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Selected Projects

Check projects for the entire list

Self Driving Car DQN
Implemented a self driving car through a Deep Q Network Reinforcement Learning Model. Tested multiple networks includind a Convolutional Neural Network, Recurrent Neural Network and a hybrid model.

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SeeMath
Developed a companion application to Hypatia's math editor software for mathematical visualizations. Demo walkthrough of the project available here.

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Break & Enter Crimes in Toronto
Work for the Toronto Police to analyze and find patterns in Break and Enter Crimes in the Greater Toronto Area (GTA).

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Awards

2019

Literature Review Competition First Place Award
Sponsored by the STEM Fellowship in recognition of outstanding academic writing and abilities in science communication for the manuscript Artificial Neural Networks on Recruiting Athletes.
2015

2x Bronze Medalist (2014 & 2015): National Astronomy Olympiad
This single phased olympiad fetured 784.390 competitors from all over Brazil. Medals were distributed based on score thresholds.
2014

Silver Medalist: National Physics Olympiad
This Brazilian Olympiad featured 155.968 competitors from all over the country. It is divided into 3 stages in which students must have a threshold score to advance to the next phase.

Publications

01
Artificial Neural Networks on Recruiting Athletes

Aug 2020

STEM Fellowship Journal
In this viewpoint written during my senior year of High School, I explored three implementations of artificial neural networks in sports to learn their efficacy for athlete recruitment in the professional level. https://doi.org/10.17975/sfj-2020-005

Main Interests

Significant Courses

01
Intelligence, Artificial
and Human

2020

University of Toronto
This course presents students with the opportunity to integrate their computer science interests with philosophy, history, and literature by answering questions that include: what is human intelligence? How close are we to replicating it? What ethical challenges are posed by AI on workers, society, and the environment?
02
Data Structures
and Analysis

2020

University of Toronto
Standard abstract data types, such as graphs, dictionaries, priority queues, and disjoint sets. A variety of data structures for implementing these abstract data types, such as balanced search trees, hashing, heaps, and disjoint forests. Design and comparison of data structures. Algorithm analysis: worst-case, average-case, and amortized complexity.
03
Software Tools and
Systems Programming

2020

University of Toronto
Software techniques in a Unix-style environment, using scripting languages and a machine-oriented programming language (typically C). What goes on in the operating system when programs are executed. Core topics: creating and using software tools, pipes and filters, file processing, shell programming, processes, system calls, signals, basic network programming.

Hi, I am a 3rd year student at the University of Toronto pursuing a: Computer Science Specialist Statistics Minor Mathematics Minor