The way a prepare the classes depend on the subjects I'm teaching, the level required, and the goal that the student has on learning it.
For subjects that involve coding and applied methods, I like to prepare extra content in form of jupyter notebooks and tutorial scripts. I have seen good results using these methods.
For more theoretical subjects I focus more time in trying to make the foundations clear and intuitive, so it becomes easier to address specific problems.
I am a PhD student in Math working with machine learning at NYU.
I also have a masters in Computer Graphics.
My expertise lie in probability, statistics, data science, applied maths, and extremal combinatorics.
I tutored calculus, and linear algebra for undergrad level.
I also tutored linear algebra, probability theory, and image processing on graduate level.
I have taught a course in computer vision and deep learning.
Federal University of Santa Catarina, Brazil
Bachelor degree in Mathematics - 2011 to 2014
• With Honors - best academic performance
Instituto Nacional de Matemática Pura e Aplicada (IMPA),
Rio de Janeiro, Brazil
PhD in Mathematics - 2017 to 2020 (expected)
• Advisors: Roberto Imbuzeiro Oliveira.
M.Sc in Mathematics (Computer Graphics) - 2015 to 2017
• Advisor: Diego Nehab - Augusto Teixeira.
• Master Thesis: "Generative Adversarial Networks".
New York University (NYU) - 2019-2020
• J-1 Research Scholar on Computer Science at Courant Institute (NYU)
• Advisor: Davi Geiger
Research in Epidemiology (2014-2017)
Assisted Wladimir J. Alonso (NIH) on research projects and on the development of several analytical tools for processing epidemiological data (mostly timeseries). See EPIPOI and publications.
Programming Skills: Experience with Python, C++, Lua, MatLab, OpenCV,
TensorFlow and PyTorch.
Teaching Assistant(2017-2018): Assisted the course of “Fundamentals and Trends in Computer Vision and Image Processing”, both on preparing the material and teaching some of the classes. My contribution consisted mainly on presenting Deep Learning and Generative Modeling methods for applications in Computer Graphics and Vision.
|at his home||at your home||By webcam|
|1 hour||Not available||$50||$50|