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The Internet, which has spanned several networks in a wide variety of domains, is having a significant impact on every aspect of our lives. The next generation of networks
will utilize a wide variety of resources with significant sensing capabilities. Such networks will extend beyond physically linked computers to include multimodal-
information from biological, cognitive, semantic, and social networks. This paradigm shift will involve symbiotic networks of smart medical devices, and smart phones or
mobile personal computing and communication devices (mPCDs). These devices – and the network -- will be constantly sensing, monitoring, and interpreting the environment;
this is sometimes referred to as the Internet of Things (IoT). Additionally, we are also witnessing considerable interest in the “Omics” paradigm, which can be viewed as the study of a domain in a massive scale, at different levels of abstraction, in an integrative manner. The IoT revolution combined with the Omics revolution (genomics and sociomics or social networks) will have significant implications on the way health care is delivered in the United States. In this talk I will discuss the following: 1) The P9 concept of smart health care; 2) The evolution of IoT – sensing, monitoring, and interpreting the environment ; 3) The omics revolutions – genomics and sociomics; and 4) Artificial Intelligence/Machine Learning applications.
With the burgeoning use of machine learning models in an assortment of applications, there is a need to rapidly and reliably deploy models in a variety of environments. These trustworthy machine learning models must satisfy certain criteria, namely the ability to: (i) adapt and generalize to previously unseen worlds although trained on data that only represent a subset of the world, (ii) allow for non-iid data, (iii) be resilient to (adversarial) perturbations, and (iv) conform to social norms and make ethical decisions.
In this talk, towards trustworthy and generally applicable intelligent systems, I will cover some reinforcement learning algorithms that achieve fast adaptation by guaranteed knowledge transfer, principled methods that measure the vulnerability and improve the robustness of reinforcement learning agents, and ethical models that make fair decisions under distribution shifts.
Markov Chain Monte Carlo for High-dimensional, Nonlinear Problems in Earth Science | HDSI Seminar Series
Earth science nearly always requires estimating models, or model parameters, from data. This could mean to infer the state of the southern ocean from ARGO floats, to compute the state of our atmosphere based on atmospheric observations of the past six hours, or to construct a resistivity model of the Earth’s subsurface from electromagnetic data. All these problems have in common that the number of unknowns is large (millions to hundreds of millions) and that the underlying processes are nonlinear. The problems also all have in common that they can be formulated as the problem of drawing samples from a high-dimensional Bayesian posterior distribution...