Many problems in science and engineering involve a delicate interplay between order and disorder. For example, this plays an important role in the study of ground states of interacting particle systems, as well as related problems such as designing error-correcting codes for noisy communication channels. Some solutions of these optimization problems exhibit beautiful long-range order while others are amorphous, and finding a clear basis for this dichotomy is a fundamental mathematical problem. It's natural to assume that any occurrence of dramatic structure must happen for a good reason, but is that really true? In this talk, I'll describe several examples of particle systems and codes showing disparate phenomena (without assuming any specialized background on the part of the audience). The strangest will be some codes Abhinav Kumar, Greg Minton, and I recently found. Although they are highly structured, they seemingly exist just because they can: a parameter count allows them to exist, and they take advantage of this possibility with no evidence of any deeper reason or regularity. They thus form an intriguing test case for order vs. disorder.

In the last decades granular materials have been used as physical models of complex phenomena: Jamming transition, Self-organized Criticality, Earthquakes, etc. I will present two granular experiments aiming the prediction of large events in a dynamics of power-law distributed avalanches. The first one is a sandpile experiment where an increase of disorder in the internal structure of the system serves as a precursor of large and very large avalanches. The second experiment mimics the behavior of a tectonic fault. It shears continuously a compressed granular layer and uses acoustics as the main source of information.

Much of our understanding of spontaneous pattern formation in spatially extended systems was developed in the “wetlands" of fluid mechanics. That setting allowed well-controlled table-top laboratory experiments; it came with fundamental equations governing the system; it benefitted from a back-and-forth between theory and experiment. These investigations identified robust mechanisms for spontaneous pattern formation, and inspired the development of equivariant bifurcation theory. Recently, these pattern formation perspectives have been applied to modeling the vegetation in dryland ecosystems, where satellite images have revealed strikingly regular spatial patterns on large scales. Ecologists have proposed that characteristics of vegetation pattern formation in these water-limited ecosystems may serve as an early warning sign of impending desertification. We use the framework of equivariant bifurcation theory to investigate the mathematical robustness of this approach to probing an ecosystem’s robustness. Additionally, we identify new applied pattern formation research directions in this far-from-pristine setting, where there are no fundamental equations and no controlled laboratory experiments.

Machine-learning tasks frequently involve problems of manipulating and classifying large numbers of vectors in high-dimensional spaces. Quantum computers are good at manipulating high-dimensional vectors in large tensor product spaces. This talk shows how quantum computers can provide an exponential speed-up over their classical counterparts for a variety of problems in machine learning and big data analysis.

The long-lived noble-gas isotope 81Kr is the ideal tracer for water and ice with ages of 10^5 – 10^6 years, a range beyond the reach of 14C. 81Kr-dating, a concept pursued over the past five decades by numerous laboratories employing a variety of techniques, is finally available to the earth science community at large. This is made possible by the development of the Atom Trap Trace Analysis (ATTA) method, in which individual atoms of the desired isotope are captured and detected. ATTA possesses superior selectivity, and is thus far used to analyze the environmental radioactive isotopes 81Kr, 85Kr, and 39Ar, These three isotopes have extremely low isotopic abundances in the range of 10^-16 to 10^-11, and cover a wide range of ages and applications. In collaboration with earth scientists, we are dating groundwater and mapping its flow in major aquifers around the world. We have also demonstrated for the first time 81Kr-dating of old ice.

The need of advanced materials for sustainable energy resources and next generation information technology requires the development of integrated scientific strategies, encompassing theoretical innovations, and computational and laboratory experiments. Substantial progress has been made in the last two decades in understanding and predicting the fundamental properties of materials and molecular systems from first principles, i.e. from numerical solutions of the basic equations of quantum mechanics. However the field of ab initio predictions is in its infancy; some formidable theoretical and computational challenges lie ahead of us, including the collection and use of data generated by simulations. We will describe recent progress and successes obtained in predicting properties of matter by quantum simulations, and discuss algorithmic challenges in connection with the use of evolving high-performance computing architectures. We will also discuss open issues related to the validation of the approximate, first principles theories used in large-scale quantum simulations.

