My research interests include algorithmic/computational mathematics and statistics, multiple testing, Monte Carlo methods, quantum computing and quadratic unconstrained binary optimisation, mathematical optimisation and information theory, but also algorithmic number theory. My interest in algorithmic mathematics and statistics includes, amongst others, the analysis and development of efficient/fast algorithms (also randomised algorithms) for specialised applications together with an implementation and a proof of correctness.
To be precise, the main focus of my research has consistently been in the area of computational statistics and data-driven mathematics, with particular interest in Monte Carlo inference, entropy estimation and statistical data compression (Part III, University of Cambridge, 2009-10; resulting admission as a Scholar of Churchill College Cambridge in 2010), Monte Carlo based multiple hypothesis testing (Ph.D., Imperial College London, 2011-15; Ph.D. awarded without corrections), smoothing approaches for broken stick/plane regression (post-doc, Columbia University, 2015-16), and testing for changepoints in multivariate data (StatScale, 2017-19). I also hold close ties with Los Alamos National Laboratory, the world's largest research laboratory, to which I am invited regularly to conduct research for innovative and timely projects (2015, 2016, 2017, 2019 with more than six months total spent at the laboratory; projects comprised quantum molecular dynamics, combinatorial optimisation on a quantum annealer, and theoretical research on NP-complete optimisation problems). In 2018 I visited Lawrence Livermore National Laboratory for a research project on quadratic unconstrained binary optimisation and neuromorphic computing.