I am an Assistant Professor in Data Science at Northeastern University – London, teaching courses in Data science and Machine Learning and an Honorary Research Fellow at the Centre for Climate Finance & Investment at Imperial College London.
My biggest research passion is diving into data. I refer to myself as a DataMetrician since I base my research and analytical methods on observational data.
I define DataMetrics as the intersection of Data analytics, Statistical Learning, and Computational science. To be more precise, I see DataMetrics in between (rather than the intersection) Data Science and Econometrics (or any other field-based statistics) where the Data is pivotal (as in Data Science) but the cause-effect relationship plays a huge role (Econometrics). However, it differs from them as the analysis is done by taking the background environment in mind (Finance in my case) rather than the optimization of an algorithm over a specific Dataset but at the same time without imposing an a priori strong theoretical structure in the modeling approach.
My idea of DataMetrics can be synthesized in the following quote:
“It is not the data that should fit models, but models that should fit the data”
However, this should not be taken literary. I do think that models are important: giving a structure to our techniques is necessary. Nevertheless, I do think that data should be the main driver. This means that instead of trimming data at our necessity to fit existing models, researchers should develop new models to reflect the complexity of the data. In particular, even if I see the data as the pivotal ingredient of the analysis, I do not advocate for orthodox data-driven models as I believe there is room for conceptual models to be of great use to make inference and better predictions.
My background is in Economics and Finance, with a specific interest in Applied Econometrics. This lead me to first enroll in a Master in (Quantitative) Finance in which much of the theory was then applied to the real world and then to pursue a Ph.D. in Economics where I analyzed multidimensional Economic dataset through the lenses of Tensor methods, which is a mathematical formulation mostly used in machine learning to analyze multidimensional objects with interrelations among the data points.
After concluding my Ph.D. in Economics, I started a second Ph.D. in Applied Mathematics at King’s College London with the objective to study and produce research with DataMetrics in mind. In the three years of the Ph.D., I studied financial markets using models and techniques borrowed from Complex Systems, Machine learning, Econometrics, and Quantitative Finance (including Econophysics and Mathematical Finance).
My papers are mostly related to applied research in Finance using different analytic tools borrowed from different research fields. I believe in the transversality of science and the possibility, when properly and wisely adapted, to use methodologies from other fields to extract useful information from the Data analyzed.