Systems with short-range interactions show sqrt(N) fluctuations in equilibrium, except near a critical point, where the fluctuations diverge, with different scaling. We study a class of non-equilibrium systems with a continuous phase transition separating an “absorbing” phase (where the dynamics ultimately ceases) from an “active” phase (where diffusive-like dynamics persists forever). Remarkably even though the interactions are finite ranged, the dynamics lead to a “hyperuniform” state with diminished density fluctuations at the critical point. This prediction can be tested experimentally using a system of sheared colloids. In addition, we derive a scaling relation that relates the anomalous density fluctuations to other known exponents and study the effect of infinitesimal diffusion.

Concentrated suspensions of solid particles in a simple liquid exhibit highly non-Newtonian responses to applied forcing. At rest these suspensions appear liquid-like, but when sheared or impacted they can transition into states of vastly increased viscosity and in some cases even fracture like solids. Over the last few years this remarkable and quite counter-intuitive behavior has made suspensions an active area of research for the investigation of far-from-equilibrium physics. I will discuss some of these materials’ complex dynamic properties, both under steady-state shear and as transient response during impact, and highlight problems in our understanding that so far have remained unresolved.

In contrast to conventional matter, which is made up of chemically bonded constituents (e.g., atoms, molecules, etc.), "optical matter" is bound by light. This form of matter exists in a range of contexts - from optically arranged atomic lattices to close-packed colloidal assemblies to self-organizing arrays of nanoparticles. This talk will focus on the last category, wherein the interactions are created by the electrodynamic interactions amongst the constituent nanoparticles. This is in contrast to well-known methods for creating a user-defined optical lattices or collection of optical tweezers. The primary interactions in nanoparticle-based optical matter are dipolar - where the small metal nanoparticles are well described as point dipoles in the Raleigh limit. They are not near-field but rather originate from the typically neglected intermediate-scale term in the Green's function description of dipolar interactions. As our studies are conducted in liquid environments at room temperature, the relevant energy scale is kT; in fact, attractive and repulsive inter-particle interactions 10-fold greater can be achieved. We use minimally shaped, focused optical beams in which the particles create their long range interactions (essentially an interference effect of mutually scattered light). Therefore, arrays of optically interacting particles represent a many-body problem that requires self-consistent numerical methods to solve Maxwell's equations to model forces and interactions. User defined optical gradient forces and phase gradient forces will be demonstrated as ways to manipulate and control the shape and material properties of particle arrays. These properties include directional stress-strain relationships and yield stress that can result in structural transformations in finite size clusters of nanoparticle-based optical matter. Well defined optical matter arrays also allow exploring the behavior of driven non-equilibrium systems, including elucidating explicit dynamics (particle trajectories) in driven Kramers barrier crossing processes and examining the role of noise (from driving) to create what appear to be hyperuniform states.

I will present recent work conducted in my group realizing topological phases of photons. Beginning with a spin-hall meta-material for RF photons in coupled resonators, I will proceed to a description of recent progress engineering Landau levels for photons in non-planar (twisted) optical resonators, and mediating strong interactions between harmonically confined, massive photons using resonator Rydberg-EIT. This work holds short-term promise for dissipative production of photonic Laughlin states, and long-term potential as a route to controlled studies of anyons.

Although current observations of exoplanets focus on large, hot and gaseous planets, it is very likely that we will be able to study the atmosphere of a rocky planet around a nearby star within the next 5 years. The extent to which such a planet and its atmosphere might resemble the planets in our own solar system is still unknown. In this talk I will give an overview of the rapidly-evolving observations, before focusing on how dimensional analysis and scaling arguments can help us understand them. Starting with toy models of an atmosphere and then adding complexity, I will review basic scaling estimates for planetary temperature structures and wind speeds. In many cases these arguments can capture broad features of atmospheric circulations and more complex numerical models. I will then discuss how such theories can be applied to observations. In particular, near-future observations might be able to measure the day-night temperature contrast of exoplanets, which for many planets will be largely set by the atmospheric energy transport. I will show how dimensional analysis coupled with computation allows us to interpret these observations in new ways, and might even be used to infer the surface pressure of terrestrial exoplanets. Such constraints will be important for understanding the atmospheric evolution of terrestrial exoplanets, and for characterizing the surface conditions of potentially habitable planets.

Modeling of particle laden flow, especially in the case of higher particle concentrations, does not readily allow for first principles models. Rather, semi-empirical models of the bulk dynamics require careful comparision with experiments. At UCLA we have developed this theory for the geometry of viscous thin film flow with non-neutrally buoyant particles. We have found that for these slower flows, that diffusive flux models, involving a balance between shear-induced migration and hindered settling, can provide reasonably accurate predictive models. I will discuss the current state of this work including recent extensions to bidensity slurries and the relevant mathematics needed to understand the dynamics. Lubrication theory can be derived for this problem and results in a coupled system of conservation laws including regular shock dynamics and singular shocks. I will also briefly discuss relevant applications such as spiral separators.

