Our Story

MATRA AI is a collective of leading AI researchers and seasoned business leaders, united by a shared vision of delivering bespoke Responsible AI solutions. Our team’s expertise in Responsible AI, AI Policy, and Human-Computer Interaction (HCI), all rooted in groundbreaking academic research from the University of Toronto, ensures that MATRA operates at the forefront of technological innovation and ethical responsibility.

For the past decade, MATRA AI’s Research Scientists, Prof. Ishtiaque Ahmed and Prof. Shion Guha, have accrued a wealth of experience from their research and consulting work in Responsible AI at the University of Toronto, where they have collaborated with organizations such as Microsoft, Meta, and Health Canada.

With the business expertise and practical insights of the rest of the founding team, we joined together to form MATRA AI, providing us with a multidisciplinary approach empowering us to design solutions that are both technically robust and strategically aligned with the needs of the organizations we serve.

MATRA AI bridges the gap between compliance and innovation, enabling organizations to unlock AI’s full potential safely and responsibly. HCI is central to our philosophy, allowing us to identify inefficiencies and inadequacies in responsibility and create truly human-tailored solutions that drive value and foster trust. Our commitment to integrity, responsibility, and technical excellence makes us a trusted partner in shaping an AI-driven future that benefits both businesses and society.

Our Team

  • Ishtiaque Ahmed, PhD, MSc,

    Co-Founder, Research Scientist
    Tenured CS Professor @ UofT

    Syed Ishtiaque Ahmed is an Associate Professor of Computer Science at the University of Toronto and the founding director of the ‘Third Space'' research group. His research interest is in the intersection between Human-Computer Interaction (HCI) and Artificial Intelligence (AI). Ahmed received his PhD and Masters from Cornell University in the USA, and his Bachelor's and Master’s from BUET in Bangladesh. In the last fifteen years, he studied and developed successful computing technologies with various marginalized communities in Bangladesh, India, Canada, USA, Pakistan, Iraq, Turkey, and Ecuador. He has published over 100 peer-reviewed research articles and received multiple best paper awards in top computer science venues including CHI, CSCW, ICTD, and FaccT.

    Ahmed has received numerous honors and accolades, including the International Fulbright Science and Technology Fellowship, the Intel Science and Technology Fellowship, the Fulbright Centennial Fellowship, the Schwartz Reisman Fellowship, the Massey Fellowship, the Connaught Scholarship, Microsoft AI & Society Fellowship, Google Inclusion Research Award, and Facebook Faculty Research Award. His research has also received generous funding support from all three branches of Canadian tri-council research (NSERC, CIHR, SSHRC), USA’s NSF and NIH, and Bangladesh government’s ICT Ministry. Ahmed has been named the “Future Leader” by the Computing Research Association in 2024.

  • Shion Guha, PhD, MSc

    Co-Founder, Research Scientist
    Data Science & CS Professor @ UofT

    Dr. Shion Guha is an Assistant Professor at the University of Toronto, jointly appointed in the Faculty of Information and the Department of Computer Science. He leads the Human-Centered Data Science Lab and coordinates the Human-Centered Data Science program in the Master of Information. His work integrates computational methods such as AI, machine learning, and statistical modeling with critical social science approaches like ethnography and content analysis to address pressing societal challenges. Dr. Guha's research focuses on the ethical and practical implications of AI in high-stakes domains, including child welfare, public health, education, and law enforcement. His collaborations span governmental agencies, civil society organizations, and healthcare institutions in North America, including the ACLU, Health Canada, and the City of Toronto.

    His contributions have shaped policy and practice through roles such as member of the CIFAR-MILA AI for Policymakers Expert Group and Visiting Senior Research Scientist at Parkview Health. His work has been recognized with several awards, including the Way-Klingler Early Career Award and the Schwartz-Reisman Institute Faculty Fellowship.

    Dr. Guha holds a Ph.D. in Information Science and Statistics from Cornell University and has previously held academic positions at Marquette University.

  • Zain Bari is the Co-Founder of MATRA, an AI company making AI solutions more responsible and accessible

    Zain Bari, MSc, MA

    Co-Founder, CEO

    Zain Bari is a seasoned professional with over 15 years of global experience across diverse industries. She has held senior leadership roles in multinational organizations, including Siemens, where she led large-scale projects such as post-merger integrations and operational transformations, earning recognition for delivering results under high-stakes conditions.

    Her career spans governance, financial management, and strategic advisory roles with organizations like the International Finance Corporation and Canada Life. Zain’s expertise lies in driving operational efficiency, fostering collaboration, and navigating complex challenges with a strategic and results-oriented approach.

    Having lived and worked across North America, Europe, and Asia, Zain’s global experiences have shaped her ability to lead multicultural teams and deliver impactful solutions across industries.

  • Safwan Zahid, MBA/MGA

    Co-Founder, Business Lead

    Safwan Zahid is pursuing a Master of Global Affairs and Master of Business Administration at the Munk School of Global Policy and Public Affairs and the Rotman School of Management at the University of Toronto. His research focuses on Responsible Artificial Intelligence and Innovation Policy.

    He has professional experience as a Research Assistant in the Computer Science Department at UofT, as well as internships in Corporate Finance at KPMG and Special Asset Management at Bank Asia in Bangladesh.

    Safwan was an International Undergraduate Entrance Scholar at UofT and has received accolades, including graduating with high distinction, being a Dean’s List Scholar, completing a nominated Exchange Program at SciencesPo Paris, and earning an Academic Merit Scholarship at the start of his time at Munk.

  • Matthew Tamura, B.ASc.

    Technical Lead

    Matthew is a fourth-year Engineering Science Student at the University of Toronto. He plays an active role in AI research across disciplines, being part of the AI Fact Labels research group at the American Psychiatric Association, a research assistant at the Human Centered Data Science Lab, and working on a robotic dexterous manipulation learning project at the Robotic Vision & Learning Lab. 

    Matthew is also Co-President of the University of Toronto Machine Intelligence Student Team (UTMIST), the largest Canadian student organization on ML/AI.

Our Past Work

The Meta Advertising tool collected data with uninformed permission.

The solution was the development of a differential privacy model to minimize privacy breaches and keeping more users data protected.

This improved the privacy maintenance of the 3.5+ billion Meta users subject to the advertisement tool

The images produced by Microsoft’s “Dall-E” AI system had geographical biases.

The solution was the development of copyrighted representative image datasets, and active learning algorithms.

This improved performance for producing images attuned to Global South aesthetics & culture for the 2 million images produced per day by the 1.5 million users of Dall-E

Parkview Health (US) used an AI tool to schedule patients to maximize revenue in Orthopedic line. This leads to racial/gender biases.

The solution was adding human-centred AI design process to balance revenues and patient care to fix demographic biases.

The updated tool implemented fair scheduling for 4000 patients, capturing revenue from prior excluded marginalized groups

Health Canada used a type-2 diabetes AI prediction tool to identify at-risk patients. Deployed in Peel Region and causes biases.

The Solution was adding hybrid-based fairness metrics to AI prediction tool as well as clinical narrative analysis to fix demographic biases.

This boosted prediction accuracy capturing 50,000 previously unidentified diabetes patients.