Talks

Abstract Online social networks often mirror community formation in real-world networks (based on common demographics, interests, or affinities). Such patterns are often picked up and used by algorithms that leverage social data for the purpose of providing recommendations, diffusing information, or forming groups. In this talk, we'll discuss the influence maximization problem where multiple communities exist, showing that common centrality metrics may exclude minority communities from adopting the information diffused. Using the preferential attachment model with unequal communities, we'll characterize the relationship between homophily, network centrality, and bias through the power-law degree distributions of the nodes, and study the conditions in which diversity interventions can actually yield more efficient and equitable outcomes. We find a theoretical condition on the seedset size that explains the potential trade-off between outreach and diversity in information diffusion. To wrap up, we’ll discuss a novel set of algorithms that leverage the network structure to maximize the diffusion of a message while not creating disparate impact among participants based on community affiliation.

Abstract We study how communication platforms can improve social learning without censoring or fact-checking messages, when they have members who deliberately and/or inadvertently distort information. Message fidelity depends on social network depth (how many times information can be relayed) and breadth (the number of others with whom a typical user shares information). We characterize how the expected number of true minus false messages depends on breadth and depth of the network and the noise structure. Message fidelity can be improved by capping depth or, if that is not possible, limiting breadth, e.g., by capping the number of people to whom someone can forward a given message. Although caps reduce total communication, they increase the fraction of received messages that have traveled shorter distances and have had less opportunity to be altered, thereby increasing the signal-to-noise ratio.  

One fruitful strategy of tackling quantum gravity is to adapt quantum field theory to the situation where spacetime geometry is dynamical, and to implement diffeomorphism symmetry in a way that is compatible with regularization and renormalization. It has taken a while to address the underlying technical and conceptual challenges and to chart a quantum field-theoretic path toward a theory of quantum gravity that is unitary, essentially unique and can produce "numbers" beyond perturbation theory. In this context, the formulation of Causal Dynamical Triangulations (CDT) is a quantum-gravitational analogue of what lattice QCD is to nonabelian gauge theory. Its nonperturbative toolbox builds on the mathematical principles of “random geometry” and allows us to shift emphasis from formal considerations to extracting quantitative results on the spectra of invariant quantum observables at or near the Planck scale. A breakthrough result of CDT quantum gravity in four dimensions is the emergence, from first principles, of a nonperturbative vacuum state with properties of a de Sitter universe. I will summarize these findings, highlight the nonlocal character of observables in quantum gravity and describe the interesting physics questions that are being tackled using the new notion of quantum Ricci curvature.

Zoom Link: https://pitp.zoom.us/j/92791576774?pwd=VEg3MEdKOWsxOEhXOHVIQUhPcUt0UT09

Abstract Identifying the optimal set of individuals to first receive information (‘seeds’) in a social network is a widely-studied question in many settings, such as diffusion of information, spread of microfinance programs, and adoption of new technologies. Numerous studies have proposed various network-centrality based heuristics to choose seeds in a way that is likely to boost diffusion. Here we show that, for the classic SIR model of diffusion and some of its generalizations, randomly seeding s + x individuals can prompt a larger diffusion than optimally targeting the best s individuals, for a small x. We prove our results for large classes of random networks, and verify them in several small, real-world networks. Our results identify practically relevant settings under which collecting and analyzing network data to boost diffusion is not cost-effective.

The growing catalog of gravitational-wave signals from compact object mergers has allowed us to study the properties of black holes and neutron stars more precisely than ever before and has opened a new window through which to probe the earliest moments in our universe’s history. In this talk, I will demonstrate how current and future gravitational-wave observations can be uniquely leveraged to learn about astrophysics and cosmology. With the current catalog of events detected by the LIGO and Virgo gravitational-wave detectors, I will present evidence for a correlation between the redshift and spin distributions of binary black holes and discuss its astrophysical implications. With joint observations of short gamma-ray bursts and binary neutron star mergers accessible in the next few years, I will describe how to constrain the jet geometry and shed light on the central engine powering these explosions. Finally, with the sensitivities expected for the next generation of gravitational-wave detectors, I will present the statistically optimal method for the simultaneous detection of a foreground of compact binary mergers and a stochastic gravitational-wave background from early-universe processes.

