Experience the AI4Good Lab in Toronto.

For empowerment. For justice. For society.
What will you build AI for?

Hosted in collaboration with Toronto Metropolitan University.

Toronto Metropolitan University is Canada's comprehensive innovation university, addressing real-world challenges to drive economic growth and improve quality of life for Canadians.

The work of our researchers in artificial intelligence covers areas that range from intelligent transportation systems to tools for health care applications. As the home of the Responsible Artificial Intelligence (RAI) training initiative, Toronto Metropolitan University is also creating a pipeline of highly qualified trainees who will be our future experts in AI ethics, privacy-enhanced analytics and accountability.

History of the Toronto Program.

The Toronto Program debuted in 2022 as the third regional location in our national expansion across Canada. The Toronto Program was made possible by the continued support of CIFAR.

CIFAR is a Canadian-based global research organization that convenes extraordinary minds to address the most important questions facing science and humanity. Since 2017, the CIFAR Pan-Canadian AI Strategy has been transforming AI research, training and innovation in Canada by building strong AI ecosystems and attracting, creating, and supporting talent across the nation.

AI4Good Lab students
AI4Good Lab students

Discover the AI Ecosystem in Ontario.

Find yourself at the epicenter of AI research and innovation in Toronto’s diverse and competitive landscape. The Toronto program welcomes 30 participants each year.

Different regions, one Lab.

Like different classes for the same course, the regional programs all offer the same core education, while at the same time showcasing the local identity of each vibrant AI community. Whether you join us from Toronto, Montreal, or Edmonton, the skills and network you gain at the Lab will kickstart your journey into AI.

Our program.

Our 7-week program (May 1 – June 20, 2023) is designed to prepare you with the necessary skills to build your own machine learning projects, from start to finish, while offering an unmatched opportunity for mentorship and career exploration.

Weeks 1 — 4 ML Bootcamp.

Program Launch.

The first day of the program (May 1) kicks off with words of advice and encouragement from our co-founders and partners. Get the chance to meet your peers and TAs and become familiar with the flow of the next 7 weeks.

Lectures.

Get experience with processing data, training models, and using ML tools. Progress from the fundamentals of machine learning through neural networks (NNs, CNNs, RNNs) and on to reinforcement learning.

Big Picture.

Learn how AI is changing industries, explore the cutting edge of AI research, and discuss issues on social good and AI ethics with our guest speakers.

Network.

Get excited for Industry Night where you will meet representatives from our partner organizations, expand your network, and explore the opportunities available to you in the industry.

Practice.

Solve hands-on exercises, put your new skills into action at our mini data-processing hackathon, and have all your questions answered by your graduate-level TAs.

Weeks 5 — 7 Team Projects.

Experience.

Cement your newly acquired knowledge in AI by developing a machine learning prototype, from start to finish.

Teamwork.

Discover the best way to collaborate on technical projects by working in teams to build your prototype.

Mentorship.

Enhance your projects with guidance from your mentors who join us from our partners in the AI industry.

For Good.

Use your project as an opportunity to explore solutions to real-world problems and evolve your understanding of ethical design principles.

Demo Day.

The final day of the program (June 20) is a celebration of the hard work put in by all the project teams in your cohort. Pitch your project to a panel of AI experts and mingle with all those who’ve supported you along the way. Select teams may be chosen to extend their projects with additional support from the AI4Good Lab and our collaborators.

Is this program right for me?

Passion

You want to gain technical skills and hands-on experience in AI.

You want to build relationships with AI professionals.

You care about enhancing society through technology.

You value teamwork and diverse perspectives.

Eligi­bility

You are a woman+.

You have a valid status in Canada (citizen, resident, study or work permit).

You are registered at a post-secondary institution (Bachelor’s, Master’s, or college) or have recently graduated.

You are not a PhD student.

Qualifi­cations

You have some experience programming in Python.

You have a strong background in at least one math subject: linear algebra calculus, statistics, probability.

No prior experience in machine learning necessary!

FAQ.

Lab overview

The AI4Good Lab is an introductory program to machine learning for women+ with some programming background and intermediate to no experience in machine learning. The Lab runs for 7 weeks during the summer and is split into 2 sections (the Bootcamp and Project weeks). See a detailed description of the program.