The forced radial expansion of a spherical liquid shell by an exothermic chemical reaction is a prototypical configuration for the explosion of cohesive materials in three dimensions. The shell is formed by the capillary pinch off of a thin liquid annular jet surrounding a jet of reactive gaseous mixture at ambient pressure. The encapsulated gas in the resulting liquid bubble is a mixture of hydrogen and oxygen in controlled relative proportions, which is ignited by a laser plasma aimed at the center of the bubble. The strongly exothermic combustion of the mixture induces the expansion of the hot burnt gas, pushing the shell radially outwards in a violently accelerated motion. That motion triggers the instability of the shell, developing thickness modulations ultimately piercing it in a number of holes. The capillary retraction of the holes concentrates the liquid constitutive of the shell into a web of ligaments, whose breakup leads to stable drops. We offer a comprehensive description of the overall process, from the kinematics of the shell initial expansion, to the final drops size distribution as a function of the composition of the gas mixture, and the initial shell radius and thickness of the bubble. This problem, in which the fragments distribution is the result of a competition between deformation, breakup and cohesion, is relevant to a collection of phenomena spanning over a broad range of length scales, among which are: Exploding blood cells in the human body, spore dispersal from plants, boiling droplets, underwater explosions, magma eruption in volcanoes, up to the torn patterns of supernovae in the Universe.

I will describe a potential mechanism for a singular solution of the Euler equation. The mechanism involves the interaction of vortex filaments, but occurs sufficiently quickly and at a small enough scales that could have plausibly evaded experimental and computational detection. Joint work with Sahand Hormoz and Alain Pumir.

The maximum entropy production (MEP) principle holds that non equilibrium systems with sufficient degrees of freedom will likely be found in a dynamic state that maximizes entropy production or, analogously, maximizes potential energy destruction rate. The theory does not distinguish between abiotic or biotic systems; however, I will show that systems that can coordinate function over time and/or space can potentially dissipate more free energy than purely Markovian processes (such as fire or a rock rolling down a hill) that only maximize instantaneous entropy production. Biological systems have the ability to store useful information acquired via evolution and culled by natural selection in genomic sequences that allow them to execute temporal strategies and coordinate function over space. For example, circadian rhythms allow phototrophs to “predict” that sun light will return and can orchestrate metabolic machinery appropriately before sunrise, which not only gives them a competitive advantage, but also increases the total entropy production rate compared to systems that lack such anticipatory control. Similarly, coordination over space, such a quorum sensing in microbial biofilms, can increase acquisition of spatially distributed resources and free energy and thereby enhance entropy production. In this talk a computational modeling framework will be presented to describe microbial biogeochemistry based on the MEP conjecture constrained by information and resource availability. Results from model simulations will be compared to laboratory experiments to demonstrate the approach.

Advances in technology have begun to allow for the production of large groups, or swarms, of robots; however, there exists a large gap between their current capabilities and those of swarms found in nature or envisioned for future robot swarms. These deficiencies are the result of two factors, difficulties in algorithmic control of these swarms, and limitations in hardware capabilities of the individuals. Creating a hardware system for large robotic swarms is an open challenge; cost and manufacturability pressure hardware designs to be simple with minimal capabilities, while algorithm design favors more capable hardware. The robot design must balance these factors to create a simple robot that is, at the same time, capable of performing the desired behaviors. To investigate these challenges, I created the Kilobot robot swarm, a swarm of 1024 (“kilo”) robots. In this talk, I will discuss the many challenges associated with creating a robot swarm at this scale and the implications this has for creating even larger, more capable swarms in the future.

Controlling these swarms is also a challenge, as the properties desired from these systems, e.g. shape, locomotion, are generally a global property; however, we can only control local interactions between individuals. Furthermore, the mapping between controllable local behaviors and desired global results is not well understood. Their control is further complicated by the very nature of these systems which are composed of decentralized, distributed, asynchronous, error-prone individuals with often limited capabilities. I will discuss two examples of algorithms recently implemented on the Kilobot swarm, self-assembly of user-defined 2D shapes, and the collective transport of objects. Both of these examples provide guarantees of correctness and performance bounds of the swarm, and provide examples of reliable global-to-local control over a robot swarm. I will describe unexpected challenges faced while trying to control the Kilobot swarm, and how these challenges will influence the design of future swarm algorithms.

