My research areas primarily include topological data analysis, algebraic statistics, and nonlinear algebra applied to mathematical and computational biology.
I earned my PhD from the Swiss Federal Institute of Technology in Lausanne (EPFL). Prior to joining Imperial, I held postdoctoral and visiting faculty positions at the Technion–Israel Institute of Technology, Duke University, Columbia University in the City of New York, and Tel Aviv University.
More information about me and my professional activities, including available opportunities to work with me, can be found on my personal webpage; more detail on my research is available at the Research tab above.
I have raised over US $800'000 in research funding from internal, external, and international sources as PI and co-PI; I received two funding awards within 6 months of starting at Imperial.
I have had four proposals accepted to host 5-day workshops at the Banff International Research Station.
I was featured as a Nonlinear Algebra Researcher at the Max Planck Institute for Mathematics in the Sciences.
et al., 2020, Linking transcriptomic and imaging data defines features of a favorable tumor immune microenvironment and identifies a combination biomarker for primary melanoma, Cancer Research, Vol:80, ISSN:0008-5472, Pages:1078-1087
et al., 2019, Predicting clinical outcomes in Glioblastoma: an application of topological and functional data analysis, Journal of the American Statistical Association, Vol:115, ISSN:0162-1459, Pages:1139-1150
et al., 2019, Tropical sufficient statistics for persistent homology, Siam Journal on Applied Algebra and Geometry, Vol:3, ISSN:2470-6566, Pages:337-371
et al., 2018, Quantitative analysis of immune infiltrates in primary melanoma, Cancer Immunology Research, Vol:6, ISSN:2326-6066, Pages:481-493
Monod A, 2014, Random Effects Modeling and the Zero-Inflated Poisson Distribution, Communications in Statistics-Theory and Methods, Vol:43, ISSN:0361-0926, Pages:664-680