ML Bootcamp: The curriculum section is an intensive introductory bootcamp to machine learning and AI, which takes place over the first 4 weeks of the program. During this time, participants attend lectures, talks, workshops, and networking events. Participants will work in small groups with their assigned Teaching Assistant (TA) to practice what they’ve learned through exercises. The TAs are typically PhD students in Computer Science with specializations in Machine Learning or AI.
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Team projects: The Project section takes place over the last 3 weeks, during which participants use the knowledge gained during the bootcamp and work in teams to develop an AI/ML prototype that addresses a social good issue. Teams are supported by their TAs and up to 3 mentors who work in AI.

The AI4Good Lab is a non-profit program supported by a variety of partners and run by our management team. Our founding partners are the OSMO foundation and CIFAR, and we are supported by corporate, academic, and government organizations. The AI4Good Lab headquarters is stationed in Montreal, where we run the local Montreal program and organize expansion across Canada. The Edmonton program is run in collaboration with the Alberta Machine Intelligence Institute (Amii) and the Toronto program is run in collaboration with Toronto Metropolitan University.

We accept 30 participants per regional program for a total of 90 participants per year. The small cohort size is chosen to ensure we can prioritize the growth and understanding of our participants, while creating an environment that enables them to form meaningful relationships with their peers and educators.

The AI4Good Lab Program is entirely conducted in English. The application form is available in English and French.

Opportunities for individual learning are abundant, but the Lab addresses one of the biggest barriers for underrepresented individuals in the industry: access to mentorship and deep professional networks. We connect participants with mentors and educators who are experienced AI practitioners in the industry. These experts have joined us to guide our participants in their learning, and are eager to welcome them into the AI community.

Our goal is to open doors for those whose genders are underrepresented in the AI industry. We aim to achieve this in the following ways:

  • We accept participants from a broad range of educational backgrounds and experiences.
  • Our program is tuition-free and we provide stipends and reimbursements to ensure equal access to the program.
  • Our education model centres curiosity-driven exploration and collaborative learning instead of competition and grades.

From mentorship and financial support to our educational ethos, this program is unmatched in its ambition to drive the culture of technology towards inclusion and representation.

Program details

The 2023 Lab will follow a hybrid model. Some components will be hosted online, and in-person components will be hosted in the three regional locations: Montreal, Toronto, Edmonton. Although it is strongly encouraged to take advantage of in-person networking opportunities, participants will not be obligated to attend in-person events. Alternate online options will be made available. Each location will determine which components of the program will be hosted in person.

All selected participants will be given further details to allow for advanced planning. Regardless of the format, the Lab will continue to ensure the program’s accessibility for all participants.

The 2023 program is running in Montreal, Edmonton, and Toronto from May 1st to June 20th. It is a full-time commitment that runs roughly during work hours (11AM - 6:30PM ET), with scheduled breaks throughout the day. The hours vary across Canadian time zones so virtual activities can be run synchronously. Each day features four 90 minute sessions with 15 minute breaks in between and a one hour lunch. Due to the intensive nature of the projects, many participants choose to work on their projects outside of Lab hours, including evenings and/or weekends. The program is designed to be fully immersive, so attendance at all lectures, talks, and workshops is required for all participants.

In-person attendance is strongly encouraged but not mandatory. The virtual program is accessible to anyone who is available during the program hours (11AM - 6:30PM EDT) and has a valid status to study or work in Canada. The Lab is exploring options for providing travel reimbursements to eligible participants for certain events, however the Lab will not be able to fully fund participants to move to a new city for the duration of the program. The stipend provided to all participants at the end of the program may be used to cover living expenses and travel costs as well.

Like different classes for the same course, each regional program of the AI4Good Lab offers participants the same value of education and opportunities for career advancement.

In 2023, the regional programs will overlap virtually for the foundational components of the program which includes lectures, speakers, and some networking events.

The distinct identities of each program arise from the different people involved; each program is run by a different team, has its own distinct cohort of participants and TAs, and has some differences in the regionally hosted events. The Montreal program is run by the AI4Good Lab headquarters, the Edmonton program is run in collaboration with Amii, and the Toronto program is run in collaboration with Toronto Metropolitan University.

Each regional program will run its own study and project groups, as well as some events and social activities. Each location will determine which components of the program will be hosted in person and participants will be notified of these decisions to allow for advanced planning.

