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Dive into the research topics where Evan Kodra is active.

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Featured researches published by Evan Kodra.


Climate Dynamics | 2014

Regional and seasonal intercomparison of CMIP3 and CMIP5 climate model ensembles for temperature and precipitation

Devashish Kumar; Evan Kodra; Auroop R. Ganguly

Abstract Regional and seasonal temperature and precipitation over land are compared across two generations of global climate model ensembles, specifically, CMIP5 and CMIP3, through historical twentieth century skills and multi-model agreement, and twenty first century projections. A suite of diagnostic and performance metrics, ranging from spatial bias or model-consensus maps and aggregate time series plots, to measures of equivalence between probability density functions and Taylor diagrams, are used for the intercomparisons. Pairwise and multi-model ensemble comparisons were performed for 11 models, which were selected based on data availability and resolutions. Results suggest little change in the central tendency or variability or uncertainty of historical skills or consensus across the two generations of models. However, there are regions and seasons, at different levels of aggregation, where significant changes, performance improvements, and even degradation in skills, are suggested. The insights may provide directions for further improvements in next generations of climate models, and in the meantime, help inform adaptation and policy.


Computers & Geosciences | 2012

Estimating future global per capita water availability based on changes in climate and population

Esther S. Parish; Evan Kodra; Karsten Steinhaeuser; Auroop R. Ganguly

Human populations are profoundly affected by water stress, or the lack of sufficient per capita available freshwater. Water stress can result from overuse of available freshwater resources or from a reduction in the amount of available water due to decreases in rainfall and stored water supplies. Analyzing the interrelationship between human populations and water availability is complicated by the uncertainties associated with climate change projections and population projections. We present a simple methodology developed to integrate disparate climate and population data sources and develop first-order per capita water availability projections at the global scale. Simulations from the coupled land-ocean-atmosphere Community Climate System Model version 3 (CCSM3) forced with a range of hypothetical greenhouse gas emissions scenarios are used to project grid-based changes in precipitation minus evapotranspiration as proxies for changes in runoff, or fresh water supply. Population growth changes, according to Intergovernmental Panel on Climate Change (IPCC) storylines, are used as proxies for changes in fresh water demand by 2025, 2050 and 2100. These freshwater supply and demand projections are then combined to yield estimates of per capita water availability aggregated by watershed and political unit. Results suggest that important insights might be extracted from the use of the process developed here, notably including the identification of the globes most vulnerable regions in need of more detailed analysis and the relative importance of population growth versus climate change in altering future freshwater supplies. However, these are only exemplary insights and, as such, could be considered hypotheses that should be rigorously tested with multiple climate models, multiple observational climate datasets, and more comprehensive population change storylines.


Scientific Reports | 2015

Asymmetry of projected increases in extreme temperature distributions

Evan Kodra; Auroop R. Ganguly

A statistical analysis reveals projections of consistently larger increases in the highest percentiles of summer and winter temperature maxima and minima versus the respective lowest percentiles, resulting in a wider range of temperature extremes in the future. These asymmetric changes in tail distributions of temperature appear robust when explored through 14 CMIP5 climate models and three reanalysis datasets. Asymmetry of projected increases in temperature extremes generalizes widely. Magnitude of the projected asymmetry depends significantly on region, season, land-ocean contrast, and climate model variability as well as whether the extremes of consideration are seasonal minima or maxima events. An assessment of potential physical mechanisms provides support for asymmetric tail increases and hence wider temperature extremes ranges, especially for northern winter extremes. These results offer statistically grounded perspectives on projected changes in the IPCC-recommended extremes indices relevant for impacts and adaptation studies.


affective computing and intelligent interaction | 2013

Measuring Voter's Candidate Preference Based on Affective Responses to Election Debates

Daniel McDuff; Rana el Kaliouby; Evan Kodra; Rosalind W. Picard

In this paper we present the first analysis of facial responses to electoral debates measured automatically over the Internet. We show that significantly different responses can be detected from viewers with different political preferences and that similar expressions at significant moments can have very different meanings depending on the actions that appear subsequently. We used an Internet based framework to collect 611 naturalistic and spontaneous facial responses to five video clips from the 3rd presidential debate during the 2012 American presidential election campaign. Using this framework we were able to collect over 60% of these video responses (374 videos) within one day of the live debate and over 80% within three days. No participants were compensated for taking the survey. We present and evaluate a method for predicting independent voter preference based on automatically measured facial responses and self-reported preferences from the viewers. We predict voter preference with an average accuracy of over 73% (AUC 0.779).


Environmental Research Letters | 2012

Evaluation of global climate models for Indian monsoon climatology

Evan Kodra; Subimal Ghosh; Auroop R. Ganguly

The viability of global climate models for forecasting the Indian monsoon is explored. Evaluation and intercomparison of model skills are employed to assess the reliability of individual models and to guide model selection strategies. Two dominant and unique patterns of Indian monsoon climatology are trends in maximum temperature and periodicity in total rainfall observed after 30 yr averaging over India. An examination of seven models and their ensembles reveals that no single model or model selection strategy outperforms the rest. The single-best model for the periodicity of Indian monsoon rainfall is the only model that captures a low-frequency natural climate oscillator thought to dictate the periodicity. The trend in maximum temperature, which most models are thought to handle relatively better, is best captured through a multimodel average compared to individual models. The results suggest a need to carefully evaluate individual models and model combinations, in addition to physical drivers where possible, for regional projections from global climate models.


