Quantum-enhanced integrative modelling
Currently, I conduct research as part of our research group at ProteinQure. The focus of my research is on marrying near term quantum computing and machine learning techniques with biology applications, in particular in the areas useful to drug design.
If this sparks your interest, have a look at our papers approaching coarse-grained protein folding using QAOA with hard and soft constraints on Rigetti’s 19-Acorn chip or using quantum annealing on D-Wave’s 2000Q.
I am interested in pratical applications of current and near term quantum computing devices, especially in the areas of optimization and machine learning. In the thesis Quantum-enhanced sampling for probabilistic inference in undirected graphical models I looked closely at computational advantages of D-Wave 2000Q quantum processing chip against classical MCMC sampling methods.
Being a firm believer in the openness, collaboration and ultimately deduplication of work, I strongly advocate for adoption of open source in a nascent field like this.
As the realm of computers becomes more and more intertwined with our lives, it is fundamental that we are able to protect ourselves from nefarious actors. I am particularly interested in analysis of side-channel information that software products might unintentionally leak. For this purpose, I have created User action detection toolkit, which can be used to analyze applications for network-based side channel information leaks.
In my master’s thesis Detecting user actions from encrypted traffic I investigated how machine learning techniques applied to traffic analysis can be used to detect exactly what are we doing within applications on our phones.
While majority of my current research interests revolve around the world of the very small, I have a long-term fondness of the world of very big. On this blog, you can find several posts that revolve around analysis of astronomical observations.
List of publications
Updated list of my academic works can be found on Google Scholar.