The AI4Good Lab is a full-time commitment (11 AM to 6:30 PM ET) that includes daily attendance at mandatory events and intensive studying and/or group work. The hours vary across Canadian time zones so virtual activities can be run at the same time. Each day features four 90 minute sessions with 15 minute breaks in between, including a one hour lunch. In our experience, it is impossible to balance external responsibilities with full participation in the AI4Good Lab. If you are accepted and currently employed, we recommend receiving a leave of absence from your employer before accepting your spot.

Participants will be expected to have access to a laptop/computer, high-speed internet (at least 70mbps download speeds), and earphones/headphones with a microphone. We may offer reimbursements for internet and other tech accessories to ensure all participants can access the program without barriers.

At the AI4Good Lab, we prioritize a peer-driven, curiosity-based learning environment. No grades are given. Exercises will not be graded, but may be reviewed with TAs in work groups to enhance participant understanding of the material. Attendance at lectures, talks, and workshops, however, is required for completion of the program.

All AI4Good Lab participants who successfully complete the program will receive a certificate of completion. The AI4Good Lab is not an accredited institution and does not give official degrees.

Eligibility

The ideal AI4Good Lab applicant is a woman+ who has a strong math background as well as some knowledge of Python (through coursework, personal projects, work experience, etc.).

All participants must be current students at a post-secondary institution (University, college, etc.), or recent graduates. The program is an introductory program to machine learning and therefore not open to high school students, PhD students, or those who are already experienced in ML.

To be accepted, applicants must have a valid status in Canada (citizen, resident, study or work permit, etc.).

Our program is open to women+, which includes cisgender women, transgender women, non-binary people, and anyone who identifies as having experienced misogyny. The “+” in “woman+” is in no way intended to erase or minimize those for whom the term ‘woman’ doesn't apply or fully encapsulate their personal experiences. Rather the “+” is included to reach those who would benefit from our mission in creating opportunities for people whose genders have historically been underrepresented in the AI industry. We are committed to listening and growing as the conversation about the nuances and complexity of gender and its intersections evolves. Please read Our Story to learn more about why the program exists to support women+.

The purpose of the program is to introduce machine learning to those who otherwise may not have the opportunity to explore it in-depth. We welcome applicants with diverse educational backgrounds! Participants do not need to have a mathematics or computer science degree to succeed in the program, however it is an intensive and fast-paced program that requires previous knowledge of coding and math or the courage to pick up challenging concepts at a fast pace.

To keep up with the math concepts introduced in the lectures, participants should at least be familiar with linear algebra, probability, and/or calculus. The lecturers will offer a basic overview of what you’ll need to know as it applies to machine learning with the assumption that you have some background knowledge to build off of. You may review the following topics for further insight: probability distribution, Bayesian estimation, basics of linear algebra, calculus derivatives, and regression.

The program does not teach its participants to program, and all coursework is performed in Python. Participants are expected to have prior experience coding in Python from activities such as coursework, personal projects, work experience, etc. If you are accepted into the program and feel uncertain about your Python skills, we recommend reviewing the basics before the start of the program.

Applicants do not need to know any machine learning prior to the program.

Unfortunately we are not available to review your background or application. If you are uncertain, we encourage you to apply.

To reduce economic barriers, all participants are provided with a stipend. It is the responsibility of the participant to ensure that their status in Canada enables them to receive this payment, which may be issued in the form of a Canadian cheque, and be able to report this income on their applicable taxes.

Economic support during the program is a key pillar that equalizes all participants. It is a required part of the program.

Applications

When you apply, you will not have the option to select a specific region. The selection committee will categorize applicants into the appropriate regional cohort (Montreal, Edmonton, Toronto) based on your location in Canada during the program. Although your preference may be taken into consideration, your cohort will be chosen to maximize the possibility of travel to in-person events while enabling you to collaborate virtually with peers in similar time-zones.

Applicants will need to log into their Google accounts to submit to our Google Form application.

To apply, applicants will need to submit their CV/Resume and a copy of their most recent transcript (unofficial transcripts are accepted). The application also consists of 3 short-essay questions (500 words max):

  1. Provide a brief description of why you'd like to join the Lab.
     
  2. The AI4Good Lab is a collaborative learning environment, where all participants will work in groups throughout the curriculum and project phases. Please describe your experiences with collaborative learning and describe how you believe you will contribute to a supportive peer-learning environment.
     
  3. The AI4Good Lab is devoted to the use of AI as a tool for social good. Please describe how this mission speaks to you.

We encourage applicants to provide concrete examples for every essay question, drawing from personal history, projects, or activities that they were directly involved in to support their responses. We are unable to give feedback on individual applications.

