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Iyad Batal
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5406 Sennott
Square, Department of Computer Science, Email:
iyad@cs.pitt.edu
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Biography
I was born and raised in Damascus, the oldest continuously inhabited city in the world. I joined the computer science department at the University of Pittsburgh in 2006 for MS degree and later joined the PhD program in 2008. Currently, I’m a PhD candidate in machine learning.
Research Interests
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Data
Mining and Machine Learning o Temporal data mining. o Pattern-based classification. o Time Series Classification. o Feature selection/construction for high
dimensional data. ·
Machine
learning for medical applications. ·
Information
Retrieval and text mining. ·
Advanced
Database applications: Indexing and querying spatial, temporal and XML
databases.
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Publications
I. Batal, D. Fradkin, J. Harrison, F. Moerchen
and M. Hauskrecht. Mining Recent
Temporal Patterns for Event Detection in Multivariate Time Series Data. ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD). Beijing, China, 2012.
I. Batal, H. Valizadegan,
G. Cooper and M. Hauskrecht. A Temporal Pattern
Mining Approach for Classifying Electronic Health Record Data. ACM Transaction on Intelligent Systems and
Technology (ACM TIST), Special Issue on Health Informatics, 2012.
I. Batal, H. Valizadegan, G. Cooper and M. Hauskrecht. A Pattern Mining Approach for Classifying
Multivariate Temporal Data. IEEE
International Conference on Bioinformatics and Biomedicine (BIBM)
(acceptance rate 20%). Atlanta, GA, 2011. [NSF travel award]
I. Batal and M. Hauskrecht. Constructing Classification Features using
Minimal Predictive Patterns. ACM Conference on Information and Knowledge
Management (CIKM) (acceptance rate 13.4%). Toronto, Canada, 2010.
I. Batal and M. Hauskrecht. Mining Clinical Data using Minimal Predictive
Rules. Annual American Medical Informatics Association (AMIA) Conference.
Washington, DC, 2010.
M. Hauskrecht, M. Valko, I.Batal, G.
Clermont, S. Visweswaran, G. Cooper. Conditional
Outlier Detection for Clinical Alerting. Annual American Medical Informatics
Association (AMIA) Conference. Washington, DC, 2010. [Homer Warner Best
Paper Award]
I. Batal and M. Hauskrecht. A Concise Representation of Association Rules
using Minimal Predictive Rules. The European Conference on Machine Learning
and Principles of Knowledge Discovery in Databases (ECML PKDD) (acceptance
rate 16%). Barcelona, Spain, 2010.
I. Batal and M. Hauskrecht. A Supervised Time
Series Feature Extraction Technique using DCT and DWT. International
Conference on Machine Learning and Applications (ICMLA), 2009.
I. Batal, L. Sacchi, R. Bellazzi
and M. Hauskrecht. A Temporal Abstraction Framework
for Classifying Clinical Temporal Data. Annual American Medical Informatics
Association (AMIA), 2009.
I. Batal and M. Hauskrecht. Boosting KNN Text
Classification Accuracy by using Supervised Term Weighting Schemes. ACM
Conference on Information and Knowledge Management (CIKM), 2009.
I. Batal, L. Sacchi, R. Bellazzi, and M. Hauskrecht. Multivariate Time Series Classification with
Temporal Abstractions. International Florida AI Research Society Conference
(FLAIRS), 2009.
I. Batal and A. Labrinidis. QuickStack: A Fast
Algorithm for XML Query Matching, Technical Report TR-08-155, 2008.
Presentations and talks
Introduction to Principal Components Analysis (PCA)
Dimensionality Reduction techniques
Association Rule Mining
Temporal Data Mining
Teaching
[CS131] SOFTWARE FOR PERSONAL COMPUTING (Fall 2008).
[CS 449] INTRODUCTION TO SYSTEMS SOFTWARE (Spring
2007).
[CS 110] INTRODUCTION TO PERSONAL COMPUTERS AND THE INTERNET (Fall 2006).
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Quick Links: ACM SIGKDD home page (data mining
tools, datasets and other resources) ACM SIGMOD home page (DBMS resources, free
software, papers, conference info) |
My graduation from |
