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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.
CV Resume


Papers:

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

On the evolution in the configuration model, submitted.

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

Permanents of heavytailed random matrices with positive elements, submitted.

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

Tugofwar and infinity
Laplace equation with vanishing Neumann boundary condition (with Yuval Peres, Scott Sheffield and Stephanie
Somersille),
Comm. Partial Differential Equations, 37(10):18391869, 2012

Isolated zeros for Brownian
motion with variable drift (with Krzysztof Burdzy, Yuval Peres and Julia Ruscher),
Electronic Journal of
Probability, 16:no. 65, 17931815, 2011.

Brownian motion with variable
drift can be spacefilling (with Yuval Peres and
Brigitta Vermesi),
Proceedings of the American Mathematical Society, 139(9):33593373, 2011.

Equality of Lifshitz and van
Hove exponents on amenable Cayley graphs (with Ivan Veselić),
Journal de
Mathématiques Pures et Appliquées, 92(4):342362, 2009.

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 129. Birkhäuser Verlag, Basel, 2009.

Sharpness of the phase
transition and exponential decay of the subcritical cluster size for percolation on quasitransitive graphs (with
Ivan Veselić),
Journal
of Statistical Physics, 130(5):9831009, 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):



