Francis Duplessis, Ph.D.

Contact

Information

Phone: 480-***-**** LinkedIn: linkedin.com/in/francisduplessis Email: ac0erm@r.postjobfree.com Github: github.com/fdupless Canadian Citizen (with OPT visa in the US) Personal Website: fduplessis.com Profile Recent Physics PhD graduate with a strong background in mathematics and numerical simulations. Currently transitioning to the eld of data science with an emphasis on applying state of the art machine learning tools to real world problems.

Experience Research Assistant, Arizona State University Aug 2013 { May 2017

Studied the di erential equations describing cosmological in

ation and one of its alternative, the matter bounce, to determine their ability to reproduce observed data.

Investigated theories of modi ed gravity that expand upon General Relativity.

Performed extensive numerical simulations involving Gaussian Random Fields, Monte Carlo and stochastic processes to test astrophysical theories. Examples can be found on my personal webpage.

Teaching Assistant, Arizona State and McGill University Aug 2011 { May 2017

Lectured and taught on topics ranging from Introductory Mechanics to General Relativity.

Prepared the lectures and tutorials to present to classes of up to 40 students. Relevant

Numerical

Projects

Analysis of the

-ray distributions from Blazar Halos { Python github.com/fdupless/halomorp

Analyzed Fermi telescope data scraped o the Fermi Collaboration website.

Generated Monte Carlo simulations of the appearance of Blazar halos to Earth bound observers.

Using the Central Limit Theorem, we showed that our statistical tools were sensitive to quantitative details of the halos parameter.

Activity Recognition { Python

github.com/fdupless/AR

Trained a Random Forest Classi er to detect activities done by individuals.

Using real-time input from inertial sensors, the RFC could accurately classify a total of 18 activities. Kaggle Leaf Classi cation { Python

github.com/fdupless/HLML

Engineered useful features to classify 990 Leaf images containing 100 di erent species.

The features ranged from dimensionless ratios to Fourier analysis of the image’s contours.

A 90% test accuracy was achieved using an Linear Discriminant Analysis. Galactic Radio Signal from Dark Matter Clumps { C++ www.public.asu.edu/~fdupless/Computational.html

Used Gaussian Random Fields and Convolutions to simulate the radio emission.

Compared Power Spectrum distributions to test the null hypothesis. Scientific

Contributions

Authored 7 Astrophysics and Cosmology publications in highly reputable journals, namely the Journal of Cosmology and Astroparticle Physics [JCAP] and Physical Review D [PRD]. The papers are listed on: inspire arXiv GoogleScholar Lay summaries of the research can be found at www.public.asu.edu/ fdupless/Research.html.

Refereed for the Journal of High Energy Physics [JHEP].

Gave 12 Seminar, conference or technical talks spread over 5 institutions in 3 di erent countries. Education Ph.D., Physics, Arizona State University, Advisor: D. Easson 2017 Partially funded by an FQRNT B2 Doctoral Scholarship, $60,000.00 CAD

M.Sc., Physics, McGill University, Advisor: Robert Brandenberger 2013

B.Sc., Joint Honours in Mathematics and Physics McGill University 2011 Skills Languages: Python, C++, R, Fortran, Bash, MySQL, English, French Libraries: Pandas, sklearn, TensorFlow, Mathematica, Matlab, LATEX Math: (Linear) Algebra, Calculus, Monte Carlo, Random Process, Numerical Modeling

Contact this candidate