Chinmay Kulkarni, a Ph.D candidate in the MSEAS group has won the first place in the de Florez Competition 2019. This year’s competition saw 44 entries, among which Chinmay was placed first (in a four-way tie) under the Graduate Science category, with his presentation titled “Persistent Rigid Sets in Ocean Flows Help Predict the Environmental Impact of Deep Sea Mining Activities”. The competition website is here.

Chinmay Kulkarni, Ph.D candidate in MSEAS group has been awarded a 2019 Wunsch Foundation Silent Hoist and Crane Award for Outstanding Graduate Research. Congratulations Chinmay!

Abstract: Understanding how social influence shapes biological processes is a central challenge in contemporary science, essential for achieving progress in a variety of fields ranging from the organization and evolution of coordinated collective action among cells, or animals, to the dynamics of information exchange in human societies. Using an integrated experimental and theoretical approach I will address how, and why, animals exhibit highly-coordinated collective behavior. I will demonstrate imaging and virtual reality technology that allows us to reconstruct (automatically) the dynamic, time-varying networks that correspond to the visual cues employed by organisms when making movement decisions. Sensory networks are shown to provide a much more accurate representation of how social influence propagates in groups, and their analysis allows us to identify, for any instant in time, the most socially-influential individuals within groups, and to predict the magnitude of complex behavioral cascades before they actually occur. I will also investigate the coupling between spatial and information dynamics in groups and reveal that emergent problem solving is the predominant mechanism by which mobile groups sense, and respond to complex environmental gradients. Evolutionary modeling demonstrates such ‘physical computation’ readily evolves within populations of selfish organisms, allowing individuals to compute collectively the spatial distribution of resources. Finally, I will reveal the critical role uninformed, or unbiased, individuals play in effecting fast and democratic consensus decision-making in collectives, and will test these predictions with experiments involving schooling fish and groups of wild storks and baboons.

Biography: Iain Couzin is Director of the Max Planck Institute for Ornithology, Department of Collective Behaviour and the DFG Excellence Cluster ‘Centre for the Advanced Study of Collective Behaviour’, and Chair of Biodiversity and Collective Behaviour at the University of Konstanz, Germany. Previously he was an Assistant- and then Full-Professor in the Department of Ecology and Evolutionary Biology at Princeton University, and prior to that a Royal Society University Research Fellow in the Department of Zoology, University of Oxford, and a Junior Research Fellow in the Sciences at Balliol College, Oxford. His work aims to reveal the fundamental principles that underlie evolved collective behavior, and consequently his research includes the study of a wide range of biological systems, from insect swarms to fish schools and primate groups. In recognition of his research he has been recipient of the Searle Scholar Award in 2008, top 5 most cited papers of the decade in animal behavior research 1999-2010, the Mohammed Dahleh Award in 2009, Popular Science’s “Brilliant 10” Award in 2010, National Geographic Emerging Explorer Award in 2012, the Scientific Medal of the Zoological Society of London in 2013 and Clarivate Analytics (formerly Thompson Reuters) Global Highly Cited Researcher in 2018.

We propose a novel numerical methodology to compute the advective transport and diffusion-reaction of tracer quantities. The tracer advection occurs through flow map composition and is super-accurate, yielding numerical solutions almost devoid of compounding numerical errors, while allowing for direct parallelization in the temporal direction. It is computed by implicitly solving the characteristic evolution through a modified transport partial differential equation and domain decomposition in the temporal direction, followed by composition with the known initial condition. This advection scheme allows a rigorous computation of the spatial and temporal error bounds, yields an accuracy comparable to that of Lagrangian methods, and maintains the advantages of Eulerian schemes. We further show that there exists an optimal value of the composition timestep that yields the minimum total numerical error in the computations, and derive the expression for this value. We develop schemes for the addition of tracer diffusion, reaction, and source terms, and for the implementation of boundary conditions. Finally, the methodology is applied in three flow examples, namely an analytical reversible swirl flow, an idealized flow exiting a strait undergoing sudden expansion, and a realistic ocean flow in the Bismarck sea. New benchmark problems for advection-diffusion-reaction schemes are developed and used to compare and contrast results with those of classic schemes. The results highlight the theoretical properties of the methodology as well as its efficiency, super-accuracy with minimal numerical errors, and applicability in realistic simulations.