David Manheim works with academics and policymakers to reduce risks from future large-scale catastrophes. He is currently involved in understanding and building policy to mitigate large scale risks to humanity in different domains, focusing on biosecurity and artificial intelligence alignment, looking at both technical issues in risk evaluation and mitigation, and public policy and legislative approaches for reducing risks. David is also a Superforecaster with Good Judgement Inc., and was one of the original superforecasters involved in the Good Judgement Project, winner of an IARPA competition for forecasting, and is a former top-100 Metaculus forecaster. He completed his doctoral dissertation at Pardee RAND, part of the RAND Corporation, on topic of "Value of Information in Policy Analysis." That research applied both informal methods, Bayesian networks, and Markov-chain Monte-Carlo analysis to the question of how decision makers can make better decisions about information gathering for large scale decisions related to homeland security and biological risks. During his time at RAND, he was also involved in a variety of projects ranging from analysis of social media, to increasing resilience in military applications, to various types of risk modeling for public policy. Other work includes public policy decision making for infectious diseases, the implications of cryptocurrency for US national security, and several projects relating to NYC flood risk and risk reduction, the contributions of the US Army Corps of Engineers and others in multi-stakeholder planning in the region.Before graduate school, David worked with modeling risk in both insurance and equity derivatives. He has specialized in extreme tail risk and managing large datasets. David has worked on a variety of projects, leveraging his background in programming and risk analysis. He has assisted or led reviewing the RMS terrorism models, led internal and external training development for the explanation of complex models, automated business processes, performed consulting projects on accumulations and loss modeling, and helped build applications to monitor risk by combining models.