I am a Computer Science Master’s student at the Technion, Israel, studying under the supervision of Prof. Yuval Filmus. I obtained my Bachelor’s degree in Computer Science from the Technion.

Research interests

I am interested in theoretical computer science, especially in theoretical machine learning and complexity. My recent works are of the topics of information compleity, Huffman codes, multi armed bandits and online algorithms.

Contact information


yuval.dagan at cs . technion . ac . il



Main research works

  • Dagan, Y., Filmus, Y., Gabizon, A., & Moran, S. (2017, June). Twenty (simple) questions. In Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing (pp. 9-21). ACM.
    Invited to HALG 2018
  • Dagan, Y., Filmus, Y., Hatami, H., & Li, Y. (2017, June). Trading information complexity for error. In 32nd Computational Complexity Conference (pp. 16:1-16:59).
    Invited to CCC2017 Special Issue
  • Yuval Dagan and Ohad Shamir. Detecting correlations with little memory and communication. Proves that one requires quadratic memory in order to optimally reveal a correlation in data using a streaming algorithm. Link
  • Yuval Dagan and Koby Crammer. A Better Resource Allocation Algorithm with Semi-Bandit Feedback. Accepted to the International Conference on Algorithmic Learning Theory (ALT 2018). Link

Other research work

  • Yuval Dagan and Saar Zehavi. There is no online sparse regressor with sublinear regret. Presents an answer to the main question asked by Dr. Satyen Kale, published in the open problem section of Conference on Learning Theory 2014. We showed that under standard assumptions on hardness of computation, there is no efficient sparse online learning algorithm with sublinear regret. We did not publish our result.  Link
  • Reuven Cohen, Yuval Dagan and Gabi Nakibly. Procative rerouting in network overlays. Suggests a rerouting algorithm for computer networks. Link

Teaching assistance

Role: delivering tutorials, creating and checking homework assignments and exams.


  • Introduction to Machine Learning (winter semester 14/15)
  • Automata and Formal Languages (spring semester 14/15, winter semester 15/16)
  • Complexity Theory (spring semester 15/16)