The William H. Gates Sr. Fellowship from the AD Data Initiative

Save the Date - Applications for Cohort II

The Alzheimer’s Disease Data Initiative invites you to apply for our two-year fellowship program, named in honor of the life and legacy of William H. Gates, Sr., who passed away from Alzheimer’s disease in 2020.  

We’re looking for early- to mid-career fellowship applicants from diverse backgrounds. Applicants should have expertise in computational, machine learning, statistical, or other data science methods, and a demonstrated interest in brain research, neuroscience, or other relevant fields. A proven track record in generative AI methods and applications to neuroscience or neurodegenerative diseases is required.  

We will select up to four applicants for our second cohort. Awards will include financial assistance for your research, networking and mentorship opportunities, conference attendance, and more.  

Applications and additional details will be made available October 24. 

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About the Fellowship

The William H. Gates Sr. Fellowship from the Alzheimer’s Disease Data Initiative was established to support a new generation of researchers who have novel approaches to end Alzheimer’s disease and related dementias (ADRD).

The two-year program encourages and supports researchers from around the world to be bold, creative, and innovative in their approach to ADRD research. This may include generating new hypotheses about the biology of dementia or challenging existing ones; and using novel data analysis ideas or statistical approaches on human or human-derived anonymized data to make new discoveries in ADRD. The Fellowship provides researchers a $100,000 USD financial award, mentoring and network opportunities, and other supports such as publication assistance and conference attendance.

Named after William H. Gates Sr., this Fellowship honors his legacy of advocacy and philanthropy. We are inspired by the actions of Mr. Gates Sr., who passed away from Alzheimer’s disease in 2020, and we hope he can be an inspiration to the global research community.

About the 2023 Inaugural Cohort

The Alzheimer’s Disease Data Initiative is pleased to announce the selection of seven Fellows into our inaugural cohort. This year’s Fellows advanced through a highly competitive selection process and are a remarkably accomplished, passionate, and talented group from around the world. They have a broad array of research interests such as artificial intelligence/machine learning, biostatistical modeling, functional genomics, and neuroepidemiology.

The AD Data Initiative is proud to support these Fellows as they conduct their research and share their findings with researchers from around the world. This inaugural cohort is at the forefront of building a community and increasing collaborations as we move further and faster to end ADRD.

Meet the Gates Sr. AD Fellows


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Geetha Chilla, Ph.D.
Professional Background:

Dr. Geetha Chilla is a scientist at the A*STAR’s Bioinformatics Institute (BII), Singapore. She investigates neurodevelopment, neuropsychiatry, and other neurodegenerative diseases using medical imaging and artificial intelligence. She graduated from Nanyang Technological University, Singapore, with a Ph.D. in biomedical engineering.

Research Focus:

Neuro-radiomics, medical imaging and image processing, and artificial intelligence.

Proposed Research:

Explore regional alterations in subjects stratified with risk of dementia based on image-based markers obtained from mild and moderate Alzheimer’s disease and evaluate them with respect to changes in cognitive measures. Also, explore the possibility of predicting AD and dementia based on brain morphometry measures prior to the onset of symptoms.


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Ricardo D’Oliveira Albanus, Ph.D.
Professional Background:

Dr. Ricardo D’Oliveira Albanus is a biologist and postdoctoral associate at the Washington University School of Medicine in St. Louis, USA. His research focuses on the interplay between aging and neurodegeneration from a genomics viewpoint. He is especially interested in mapping the interface between genetics and gene regulation. He graduated from the University of Michigan, USA, with a Ph.D. in bioinformatics.

Research Focus:

Functional genomics of AD and aging.

Proposed Research:

Understand how aging predisposes to AD and identify the differences between healthy aging and AD. This will be done with deep molecular characterization of human brain tissues across a broad spectrum of ages and neuropathological states, including cognitively healthy centenarians.


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Sam Danso, Ph.D.
Professional Background:

Dr. Sam Danso is a senior lecturer in computer science at the University of Sunderland, UK. He is also a principal for the Ghana Prevent Programme, a project to develop a cohort of underrepresented populations for neurodegenerative disorders. He graduated from the University of Leeds, UK, with a Ph.D. in artificial intelligence.

Research Focus:

Applied artificial intelligence and data science with a focus on explainable artificial intelligence (XAI) for brain health.

Proposed Research:

Develop a deep phenotype of data that combines data from heterogeneous sources and different populations to obtain a life-course cohort of representative sample using already existing data. Also, explore and develop early detection XAI models using the harmonized life-course cohort.


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Jiyang Jiang, Ph.D.
Professional Background:

Dr. Jiyang Jiang is a research fellow at the Centre for Healthy Brain Ageing, University of New South Wales, Australia. His primary research interest is applying advanced computational skills to resolve complex questions in medicine. He graduated from the University of New South Wales, Australia with a Ph.D. in psychiatry.

Research Focus:

Machine learning, medical imaging, brain and cognitive aging, and dementia.

Proposed Research:

Develop robust prediction models for dementia in low- and middle-income countries (LMICs), using data that are easily accessible to LMICs such as demographics, risk factors, cognition, and physical function.


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Hangfan Liu, Ph.D.
Professional Background:

Dr. Hangfan Liu is an assistant professor at the Center for Advanced Imaging Research (CAIR), University of Maryland, USA. He is especially interested in exploring the disease heterogeneity within ADRD and improving disease subtyping. He graduated from Peking University, China, with a Ph.D. in computer science.

Research Focus:

Image processing, machine learning, and neuroimaging analysis.

Proposed Research:

Use innovative applications of machine learning, image processing, and neuroimaging analysis for the advancement of personalized treatment strategies. Efforts will include three key phases to disentangle the heterogeneity of ADRD – signal-adaptive image enhancement, joint feature representation learning, and robust collaborative clustering.


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Jermaine Ross, Ph.D.
Professional Background:

Dr. Jermaine Ross is the CEO and co-founder of Alleo Labs and a board member for the Alzheimer’s Association in New York City. He is interested in examining AD as a syndrome rather than a single disease, analyzing disease progression by AD subgroups. He graduated from Brown University, USA, with a Ph.D. in neuroscience.

Research Focus:

Alzheimer’s disease, Lewy body dementia, and Parkinson’s disease.

Proposed Research:

Advance biomarker research for early detection and diagnosis by examining the relationship between levels of markers such as insulin-like growth factor-1 and AD progression.


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Natalia Vilor-Tejedor Ph.D.
Professional Background:

Dr. Natalia Vilor-Tejedor leads the genetic neuroepidemiology and biostatistics team at Barcelona Beta Brain Research Center, Spain. Her research focuses on genetic (non-modifiable) and environmental (modifiable) factors in the development of neurological disorders and related traits. She graduated from Pompeu Fabra University, Spain, with a Ph.D. in biomedicine.

Research Focus:

Genetic neuroepidemiology and biostatistical modelling.

Proposed Research:

Develop a modeling framework that can generate functional predictive omics scores to identify endophenotypes of neurodegeneration at early, preclinical, and advanced stages. The goal is to enable the identification of specific molecular mechanisms of neurodegenerative disease prior to symptom development.




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