Inspired by biological functions such as ciliary beating and cytoplasmic streaming, we developed a highly tunable and robust model system from cytoskeletal components that self-organizes to produce a broad range of far-from-equilibrium materials with remarkable emergent properties. Using only simple components -- microtubules, kinesin motor clusters, and a depletion agent that bundles MTs -- we reconstituted analogues to several essential biological functions, including cilia-like beating, metachronal waves in bundle arrays, and internally generated flows in active cytoskeletal gels. Beyond these biomimetic behaviors, we have also used the same components to engineer novel active materials which have no biological analogues: active streaming 2D nematics, self-propelled emulsion droplets, and self-deforming vesicles. Since these initial observations, theoreticians have recapitulated many of these experimental results with physical models of cytoskeletal mixtures. This underscores the value of model systems such as ours for better understanding the fundamental principles that drive self-organized processes. This could one day lead to the systematic engineering of far-from-equilibrium materials with highly sought-after collective and biomimetic properties.

In my graduate work, I systematically varied energy levels (ATP) and characterized the response in our system’s collective dynamics. I will also discuss my current research, investigating the possible effects that varying cellular energy levels may have on the self-organized properties of the mitotic spindle. The spindle is also composed of microtubules in a liquid crystalline phase and motor proteins, and is essential to life because it mediates chromosome segregation. In vivo, energy levels are determined by the mitochondria and the cell's metabolism. We are able to quantitatively characterize the metabolic state of cells using Fluorescence Lifetime Imaginge Microscopy (FLIM), and are now investigating whether metabolic activity affects spindle function and chromosome segregation.

Human cognitive and social systems are perhaps the final frontier for mathematical scientific theory. While well-known methods of statistical physics and scientific computation are useful as entry points to a fast growing body of data, critical formal innovations are also necessary that describe these systems in their own terms.

Cities, in particular, provide a rich, novel and increasingly empirically available set of problems where open-ended adaptation at different scales builds large-scale socioeconomic networks in interaction with infrastructural systems embedded in space and time.

In this talk, I will describe the emerging mathematics of cities. The crucial starting element deals with the quantification of the general properties of urban areas, which become apparent through scaling analysis and associated statistics. Based on a set of regularities that I will demonstrate empirically, I then build a mean-field theory that derives the scaling of many socioeconomic, infrastructural and physical properties of cities and reveals the basic trade-offs involved in these systems.

I will then demonstrate how the detailed fabric of cities can be understood through a process of spatial selection and show how the complexity of explanations at the local level (groups, neighborhoods) can be quantified in units of information relative to more coarse-grained descriptions, in a way analogous to renormalization group transformations in statistical physics.

I will end with some general (speculative) thoughts on the convergence between methods of statistical physics, the mathematics of selection and basic aspects of human social behavior and cognition that may provide a path to a more integrated quantitative understanding of complex adaptive systems.

“Big computing” – petascale systems and the multicore paradigm – has enabled rapid, large-scale biomolecular simulation and property prediction. Similarly, “big biology” – high-throughput sequencing and the “-omics” revolution – has heralded voluminous bioinformatics databases. These large data sets present exciting opportunities to advance scientific understanding, but their size presents new challenges, and demands new paradigms, for their analysis. In the first part of this talk, I will discuss the translation of clinical sequence databases into viral fitness landscapes based on spin glass models from statistical physics. In an application to hepatitis C virus, we identified particular viral vulnerabilities and rationally designed T-cell vaccines to hit the virus where is hurts. In the second part of this talk, I will describe an approach integrating ideas from dynamical systems theory and nonlinear machine learning to infer multidimensional biomolecular folding funnels from univariate experimental measurements.

A bag of sand is a slightly hypostatic system: the number of constraints is just a little smaller than the number of degrees of freedom. As a result, several linearly independent modes of motion are available at zero energy cost. The question naturally arises: can we use these modes to transmit information from one side of the system to the other? In this talk, I will explain why we cannot. Even though each mode considered on its own spans a large portion of the system, combining the modes yields only a few independent long range modes, and many localised modes. Thus the effective number of free modes seen by any small portion of the system is much smaller than we would have guessed based on Maxwell counting. This provides an unexpected limitation on the perturbations that can be applied, and even most of those that are accessible are not transmitted.

