I focus on designing scalable, interpretable algorithms to enable novel scientific discovery from biomedical imaging data at multiple scales ranging from diffuse optical tomography (DOT) to confocal and superresolution microscopy (SRM, STED, SMLM, dSTORM). I leverage and extend concepts from several fields: fuzzy computing (belief theory, information fusion) to simulation (discrete event simulators, agent based simulation), graph algorithms, vector fields, recurrent neural networks, and structural causal discovery from complex data. 
Application domains range from degenerative (Alzheimer, ageing) to metabolic (diabetes) and infectious disease, with a specific focus on elucidating subcellular function from complex data. Whenever possible I like to design algorithms that push beyond the empirical resolution limits of modalities and that are adaptive or robust to complex noise models.

I started my research career developing a paralell discrete event simulator in C++ with the capability to switch at runtime between simulation paradigms enabling simulation designers to find automatic optima for their domain uses. In my Master's I combined distributed metaheuristics with hyperheuristics to enable symbolic regression for complex epidemiological simulations. 
 My doctorate focused on designing algorithms that extract interactions below observable empirical precision from superresolution microscopy. 
In my postdoctoral fellowship I work on inferring equilibria from causal topological signatures in noisy biomedical imaging modalities.