Stefano Meschiari, Ph.D.
Senior Data Scientist, Duo Security
I am an experienced data scientist with a background in Astrophysics research. I have worked on projects that involved analyzing complex datasets, building novel production ETL and machine learning platforms, researching new algorithms and approaches to attacking hard problems, and creating functional data tooling for other teams.
I enjoy working with cross-functional teams to create, polish, and evolve from napkin ideas to production. In particular, I love collaborating with product teams to coherently integrate data science into their vision, and helping them test out new concepts and visualizations with data science-driven prototypes.
July 2017-present, Duo Security Senior Data Scientist
- I am part of a stellar data science group at Duo. We are focused on creating new data science-powered solutions to help companies detect threats and protect themselves.
At Duo, I lead the technical development of the data science platform, which powers the UEBA product feature. Our platform uses Spark at scale to analyze data, build models, and surface possible threats and authentication anomalies.
Feb 2016-June 2017, Civitas Learning Product Data Scientist
- Created and improved on prototype machine learning tools and pipelines to model student outcomes.
- Prototyped new product ideas and internal tooling that employ machine learning, novel summary statistics and visualizations.
- Maintained and improved the custom modeling platform. Reduced training and scoring running time (and cloud costs) by half.
- Independently developed components for end-to-end Data Science projects:
- Back-end (Node.JS/Express, created new APIs and services exposing new functionality to internal services)
- Front-end (React, HighCharts, and custom-built components and visualizations).
2012-2016, University of Texas at Austin W. J. McDonald Postdoctoral Fellow
- Led the data analysis effort for the Lick-Carnegie science collaboration (~20 scientists across the United States). Analyzed time series data using my Markov-Chain Monte Carlo code, Systemic. Systemic has been used to discover more than 40 new planetary systems.
- Wrote high-performance, parallelized codes to solve ordinary and partial differential equations modeling planet formation.
2014-2016, University of Texas at Austin SAVE/Point, Principal Investigator
- Principal Investigator of SAVE/Point, a collaboration of astronomers and educators creating cutting-edge edtech games, apps, and interactive touch kiosks, running on the latest Web technologies.
- Developed Super Planet Crash, an HTML5/JS game that was played more than 15 million times and was covered by The Verge, IO9, Huffington Post, and others.
- Developed Systemic Live, an HTML5/JS web app that teaches students about the process of data analysis and scientific discovery. It is used in MOOC classes in Coursera, and classes at Caltech, UF, UT, MIT, SJSU, UD, Yale, Columbia, UCSC, SFSU, and others.
2010-2011, VN7 Dynamic LP; 2013-2014, EFFEX Capital Research Analyst/Contractor
- Led the development of a sophisticated desktop application to monitor the real-time performance of strategies on high-frequency stock trading.
- Machine Learning: Classification, regression, feature engineering, propensity score matching, building high-volume ETL and machine learning pipelines.
- Statistical Methods and Numerical Algorithms: Hypothesis testing, bootstrapping, Markov-Chain Monte Carlo, numerical optimization using local and global methods, numerical simulations, integration of differential equations, time series analysis.
- Programming Languages:
- R (including
xgboost, and the
- Scala (including
- C and C++
- Others: Lua, Matlab. Basic knowledge of Python, Fortran, Objective-C.
- R (including
- 2012 – Doctor of Philosophy (Astronomy & Astrophysics), University of California at Santa Cruz. Received Whitford Prize for highest achievement in research, coursework, and teaching.
- 2006 – Master of Science (Astronomy, with highest honors), University of Bologna
- 2004 – Bachelor of Science (Astronomy, with highest honors), University of Bologna
Publications, Academic Honors and Awards
- Published 8 first-author publications on time series analysis, numerical optimization, and Monte-Carlo simulations (cited 224 times); a total of 17 refereed papers (cited 992 times). See research page.
- 2014 – Meschiari, S. (PI), Ludwig, R., Green, J., Interactive Education Tools in the Public Square (Award: $2,800, for purchasing iPad Air tablets and wall mounts)
- 2014 – Meschiari, S. (PI), Green, J., Ludwig, R., Bringing the Tools of Research Direct to the UT Class- room: Systemic, a Virtual Lab for Students (Award: $87,710)
- 2010 – Award for Excellence in Teaching
- 2008 – Whitford Prize for graduate academic performance
- 2006 – Regents’ Fellow, University of California