Pinar Yanardag

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Featuring a dress and a necklace generated by GANs -- made by human designers at Conference of the Future'19, Chile.


My principal research interest lies in developing deep learning approaches to enable human-AI collaboration, creativity and interpretability. My research is published at top computer science conferences such as NeurIPS, ICCV and KDD, and featured in mainstream media (e.g., The Washington Post, BBC, CNN) and magazines (e.g., Motherboard, Rolling Stone). For more information, see the Publications page.

Currently, I am leading a research lab at Bogazici University under the TUBITAK Outstanding Research Fellows program. Previously, I was a postdoctoral research associate at MIT Media Lab. I did my Ph.D. in Computer Science at Purdue, under the supervision of SVN Vishwanathan. During my Ph.D., I also spent time at Amazon (P13N team) and VMware. I received my Master’s degree from Bogazici University under the supervision of Ethem Alpaydin. During my graduate studies, I was supported by Fulbright PhD Fellowship and Google Anita Borg Scholarship.

I am also the founder of AI Fiction, a creative design studio specializing in artificial intelligence. Some of our work includes designing AI algorithms for HBO’s Westworld, for which I was a Creative Director nominee at 72nd Primetime Emmy Awards. I am also a fashion designer and co-founded GLITCH–the world’s first clothing line with fashion designs created by GANs and made in real life by human collaborators.

I regularly teach the course How to Generate (Almost) Anything which focuses on generative models and human-AI collaboration. I am also interested in teaching AI to fashion designers and taught a course at London College of Fashion called “AI & Fashion”. See Courses section for more information.

For a full CV, please see this PDF.


Aug 15, 2021 Graph2Pix is accepted to AIM workshop at ICCV!
Jul 23, 2021 LatentCLR is accepted to ICCV!
May 22, 2021 Two of our senior projects are selected among Top 3 in the department! :sparkles:

selected publications

  1. ICCV
    LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
    Oguz Kaan Yuksel, Enis Simsar, Ezgi Gulperi Er, and Pinar Yanardag
    International Conference on Computer Vision (ICCV) 2021
  2. WACV
    StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation
    Umut Kocasari, Alara Dirik, Mert Tiftikci, and Pinar Yanardag
    Winter Conference on Applications of Computer Vision (WACV) 2021