Alexisz Gaál

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I am a PhD graduate from the Courant Institute co-advised by Charles Newman and Daniel Stein. I am interested in probability theory, statistics, mathematical physics and neuroscience.

During my PhD, I worked on statistical mechanics of point processes, computational neuroscience, stochastic processes and co-developed a neural learning algorithm. My work on point processes for physical systems, focuses on showing long-range orientation order of such processes, that relates to giving mathematical rigorous proof of crystallization in solids. I also co-developed a neural learning algorithm that modifies the well known TD-lambda algorithm from reinforcement learning to suit the biological constraints of neuroscience.

I am currently a quantitative Python developer, and quant analyst at GSR. I have advanced coding experience in Python and C. I have management experience, I led a wonderful team at a Hungarian company in 2019.


Mathematical Statistics, Spring 2019

Probability seminar (undergrad/master), Fall 2018

Theory of Probability, Summer 2018

Analysis, Spring 2017

Mathematical Statistics, Spring 2017

Stochastic Calculus, Summer 2016


  • Exit time asymptotics for dynamical systems with fast random switching near an unstable equilibrium, with Yuri Bakhtin, Stochastics and Dynamics 20(1) (2020)
  • Long-range orientational order of a random near lattice hard sphere and hard disk process, first version, Journal of Applied Probability 57(2) (2020)
  • Decision Making and Learning in Artificial Physical Systems, Doctoral dissertation (2019)
  • Prospective Coding by Spiking Neurons, with Brea J., Urbanczik R., Senn W., PLoS Comput. Biol. 12(6) (2016)
  • Long-range order in a hard disk model in statistical mechanics, Electron. Commun. Probab. Volume 19 (2014)
  • Spontaneous breaking of rotational symmetry in a probabilistic hard disk model in Statistical Mechanics, Master thesis (2013)
  • Große Abweichungen für empirische Verteilungen, Bachelor thesis (2011)