Hello there!
I am a final-year Ph.D. Candidate in Computer Science at
Columbia University, advised by Prof.
Eitan Grinspun and Prof.
Changxi Zheng.
My research interests include
Physics-based Simulation,
Machine Learning, and
Computational Fabrication.
I am on the job market this year. If you are interested, please contact me!
Currently, I am interested in
merging simulation techniques with machine learning, hoping to trade-off bottlenecks and inaccuracies with data-driven techniques.
Machine-learning and model-reduction methods benefit from large demonstrative datasets; the more data present, the easier it becomes to converge on a solution.
Aggregating and formatting data is not easy however, examples are not always readily available and the collected samples must be labeled or tagged appropriately.
Fortunately, accurate simulations allow for the generation of limitless data under controlled scenarios. With every variable accounted for, the overhead of gathering and preparing the data dissapears.
Coming full circle, this data can then be used to train stable and efficient alternatives to physics simulations that can improve time-bound applications or even produce
differentiable simulations that can be tuned to achieve specific dynamics.
In the past, I have worked on tagging
physical hyperlinks onto
intricate everyday objects, as well as exploring
side-channel exploits of neural architechtures running on modern
GPUs. I also love to tinker, build, and hack. I've been known to augment 3D printers and
design award winning robots.
Outside of research you can find me playing ⚽ or 🏐. I also love to cook and
explore the food scene around me, feel free to reach out to me for recommendations!
I am originally from
São Paulo,
Brasil but have spent the last few years living in (and photographing)
New York City.
For more information about me, see my
resume or
contact me.