Zoom Link: https://pitp.zoom.us/j/95280675686?pwd=RThMeStWeWl1VlBuV1cvYW8zTXgydz09

Abstract   This talk will revisit a 2011 Review of Economic Studies paper written with Daron Acemoglu, Munther Dahleh and Asuman Ozdaglar. We consider the canonical social learning model but where observations of past actions are constrained by a social network. The network is generated stochastically and neighborhoods can have arbitrary distributions. We are interested in what kinds of networks and signal structures lead to asymptotic learning (convergence in probability to the correct action). We prove a necessary and sufficient condition for asymptotic learning if signals are of unbounded strength, as well as network properties that allow learning irrespective of the signal structure.  

Take a guided tour of the invisible universe on Dark Matter Night.

In a hybrid event (in-person and live webcast) on October 26, dark matter researchers Katie Mack and Ken Clark will share insights into the ubiquitous, mysterious matter that makes up the majority of stuff in our universe.

Dark Matter Night will be webcast live from two locations. Starting at 7:30 pm ET, Katie Mack will discuss the theoretical and observational foundations of dark matter at Perimeter Institute, where she holds the Hawking Chair in Cosmology and Science Communication. Next, Ken Clark, an associate professor at the Arthur B. McDonald Canadian Astroparticle Physics Research Institute, will share experimental approaches that could help solve the riddle of dark matter. We’ll also get a guided video tour of SNOLAB, the state-of-the-art underground laboratory two kilometres beneath Sudbury.

Abstract Due to noisy data and nonlinear dynamics, even simple stochastic epidemic models such as the Susceptible-Infectious-Removed (SIR) present significant challenges to inference. In particular, computing the marginal likelihood of such stochastic processes conditioned on observed endpoints a notoriously difficult task. As a result, likelihood-based inference is typically considered intractable in missing data settings typical of observational data, and practitioners often resort to intensive simulation methods or approximations. We discuss recent contributions that enable "exact" inference, focusing on a perspective that makes use of latent variables to explore configurations of the missing data within a Markov chain Monte Carlo framework. Motivated both by count data from large outbreaks and high-resolution contact data from mobile health studies, we show how our data-augmented approach successfully learns the interpretable epidemic parameters and scales to handle large realistic data settings efficiently.

Abstract In this talk, we present a modeling framework to study the effects of testing policy on voluntary social distancing and the spread of an infection. Agents decide their social activity level, which determines the social network over which the virus spreads. Testing enables the isolation of infected individuals, slowing down the infection. But greater testing also reduces voluntary social distancing or increases social activity, exacerbating the spread of the virus. We show that the effect of testing on infections is non-monotone. This non-monotonicity also implies that the optimal testing policy may leave some of the testing capacity of society unused. This also implies that testing should be combined with mandatory social distancing measures to avoid these adverse behavioral effects.

We revisit the problem of how interactions emerge in quantum gravity. Namely, we show that bulk scattering of multiple particles in the AdS space requires multipartite entanglement on the boundary. This statement can be proven by two totally different methods, 1) general relativity and 2) quantum cryptographic argument. Furthermore, we argue that interactions among particles in the scattering event emerge from the mechanism of entanglement-assisted quantum error-correcting codes (EAQECCs) which utilize pre-existing multipartite entanglement in CFT. We also propose a concrete protocol to implement a certain class of multi-partite unitary interactions by using transversal logical operators of quantum codes. This talk is based on a (very) recent work with Alex May and Jonathan Sorce.

Zoom Link: https://pitp.zoom.us/j/91349028320?pwd=TGF2Q2ZNdTZtZGxkQ0NiMURLdW5Zdz09

A complex contagion is an infectious process in which individuals may require multiple transmissions before changing state. These are used to model behaviours if an individual only adopts a particular behaviour after perceiving a consensus among others. We may think of individuals as beginning inactive and becoming active once they are contacted by a sufficient number of active partners.  Here we study the dynamics of the Watts threshold model (WTM).  We adapt techniques developed for infectious disease modelling to develop an analyse analytic models for the dynamics of the WTM in configuration model networks and a class of random clustered (triangle-based) networks.  We derive conditions under which cascades happen with an arbitrarily small initial proportion active. We also observe hybrid phase transitions when cascades are not possible for small initial conditions, but occur for large enough initial conditions.