Talks

High resolution hydrodynamic simulations of galaxy formation are powerful tools, which can provide important information on the properties of the dark matter halo. Combined with the information obtained from the second data release of the Gaia satellite, simulations can significantly improve our understanding of the dark matter distribution in the Solar neighborhood. Determining the local dark matter velocity distribution is crucial for the correct analysis and interpretation of data from dark matter direct detection experiments. I will discuss the local dark matter distribution of Milky Way-like galaxies extracted from state-of-the-art hydrodynamic simulations, and present an analysis of direct detection data using this distribution. I will also discuss the properties of the dark matter component of the radially anisotropic stellar population recently discovered in the Gaia data, using the Auriga simulations. In particular, I will present the local dark matter density and velocity distributions of the simulated Milky Way-like galaxies with and without the anisotropic substructure, and discuss their implications for dark matter direct detection.

We discuss a new class of quantum phase transitions --- Deconfined Mott Transition (DMT) --- that describe a continuous transition between a Fermi liquid metal with a generic electronic Fermi surface and an insulator without emergent neutral Fermi surface. We construct a unified U(2) gauge theory to describe a variety of metallic and insulating phases, which include Fermi liquids, fractionalized Fermi liquids (FL*), conventional insulators and quantum spin liquids, as well as the quantum phase transitions between them. Using the DMT as a basic building block, we propose a distinct quantum phase transition --- Deconfined Metal-Metal Transition (DM2T) --- that describes a continuous transition between two metallic phases, accompanied by a jump in the size of their Fermi surfaces (also dubbed a 'Fermi transition'). We study these new classes of deconfined metallic quantum critical points using a renormalization group framework at the leading nontrivial order in a controlled double-expansion and comment on the various interesting scenarios that can emerge going beyond this leading order calculation.

Causal reasoning is vital for effective reasoning in science and medicine. In medical diagnosis, for example, a doctor aims to explain a patient’s symptoms by determining the diseases causing them. This is because causal relations---unlike correlations---allow one to reason about the consequences of possible treatments. However, all previous approaches to machine-learning assisted diagnosis, including deep learning and model-based Bayesian approaches, learn by association and do not distinguish correlation from causation. I will show that these approaches systematically lead to incorrect diagnoses. I will outline a new diagnostic algorithm, based on counterfactual inference, which captures the causal aspect of diagnosis overlooked by previous approaches and overcomes these issues. I will additionally describe recent algorithms from my group which can discover causal relations from uncontrolled observational data and show how these can be applied to facilitate effective reasoning in medical settings such as deciding how to treat certain diseases.

We study the defect operator product expansion (OPE) of displacement operators in free and interacting conformal field theories using replica methods. We show that as n approaches 1 a contact term can emerge when the OPE contains defect operators of twist d−2. For interacting theories and general states we give evidence that the only possibility is from the defect operator that becomes the stress tensor in the n→1 limit. This implies that the quantum null energy condition (QNEC) is always saturated for CFTs with a twist gap. As a check, we show independently that in a large class of near vacuum states, the second variation of the entanglement entropy is given by a simple correlation function of averaged null energy operators as studied by Hofman and Maldacena. This suggests that sub-leading terms in the the defect OPE are controlled by a defect version of the spin-3 non-local light ray operator and we speculate about the possible origin of such a defect operator. For free theories this contribution condenses to a contact term that leads to violations of QNEC saturation.

A multitude of protein bodies have recently been found to be non-membrane-bound liquid droplets that phase-separate spontaneously from the cellular environment, both in the cytoplasm and in the nucleus. For this reason, they are often compared to oil droplets in water. In this talk, I will challenge this analogy at two different levels. First, protein liquids are not quite like oil - they are generally caused by the specific attractive interactions between complementary protein domains, which result in surprising phase behavior and allows cells to fine-tune their dynamical properties. Second, the cellular environment, and in particular the nucleoplasm, is not quite like water - it is crowded, visco-elastic, and highly complex. Using simple theoretical arguments, I will show that the interplay between the elasticity of chromatin and the surface tension of droplets controls phase separation - and I will predict that this could result in exotic, yet-unobserved new phases of matter.

A key feature of living systems is their ability to consume chemical energy to actively generate the forces they use to move and change shape. These forces are typically generated at the nanometer scale by motor proteins, and transmitted to larger scales by networks of fibers. I will first discuss the transmission of these active forces through the cell cytoskeleton and the extracellular matrix. I will show how the nonlinear mechanical properties of these biopolymers crucially affect force transmission by selecting and amplifying contractile stresses. We experimentally confirm these results using a novel stress measurement technique, Nonlinear Stress Inference Microscopy. In a second part, I will discuss how active forces emerge from Brownian noise at the sub-micron scale. From an observer's point of view, there is a fundamental bound to the amount of information that can be recovered by monitoring the dynamics of such systems. I will propose a practical method, Stochastic Force Inference, that efficiently utilizes this limited information to reconstruct force fields and infer dissipative currents in Brownian systems.