The call for applications opens in 2023 on January 10th, and closes on January 31st.

After the call closes, the selection committee will process the applications in 3-4 weeks. The selection committee consists of our founders, management team, and committee members from our partners.

All applicants will be notified of their selection status by March 1st at the latest. Those on the waitlist will be notified of selection on a rolling basis. Applicants who do not hear from us by March 1st may contact us at info@ai4goodlab.com to inquire as to the status of their application. The most common reason for a lack of response is because we received an incorrect email address from an applicant.

Each year we evaluate a competitive pool of applicants for limited spots, so we encourage rejected candidates to re-apply the following year. We are unable to give feedback or suggestions to those who are not accepted.

The waitlist does not have a specific order. Participants are selected off the waitlist due to a variety of factors, and we cannot give further information on one’s chances of receiving a spot if they are placed on the waitlist.

Accepted applicants will have 4 business days to confirm their spot before the opportunity is passed to someone on the waitlist. Given the full-time requirements of the program, many participants will need to make arrangements well in advance to attend. This means applicants who receive acceptances need to notify us ASAP if they are unable to attend the program in order to allow others the opportunity to plan ahead.

Costs

The only cost to attend the Lab is a $75 registration fee for accepted applicants to confirm their spot in the program.

There is no cost to apply and there are no tuition fees.

Every participant receives a stipend after completion of the program. The minimum stipend amount is $500, and the final amount is determined on a yearly basis by the end of the program. The final amount is determined by budgeting activities and is not dependent on the performance of the participants.

Other questions? Please email us at info@ai4goodlab.com

Learn from experts in the field.

Rasha Kashef

Rasha Kashef

Assistant professor, Mila, University of Montréal

AI4Good Lab

Sanaz Mohammadjafari

PhD Candidate, Toronto Metropolitan University

Patrick Pilarski

Patrick Pilarski

Associate Professor, Canada CIFAR Artificial Intelligence Chair

Doina Precup

Dr. Doina Precup

Lab Founder, DeepMind, McGill University, Mila

Guillaume Rabusseau

Guillaume Rabusseau

Canada CIFAR AI Chair
Assistant Professor
Université de Montréal
Mila

Samrudhdhi Rangrej

Samrudhdhi Rangrej

PhD Candidate
McGill University

Siva Reddy

Siva Reddy

Facebook CIFAR AI Chair
Assistant Professor
McGill University
Mila

Matthew Schlegel

Matthew Schlegel

PhD Candidate
Amii, RLAAI
University of Alberta

Martha White

Martha White

Associate Professor
University of Alberta
RLAI and PI at AMII

Meet and work with AI leaders.

Alaa Abdelgawad

Alaa Abdelgawad

Zafarali Ahmed

Zafarali Ahmed

Manager, PwC

Sedef Akinli Kocak

Dr. Sedef Akinli Kocak

Applied AI Project Manager
Vector Institute

Adrien Ali Taïga

Adrien Ali Taïga

Research associate, Google

Ehsan Amjadian

Ehsan Amjadian

Director of AI, RBC

Muhammad Anwar Ul Haq

Muhammad Anwar Ul Haq

PhD candidate, University of Toronto, Vector

Arjun Asokakumar

Arjun Asokakumar

Director, Data Science, RBC

Nadia Blostein

Nadia Blostein

MS, Integrated Program in Neurosceience @ McGill University

Chantelle Bourgeois

Chantelle Bourgeois

MNP

Alissa Brayley

Alissa Brayley

Data Scientist, RBC

Neil Burch

Neil Burch

MSc Student, McGill University

Ann Crabtree

Ann Crabtree

Clinical Associate Professor, University of Calgary

Sara El Shawa

Sara El-Shawa

Applied Machine Learning Intern
Vector Institute

Joud El-Shawa

Joud El-Shawa

Andy Forest

Andy Forest

Executive Director, STEAMlabs

Alexandre Frechette

Alexandre Frechette

Research Engineer, DeepMind

Alona Fyshe

Alona Fyshe

Assistant Professor, Amii

Alyssa Gagne

Alyssa Gagne

Senior Associate, PwC

Anita Gergely

Anita Gergely

Research Developer
DeepMind

Maria Gini

Maria Gini

CSE Distinguished Professor, Department of Computer
Science and Engineering, University of Minnesota

