Emanuele Guidotti

Emanuele Guidotti

Postdoctoral Researcher

USI Lugano


I am a physicist turned into a quant and computer scientist with a strong interdisciplinary mindset. I am interested in financial markets, machine learning, and data science, and my long-term goal is to study price dynamics in financial markets through the lens of deep learning and the analysis of big data.

I am currently working on price formation, efficient estimation of bid-ask spreads, asymptotic expansion formulas for diffusion processes, and explainable AI.

Among other things, I am the developer of COVID-19 Data Hub (Scientific Data), I have authored a classification algorithm inspired by Born’s rule (NeurIPS), and I maintain several R and Python packages including a package for high dimensional numerical and symbolic calculus in R (JSS).

For my contributions, I have received grants and awards from IVADO, the R Consortium, and Google Cloud.

Follow me on LinkedIn and GitHub

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Recent Publications

(2022). calculus: High Dimensional Numerical and Symbolic Calculus in R. Journal of Statistical Software, vol. 104(5), pag. 1–37.

PDF DOI Cite Website GitHub CRAN

(2022). A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution. Scientific Data, vol. 9(1), pag. 1-7. Nature Publishing Group.

PDF DOI Cite Data Website GitHub CRAN PyPI

(2020). COVID-19 Data Hub. Journal of Open Source Software, vol. 5(51), pag. 2376.

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Recent & Upcoming Talks

Grants & Awards

Awarded to enable performance tests on GPU for the paper Text Classification with Born’s Rule
Awarded to support the maintainance of COVID-19 Data Hub
Awarded to support the development of COVID-19 Data Hub