Understanding how particles come together to form ordered structures is a central goal of material science. Mechanisms which rely on geometric aspects of the system, for example the shape of the constituent particles, often lead to more robust ways of influencing a material structure because they are independent of the chemical details. These geometric influences may enter at the particle scale or at the scale of the underlying environment, for example through the curvature of the substrate. We experimentally studied the 2-D assembly of attractive colloidal particles with varying shapes as well as repulsive spheres inside spatially varying external fields. In each of these cases, we use geometry to gain insight on the resulting structure.

Kinneyia are a class of microbially mediated sedimentary fossils. Characterized by clearly defined ripple structures, Kinneyia are generally found in areas that were formally littoral habitats and covered by microbial mats. To date, there has been no conclusive explanation of the processes involved in the formation of these fossils. Microbial mats behave like viscoelastic fluids. We propose that the key mechanism involved in the formation of Kinneyia is a Kelvin–Helmholtz-type instability induced in a viscoelastic film under flowing water. A ripple corrugation is spontaneously induced in the film and grows in amplitude over time. Theoretical predictions show that the ripple instability has a wavelength proportional to the thickness of the film. Experiments carried out using viscoelastic films confirm this prediction. The ripple pattern that forms has a wavelength roughly three times the thickness of the film. This behaviour is independent of the viscosity of the film and the flow conditions. Laboratory-analogue Kinneyia were formed via the sedimentation of glass beads, which preferentially deposit in the troughs of the ripples. Well-ordered patterns form, with both honeycomb-like and parallel ridges being observed, depending on the flow speed. These patterns correspond well with those found in Kinneyia, with similar morphologies, wavelengths and amplitudes being observed.

From the tiny fluctuations in the temperature of the cosmic microwave background to the distribution of galaxies seen today, the universe is rich with structure. The gravitational evolution of this structure is a sensitive probe of the matter contents and evolutionary history of the universe. I will discuss how the statistical distribution of cosmic structure can be used to test dark matter, dark energy, and cosmic inflation (the epoch just before the big bang during which all of the structure of the universe was generated by quantum fluctuations).

Physics consists in identifying repeatable sequences in our environment and finding the simplest underlying laws. Here, the environment is Sport. We will first start with sports ballistics and classify the different paths that can be observed according to the symmetry of the "particles". This will allow us to discuss the diversity of trajectories with spheres, the flip properties of shuttlecocks and the stability issues in ski-jumping. The second part will be dedicated to weightlifting. The questions we address are: How does a human lift a weight? Can we use the dynamics of lift as a muscle rheometer?

Although quantum mechanics has enabled astounding advances in semiconductor technology, these technologies still do not fully exploit aspects of quantum physics, such as entanglement. A second revolution in semiconductor technology will stem from the successful control and implementation of entanglement and other exotic features of quantum physics. In this seminar, we discuss two material platforms that may lead to new types of quantum electronics building upon mature semiconductor microelectronic technologies, and we describe how first-principles theoretical modeling techniques can be used to design these material systems and guide experimental efforts. First, we will discuss how scanning tunneling microscopy can be used to create systems of atomically precise defects. These defects can be used as long-lived quantum dot qubits, networks of interacting qubits, atomic-scale transistors, and more. We will show how computational methods can provide precise detail on the electronic structure of these defects, and identify new dopants and defect geometries. In the second part of the talk, we will discuss electronic spins bound to atom-like point defects in semiconductors. We will present our recent work on the quantum decoherence dynamics of the divacancy spins in silicon carbide using the cluster-correlation expansion method and our work on the exploration of new quantum defect spins in piezoelectric aluminum nitride using density functional theory.

Measurements of the cosmic microwave background (CMB) have driven spectacular advances in our understanding of the universe. This has led to a standard cosmological model (LCDM) that requires only six cosmological parameters to fit all cosmological data sets, which imply a universe dominated by dark matter and dark energy. Next-generation CMB experiments aim to answer some of the most exciting questions in cosmology: to understand the physical origin of dark energy, to test and constrain physics at Planck energy scales (1e16 GeV), to measure the sum of the neutrino masses at a level below the minimum mass expected from neutrino oscillations (<0.06 eV), and to precisely constrain the relativistic energy density of the universe and any "dark radiation" component. I will review some of the latest results from the CMB, and describe how CMB experiments use superconducting detectors to go from detector time-streams, to microwave maps of the sky, and finally to constraints on cosmology.

