Dr. Mohammad Jalali, Harvard Medical School, A simulation-optimization approach to aggregate prior statistical findings, 14:00-15:00, May 9th, 2019, Thursday, Room N412, Shunde building 2019.05.07

[Time] 14:00-15:00, May 9th, 2019, Thursday

[Venue] Room N412, Shunde building

[Speaker] Dr. Mohammad Jalali, Harvard Medical School

[Host] Dr. Xiaolei Xie

[Title] A simulation-optimization approach to aggregate prior statistical findings

[Abstract] Rapid growth in scientific output requires methods for quantitative synthesis of prior research, yet current meta-analysis methods limit aggregation to studies with similar designs. Here we describe and validate Generalized Model Aggregation (GMA), which allows researchers to combine prior estimated models of a phenomenon into a quantitative meta-model, while imposing few restrictions on the structure of prior models or on the meta-model. In an empirical validation, building on 27 published equations, GMA provides a predictive equation for Basal Metabolic Rate that outperforms existing models, identifies novel nonlinearities, and estimates errors in various measurement methods. Additional numerical examples demonstrate the ability of GMA to obtain unbiased estimates from potentially mis-specified prior studies. Thus, in various domains, GMA can leverage previous findings to compare alternative theories, advance new models, and assess the reliability of prior studies, extending meta-analysis toolbox to many new problems.

[Bio] Dr. Mohammad Jalali (aka, ‘MJ’) is an assistant professor at Harvard Medical School and a senior scientist at MGH Institute for Technology Assessment. He was previously a research faculty at MIT Sloan School of Management and a consultant at the World Bank. MJ uses analytics and simulation-based approaches to help policymakers identify and develop high-leverage policies that not only are effective over the long haul, but also are not thwarted by unanticipated side effects. To achieve this goal, he spends a great deal of time working with decision-makers and policymakers, doing fieldwork and collecting different types of data that can inform richer models and analyses. MJ’s work has been featured by various national and international media outlets, including Associated Press, The Hill, Newsweek, Scientific American, Business Insider, and NPR. He is an associate editor of System Dynamics Review and is on the editorial board of the Journal on Policy and Complex Systems. He is the recipient of the 2015 Dana Meadows Award, the 2015 WINFORMS Excellence Award, and the 2014 Lupina Young Researcher Award. MJ received his PhD in Systems Engineering, with a concentration on management and health care systems, from Virginia Tech in 2015.

All interested are welcome!

联系电话: 010-62772989

Copyright © 2014-2021 清华大学工业工程系 版权所有