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Thanks for your interest in Neurogenesis! While in-person registration for this event is currently closed, .

Neurogenesis: HBHL's New Faculty Recruit Speaker Series

Thursday, February 9, 2023
4:00–5:00 p.m.

Hybrid seminar followed by an informal, post-event reception with refreshments for those attending in person

°Õ³ó±ðÌý±·±ð³Ü°ù´Ç²µ±ð²Ô±ð²õ¾±²õ speaker series will give you the opportunity to get to know HBHL’s new recruits firsthand—to learn about their research, ask questions and get to know them over refreshments.

Each event in the series will feature two HBHL faculty recruits whose research areas provide an interesting contrast or intersection for discussion.

February speakers:

• , will talk about her work on ex vivo brain scanning and neurodegenerative disease; and
• , will present his work on polygenic risk score modelling and topic-guided PheWAS using UK Biobank data

Can't make it in person? No worries!

To better meet the accessibility needs of the ÎÛÎÛ²ÝÝ®ÊÓƵ community, HBHL is now offering the possibility to attend the Neurogenesis presentation session remotely (childcare subsidy program also available for those interested in attending in person).

Speakers

Mahsa Dadar

Mahsa Dadar's team aims to investigate the role of cerebrovascular pathology in aging and neurodegenerative disease populations. Her research program has three main components: (1) Developing neuroimaging and machine learning tools to accurately detect and track signs of cerebrovascular and neurodegenerative pathologies; (2) Investigating the relationship between cerebrovascular and neurodegenerative pathologies, the impact of lifestyle and environmental factors on these diseases, and the impact of cerebrovascular pathology on clinical outcomes in neurodegenerative disease populations; and (3) Ex-vivo assessment of cerebrovascular disease using post-mortem MRI and histology.

Yue Li

Yue Li's research is focused on developing interpretable probabilistic-learning and deep-learning models of genetic, epigenetic, electronic health record and single-cell genomic data. By systematically integrating multimodal and longitudinal data, his lab aims to have impactful applications in computational medicine, including building intelligent clinical recommender systems, forecasting patient health trajectories, offering personalized polygenic risk predictions, characterizing multi-trait functional genetic mutations, and dissecting cell-type-specific regulatory elements that are underpin complex traits and diseases in humans. His research program covers three main research areas involving applied machine learning in: (1) healthcare and public health; (2) computational genomics; and (3) population genetics.