97 citations · 168 across the 18 of their papers we have counts for
19 papers
Variational Exploration Module VEM: A Cloud-Native Optimization and Validation Tool for Geospatial Modeling and AI Workflows
Julian Kuehnert, Hiwot Tadesse, Chris Dearden +7
Geospatial observations combined with computational models have become key to understanding the physical systems of our environment and enable the design of best practices to reduc…
Learning to Act: Novel Integration of Algorithms and Models for Epidemic Preparedness
Sekou L. Remy, Oliver E. Bent
In this work we present a framework which may transform research and praxis in epidemic planning. Introduced in the context of the ongoing COVID-19 pandemic, we provide a concrete…
A Collaborative Approach to the Analysis of the COVID-19 Response in Africa
Sharon Okwako, Irene Wanyana, Alice Namale +6
The COVID-19 crisis has emphasized the need for scientific methods such as machine learning to speed up the discovery of solutions to the pandemic. Harnessing machine learning tech…
Surrogate Ensemble Forecasting for Dynamic Climate Impact Models
Julian Kuehnert, Deborah McGlynn, Sekou L. Remy +2
As acute climate change impacts weather and climate variability, there is increased demand for robust climate impact model predictions from which forecasts of the impacts can be de…
Deep Temporal Interpolation of Radar-based Precipitation
Michiaki Tatsubori, Takao Moriyama, Tatsuya Ishikawa +5
When providing the boundary conditions for hydrological flood models and estimating the associated risk, interpolating precipitation at very high temporal resolutions (e.g. 5 minut…
Overcoming Digital Gravity when using AI in Public Health Decisions
Sekou L Remy, Aisha Walcott-Bryant, Nelson K Bore +2
In popular usage, Data Gravity refers to the ability of a body of data to attract applications, services and other data. In this work we introduce a broader concept, "Digital Gravi…
An Analysis of Reinforcement Learning for Malaria Control
Ndivhuwo Makondo, Arinze Lawrence Folarin, Simphiwe Nhlahla Zitha +1
Previous work on policy learning for Malaria control has often formulated the problem as an optimization problem assuming the objective function and the search space have a specifi…
Disease Progression Modeling Workbench 360
Parthasarathy Suryanarayanan, Prithwish Chakraborty, Piyush Madan +9
In this work we introduce Disease Progression Modeling workbench 360 (DPM360) opensource clinical informatics framework for collaborative research and delivery of healthcare AI. DP…
Narratives and Counternarratives on Data Sharing in Africa
Rediet Abebe, Kehinde Aruleba, Abeba Birhane +4
As machine learning and data science applications grow ever more prevalent, there is an increased focus on data sharing and open data initiatives, particularly in the context of th…
WNTRAC: AI Assisted Tracking of Non-pharmaceutical Interventions Implemented Worldwide for COVID-19
Parthasarathy Suryanarayanan, Ching-Huei Tsou, Ananya Poddar +23
The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease…
Promoting Distributed Trust in Machine Learning and Computational Simulation via a Blockchain Network
Nelson Kibichii Bore, Ravi Kiran Raman, Isaac M. Markus +8
Policy decisions are increasingly dependent on the outcomes of simulations and/or machine learning models. The ability to share and interact with these outcomes is relevant across…
Subset Scanning Over Neural Network Activations
Skyler Speakman, Srihari Sridharan, Sekou Remy +2
This work views neural networks as data generating systems and applies anomalous pattern detection techniques on that data in order to detect when a network is processing an anomal…
Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach
Ravi Kiran Raman, Roman Vaculin, Michael Hind +6
Large-scale computational experiments, often running over weeks and over large datasets, are used extensively in fields such as epidemiology, meteorology, computational biology, an…
Reshaping the use of digital tools to fight malaria
Sekou L. Remy, Oliver Bent, Nelson Bore
In this extended abstract we present our approach to marshal digital tools in the fight against malaria. We describe our scalable infrastructure which leverages abstractions to sup…
Assessing virtualization effects in simulations of distributed robotics
Sekou L. Remy
In this work, our aim is to identify whether the choice of virtualization strategy influences the performance of simulations in robotics. Performance is quantified in the error bet…
Characterizing the hyper-parameter space of LSTM language models for mixed context applications
Victor Akinwande, Sekou L. Remy
Applying state of the art deep learning models to novel real world datasets gives a practical evaluation of the generalizability of these models. Of importance in this process is h…
Novel Exploration Techniques (NETs) for Malaria Policy Interventions
Oliver Bent, Sekou L. Remy, Stephen Roberts +1
The task of decision-making under uncertainty is daunting, especially for problems which have significant complexity. Healthcare policy makers across the globe are facing problems…
Using Genetic Algorithms to Benchmark the Cloud
Jeff Kinnison, Sekou L. Remy
This paper presents a novel application of Genetic Algorithms(GAs) to quantify the performance of Platform as a Service (PaaS), a cloud service model that plays a critical role in…
Web Standards as Standard Pieces in Robotics
Sekou L. Remy
Modern robotics often involves the use of web technologies as a means to cope with the complexity of design and operation. Many of these technologies have been formalized into stan…