Tonći Antunović

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From 07/2012 till 06/2015 I was a Hedrick Assistant Adjunct Professor in the Department of Mathematics at UCLA. I received my PhD in mathematics from UC Berkeley and was advised by Sourav Chatterjee and Yuval Peres. My research interests are probability theory and stochastic processes. I did my undergraduate studies in the Math Department at University of Zagreb.

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Centar izvrsnosti matematike


  1. Stationary Eden model on groups, (with Eviatar B. Procaccia), submitted.

  2. On the evolution in the configuration model, submitted.

  3. Coexistence in preferential attachment networks (with Elchanan Mossel and Miklos Racz), submitted.

  4. Permanents of heavy-tailed random matrices with positive elements, submitted.

  5. Competing first passage percolation on random regular graphs (with Yael Dekel, Elchanan Mossel and Yuval Peres), submitted.

  6. Tug-of-war and infinity Laplace equation with vanishing Neumann boundary condition (with Yuval Peres, Scott Sheffield and Stephanie Somersille),
    Comm. Partial Differential Equations, 37(10):1839-1869, 2012

  7. Isolated zeros for Brownian motion with variable drift (with Krzysztof Burdzy, Yuval Peres and Julia Ruscher),
    Electronic Journal of Probability, 16:no. 65, 1793--1815, 2011.

  8. Brownian motion with variable drift can be space-filling (with Yuval Peres and Brigitta Vermesi),
    Proceedings of the American Mathematical Society, 139(9):3359--3373, 2011.

  9. Equality of Lifshitz and van Hove exponents on amenable Cayley graphs (with Ivan Veselić),
    Journal de Mathématiques Pures et Appliquées, 92(4):342--362, 2009.

  10. Spectral asymptotics of percolation Hamiltonians on amenable Cayley graphs (with Ivan Veselić),
    Methods of spectral analysis in mathematical physics,
    volume 186 of Oper. Theory Adv. Appl., pages 1--29. Birkhäuser Verlag, Basel, 2009.

  11. Sharpness of the phase transition and exponential decay of the subcritical cluster size for percolation on quasi-transitive graphs (with Ivan Veselić),
    Journal of Statistical Physics, 130(5):983--1009, 2008.

Neural network reinforcement learning for an avoidance game
A neural network learning to play a game evolving as a stationary Markov process via local dynamic programming (reinforcement learning) and stochastic gradient descent. See video for the purpose and the report.

Teaching at UCLA:

  • Math 31A (Fall 2013)
  • Math 33B (Winter 2013)
  • Math 170A (Fall 2012, Winter 2015)
  • Math 170B (Winter 2013, Spring 2013, Winter 2014, Fall 2014, Spring 2015)
  • Math 171 (Spring 2014, Spring 2015)
  • Math 275A (Fall 2013)

Previous teaching (at UC Berkeley):