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DTSTART;TZID=America/New_York:20220401T090000
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SUMMARY:Diffusive growth sourced by topological defects
DESCRIPTION:Member Seminar \nSpeaker: Farzan Vafa \nTitle: Diffusive growth sourced by topological defects \nAbstract: In this talk\, we develop a minimal model of morphogenesis of a surface where the dynamics of the intrinsic geometry is diffusive growth sourced by topological defects. We show that a positive (negative) defect can dynamically generate a cone (hyperbolic cone). We analytically explain features of the growth profile as a function of position and time\, and predict that in the presence of a positive defect\, a bump forms with height profile h(t) ~ t^(1/2) for early times t. To incorporate the effect of the mean curvature\, we exploit the fact that for axisymmetric surfaces\, the extrinsic geometry can be deduced entirely by the intrinsic geometry. We find that the resulting stationary geometry\, for polar order and small bending modulus\, is a deformed football.\nWe apply our framework to various biological systems. In an ex-vivo setting of cultured murine neural progenitor cells\, we show that our framework is consistent with the observed cell accumulation at positive defects and depletion at negative defects. In an in-vivo setting\, we show that the defect configuration consisting of a bound +1 defect state\, which is stabilized by activity\, surrounded by two -1/2 defects can create a stationary ring configuration of tentacles\, consistent with observations of a basal marine invertebrate Hydra
URL:https://cmsa.fas.harvard.edu/event/4-1-2022-member-seminar/
LOCATION:Virtual
CATEGORIES:Member Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220408T084500
DTEND;TZID=America/New_York:20220408T101500
DTSTAMP:20260509T235516
CREATED:20240214T084325Z
LAST-MODIFIED:20240301T105121Z
UID:10002595-1649407500-1649412900@cmsa.fas.harvard.edu
SUMMARY:Synthetic Regression Discontinuity: Estimating Treatment Effects using Machine Learning
DESCRIPTION:Speaker: Jörn Boehnke \nTitle: Synthetic Regression Discontinuity: Estimating Treatment Effects using Machine Learning \nAbstract:  In the standard regression discontinuity setting\, treatment assignment is based on whether a unit’s observable score (running variable) crosses a known threshold.  We propose a two-stage method to estimate the treatment effect when the score is unobservable to the econometrician while the treatment status is known for all units.  In the first stage\, we use a statistical model to predict a unit’s treatment status based on a continuous synthetic score.  In the second stage\, we apply a regression discontinuity design using the predicted synthetic score as the running variable to estimate the treatment effect on an outcome of interest.  We establish conditions under which the method identifies the local treatment effect for a unit at the threshold of the unobservable score\, the same parameter that a standard regression discontinuity design with known score would identify. We also examine the properties of the estimator using simulations\, and propose the use machine learning algorithms to achieve high prediction accuracy.  Finally\, we apply the method to measure the effect of an investment grade rating on corporate bond prices by any of the three largest credit ratings agencies.  We find an average 1% increase in the prices of corporate bonds that received an investment grade as opposed to a non-investment grade rating.
URL:https://cmsa.fas.harvard.edu/event/4-8-2022-member-seminar/
LOCATION:Virtual
CATEGORIES:Member Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220429T093000
DTEND;TZID=America/New_York:20220429T110000
DTSTAMP:20260509T235516
CREATED:20240215T100221Z
LAST-MODIFIED:20240229T090935Z
UID:10002735-1651224600-1651230000@cmsa.fas.harvard.edu
SUMMARY:Machine Learning the Gravity Equation for International Trade
DESCRIPTION:Member Seminar \nSpeaker: Sergiy Verstyuk \nTitle: Machine Learning the Gravity Equation for International Trade \nAbstract: We will go through modern deep learning methods and existing approaches to their interpretation. Next\, I will describe a graph neural network framework. You will also be introduced to an economic analog of gravity. Finally\, we will see how these tools can help understand observed trade flows between 181 countries over 68 years. [Joint work with Michael R. Douglas.]
URL:https://cmsa.fas.harvard.edu/event/4-29-2022-member-seminar/
LOCATION:Virtual
CATEGORIES:Member Seminar
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