Cristina Conati, University of British Columbia

Research

Read how they describe their research on their University of British Columbia profile.

Latest papers

Machine learning models explanations as interpretations of evidence: a theoretical framework of explainability and its implications on high-stakes biomedical decision-making.
BMC medical research methodology · 2025 · senior author
Evaluation of a Deep Learning and XAI based Facial Phenotyping Tool for Genetic Syndromes: A Clinical User Study.
medRxiv : the preprint server for health sciences · 2025
Evaluating Web-Based Automatic Transcription for Alzheimer Speech Data: Transcript Comparison and Machine Learning Analysis.
JMIR aging · 2022

All 4 publications

Latest funding

Stakeholder perspectives and impacts of explainable artificial intelligence: A case study in a British Columbia health authority
SSHRC · 2023 · Co-investigator
$74,210
Toward Personalized Explainable AI
NSERC · 2022 · Principal investigator
$144,000
AI-Driven personalized support to foster computational thinking skills in early K12 education
NSERC · 2021 · Principal investigator
$215,334

All 18 grants and awards

4 publications.

01

Machine learning models explanations as interpretations of evidence: a theoretical framework of explainability and its implications on high-stakes biomedical decision-making.

Rizzo M, Veneri A, Marcuzzo M, Zangari A, Albarelli A, Lucchese C, Nobile MS, Conati C

BMC medical research methodology2025Senior author

DOI: 10.1186/s12874-025-02703-1 PMID: 41469544

02

Evaluation of a Deep Learning and XAI based Facial Phenotyping Tool for Genetic Syndromes: A Clinical User Study.

Sümer Ö, Huber T, Duong D, Ledgister Hanchard SE, Conati C, André E, Solomon BD, Waikel RL

medRxiv : the preprint server for health sciences2025

DOI: 10.1101/2025.06.08.25328588 PMID: 40568665

03

Evaluating Web-Based Automatic Transcription for Alzheimer Speech Data: Transcript Comparison and Machine Learning Analysis.

Soroski T, da Cunha Vasco T, Newton-Mason S, Granby S, Lewis C, Harisinghani A, Rizzo M, Conati C, Murray G, Carenini G, Field TS, Jang H

JMIR aging2022

DOI: 10.2196/33460 PMID: 36129754

04

Classification of Alzheimer's Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data.

Jang H, Soroski T, Rizzo M, Barral O, Harisinghani A, Newton-Mason S, Granby S, Stutz da Cunha Vasco TM, Lewis C, Tutt P, Carenini G, Conati C, Field TS

Frontiers in human neuroscience2021

DOI: 10.3389/fnhum.2021.716670 PMID: 34616282

Stakeholder perspectives and impacts of explainable artificial intelligence: A case study in a British Columbia health authority

Co-investigatorSSHRC2023Insight Development Grant

Principal investigators: Ronquillo, Charlene

Keywords: explainable AI; machine learning; decision support; systems operations; human factors; stakeholder perspectives; information systems; technology implementation; health systems; impact of technologies

$74,210

Toward Personalized Explainable AI

Principal investigatorNSERC2022Discovery Grants Program - Individual

Principal investigators: Conati, Cristina

Keywords: explainable ai; personalization; user modeling; artificial intelligence; human computer interaction; human-centered ai

$144,000

AI-Driven personalized support to foster computational thinking skills in early K12 education

Principal investigatorNSERC2021Alliance Grants

Principal investigators: Conati, Cristina C

$215,334

Eye-tracking for Intelligent Personalization of Information Visualization

Principal investigatorNSERC2019Research Tools and Instruments

Principal investigators: Conati, Cristina

$123,076

Adaptation and Personalization for Information Visualization

Principal investigatorNSERC2016Discovery Grants Program - Accelerator Supplements

Principal investigators: Conati, Cristina

$120,000

Adaptation and Personalization for Information Visualization

Principal investigatorNSERC2016Discovery Grants Program - Individual

Principal investigators: Conati, Cristina

$276,000

Increasing users' engagement by providing personalized notifications in private social networks for healthcare

Principal investigatorNSERC2015Engage Grants Program

Principal investigators: Conati, Cristina

$25,000

Assessing and improving students' self-regulation in inquiry tasks with adaptive online physics simulations

Co-investigatorSSHRC2012Insight Development Grant

Principal investigators: Roll, Ido

Keywords: self-regulated learning; science education; physics education research; interactive online simulations; intelligent tutoring systems; inquiry learning; discovery learning; cognitive modeling; educational data mining; adaptive support; phet simulations.

$34,516

User modelling for Smart devices

Principal investigatorNSERC2012Engage Grants Program

Principal investigators: Conati, Cristina

$23,500

Examining the impact of pedagogical agents' scaffolding on students' affect during learning with digital media

Co-investigatorSSHRC2011Insight Development Grant

Principal investigators: Azevedo, Roger

Keywords: learning; emotions; digital media; technology; pedagogical agents

$75,000

From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.

Frequent collaborators

MacIsaac School of Nursing
Computer Information Systems
Other

Co-authors at University of British Columbia, colored by department. Thicker lines mean more shared papers; select anyone to open their profile and their own map.

Sally Newton-Mason

MacIsaac School of Nursing

2 shared papers, latest 2022

Giuseppe Carenini

Faculty

2 shared papers, latest 2022

Gabriel Murray

Computer Information Systems

1 shared papers, latest 2022

Write to this professor

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Applying for

Paper to mentionMention their NSERC grant from 2022

Subject: Prospective PhD student Dear Professor Conati, My name is [your name], and I am a [your current degree or role] at [your institution]. I am applying for a PhD position starting in [term and year]. I read your paper "Machine learning models explanations as interpretations of evidence: a theoretical framework of explainability and its implications on high-stakes biomedical decision-making." (2025), and it connects closely to my own work on [your research topic]. [One or two sentences on what you did and what you learned.] I also saw your NSERC-funded project "Toward Personalized Explainable AI" (2022), and I would be glad to contribute to work in that direction. Are you accepting new PhD students for [term and year]? I have attached my CV and transcripts, and I would be glad to talk at a time that suits you. Thank you for your time. Kind regards, [your name]