How cells maintain stable shapes and sizes through the cycles of birth and replication poses a fundamental question at the interface of physics and modern biology. Recent technological advances in single cell imaging have yielded unprecedented amounts of quantitative information about the shapes of single bacteria as they grow and divide. These single-cell studies are generating tremendous interest because they reveal unanticipated relationships between cell shape, growth rate and the timing of division events. To understand these relationships, we developed a general theoretical framework from a principle of minimal energy dissipation that relates cell geometry to the kinetics of growth and division. The model accounts for counterintuitive dependencies between growth and shape and gives rise to predictions that we verify in our experimental studies of the bacterium Caulobacter crescentus. In particular we describe how mechanical stresses regulate cell wall growth and drive shape transitions during cell wall constriction. Using our theoretical model and experimental data we further establish that cell growth and constriction are both driven by the synthesis of new cell wall material and are thus controlled by a single timescale. Our work brings new perspectives on how shapes of bacteria can impact their growth and survival.

Planet Lab is a network for hands-on STEM, SCIENCE, TECHNOLOGY, ENGINEERING AND MEDICINE exploration and learning. Radha Ramachandran (Physics PhD 2014, Science Education and Outreach Coordinator MRSEC & RDCEP) and Eve Tulbert (literacy and learning researcher) will describe how they decided to co-found an educational technology start-up dedicated to expanding opportunities for STEM education and engagement. In the first part of the talk, Ramachandran and Tulbert will describe the technology start-up process. Then, they will describe the current research around learning in the concept called "STEM Ecosystems," and discuss ideas with the group for creating learning pathways for K-12 students.

Is turbulence really scale invariant ? This assumption was already used in the 40’s by Kolmogorov to predict the repartition of energy through scales in a turbulent flow. Even if the first order of such a theory has been observed in many situations, it fails to predict the typical fluctuations of velocity observed in real experimental conditions. This phenonemon of scale invariance break down is often called “intermittency” and it is somehow related to the existence of structures of preferred size. Following these structures and the associated dissipation as a function of time is an experimental challenge, as they travel neither with the individual fluid particles nor with the averaged mean flow.

An insight can however be obtained in zero mean flow experiments, in which all the fluctuations are caught in a closed box. By accelerating a square grid downward in a water tank, we generate an array of wakes that induces a 3 dimensional turbulent flow. After the impulse excitation (about 100ms), a decay of the turbulent flow due to dissipation at small scales is observed. The entire decay process lasts for hours, the dissipation scale rising up through scales with time. Using an experimental trick, I will show that it is possible to follow and characterized this turbulent decay process through several order of magnitude in time using one single movie.

In a second part of my talk, I will briefly introduce some possible path of my future research. I will present in particular two experiments, one focusing on the interaction between a vortex ring and a turbulent background flow, the other on the dynamics of vortex filaments in a sheared boundary layer.

It requires a lot of force to quickly pull out an object immersed in a bath of dense granular suspension like corn starch in water To understand such striking force response under extensional flow, we experimentally measure the normal force required for pulling out a rod vertically from the suspension at a controlled velocity. We observe that for slow velocities the force response is similar to that of highly viscous fluids but above a threshold velocity the force show a diverging behavior soon after the initial viscous-like response. We use non-optical methods like X-ray and fast ultrasound imaging to map out the local velocity profiles inside the optically opaque suspension during extensional flow. These measurements clearly show that under rapid extension, there is a growing jammed region inside the suspension that interacts with the rigid boundaries of the container giving rise to the observed force divergence. Our findings suggest that under rapid extension the force repose in dense granular suspensions is very similar to impact activated solidification even though the concept of jamming under extension is highly non-intuitive.

The actin cytoskeleton is a dynamic, biological structural material that drives cellular scale deformations during processes such as cell migration and protrusion. Motor proteins actively drive deformations by buckling and translocating actin filaments. However, there is evidence that constriction of the contractile ring, an event that aids in the separation of cells during division, can occur mediated by crosslinker proteins without motor activity. How do crosslinkers, independent of motors, drive contraction of an actin bundle? We propose that crosslinkers are analogous to molecular cohesion and create an effective surface tension that drives bundle shortening and deformation. Crosslinkers induce short actin filaments to bundle into micron-sized tactoids, similar to granules found at the isotropic-nematic phase transition in liquid crystals. This contrasts sharply with long filaments, which coarsen and anneal into a steady state of bundles that are frozen in a network. Intermediate filaments form bundles that shorten until reaching a steady shape. Our results, that crosslinked bundles of short biological filaments behave like liquid droplets, suggest a motor-independent mechanism for contractility in biological materials.

