Moumanti Podder
IISER, Pune
Moumanti Podder completed her Bachelor of Statistics (Hons.) and Master of Statistics, with a specialization in mathematical statistics and probability, from the Indian Statistical Institute, Kolkata (2008-2011 and 2011-2013 respectively). She then went on to complete her PhD in mathematics from the Courant Institute of Mathematical Sciences, New York University, under the supervision of Prof. Joel Spencer (2013-2017). She served as a Postdoctoral Fellow at the School of Mathematics, Georgia Institute of Technology (2017-2018), an Acting Assistant Professor at the Department of Mathematics, University of Washington (2018-2019), and an Assistant Professor of Practice (non-tenure-track) at the NYU-ECNU Institute of Mathematical Sciences at New York University's Shanghai Campus (2019-2020), before starting as an Assistant Professor (tenure-track) at the Department of Mathematics, Indian Institute of Science Education and Research (IISER) Pune (2021-present). She primarily works on two-player combinatorial games played on random graphs, percolation games, etc. Selected as Associate in 2023.
Session 1E - Lectures by Fellows and Associates
Meena B Mahajan, IMSc, Chennai
Combinatorial games on random premises, their connections with percolation, probabilistic automata and statistical mechanics
Percolation games and their intimate ties with the popularly studied topic of percolation in physics can be of particular interest to researchers. The talk will briefly discuss the various versions of percolation games studied on directed 2-dimensional lattices and rooted random trees, with emphasis upon the phenomenon of phase transition pertaining to the probability of draw (i.e., exploring the 'regimes', in terms of the parameter(s) involved, where the probability of draw in such a game is 0, and where it is strictly positive). Such phase transition phenomena also bear close connections with the ergodicity of suitably related probabilistic cellular automata/probabilistic tree automata, as well as weak spatial mixing properties of suitably related models of statistical mechanics.