## About

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 senior analyst at Citibank. I have advanced
coding experience in Python and C. I have management experience, I led a wonderful team at a
Hungarian company in 2019.

## Teaching

#### Mathematical Statistics, Spring 2019

Recitations take place Fridays 2pm to 3:15pm in WWH 201 for Thomas Leblé's class and 3:30pm to 4:45pm in WWH 312 for Yisong Yang's class. Please note that the two recitations are not completely interchangeable.

Office hours are Tuesdays 2pm to 3pm in WWH 805 and Fridays 5:15pm to 6:15pm in WWH 524.

For a concise review of probability see Review of Probability Theory by Arian Maleki and Tom Do. For general purposes, Hogg and McKean's Introduction to Mathematical Statistics is a book that I can recommend.

Optional coding homework:
- A .cvs file with a list of 4000 zeros and ones was created using the Python 3 code. Here is a short description.
- Two .csv files (file1, file2) with a list of 50 data points (x_i, y_i) are provided. The data are generated from the same model in both cases with different parameters though. Find the suitable model and fit your parameters to the data.

#### Probability seminar (undergrad/master), Fall 2018

Dates and location: Mon. 6pm to 7:30pm (09/17 through 12/10) in WWH 805.

#### Theory of Probability, Summer 2018

#### Analysis, Spring 2017

Homework problems are from

*Foundations of Mathematical Analysis* by Richard Johnsonbaugh, and W. E. Pfaffenberger.

Changes in the pdf's are shown in red.

#### Mathematical Statistics, Spring 2017

#### Stochastic Calculus, Summer 2016

## Research/Theses

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)