figshare preprint 10324883

We propose a design for securing novelty of archived content in distributed ledgers, called Proof of Novelty. What constitutes as novel is decided through a consensus mechanism together with a similarity function, which is selected according to the content type (e.g. full-motion videos, textual documents). Scalability is guaranteed by forming a validation committee with cryptographic sortition, which use statistical hypothesis testing to decide on the probability of a content being novel or not. The system can trade-off computational with statistical performance by manipulating parameters. We discuss the usage of this design to secure the novelty of full-motion videos and end with a proposal of future lines of research that can extend the systems capabilities.

arXiv preprint arXiv:1910.00752 (2019)

We present a proxy dataset of vital signs with class labels indicating patient transitions from the ward to intensive care units called Ward2ICU. Patient privacy is protected using a Wasserstein Generative Adversarial Network to implicitly learn an approximation of the data distribution, allowing us to sample synthetic data. The quality of data generation is assessed directly on the binary classification task by comparing specificity and sensitivity of an LSTM classifier on proxy and original datasets. We initialize a discussion of unintentionally disclosing commercial sensitive information and propose a solution for a special case through class label balancing

figshare preprint 10324868

This report outlines, as well as provides a mathematical proof of functionality, of a highly efficient pseudo-random number generator: The Ziggurat Method. A simple ready-to-use code has been provided by previous authors. We contribute to this with a speed test on a modern Intel processor, as well as a Python script that generates all the necessary information to implement a specific version of the algorithm.

The Vector Scholarship in Artificial Intelligence supports the recruitment of top students to AI-related master’s programs in Ontario. Valued at $17,500 for one year of full-time study at an Ontario university, these merit-based entrance awards recognize exceptional candidates pursuing a master’s program recognized by the Vector Institute or who are following an individualized study path that is demonstrably AI-focused.

https://vectorinstitute.ai/aimasters

Won 1st place at the VDC hosted by The University of British Columbia with my paper Proof of Novelty. Received a cash prize of $ 3,000.00.

https://blockchain.ubc.ca/virtual-design-challenge-authenticating-and-protecting-full-motion-videos

Graduated with the highest GPA ever obtained (at the time) for my major.

Elected ”Best Student” by the faculty of Electrical & Electronics Engineering at the Federal University of Santa Catarina

Awarded a full scholarship that covered tuition, transportation, necessary materials and living costs to study 2 academic semesters at the University of Toronto.