We also show, by means of an analytical model, that the bias arising from the higher point functions of the CIB itself ought to be negligible. However, mitigation techniques based on multifrequency cleaning appear to be very effective.
Then, using non-Gaussian simulations of Galactic dust – extrapolated to the relevant frequencies, assuming the spectral index of polarized dust emission to be fixed at the value determined by Planck – we show that the bias to any primordial signal is small compared to statistical errors for ground-based experiments, but might be significant for space-based experiments probing very large angular scales. To quantify these, we first estimate the dust residuals in currently available CIB maps and upcoming, foreground-cleaned Simons Observatory CMB data. We find that higher point functions of the CIB and Galactic dust such as 〈 BEI〉 c and 〈 EIEI〉 c can, in principle, bias the power spectrum of delensed B-modes. In this work, we characterize how foregrounds impact the delensing procedure when CIB intensity, I, is used as the matter tracer. For near-future experiments, the best estimate of the latter will arise from co-adding internal reconstructions (derived from the CMB itself) with external tracers such as the cosmic infrared background (CIB). Fortunately, the lensing effects can be partially removed by combining high-resolution E-mode measurements with an estimate of the projected matter distribution. This approach must face the challenge posed by gravitational lensing of the CMB, which obscures the signal of interest. Early requests are strongly encouraged to allow sufficient time for meeting your access needs.The most promising avenue for detecting primordial gravitational waves from cosmic inflation is through measurements of degree-scale cosmic microwave background (CMB) B-mode polarization. If you will need disability-related accommodations in order to participate in any of the programs or events listed, please email the contact person or the iSchool Help Desk. Please contact Christine Hopper for Zoom link. His PhD research further leads to a startup Kumo AI, which demonstrates significant real-world impact. He was the lead organizer of NeurIPS New Frontiers in Graph Learning Workshop, a co-organizer of the Stanford Graph Learning Workshop, and a program committee member at numerous top-tier AI conferences and journals. Fellowship, AAAI Best Student Paper Award, World Bank Best Big Data Solution, and Outstanding TA Award from Stanford CS. He has published 12 first-author papers in NeurIPS, ICML, ICLR, AAAI, KDD, and Jiaxuan has received multiple prestigious awards, including a JPMorgan Chase Ph.D.
His research investigates scientific and industrial problems through the lens of graphs and develops graph AI methods to solve these problems. Overall, the talk will outline the promising path toward bridging interdisciplinary research and extending the frontiers of AI with graphs.īio: Jiaxuan You received his PhD in computer science from Stanford University, advised by Professor Jure Leskovec. I will specifically cover my research in representing neural networks as relational graphs, which advances the design and understanding of deep neural networks and connects to network science and neuroscience. Lastly, I will show that graphs can further power AI problems in general domains. This line of work has broad applications in molecule design and drug discovery. Next, I will demonstrate my pioneering research in deep graph generative models which can generate novel realistic graph structures toward desirable objectives. The research has democratized graph AI for domain experts and helped them with scientific discoveries. Specifically, I will present my research on accelerating graph AI research by systematically investigating the design space and task space for graph deep learning. In this talk, I will present my research on investigating the interconnected world through the lens of graphs. Jiaxuan You will present "Learning from the Interconnected World with Graphs."Ībstract: The fact that our world is fundamentally interconnected presents unique challenges for modern data-driven research.
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