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CAREER: Bridging the Data-Model Gap -- Leveraging Surveillance for Propagation Mining over Networks

职业：弥合数据模型差距——利用网络传播挖掘监控

基本信息

批准号：
2028586
负责人：
B Aditya Prakash
金额：
$ 44.31万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-14 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028586&HistoricalAwards=false
关键词：
CAREER Bridging Data Model Gap

项目摘要

The long-term goal of the proposed research is to understand, manage efficiently, and utilize dynamical mechanisms like propagation on large networks, occurring across natural, social, and technological systems. Understanding such processes enables us to manipulate them for our benefit. Propagation and networks have numerous applications in areas as diverse as public health and epidemiology, systems biology, cyber security, viral marketing and social media---hence progress in this domain promises scientific, commercial and social benefits. The proposed research aims to develop extensible, data-driven frameworks for propagation-related problems getting more implementable and generalizable tools. The PI's investigations will lead to novel mining and learning problems and scalable techniques which can be applied to massive datasets, helping make more informed choices for future. Educational activities are also closely integrated with this research agenda, including integrating research with education through courses, tutorials, and other university programs. Most current work in propagation mining assume the existence of well-calibrated models. Performing model calibration is typically very expensive, and not robust. Indeed, in many situations it is not clear which parameterized model should be calibrated. However there is an increasing availability of surveillance data like online media and medical health records. The PI's approach is unique in the sense that the aim is to directly use surveillance data and formulate optimization problems based on the data and network together. The proposed problems include inventing data-driven immunization policies for diseases like influenza, automatically finding missing infections/activations in cascade datasets, and automatically learning graph summaries based on distributed feature representations of propagation data as well as the network. The PI proposes to develop a flexible and expressive framework for all these problems. In addition, the developed algorithms will be applied to various domains, leveraging multiple collaborations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

拟议研究的长期目标是理解、有效管理和利用动态机制，例如在自然、社会和技术系统中发生的大型网络上的传播。了解这些过程使我们能够为了我们的利益而操纵它们。传播和网络在公共卫生和流行病学、系统生物学、网络安全、病毒营销和社交媒体等各个领域都有广泛的应用，因此该领域的进步有望带来科学、商业和社会效益。拟议的研究旨在开发可扩展的数据驱动框架，以解决与传播相关的问题，获得更具可实施性和通用性的工具。 PI 的调查将带来新的挖掘和学习问题以及可应用于海量数据集的可扩展技术，帮助为未来做出更明智的选择。教育活动也与该研究议程紧密结合，包括通过课程、教程和其他大学项目将研究与教育结合起来。当前传播挖掘中的大多数工作都假设存在经过良好校准的模型。执行模型校准通常非常昂贵且不稳健。事实上，在许多情况下，并不清楚应该校准哪个参数化模型。然而，在线媒体和医疗健康记录等监测数据的可用性越来越多。 PI 的方法是独特的，因为其目的是直接使用监视数据并基于数据和网络共同制定优化问题。提出的问题包括为流感等疾病发明数据驱动的免疫策略，自动查找级联数据集中丢失的感染/激活，以及基于传播数据和网络的分布式特征表示自动学习图摘要。 PI 建议为所有这些问题开发一个灵活且富有表现力的框架。此外，开发的算法将利用多种合作应用于各个领域。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（22）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future

Back2Future：利用回填动态改进未来的实时预测

DOI：
发表时间：
2022
期刊：
International Conference on Learning Representations (ICLR
影响因子：
0
作者：
Kamarthi, Harshavardhan;Rodriguez, Alexander;Prakash, B. Aditya
通讯作者：
Prakash, B. Aditya

Mapping Network States using Connectivity Queries

使用连接查询映射网络状态

DOI：
10.1109/bigdata50022.2020.9378355
发表时间：
2020
期刊：
2020 IEEE International Conference on Big Data (Big Data
影响因子：
0
作者：
Rodriguez, Alexander;Adhikari, Bijaya;Gonzalez, Andres D.;Nicholson, Charles;Vullikanti, Anil;Prakash, B. Aditya
通讯作者：
Prakash, B. Aditya

When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting

DOI：
发表时间：
2021-06
期刊：
2023 International Symposium on Medical Robotics (ISMR)
影响因子：
0
作者：
Harshavardhan Kamarthi;Lingkai Kong;Alexander Rodr'iguez;Chao Zhang;B. Prakash
通讯作者：
Harshavardhan Kamarthi;Lingkai Kong;Alexander Rodr'iguez;Chao Zhang;B. Prakash

Incorporating Expert Guidance in Epidemic Forecasting

将专家指导纳入疫情预测

DOI：
发表时间：
2020
期刊：
CM SIGKDD Epidemiology meets Data Mining and Knowledge Workshop 2020
影响因子：
0
作者：
Rodriguez, Alexander;Adhikari, Bijaya;Ramakrishnan, Naren;Prakash, B. Aditya
通讯作者：
Prakash, B. Aditya

CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting

DOI：
10.1145/3485447.3512037
发表时间：
2021-09
期刊：
Proceedings of the ACM Web Conference 2022
影响因子：
0
作者：
Harshavardhan Kamarthi;Lingkai Kong;Alexander Rodr'iguez;Chao Zhang;B. Prakash
通讯作者：
Harshavardhan Kamarthi;Lingkai Kong;Alexander Rodr'iguez;Chao Zhang;B. Prakash