23 September, 2019: IIM Bangalore’s Research and Publications Office will host 2 seminars on September 26 (Thursday) with speakers from National University of Singapore and Tel Aviv University, respectively.
Ram Fishman, from Tel Aviv University, will speak on ‘Impacts of the Green Revolution on Infant Mortality in the Developing World’ in Classroom P-12, at 2:30 p.m.
In his paper, Ram argues that there is considerable debate on the potential for modern staple seed varieties (MVs) to advance nutrition, and little is known about the contribution of MV diffusion during the ‘Green Revolution’ to the global reduction in mortality achieved during the 20th century. He and his co-authors provide global scale estimates of the relationship between MV diffusion and infant mortality between 1960–2000 by constructing a novel, spatially-precise indicator of MV diffusion and leveraging child-level data from over 600,000 children across 21,604 sampling locations in 37 developing countries. Their results indicate that the diffusion of MVs reduced infant mortality by 2.4–5.3 percentage points (from a baseline of 18%), with stronger associations for male infants. These findings, they say, are unaffected by adjusting the statistical model to control for a wide range of potential confounders. The sizable contribution of MV adoption to improved welfare should inform global food and development policy.
Ram’s research is concerned with the broad issues of Sustainable Development, with particular emphasis on water scarcity and climate change in developing countries, global food security, and sustainable agriculture. His research has a strong empirical field component, and is taking place in multiple locations in Africa and South Asia.
Ram holds a PhD in Sustainable Development from Columbia University, a M.Sc. in Physics from the Weitzman Institute and a B.Sc. in Mathematics from Tel Aviv University.
His full profile is at: https://english.tau.ac.il/profile/ramf
The second seminar, in Classroom P-21 at 2:30 pm on September 26, features a presentation by Vaibhav Ranjan from the National University of Singapore.
In his paper, Vaibhav says that the increasing availability of digitized clinical data presents an unprecedented opportunity to study and gain deeper understanding of diseases, develop new treatments and improve healthcare ecosystems. However, he and his co-authors point out that clinical data also poses modelling challenges due to the heterogeneity of data sources containing patient information, e.g., structured demographic variables, text in clinical notes, images in MRI scans etc. In his talk, Vaibhav will provide an overview of techniques to obtain patient representations (or features) from heterogeneous clinical data sources. He will then describe a new deep learning based technique for unsupervised learning of patient representations developed in my group, and empirical results on predicting gene-disease associations using this technique. Finally, he will briefly discuss how the technique can be used for combined data-driven knowledge-based predictive modelling.
Vaibhav Rajan is an Assistant Professor in the Department of Information Systems and Analytics at the School of Computing, National University of Singapore (NUS). Earlier, he was a Senior Research Scientist at Xerox Research where he led a project on Clinical Decision Support Systems for over four years. He has also worked as a consultant at Hewlett-Packard Labs and as Chief Data Scientist at Videoken (an education technology start-up).
His full profile is at: https://www.comp.nus.edu.sg/disa/bio/vaibhav/
23 September, 2019: IIM Bangalore’s Research and Publications Office will host 2 seminars on September 26 (Thursday) with speakers from National University of Singapore and Tel Aviv University, respectively.
Ram Fishman, from Tel Aviv University, will speak on ‘Impacts of the Green Revolution on Infant Mortality in the Developing World’ in Classroom P-12, at 2:30 p.m.
In his paper, Ram argues that there is considerable debate on the potential for modern staple seed varieties (MVs) to advance nutrition, and little is known about the contribution of MV diffusion during the ‘Green Revolution’ to the global reduction in mortality achieved during the 20th century. He and his co-authors provide global scale estimates of the relationship between MV diffusion and infant mortality between 1960–2000 by constructing a novel, spatially-precise indicator of MV diffusion and leveraging child-level data from over 600,000 children across 21,604 sampling locations in 37 developing countries. Their results indicate that the diffusion of MVs reduced infant mortality by 2.4–5.3 percentage points (from a baseline of 18%), with stronger associations for male infants. These findings, they say, are unaffected by adjusting the statistical model to control for a wide range of potential confounders. The sizable contribution of MV adoption to improved welfare should inform global food and development policy.
Ram’s research is concerned with the broad issues of Sustainable Development, with particular emphasis on water scarcity and climate change in developing countries, global food security, and sustainable agriculture. His research has a strong empirical field component, and is taking place in multiple locations in Africa and South Asia.
Ram holds a PhD in Sustainable Development from Columbia University, a M.Sc. in Physics from the Weitzman Institute and a B.Sc. in Mathematics from Tel Aviv University.
His full profile is at: https://english.tau.ac.il/profile/ramf
The second seminar, in Classroom P-21 at 2:30 pm on September 26, features a presentation by Vaibhav Ranjan from the National University of Singapore.
In his paper, Vaibhav says that the increasing availability of digitized clinical data presents an unprecedented opportunity to study and gain deeper understanding of diseases, develop new treatments and improve healthcare ecosystems. However, he and his co-authors point out that clinical data also poses modelling challenges due to the heterogeneity of data sources containing patient information, e.g., structured demographic variables, text in clinical notes, images in MRI scans etc. In his talk, Vaibhav will provide an overview of techniques to obtain patient representations (or features) from heterogeneous clinical data sources. He will then describe a new deep learning based technique for unsupervised learning of patient representations developed in my group, and empirical results on predicting gene-disease associations using this technique. Finally, he will briefly discuss how the technique can be used for combined data-driven knowledge-based predictive modelling.
Vaibhav Rajan is an Assistant Professor in the Department of Information Systems and Analytics at the School of Computing, National University of Singapore (NUS). Earlier, he was a Senior Research Scientist at Xerox Research where he led a project on Clinical Decision Support Systems for over four years. He has also worked as a consultant at Hewlett-Packard Labs and as Chief Data Scientist at Videoken (an education technology start-up).
His full profile is at: https://www.comp.nus.edu.sg/disa/bio/vaibhav/