a paired randomised measurement against a same-binary control, and results that did not survive that test are documented as rejected rather than removed.

Education

Hacettepe University PhD, Computer EngineeringHacettepe University, 2021 – present
Thesis in progress. First paper published in Applied Sciences, 2026 — Concept Tree Learner (CTL).
Hacettepe University MSc, Game Technologies and Computer AnimationHacettepe University, 2021
adaptive-voronoi-mapping: adaptive Voronoi mapping for legible visualisation of very large labelled spatial point data.
METU BSc, Computer EngineeringMiddle East Technical University, 2014

Publications

  • M. T. Ertekin and B. Genç, "Concept Tree Learner (CTL): An Incremental and Interpretable Symbolic Framework for Binary String Rule Induction", Applied Sciences, vol. 16, no. 17, art. 8542, 2026. Open access, doi:10.3390/app16178542
  • M. T. Ertekin and B. Genç, "User Oriented Visualization of Very Large Spatial Data with Adaptive Voronoi Mapping (AVM)", in Engineering Cyber-Physical Systems and Critical Infrastructures, Springer, 2023, pp. 515–536. doi:10.1007/978-3-031-31956-3_45
  • M. T. Ertekin, "Çok Büyük Konumsal Verinin Kullanıcı Odaklı Görselleştirilmesi", MSc thesis, Institute of Informatics, Hacettepe University, 2020. In Turkish, full text

Work

Bumper

Software Engineer.

Before that, national-scale public-sector systems in .NET — both still in production:

  • KYK, Turkey's student loans and housing agency — worked on a team that built 9 databases and 11 applications from scratch. Around 15 million users.
  • Turkish Employment Agency (İŞKUR) — employment and public-benefit programme systems serving 8+ million users.

Other work

  • Flight-controller firmware — modified the C sources running on a Pixhawk Cube autopilot.

Pinned Loading

  1. mantissa mantissa Public

    Low-precision numerics core for neural networks, in C. Custom float formats (tekin8/tekin32), narrow-store/wide-accumulate, Python bindings. By Tekin Ertekin.

    C

  2. adaptive-voronoi-mapping adaptive-voronoi-mapping Public

    Adaptive Voronoi mapping for legible visualization of very large labeled geospatial point data an adaptive quadtree refines detail at class boundaries, then per-region Voronoi cells are colored by …

    Python