Driving Innovation in Data Science and AI: Proposing New Frontiers with the Leeds-Africa Hub

Luisa Cutillo

Department of Statistics, SoM

University of Leeds

l.cutillo@leeds.ac.uk

Leeds Africa Hub Networking Event 2024

My main recent interests

EDI and Educational Interests

• WiML board of Direcors, mentorship program
• Data Science tools and computational practices in education
• Leeds Inst. Teach. Excellence (LITE)  Fellowship: Data Skills Literacy for Educators
• EDI and Skills Gap in Education: Office for Students (OfS) AI&DS scholarship funding (400K)

MSc Program Manager

• MSc in Data Science and Analytics

• MSc Statistics

• MSc Statistics with Applications to Finance

eps.leeds.ac.uk/maths-masters

My involvement in the

Leeds-Africa Hub

Research Focus Areas:

Health Data Science & Environmental AI

• ML for biomedical application: early disease detection, and understanding complex disease mechanisms.
• Human-Centered and Ethical AI: Focus on addressing health inequalities using qualitative data and AI models.
• Environmental Monitoring AI: Climate Impact Assessment on Biological Systems

ML for biomedical application: 

• Cancer Detection and Treatment Stratification. GmGM can integrate multiple modalities to identify specific cancer subtypes and cellular interactions within tumors. 

Building on our approach "GmGM: a Fast Multi-Axis Gaussian Graphical Model" (AISTAT 2024) can be used to:

early disease detection, and understanding complex disease mechanisms.

GmGM: a fast Gaussian graphical model for multi-modal data

ongoing PhD project (Andrew Bailey), AISTAT 2024

Gaussian multi-Graphical Model to construct sparse Graph estimation from tensor-variate data

• Fast: We exploit a closed form solution of the scalable bigraphical lasso

• Multimodal:  arbitrarily many tensor-variate datasets with shared axes

• Allows large dataset in short execution time (eg. run on datasets in the size of ~100 MB (4000x4000 64bit floats) < 1 minute)

Single tensor dataset

arbitrary set of tensors

• Counts of microbial species in stools
  -1000 peopleX2000 species (metagenomics)
• Counts of metabolites in blood plasma
  -1000 peopleX200 metabolites (metabolomics)

Human-Centered and Ethical AI 

Ongoing collaboration with the NHS on AI Analysis of Voice to Aid Laryngeal Cancer Diagnosis - Mary Paterson PhD Project (CDT Medical AI)

AI Analysis of Voice to Aid Laryngeal Cancer Diagnosis has potential to address health inequalities: providing accessible early-detection non invasive tools for at-risk or underserved populations.

Human-Centered and Ethical AI 

• Human-Centered Design and Inclusivity
• Ethics and Transparency in Model Development
• Addressing Health Inequalities through Qualitative Data Integration
• Continuous Evaluation and Bias Monitoring

Environmental Monitoring AI: 

 understanding how environmental stressors—like rising temperatures and extreme weather—affect biological systems 

 Key questions :

• How does climate change alter gene expression?
• How resilient or vulnerable are biological networks to climate stress?
• And how can we quantify uncertainties to guide decision-making?

UNRISK PhD project

Understanding Uncertainty to Reduce Climate Risks 

NERC Centre for Doctoral Training

Environmental Monitoring AI: 

 understanding how environmental stressors—like rising temperatures and extreme weather—affect biological systems 

 Impact:

• direct implications for agriculture, conservation, biodiversity, and public health.
• understanding how heatwaves affect crop development can help develop more resilient agricultural practices.
• insights into how marine life adapts to changing temperatures support biodiversity conservation and fisheries management. 

Possible Directions:

Promoting Impactful Research, Building and Supporting Research Communities

• AI Pipeline for Healthcare Impact: Development of APIs and tools for clinical applications.
• Establishing interdisciplinary groups within the HUB (e.g., 'AI in Health', ' AI for Health Equity and Environmental Resilience').
• Mentorship programs targeting PhD students and early-career researchers to build the next generation of leaders.
• Jointly create conversion programs targeting non-STEM students to diversify the data science workforce.

Thanks! 

Questions?