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  Marija Vucelja   Marija Vucelja
Assistant Professor , Theoretical Condensed Matter Physics
Ph.D., 2010, Weizmann Institute of Science

mv8h@Virginia.EDU email   434-924-6572 tel 310A JBL Office Web >

Theoretical Condensed Matter

Marija Vucelja is a statistical physicist interested in biophysics, soft condensed matter and computational physics. She studied mixing and clustering of particles in flows; problems that are relevant for understanding the formation rain droplets and planetesimals, clumping of pollutants on water surfaces and have many industrial applications. She derived the compressibility of surface flows and described the aggregation-disorder transition of particles in flows. Using “chaotic mixing” she substantially accelerated specific Monte Carlo algorithms (the main numerical tools for studying complex systems). Next, Marija investigated the emergence of clones in populations. Drawing analogies between glassy systems and population dynamics she calculated the rate of coalescence (the probability of two individuals belonging to the same clone). Lately, Marija’s focuses on anomalous thermal relaxations in various physical systems, non-reversible Markov Chains and biophysics (here especially population dynamics and evolution).  For more information on Marija’s research and group please visit: https://sites.google.com/view/marijavucelja/



E. Kussell and M. Vucelja, 
Non-equilibrium physics and evolution - adaptation, extinction, and ecology: a Key Issues review
Rep. Prog. Phys. 77, 192602 (2014) 



M. Vucelja, K. S. Turitsyn and M. Chertkov, 
Extreme-value statistics of work done in stretching a polymer in a gradient flow, 
Phys. Rev. E 91, 022123 (2015)



R. Neher, M. Vucelja, M. Mezard and B. I. Shraiman, 
Emergence of clones in sexual populations
J. Stat. Mech. 01, P01008 (2013) 


M. Vucelja, G. Falkovich and K. S. Turitsyn, 
Fractal iso-contours of passive scalar in two-dimensional smooth random flows
J. Stat. Phys. 147, 424 -- 435 (2012) 


K. S. Turitsyn, M. Chertkov and M. Vucelja, 
Irreversible Monte Carlo Algorithms for Efficient Sampling
Physica D 240, 410 — 414 (2011)