The AI algorithm helps researchers visualize ultrafast videos from any viewpoint, allowing for the study of how light propagates from multiple perspectives.
Ishtiaque Ahmed, an assistant professor of computer science, is a 2023-2024 recipient of a Connaught Community Partnership Research Program Award. His project will look at using AI to combat online hate aimed at Chinese and Muslim communities in Canada.
Faculty and students from the Department of Computer Science present their innovative work at SIGGRAPH Asia 2023, a premier event for computer graphics research.
A team of U of T computer scientists led by PhD student Sejal Bhalla has designed software that uses deep learning algorithms to decipher changes in vocal characteristics that indicate the lung condition of patients with chronic obstructive pulmonary disease.
Computational imaging researchers have developed a novel technique that allows video from highly dynamic scenes to be recorded once and then slowed down and sped up by a factor of billions.
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.
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.
U of T computer scientists have identified key variables that influence users’ experiences with text messaging systems aimed at supporting psychological well-being.
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.
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.
The Adaptive Experimentation Accelerator, led by Assistant Professor Joseph Jay Williams, aims to design inclusive and personalized learning experiences.
