homepeople › hoda eldardiry



Hoda Eldardiry

Hoda Eldardiry's research spans machine learning, knowledge discovery, and data mining -- with a focus on social network analysis, ensemble classification, and statistical relational learning. She is currently working on fraud analysis and anomaly detection, which has both government and commercial application

Hoda is particularly interested in the relational characteristics of data, including recovering and utilizing hidden structures. She has developed algorithms that combine relational knowledge from multiple sources and propagate inference information across various relational models.

Dr. Eldardiry earned her Ph.D. and M.S. in Computer Science from Purdue University, and her Bachelor's degree in Computer and Systems Engineering from Alexandria University in Egypt.


PARC publications

view publications by:  date | title | type | focus area



Time, Frequency & Complexity Analysis for Recognizing Panic States from Physiologic Time-Series

10th EAI International Conference on Pervasive Computing Technologies for Healthcare

16 May 2016


A Wearable and Mobile Intervention Delivery System for Individuals with Panic Disorder

The 14th International Conference on Mobile and Ubiquitous Multimedia

1 December 2015

Towards a Mobile and Wearable System for Predicting Panic Attacks

The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015)

7 September 2015


Multi-source anomaly detection: using across-domain and across-time peer-group consistency checks

Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications(JoWUA)

6 June 2014

Ganging Up on Big Data: Computer-Intermediated Collaborative Analysis

International Symposium on Collaborative Analysis and Reasoning Systems (CARS 2014)

22 May 2014


Fraud detection for healthcare

KDD2013 Workshop on Data Mining for Healthcare

11 August 2013

Multi-domain information fusion for insider threat detection

2013 IEEE Workshop on Research for Insider Threat (WRIT)

24 May 2013


An analysis of how ensembles of collective classifiers improve predictions in graphs

The 21st ACM International Conference on Information and Knowledge Management (CIKM 2012)

29 October 2012