Experience

  • 2022
    PhD SWE Internship / Student Researcher
    Google - CA, US
    • Research on improving self-supervised dense contrastive learning using different dense comparison methods and reconstruction decoders. Work accepted to NeurIPS 2022 Self-Supervised Learning - Theory & Practice Workshop.
  • 2021
    PhD SWE (Machine Learning) Internship
    Google - CA, US
    • Implemented & compared various visual-semantic image embedding techniques, deployed a novel Supervised Contrastive Learning based method to replace an attribute-based embedding one to assist graph-hierarchical clustering.
  • 2020
    Research Internship
    Michigan State University (MSU) – MI, US
    • Advisor: Prof. Saiprasad Ravishankar
    • Worked on dynamic estimation methods and developing block-matching algorithms with learned transforms.
  • 2019
    Graduate Research Internship
    Los Alamos National Laboratory (LANL) – NM, US
    • Worked on Machine/Deep Learning based tomographic reconstruction methods for ill-posed single view reconstruction.
  • 2017
    Internship
    ASELSAN Advanced Sensing Research Program Department – Ankara, Turkey
    • Performed time and frequency domain passive acoustic mapping, sparsity-based microbubble detection using constrained optimization methods for ultrasound imaging.
  • 2016
    Internship
    KAREL Electronics Research & Development Center – Ankara, Turkey
    • Performed image processing tasks for vehicles on ARM based NXP iMX6 board by cross compilation of OpenCV libraries.