Staff

Life’s Edge: The Search for What it Means to be Alive

In Life’s Edge, Carl Zimmer explores the nature of life and investigates why scientists have struggled to draw its boundaries. He handles pythons, goes spelunking to visit hibernating bats, and even tries his hand at evolution. Zimmer visits scientists making miniature human brains to ask when life begins, and follows a voyage that delivered microscopic animals to the moon, where they now exist in a state between life and death. From the coronavirus to consciousness, Zimmer demonstrates that biology, for all its advances, has yet to achieve its greatest triumph: a full theory of life.

Causality at the Intersection of Simulation, Inference, Science, and Learning: Post-talk Conversation

The sciences are replete with high-fidelity simulators: computational manifestations of causal, mechanistic models. Ironically, while these simulators provide our highest-fidelity physical models, they are not well suited for inferring properties of the model from data. Professor Kyle Cranmer of New York University will describe the emerging area of simulation-based inference and describe how machine learning is being brought to bear on these challenging problems.

NPA Seminar, Michael Ramsey-Musolf, University of Massachusetts, “Was There an Electroweak Phase Transition?”

Abstract: The possible existence of beyond Standard Model physics at the TeV scale or below has important implications for the thermal history of electroweak symmetry-breaking. A first order phase transition – not possible in the minimal Standard Model with a 125 GeV Higgs boson – would provide the preconditions for electroweak baryogenesis and the generation of primordial gravitational radiation. I discuss recent developments in assessing this possibility that rely on the combination of EFT methods and non-perturbative (lattice) computations.

Causality at the Intersection of Simulation, Inference, Science, and Learning

The sciences are replete with high-fidelity simulators: computational manifestations of causal, mechanistic models. Ironically, while these simulators provide our highest-fidelity physical models, they are not well suited for inferring properties of the model from data. Professor Kyle Cranmer of New York University will describe the emerging area of simulation-based inference and describe how machine learning is being brought to bear on these challenging problems.

Subscribe to RSS - Staff