Identifying the forces driving population dynamics is a major goal in ecology, especially for infectious diseases. Mechanistic models have yielded tremendous insight but are challenging to fit to noisy, nonlinear, and high-dimensional systems. A new method based on state-space construction purports to infer causal interactions in ecological systems without the need to invoke a model. The basic idea of this approach, known as convergent cross-mapping, is that if X drives Y, increasing the number of observations of Y should improve predictions of states of X. Even in idealized cases with long time series and no observation error, convergent cross-mapping tends to detect interactions when none are present. Several features of infectious disease systems, such as external forcing and nonstationarity, are especially problematic. Although different techniques can mitigate the effects of these factors, conceptual problems further constrain the usefulness of the method in natural systems.

The laws of equilibrium statistical mechanics impose severe constraints on the properties of conventional materials assembled from inanimate building blocks. Consequently, such materials cannot exhibit spontaneous motion or perform macroscopic work. Inspired by biological phenomena such Drosophilacytoplasmic streaming, our goal is to develop a new category of soft active materials assembled from the bottom-up using animate, energy-consuming building blocks. Released from the constraints of the equilibrium, such internally driven active gels, liquid crystals and emulsions are able to change-shape, crawl, flow, swim, and exert forces on their boundaries to produce macroscopic work. Active matter can serve as a platform for developing novel applications, testing theoretical models of non-equilibrium statistical mechanics and potentially even shedding light on self-organization of living cells.

Warming-induced sea level rise presents a long term hazard to coastal populations and infrastructure that is projected to increase throughout the 21st century. In addition to this gradual trend, the intensity and frequency of tropical cyclones, such as hurricanes, is expected to shift. These two effects interact nonlinearly to increase the return frequency for episodic, extreme flooding, such as water levels which characterized recent catastrophic events like Hurricanes Katrina and Sandy. I focus on recent advances in projecting the components of sea level rise and the large associated uncertainties to demonstrate the challenge of managing the risk. In particular, understanding and modeling the dynamics of ice sheet mass loss continues to inhibit reliable projection of the timescale for sea level rise. I conclude by pointing to a novel approach for improving the credibility of model projections.

Colloidal particles are often directed to assemble by applying external fields to steer them into well-defined structures at given locations. We are developing alternative strategies based on fields that arise when a colloid is placed within soft matter to form an inclusion that generates a potential field. For example, a particle adsorbed on a fluid interface can distort that interface to satisfy its wetting boundary conditions. The distortion has an associated energy field given by the product of its interfacial area and the surface tension. Fields generated by neighboring particles interact to drive assembly; preferred orientations for anisotropic objects emerge. Interface curvature couples to the particle’s capillary energy. By molding the interface to impose well defined curvature fields, we drive microparticles along pre-determined paths to well defined locations with deterministic energies. This example captures the emergent nature of the interactions, and their potential importance in schemes to make reconfigurable materials, since interfaces and their associated capillary energy landscapes can be readily reconfigured. We explore analogies in other reconfigurable soft matter systems. Liquid crystals are one important host medium. Particles immersed in liquid crystals distort the director field to elicit an elastic energy response. Preferred paths and locations for assembly can be defined by molding the director field and its associated defect structures. Particles adhered to lipid bilayer vesicles are another system in which such fields can be generated and exploited. These example systems have important analogies and pronounced differences which we seek to understand and exploit.

Frames consisting of nodes connected pairwise by rigid rods or central-force springs, possibly with preferred relative angles controlled by bending forces, are useful models for systems as diverse as architectural structures, crystalline and amorphous solids, sphere packings and granular matter, networks of semi-flexible polymers, proteins, origami, and an increasing number of lab-constructed micron-scale metamaterials. The rigidity of these networks depends on the average coordination number z of the nodes: If z is small enough, the frames have internal zero-frequency modes, and they are “floppy”; if z is large enough, they have no internal zero modes and they are rigid. The critical point separating these two regimes occurs at a rigidity threshold that for central forces in d-dimensions occurs at or near coordination number zc = 2d. At and near the rigidity threshold, elastic frames exhibit unique and interesting properties, including extreme sensitivity to boundary conditions, power-law scaling of elastic moduli with (z- zc), and diverging length and time scales.

