I am a computer scientist and engineer with great passion for entrepreneurship, knowledge acquisition, and knowledge dissemination.
I’m interested in building the future of education by personalizing the content and explanations we show students, aiming to maximize their understanding. Traditional education is based on previous limitations, dating back to medieval times, where one person must address many students at the same time. Thus, their teaching techniques had to be developed to address many at a time. Nowadays, we have the technology to build Intelligent Adaptive Teaching Systems that will personalize their teaching to the individual. This work will touch many areas such as Adaptive Algorithms, Generative Modeling, Machine Learning, Recommendation Systems, Information Retrieval, and Cognitive Science. I’m looking forward to joining efforts with people in these areas.
Sep 2022 - Present
Toronto, Canada
ML-powered visualizations for personalized visual literacy, supervised by Prof. Carolina Nobre.
Sep 2022 - Present
Sep 2022 - Apr 2023
Toronto, Canada
Sep 2022 - Apr 2023
Sep 2021 - Sep 2022
Toronto, Canada
Implement Multi-Armed Bandits algorithms in educational settings, supervised by Prof. Joseph Williams.
Sep 2021 - Sep 2022
Jan 2020 - Sep 2021
Vancouver, Canada
Developer at the Services Platform for Account & Connected Experiences team in the W+D organization contributing to different web services powering Windows’ and account.microsoft.com’s experiences while connecting with major backend services (e.g., Office, Xbox).
Jan 2020 - Sep 2021
Sep 2019 - Nov 2019
Barcelona, Spain
Study Gaussian Processes and their intersections with Quantum Computing, supervised by Prof. J.I. Latorre.
Sep 2019 - Nov 2019
Sep 2018 - Jan 2020
Caracas, Venezuela
Sep 2018 - Jan 2020
2021-Present Ph.D in Computer ScienceCGPA: 4 out of 4Extracurricular Activities:
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2016-2018 B.Sc. in Systems EngineeringCGPA: 3.86 out of 4Extracurricular Activities:
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2013-2018 B.Sc. in Production Engineering - Partial StudiesGPA: 3.66 out of 4 |
A method for virtual meeting content sharing comprises, during a virtual meeting, receiving a request to share visual interface content including one or more visual content elements rendered by a client computing device with one or more remote participant devices. For each of the one or more visual content elements, an element feature vector is determined. Each of the one or more element feature vectors are sent to a remote meeting server. From the remote meeting server, an indication is received that a specified visual content element is flagged as potentially subject to filtering, based at least in part on a difference between (1) a meeting feature vector and (2) the element feature vector for the specified visual content exceeding a content filter threshold, the meeting feature vector quantifying a plurality of meeting context parameters of the virtual meeting.
A computer-implemented method for recalling user-specific interactions is disclosed. User-specific application data for each of a plurality of different computer application programs is received at a computing system. The user-specific application data is translated into different content vectors representing different user-specific interactions between a user and one or more other users while using the plurality of different computer application programs. Each content vector includes parameters quantifying interaction attributes of the corresponding user-specific interaction. The content vectors are mapped to a high-dimensional content space. A query is received at the computing system and translated into a query vector. Geometric mathematical operations are performed to compare content vectors in the high-dimensional content space to the query vector to identify a content vector that correlates to the query vector. A response to the query that identifies a user-specific interaction corresponding to the identified content vector is output from the computing system.
A method and system for responding to a message directed to a recipient includes receiving the message including a query from a sender, receiving an indication that the recipient is unavailable to respond to the query, and providing the query to as an input to a machine-learning (ML) model to identify information requested in the query. The method further includes obtaining the information requested as an output from the ML model, determining if access to the information requested is available to the sender, based on a confidentiality group to which the sender belongs with respect to the information requested, upon determining that access to the information requested is available, generating a response to the query that includes the information requested, and providing the response to the sender. The confidentiality group to which the sender belongs may be determined based on a degree of association between the sender and the information requested.