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Research & Innovation

CS researchers design ‘CLAIRify’ framework to improve chemistry robotics planning

CS researchers design ‘CLAIRify’ framework to improve chemistry robotics planning

Computer scientists, led by PhD students Marta Skreta and Naruki Yoshikawa, have developed a framework called CLAIRify that converts natural language inputs into a domain-specific language that chemistry robots can understand and follow. 

Researchers find ‘unified foundation’ of word meaning in child language development and language evolution

Researchers find ‘unified foundation’ of word meaning in child language development and language evolution

New research, co-authored by Associate Professor Yang Xu, demonstrates that word meaning extension, observed in both children and the historical evolution of language, relies on a common cognitive foundation of knowledge and how things relate to each other.  

With digital messaging tools for mental health, context is key, U of T researchers find

With digital messaging tools for mental health, context is key, U of T researchers find

U of T computer scientists have identified key variables that influence users’ experiences with text messaging systems aimed at supporting psychological well-being.  

Interactive ‘Stargazer’ camera robot assists with how-to video creation

Interactive ‘Stargazer’ camera robot assists with how-to video creation

A team of U of T computer scientists explores how an interactive camera robot can assist in creating dynamic tutorial videos based on subtle verbal and non-verbal instructor cues. 

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New research on training decision-making AI reveals insights into normative judgments

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New research on training decision-making AI reveals insights into normative judgments

A new paper by MScAC alumna Aparna Balagopalan demonstrates why labelling data with normative prompts can yield better outcomes in machine learning models. Its co-authors include Assistant Professor, Status-Only Marzyeh Ghassemi and CS graduate student David Madras.  

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Transforming education through AI-powered adaptive experiments: U of T computer scientists’ team wins XPRIZE Digital Learning Challenge

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Transforming education through AI-powered adaptive experiments: U of T computer scientists’ team wins XPRIZE Digital Learning Challenge

The Adaptive Experimentation Accelerator, led by Assistant Professor Joseph Jay Williams, aims to design inclusive and personalized learning experiences. 

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U of T receives $200-million grant to support Acceleration Consortium's ‘self-driving labs’ research

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U of T receives $200-million grant to support Acceleration Consortium's ‘self-driving labs’ research

Led by Professor Alán Aspuru-Guzik, the consortium works to develop “self-driving labs” that combine artificial intelligence, robotics and advanced computing to discover new materials and molecules in a fraction of the usual time and cost.  

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U of T computer scientists' MOOClet framework helps personalize and improve online learning experiences

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U of T computer scientists' MOOClet framework helps personalize and improve online learning experiences

The Adaptive Experimentation Accelerator, which includes Assistant Professor Joseph Jay Williams and U of T CS students Ilya Musabirov, Mohi Reza, Pan Chen and Harsh Kumar, is one of three finalists for the XPRIZE Digital Learning Challenge. The team’s software architecture uses machine learning to design inclusive and personalized online classrooms. 

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AI code generators could make learning to code easier for young students, new research shows

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AI code generators could make learning to code easier for young students, new research shows

New research led by PhD student Majeed Kazemitabaar reveals AI code generators show promise in making programming more accessible for novice programmers learning to code.

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U of T researchers advancing telescope technology for next-generation ground-based observations

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U of T researchers advancing telescope technology for next-generation ground-based observations

A group of researchers including computer science PhD candidate Robin Swanson has built and installed new telescope equipment and developed machine learning algorithms to help unlock the full potential of large observatories.   

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University of Toronto scientists use machine learning to fast-track drug formulation development

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University of Toronto scientists use machine learning to fast-track drug formulation development

A new study co-led by Professor Alán Aspuru-Guzik demonstrates the potential for machine learning to accelerate the development of innovative drug delivery technologies.