My main research area is mathematical physics. I have a solid background in both mathematics and
physics and enjoy employing the tools and insights in both disciplines to solve interesting and meaningful problems in the realm of mathematical physics. My research programme often fits nicely into the
flourishing and productive subfield of  "String-Math" (see the annual conference since 2011).

One of the main focus of my research since 2010 is the topic of moonshine. Highlights in the past
few years include the following. 1) The discovery of umbral moonshine. 2) Uncovering the relation between umbral moonshine and string theory in the background of K3 surfaces. The recent construction
of the umbral moonshine modules for 5 of the 23 cases of umbral moonshine. 4) The classification of
mock forms of the umbral moonshine. 5) The very recent discovery of a few examples of a novel type
of connection between modular-type functions and finite group representations.

Another recent interest is to apply the theory and techniques of modular forms to help understand questions in other topics in mathematics and physics. Examples include to put constraints on
the spectrum of 2d conformal field theory admitting a weakly coupled gravity dual description (see
my paper Elliptic Genera and 3d Gravity), and a new method to compute the four-point conformal
blocks of vertex operator algebras and 2-dimensional CFTs (see my paper Modular Exercises for Four-Point Blocks - I). Another important theme of my recent research is to investigate the relation between
modular-type functions, in particular quantum modular forms, logarithmic vertex algebras, and
three-dimensional topology. I believe that this is a rich and multi-disciplinary topic which will lead
to progress in various fields including topology, algebra, number theory, and string theory. See my
paper 3d Modularity, Three-Manifold Quantum Invariants and Mock Theta Functions and a related
paper https://arxiv.org/abs/1904.06057 by Gukov and Manolescu.

Finally, my latest new interest lies in the application of mathematics and theoretical physics in artificial intelligence. See for instance the publication Covariance in Physics and Convolutional Neural
Networks.