Sait Celebi


I am Sait, a PhD student at Computer Science Department of Purdue University under the supervision of Prof. Jennifer Neville. My research interests include Machine Learning, Artificial Intelligence and Social Network Analysis. I like writing all piece of code that a machine could, possibly, understand.

You can find my resume here.

Find me on StackOverFlow, GitHub, LinkedIn and Google scholar. If you want to say hello, you are welcome:


Journal Publications

  1. Sait Celebi, Ahmet Erkan Bilmez, Feruz Davletov, Tarik Arici, and Bugra Gedik. "TopicStrand: Analyzing Social Media Discussions based on Participation Characteristics". Submitted to The Eight ACM International Conference on Web Search and Data Mining (WSDM 2015)
  2. Ali Selman Aydin, Sait Celebi, and Tarik Arici. "Low-Complexity Unsupervised Learning From High-Dimensional Data Using Random Forests". Submitted to IEEE Transactions on Knowledge and Data Engineering
  3. Tarik Arici, Sait Celebi, Ali Selman Aydin, and Talha Tarik Temiz. "Low-Complexity Shape Recognition Using Random Forest Classifiers with Random Rectangle Features". Submitted to 13th International Conference on Machine Learning and Applications (ICMLA 2014)
  4. Tarik Arici, Sait Celebi, Ali Selman Aydin, and Talha Tarik Temiz. "Robust Gesture Recognition using Feature Pre-processing and Weighted Dynamic Time Warping". Multimedia Tools and Applications, 1-18

Conference Publications

  1. Tarik Arici, Sait Celebi, Ali Selman Aydin, Talha Tarik Temiz. "Discriminant Boosted Dynamic Time Warping and Its Application to Gesture Recognition". VISAPP (2) 2014: 223-231
  2. Sait Celebi, Ali Selman Aydin, Talha Tarik Temiz, Tarik Arici. "Gesture Recognition using Skeleton Data with Weighted Dynamic Time Warping". VISAPP (1) 2013: 620-625
  3. Tarik Arici and Sait Celebi. "Flow-Driven Regularization with Regularizer Verification in Optical Flow Computation". International Conference on Image Processing. IEEE, 2012


  1. Master's Thesis: Low-Complexity Supervised Learning for Gesture and Shape Recognition


  1. Curriculum vitae (CV)