PLOS ONE | 2015

Network science based quantification of resilience demonstrated on the Indian Railways Network

Udit Bhatia; Devashish Kumar; Evan Kodra; Auroop R. Ganguly

The structure, interdependence, and fragility of systems ranging from power-grids and transportation to ecology, climate, biology and even human communities and the Internet have been examined through network science. While response to perturbations has been quantified, recovery strategies for perturbed networks have usually been either discussed conceptually or through anecdotal case studies. Here we develop a network science based quantitative framework for measuring, comparing and interpreting hazard responses as well as recovery strategies. The framework, motivated by the recently proposed temporal resilience paradigm, is demonstrated with the Indian Railways Network. Simulations inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as well as a cyber-physical attack scenario illustrate hazard responses and effectiveness of proposed recovery strategies. Multiple metrics are used to generate various recovery strategies, which are simply sequences in which system components should be recovered after a disruption. Quantitative evaluation of these strategies suggests that faster and more efficient recovery is possible through network centrality measures. Optimal recovery strategies may be different per hazard, per community within a network, and for different measures of partial recovery. In addition, topological characterization provides a means for interpreting the comparative performance of proposed recovery strategies. The methods can be directly extended to other Large-Scale Critical Lifeline Infrastructure Networks including transportation, water, energy and communications systems that are threatened by natural or human-induced hazards, including cascading failures. Furthermore, the quantitative framework developed here can generalize across natural, engineered and human systems, offering an actionable and generalizable approach for emergency management in particular as well as for network resilience in general.


ieee international conference on automatic face gesture recognition | 2013

From dials to facial coding: Automated detection of spontaneous facial expressions for media research

Evan Kodra; Thibaud Senechal; Daniel McDuff; Rana el Kaliouby

Typical consumer media research requires the recruitment and coordination of hundreds of panelists and the use of relatively expensive equipment. In this work, we compare results from a legacy hardware dial mechanism for measuring media preference to those from automated facial analysis on two television programs, a sitcom and a drama series. We present an automated system for facial action detection as well as a continuous measure of valence. The results demonstrate that automated facial analysis provides similar as well as additional insights on moment-to-moment affective response in a way that is unobtrusive, scalable and practical. Specifically, highly significant correlations are found between the dial and facial expression data. For specific moments where the two methods disagree, facial expression data provides additional traceable insights that cannot be obtained from dial data. Furthermore, this data can be obtained at a fraction of the cost; in this work, the facial expression data panel size is only about 5% of the sample size needed to obtain reliable dial data. Results have substantial implications for the future of media research and audience measurement.


knowledge discovery and data mining | 2017

DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution

Thomas Vandal; Evan Kodra; Sangram Ganguly; A. R. Michaelis; Ramakrishna R. Nemani; Auroop R. Ganguly

The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. Depending on statistical modeling choices, downscaled projections have been shown to vary significantly terms of accuracy and reliability. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework for statistical downscaling of climate variables. DeepSD augments SRCNN with multi-scale input channels to maximize predictability in statistical downscaling. We provide a comparison with Bias Correction Spatial Disaggregation as well as three Automated-Statistical Downscaling approaches in downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km) over the Continental United States. Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios.


european conference on artificial intelligence | 2012

Mining extremes: severe rainfall and climate change

Debasish Das; Evan Kodra; Zoran Obradovic; Auroop R. Ganguly

Theoretical developments for the analysis and modeling of extreme value data have tended to focus on limiting cases and assumptions of independence. However, massive datasets from models and sensors, space-time dimensionality, complex dependence structures, long-memory, long-range and low frequency processes all motivate the need for sophisticated methods for correlated and finite data that follow complex processes. The importance of extremes has been rapidly growing in areas ranging from climate change and critical infrastructures to insurance and financial markets. Here we briefly discuss the state-of-the-art and key gaps, through the case of rainfall extremes under climate change. Preliminary analysis suggests new directions and points to research areas that deserve further attention.


PLOS ONE | 2017

A large-scale analysis of sex differences in facial expressions

Daniel McDuff; Evan Kodra; Rana el Kaliouby; Marianne LaFrance

There exists a stereotype that women are more expressive than men; however, research has almost exclusively focused on a single facial behavior, smiling. A large-scale study examines whether women are consistently more expressive than men or whether the effects are dependent on the emotion expressed. Studies of gender differences in expressivity have been somewhat restricted to data collected in lab settings or which required labor-intensive manual coding. In the present study, we analyze gender differences in facial behaviors as over 2,000 viewers watch a set of video advertisements in their home environments. The facial responses were recorded using participants’ own webcams. Using a new automated facial coding technology we coded facial activity. We find that women are not universally more expressive across all facial actions. Nor are they more expressive in all positive valence actions and less expressive in all negative valence actions. It appears that generally women express actions more frequently than men, and in particular express more positive valence actions. However, expressiveness is not greater in women for all negative valence actions and is dependent on the discrete emotional state.

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Rana el Kaliouby

Massachusetts Institute of Technology

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Udit Bhatia

Northeastern University

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Rosalind W. Picard

Massachusetts Institute of Technology

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Debasish Das

Northeastern University

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