North America Research Interest Group Meeting [April 2023]
Materials from Meeting
Recording
Slides: Prototyping a Distributed, Asynchronous Workflow for Iterative Near-Term Ecological Forecasting, Dietze
Slides: Minimal Mobile Systems via Cloud-based Adaptive Task Processing, Ohn-Bar
Date: April 4, 2023
Meeting Agenda:
Collaboratory Research Incubation Projects
Join us for presentations and discussions on two 2023 Red Hat Collaboratory at Boston University Research Incubation Award projects.
Speaker: Michael Dietze, Boston University
Title: Prototyping a Distributed, Asynchronous Workflow for Iterative Near-Term Ecological Forecasting
Abstract: The ongoing data revolution has begun to fuel the growth of near-term iterative ecological forecasts: continually-updated predictions about the future state (daily to multi-year) of ecosystems and their services that allow society to anticipate environmental challenges and improve decisions on actionable timescales, while allowing researchers to accelerate scientific discovery and answer fundamental research questions about the predictability of nature.
To fuel the growth of the ecological forecasting community, there is a need to openly develop and deploy accessible, reusable, and scalable community cyberinfrastructure (CI) that can be broadly applied to make large numbers of ecological forecasts on a repeatable, frequent basis. This RHC project will prototype the beginnings of such a system, focusing on developing a cloud-native workflow that can handle an asynchronous, event-driven, and distributed approach to execution. Future applications would focus on extending this system to additional ecological forecast workflows (e.g., water resources, biodiversity, zoonotic disease, forest pests and other invasive species).
Speaker: Eshed Ohn-Bar, Boston University
Title: Minimal Mobile Systems via Cloud-based Adaptive Task Processing
Abstract: The high cost of robots today has hindered their widespread use. Specifically, a limiting factor involves extensive hardware and software computational resources required to run various real-time robot functions, from intensive inference with large neural network models to costly storage and compute (e.g., GPUs). How can cloud-enabled mechanisms efficiently bring about low-cost but highly-functional robots today?
In this project, our goal is to develop an efficient distributed computing platform between a robot and the cloud. We will develop an adaptive robot-cloud task management system that can intelligently off-load real-time computation to the cloud while enabling highly affordable and efficient on-board operation. We will also work to integrate various cloud-enabled functionalities with existing open-source tools for robotics development.