This talk will explore elastic and mechanical properties and mode structures of model periodic lattices, such as the square, kagome, pyrochlore, and jammed packings with central-force springs, that are just on verge of mechanical instability. It will discuss the origin and nature of zero modes and elasticity of these structures under both periodic (PBC) and free boundary conditions (FBC), and it will investigate lattices [1,2,3] (a) whose zero modes under the two boundary conditions are essentially identical, (b) whose phonon modes in the bulk are “gapped” with no zero modes in the periodic spectrum (except at zero wavenumber) but include zero-frequency surface Rayleigh waves in the free spectrum, and (c) whose bulk phonon modes include isolated points or lines where their frequency is zero. In case (a), lattices are generally in a type of critical state that admits states of self-stress in which there can be tension in bars with zero force on any node. Distortions away from that state gap the spectrum and give rise to surface modes under free boundary conditions whose degree of penetration into the bulk diverges at the critical state. The gapped states have a topological characterization, similar to those of polyacetylene and topological insulators, that define the nature of zero-modes at the boundary between systems with different topology. Case (c) is closely analogous to Weyl semi-metals with isolated points in the Brillouin zone where valence and conduction bands meet. These critical lattices generally have macroscopic elastic distortions, called Guest Modes, that cost no energy.

Developmental signaling networks are composed of dozens of components whose interactions are very difficult to quantify in an embryo. Geometric reasoning enumerates a discrete hierarchy of phenotypic models with a few composite variables whose parameters may be defined by in vivo data. Vulval development in the nematode Caenorhabditis elegans is a classic model for the integration of two signaling pathways; induction by EGF and lateral signaling through Notch. Existing data for the relative probabilities of the three possible terminal cell types in diverse genetic backgrounds as well as timed ablation of the inductive signal favor one geometric model and suffice to fit most of its parameters. The model is fully dynamic and encompasses both signaling and commitment. It then predicts the correlated cell fate probabilities for a cross between any two backgrounds/conditions. The two signaling pathways are combined additively, without interactions, and epistasis only arises from the nonlinear dynamical flow in the landscape defined by the geometric model. In this way, the model quantitatively fits genetic experiments purporting to show mutual pathway repression. The model quantifies the contributions of extrinsic vs. intrinsic sources of noise in the penetrance of mutant phenotypes in signaling hypomorphs and explains available experiments with no additional parameters. Data for anchor cell ablation fix the parameters needed to define Notch autocrine signaling.

In this talk I will describe efforts to construct a platform based on microwave photons propagating in superconducting structures to perform quantum simulation. Quantum simulation is the idea that you can use a well controlled quantum system (superconducting qubits, ultra-cold atoms) to construct a model Hamiltonian that would be hard to solve numerically and difficult to manipulate experimentally. This especially applies to strongly interacting quantum systems such as those found in the fractional quantum hall state. In a tight collaboration with Jon Simon we are building the elements to perform such simulations. I will describe the basic types of systems we'd like to be able to model and show how we can realize each component necessary to study them.

Five times in the past 500 million years, mass extinctions have resulted in the loss of greater than three-fourths of living species. Each of these events is associated with a significant perturbation of Earth's carbon cycle. But there are also many such environmental events in the geologic record that are not associated with mass extinctions. What makes them different? We show that natural perturbations of the carbon cycle exhibit a characteristic rate of change consistent with the cycle's maximum rate of quasistatic evolution. We identify this rate with marginal stability, and find that mass extinctions occur on the fast, unstable side of the stability boundary. These results suggest that the great extinction events of the geologic past, and potentially a "sixth extinction" associated with modern environmental change, are characterized by common mechanisms of instability.

Over the past several years, our understanding of topological electronic phases of matter has advanced dramatically. A paradigm that has emerged is that insulating electronic states with an energy gap fall into distinct classes distinguished by the topology of their band structure. Interfaces between different topological phases exhibit gapless conducting states that are protected topologically and are impossible to get rid of. In this talk, after briefly introducing topological electronic band structures, we will show that similar ideas arise in a completely different class of classical problems. Isostatic lattices are arrays of masses and springs that are at the verge of mechanical instability. They play an important role in our understanding of granular matter, glasses and other 'soft' systems. Depending on their geometry, they can exhibit zero-frequency 'floppy' modes localized on their boundaries that are insensitive to local perturbations. The mathematical relation between this classical system and quantum electronic systems reveals an unexpected connection between theories of hard and soft matter.