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      Machine Learning to Quantify Brain Activity from Astrocyte Imaging Data

      日期: 2019-03-11
      IDG麥戈文腦科學研究所學術報告
      Title: Machine Learning to Quantify Brain Activity from Astrocyte Imaging Data
      Speaker: Guoqiang Yu,Ph.D.
      Associate Professor
      the Bradley Department of Electrical and Computer Engineering
      Virginia Tech
      時間:2019年3月12日,10:00-11:30
      地點:王克楨樓1113
      Abstract: 
      Recent studies have suggested that astrocytes exert proactive regulatory effects on brain information processing and that they are deeply involved in normal brain development and disease pathology. Recording astrocyte activity is now technically feasible, due to recent advances in modern microscopy and ultrasensitive cell-type specific genetically encoded Ca2+ indicators for long-time imaging. However, there is a big gap between generating the data and extracting information from the data. Indeed, partially because of the challenges imposed by the complex patterns in astrocyte activity data, the development of sophisticated modeling and analysis tools lags much behind, and the current practice is essentially manual. This practice not only limits analysis throughput but also risks introducing bias and missing important information latent in complex, dynamic big data. In this talk, I will discuss our recent work on applying machine learning theory and techniques to flexibly and accurately quantify the astrocyte activity from the time-lapse fluorescent imaging data. For those who are interested in biology, I will demonstrate how our work can be used to assist your study of decoding the functional roles of astrocyte in brain. For those who are interested in the algorithm and tool development, I will show you how the astrocyte activity analysis is a booming research area and how the problem can serve as a motivating example for generic algorithm development.
      Speaker's Bio: 
      Guoqiang Yu is currently an Associate Professor at the Bradley Department of Electrical and Computer Engineering, Virginia Tech. He received his B.S. degree in electrical and computer engineering from Shandong University in 2001 and M.S. degree in automation from Tsinghua University in 2004. He received his Ph.D. degree in electrical and computer engineering from Virginia Tech in 2011. He did his Postdoctoral training in Bioinformatics at Stanford University. He returned to Virginia Tech as an Assistant Professor of Electrical and Computer Engineering in 2012. He was promoted to Associate Professor with tenure at 2018. His research interests are machine learning, signal and image analysis, statistical modeling, optimization techniques and their applications to developing computational tools to analyze and understand the big data in the biomedical field, particularly related to brain research. He has published 30 journal papers and 33 peer-reviewed conference papers, in journals such as Nature Medicine, Neuron, Bioinformatics and Journal of Machine Learning Research, and conferences such as NIPS, ISBI and BIBM. His work has been supported by NSF and NIH. He has 4 active projects with the personal share over 5 million dollars. He is a recipient of NSF CAREER award. He currently serves as an associate editor for the journal BMC Bioinformatics. He is a recipient of three best paper awards including the IEEE International Conference on Bioinformatics and Biomedicine (2009 and 2016) and the William A. Blackwell Award of Virginia Tech. He graduated 1 Postdoc, 3 Ph.D and 3 Master students. He is currently mentoring 7 Ph.D students and 3 Master students.
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