Fazi

I am a 4th-year PhD student in computer science at the University of Cambridge. My research focuses on generalization of machine learning approaches for vision/graphics problems. Before that, I received an MSc in signal processing and communications from the University of Edinburgh and a BSc in electrical and electronics engineering from Bogazici University, Turkey. In my 2nd year, I spent the summer in Berlin, interning at Amazon as an applied scientist.

In my spare time, I actively volunteer for Women@CL events and enjoy photography.

Research interests: Machine Learning, Computer Vision, 2D Graphics

Publications

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Hypernetworks for Generalizable BRDF Representation

Fazilet Gokbudak, Alejandro Sztrajman, Chenliang Zhou, Fangcheng Zhong, Rafal Mantiuk and Cengiz Oztireli

Under review.

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One-shot Detail Retouching with Patch Space Neural Transformation Blending

Fazilet Gokbudak, Cengiz Oztireli

20th ACM SIGGRAPH European Conference on Visual Media Production (CVMP), 2023.

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Spatial receptor allocation for a multiple access hub in nanonetworks

Fazilet Gokbudak, Bayram Cevdet Akdeniz, Tuna Tugcu, and Ali Emre Pusane

IEEE Transactions on Molecular, Biological and Multi-Scale Communications, 2019.

News

  • We are running our 2nd GirlsWhoML workshop👩‍💻. If you are interested, please sign up here.
  • Our paper "Hypernetworks for Generalizable BRDF Representation" is now available on arXiv😊. You can find the paper here.
  • Our paper "One-shot Detail Retouching with Patch Space Neural Transformation Blending" got accepted to ACM SIGGRAPH European Conference on Visual Media Production (CVMP 2023) 🎉😊.
  • Awarded with Graduate Student Travel Award by Queens' College to attend this year's Siggraph🎉. Looking forward to attending the conference and following up with emerging technologies in computer graphics🤓.
  • Supervised Artificial Intelligence course this term.
  • Completed my internship as an Applied Scientist Intern at Amazon's computer vision research team in Berlin🥳. During my internship, I worked on makeup transfer with generative models.