Ross Goroshin

Ross Goroshin

Senior Data Scientist, Manulife

AI4Good Lab

Courtney Handford

Physician – Psychiatry Resident, Dalhousie University

Nidhi Hegde

Nidhi Hegde

Associate Professor
University of Alberta

Natasha Jaques

Natasha Jaques

Research Scientist
Google
UC Berkeley

Faezeh Khabbaz

Faezeh Khabbaz

Manager Data Science, PricewaterhouseCoopers LLP (PwC)

Brenden Kumarasamy

Brenden Kumarasamy

Parisa Lak

Parisa Lak

Research scientist, DeepMind

Kate Larson

Kate Larson

Professor
University of Waterloo
DeepMind

Elaine Lau

Elaine Lau

AI4Good Lab Alumni
Data Scientist Intern at TD Bank

Chun Lei He

Chun Lei He

Lead Data Scientist, Manulife

Ayesha Liaqat

Ayesha Liaqat

Maureen Mai

Maureen Mai

PhD Candidate, University of Waterloo

AI4Good Lab

Mina Mirshahi

Data Developer, MNP Digital

AI4Good Lab

Joseph Modayil

Sr. Data Scientist, Manulife

Debarati Nath

Debarati Nath

Elmira Navidbakhsh

Elmira Navidbakhsh
Deval Pandya

Deval Pandya

Director of AI Engineering
Vector Institute

Mojgan Saeidi

Mojgan Saeidi

Senior data scientist, Manulife

Bahar Sateli

Dr. Bahar Sateli

Manager in AI & Advanced Analytics
PWC

Sylvia Shiah

Sylvia Shiah

Lead Professional Development, Vector Institue

AI4Good Lab

Brenda Shivanandan

Executive Director, STEAMlabs

Jia Song

Jia Song

Senior Director of Analytics, Global Cyber Security, RBC

David Szepesvari

David Szepesvari

Research Developer, DeepMind

Cristina Vasconcelos

Cristina Vasconcelos

Research Software Engineer
Google

Jodie Wallis

Jodie Wallis

Global Chief, Analytics Officer
Manulife

Connor Wright

Connor Wright

Partnerships Manager
Montreal AI Ethics Institute

Jie Wu

Jie Wu

Software Engineer, Google

Kelly Zheng

Kelly Zheng

Participants.

Ipek Akyol

Ipek Akyol

Natalie Neda Ashgriz

Natalie Neda Ashgriz
Selen Calgin

Selen Calgin
Lemeng Dai

Lemeng Dai

Vladyslava Diachenko

Vladyslava Diachenko
Divya Gupta

Divya Gupta
Jasmine Joy

Jasmine Joy
Bernice Lien

Bernice Lien
August Lu

August Lu

Kelly Owenya

Kelly Owenya
Ifeoluwa Deborah Oyetoran

Ifeoluwa Deborah Oyetoran

Kelsi Patel

Kelsi Patel

Gabriela Peralta

Gabriel Peralta
Jisu Qian

Jisu Qian
Anuradha Rajkumar

Anuradha Rajkumar
Mckenna Ramsay

Mckenna Ramsay
Mudita Sharma

Mudita Sharma
Qianni Wang

Qianni Wang

Anran Xu

Anran Xu

Fatemeh Yazdanpanah

Fatemeh Yazdanpanah
Chen Yutong

Chen Yutong
Shuqi Zhang

shuqi zhang

Yiyi Zhang

Yiyi Zhang

Irene Hui-Chen Zhu

Irene Hui-Chen Zhu

Past Toronto projects.

dose io

dose.io

Epi Health

Epi Health

Happy path

Happy path

Reimagine

Reimagine

2022 Toronto Accelerator Award winner: EpiHealth

See the full project report
Elissa Strome

“Women continue to be underrepresented in computing science, which is an issue not just to individual women, but society-at-large. Diverse voices and perspectives are instrumental in building robust algorithms that provide benefits across all aspects of society.”

Elissa Strome
Executive Director, CIFAR Pan-Canadian AI Strategy

See all Toronto projects

Explore now

Toronto partners.

National partners.

Founding Partners

Expansion Partners

Title sponsors

Key sponsors

Community sponsors

Explore other regional programs.

Montreal skyline

Montreal

Hosted by the AI4Good Lab headquarters team

Edmonton, Alberta

Edmonton

Hosted in partnership with the Alberta Machine Intelligence Institute