Operations & Partnerships
An experienced operations leader with an Executive MBA from Forman Christian College, specialising in programme management, strategic partnerships, and organisational coordination. He oversees operational planning and execution at the National AI Hub for MNCH, enabling cross-sector collaboration and supporting the delivery of innovative AI solutions for maternal and child healthcare.
A dentist by training and a public health professional trained at the London School of Hygiene & Tropical Medicine (LSHTM), specialising in health systems and health policy. Her work sits at the intersection of AI for health equity, climate-resilient health systems, and evidence-informed policymaking, with a focus on translating research into policy and practice through cross-sector collaboration.
AI Use Case & Discovery
An anthropologist with a Master’s in Applied Anthropology from the University of Amsterdam and a bachelor’s degree from LUMS. She explores maternal, newborn, and child health (MNCH) needs and experiences to inform human-centred AI interventions that are responsive to local contexts and the people they serve.
He applies expertise in microeconomics and primary data analysis to generate empirical insights that inform scalable AI solutions and maternal and child health interventions.
A UX researcher with a background in Psychology (BSc) and Human-Computer Interaction (MSc), she is passionate about using research to shape intuitive, user-centred experiences at the intersection of health and AI.
Data Analytics & Risk Stratification
A data scientist and machine learning engineer with over five years of industry experience spanning deep learning, computer vision, and MLOps. She develops machine learning-based risk stratification frameworks for maternal and neonatal complications.
A machine learning researcher with an MS in Artificial Intelligence from LUMS, he has research interests spanning explainable machine learning, language modelling, computer vision, and generative AI. His work focuses on developing and evaluating learning-based methods for complex real-world problems, with experience in speech recognition and healthcare AI. At the National AI Hub, he applies machine learning to maternal and newborn health research, including clinical risk prediction and decision support.
Senior data analyst with an interest in applied statistics and clinical prediction, developing the data pipelines and risk stratification models for the Hub's research on adverse maternal outcomes.
Speech & Language Technologies (SLT)
A researcher with a PhD in Machine Learning, he has research interests spanning speech processing, low-resource languages, and efficient machine learning in resource-constrained environments.
She completed her PhD in Computer Science at Information Technology University (ITU), Lahore, in 2026, with a thesis on speech emotion recognition for Urdu. Her research focuses on language and speech technologies for low-resource South Asian languages, covering machine translation, speech recognition, speech synthesis, medical AI, and evaluation.
She holds a master’s degree in Computer Science and has been working at the Centre for Speech and Language Technologies (CSaLT) for eight years. Her experience includes natural language processing, which was also the focus of her master’s research and a published paper, as well as IVR systems, data analysis, and data management.
He specialises in natural language processing, machine translation, large language models, and speech data for low-resource languages. He has experience building benchmarks, adapting large language models, and evaluating models for regional languages. His work aims to make language technology, including medical LLMs, accessible to underserved language communities.
He specialises in speech and audio AI, real-time AI systems, computer vision, and machine learning. He has experience developing and deploying production-ready AI solutions, with a focus on real-time audio processing, intelligent monitoring, and scalable AI applications.
A researcher focused on developing reliable and accessible AI systems for real-world impact, particularly in healthcare. His research interests include preference optimisation, natural language processing, and low-resource language technologies.
Monitoring, Learning & Evaluation (MLE)
A researcher with a background in Quantitative Economics, he is interested in development economics and using rigorous evidence to understand how AI is reshaping economies and societies in Pakistan and the developing world.
A researcher with a background in Economics and Political Science, she is interested in econometrics, development economics, and using data to understand complex social challenges. Her work is guided by a strong interest in evidence-based approaches to improving health outcomes.