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and Biomedical Informatics

Prospective Analysis of Large and Complex Partially Observed Temporal Social Networks

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RECENT PROJECTS

DARPA project N66001-11-1-4183

Predictive Modeling of Patient State and Therapy Optimization Principal InvestigatorObradovic Zoran Abstract This project will develop and validate effective predictive modeling technology to achieve the

US-Serbia and West Balkan Data Science Workshop

Principal Investigator: Zoran Obradovic, L.H.Carnell Professor of Data Analytics, Temple University Funding: NSF Award # IIS-1818661 Abstract The project mission is to organize a workshop

BIGDATA: Multi-level predictive analytics & motif discovery across massive dynamic spatio-temporal networks in complex socio-technical systems: An organizational genetics approach

Supported by the National Science Foundation (NSF BIGDATA NSF-14476570) Investigators: Prof. Youngjin Yoo, Management Information Systems Department, Temple University Prof. Sunil Wattal, Management Information Systems

EVENTS

Predictive Data Mining in Science, Engineering and Business

IST Colloquium Oct 05, 2000 Zoran Obradovic Predictive Data Mining in Science, Engineering and Business Wachman 32201:30PM – 03:00PM

Planning with partially observable Markov decision processes

IST Colloquium Oct 12, 2000 Milos Hauskrecht Planning with partially observable Markov decision processes Wachman 32201:30PM – 03:00PM

Data Mining in Multimedia/Medical Databases

IST Colloquium Oct 19, 2000 Vasileios Megalooikonomou Data Mining in Multimedia/Medical Databases Wachman 32201:30PM – 03:00PM

NEWS

Prof. Obradovic is giving a Keynote Lecture at the IEEE 5th World Conference on Complex Systems

Prof. Obradovic is giving a Keynote Lecture in November 2024 at the IEEE 5th World Conference on Complex Systems in Casablanca, Morocco (https://mscomplexsystems.org/wccs24/keynotes/). The topic of

Feature Article about Jovan Andjelkovic: From Serbia to Temple, Crafting a Career in Machine Learning

In a compelling interview conducted for Temple Graduate School, Jovan Andjelkovic, a PhD student in Dr. Zoran Obradovic’s lab, unveils his research—predicting cancer based on

A short film devoted to Dr. Obradovic’s Artificial Intelligence research with Dr. D. Rubin and collaborators on estimating hospitalization readmission risk for diabetes.

A short film devoted to Dr. Obradovic’s Artificial Intelligence research with Dr. D. Rubin and collaborators on estimating hospitalization readmission risk for diabetes patients is

Published: August 8, 2020

Principal Investigator:

Prof. Obradovic Zoran

Co-investigators:

Prof. Emily Fox, University of Washington Statistics Department

Prof. Katya Scheinberg, Lehigh University Industrial and Systems Engineering Department.

Abstract

The analysis of social networks often assumes a time invariant scenario, while in practice actor attributes and links in such networks evolve over time and are inextricably dependent on each other. In addition, the temporal graph is just partially observed, multiple kinds of links exist among actors, various actors have different temporal dynamics and environmental influence can be both positive and negative. This project is closely examining the hypothesis that a unified approach of jointly modeling these and related problems is beneficial for prospective analysis of large-scale partially observed temporal hypergraphs. Novel methods for analyzing large and evolving graphs developed on the project are evaluated on high impact applications related to predictive modeling of information networks, climate and human health.