Iyad Batal
	5109 Sennott Square, Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, 15213 Email: iyad@cs.pitt.edu

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CV

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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. Later on, I joined the PhD program and graduated in 2012. Currently, I’m a postdoctoral associate working with Dr Milos Hauskrecht on a variety of machine learning projects.

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Research Interests

· Data Mining and Machine Learning

o Temporal Pattern Mining

o Subgroup Discovery

o Time Series Classification and Feature Extraction

o Probabilistic Graphical Models

o Multi-label Classification

· Machine Learning for Medical Applications

· Information Retrieval and Text Mining

· Database: Indexing and Querying Spatial, Temporal and XML databases

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Publications

I. Batal, G. Cooper and M. Hauskrecht. A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules. The European Conference on Machine Learning and Principles of Knowledge Discovery in Databases (ECML PKDD). Bristol, UK, 2012. (Additional proofs/derivations that did not fit in the paper can be found here: Appendix).

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.

M. Hauskrecht, I. Batal, M. Valko, S. Visweswaran, G. Cooper and G. Clermont. Outlier-detection for Patient Monitoring and Alerting. Journal of Biomedical 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.

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Presentations and talks

Introduction to Principal Components Analysis (PCA)

Dimensionality Reduction techniques

Association Rule Mining

Temporal Data Mining

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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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Useful Links: ACM SIGKDD home page (data mining tools, datasets and other resources) Free Artificial Intelligence courses Machine Learning and Data Mining tutorials Ph.D comics

My graduation from Damascus University