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Data Science and Biology – Speaker Series provides an exciting opportunity to hear from experienced professionals about various data science techniques with applications to the biological sciences. All undergraduate/graduate students, faculty, and staff interested in STEM are encouraged to attend! RSVP Here!
Gain insight on cutting edge academic research pertaining to early detection of COVID-19 infection and unsupervised data analysis in neuroscience, as well as an industry perspective of data analysis in biopharmaceutical manufacturing. See speaker bios on the next page!
Each speaker will give a short presentation of their background and current research, followed by a brief Q&A session. Attendees will have the opportunity to ask questions to each speaker. The speakers offer a wealth of experience from both academic and industry perspectives so we encourage everyone to attend and make the most of this opportunity!
Assistant Professor, Bioengineering Department and Halicioglu Data Science Institute, UCSD.
The Smarr lab leverages domain expertise in biological rhythms and neuroendocrinology to uncover patterns in diverse sets of time series data that carry actionable information to impact health and cognitive performance. In 2020 he became the technical lead of the global collaborative TemPredict study, which developed algorithms for early detection of COVID-19 infection, and unique cyberinfrastructure to serve rapid, collaborative explorations of population-scale, personal time series data. Beyond the pandemic, Dr. Smarr contributes broadly through science outreach, popular media, and industry liaisons. His personal passions lie in advancing women’s health, and in increasing participant engagement to map physiological diversity in service to precision individual and public health.
Assistant Professor, Halicioglu Data Science Institute, UCSD.
Dr. Mishne’s research is at the intersection of signal processing and machine learning for graph-based modeling, processing and analysis of large-scale high-dimensional real-world data. She develops unsupervised and generalizable methods that allow the data to reveal its own story in an unbiased manner. Her research on unsupervised data analysis in neuroscience, includes processing of raw neuroimaging data through discovery of neural manifolds to visualization of learning in artificial and biological neural networks.
Principal Data Scientist at Resilience.
(All up to date information can be found here.)