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Hi, I am Fernando

Fernando Yánez

PhD Student at the at University of Toronto

I am a computer scientist and engineer specializing in generative AI, user-adaptive systems, entrepreneurship, and education.

My current focus is on guiding individuals and businesses to integrate Generative AI as a powerful tool to enhance various aspects of their lives and operations. I aim to demystify AI and provide practical strategies for using it in daily tasks, creative projects, and business workflows. By offering tailored coaching sessions, I help clients understand how to harness AI’s capabilities effectively, whether it’s automating processes, creating unique content, or enhancing decision-making, to drive personal and professional growth.

Personally, 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, teaching techniques had been one-size-fits-all. Nowadays, we have the technology to build Intelligent Adaptive Teaching Systems that will personalize their teaching to the individual.

Experiences

1

Toronto, Canada

Graduate Fellow

Sep 2024 - Present


Massey College

Sep 2023 - Present

Toronto, Canada

Junior Fellow

Sep 2023 - Present

Responsibilities:
  • Mentoring and Tutoring Committee Co-chair
  • Member of the Lionel Massey Fund
2

3
Radical Ventures

Sep 2023 - Dec 2023

Toronto, Canada

Radical AI Founders cohort

Radical AI Founder

Sep 2023 - Dec 2023


Toronto, Canada

ML-powered visualizations for personalized visual literacy, supervised by Prof. Carolina Nobre.

PhD Student

Sep 2022 - Present

4

5
University of Toronto

Sep 2022 - Apr 2023

Toronto, Canada

Course Instructor

Sep 2022 - Apr 2023

Responsibilities:
  • Introduction to Computer Programming (Fall 2022, Winter 2023, Fall 2023, Winter 2024).
  • Probability and Statistics (Winter 2024).

Toronto, Canada

Implement Multi-Armed Bandits algorithms in educational settings, supervised by Prof. Joseph Williams.

Research Assistant

Sep 2021 - Sep 2022

6

7
Microsoft

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).

Software Engineer

Jan 2020 - Sep 2021


Barcelona, Spain

Study Gaussian Processes and their intersections with Quantum Computing, supervised by Prof. J.I. Latorre.

Visiting Researcher

Sep 2019 - Nov 2019

8

9
Metropolitan University

Sep 2018 - Jan 2020

Caracas, Venezuela

Adjunct Instructor and Researcher

Sep 2018 - Jan 2020

Responsibilities:
  • Algorithms and Programming (Winter 2019, Summer 2019).
  • Statistics I (Winter 2019, Summer 2019).
  • Differential Equations (Winter 2019, Summer 2019).
  • Pre-Calculus (Fall 2018, Winter 2019, Summer 2019).

Education

Ph.D in Computer Science
CGPA: 4 out of 4
Extracurricular Activities:
  • Volunteer as a mentor for the Grad Application Assistance Program.
  • Volunteer as MScAC Mentor for Technical Interviews preparation.
  • Volunteer in the admission process for the Summer Program for Students Affected by War in Ukraine.
  • Volunteer Math Tutor for High School students from Black Communities.
B.Sc. in Systems Engineering
CGPA: 3.86 out of 4
Extracurricular Activities:
  • UNIMET SAE Aero Design - Member and Secretary-General.
  • Thespis Theater Group - Actor.
  • UNIMET Federation of Student Councils - Member.
  • AIESEC - Volunteer at a foster home for children in risk situations (Curitiba, Brazil).
Diploma in Advanced Math
GPA: 4 out of 4
B.Sc. in Production Engineering - Partial Studies
GPA: 3.66 out of 4

Patents

Shared Visual Content Filtering during Virtual Meetings
Microsoft 6 August 2021

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.

User-specific Computer Interaction Recall
Microsoft 12 April 2021

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.

Method and System of Intelligently Providing Responses for a User in the User’s Absence
Microsoft 23 November 2020

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.