Professional Summary

• A completed Ph.D. in Computer Science.

• Deep knowledge in Big data, Cloud Computing, Machine Learning, Probability and Statistics and Data Mining.

• Familiar with applying research in industry.

• Overall 3 years Hands on experience in Big Data technologies; Hadoop; and related tools such as HDFS, MapReduce, Mahout, Spark.

• Hands on experience in application development using python and and Linux shell scripting.

• Hands on experience in Statistical analysis and machine learning with Python.

• Cohesive team worker, having strong analytical, problem solving and interpersonal skills. Professional Experience

Post-Doctoral Fellow York University • Toronto, Ontario Canada January 2013 to July 2017

• Designed, implemented and evaluated an associative based algorithm on Apache Spark for classification using Python

• Understanding and predicting migration due to climate change based on data from communities in India and Nepal using Weka

• Implemented a statistical model for forced migration prediction in Iraq from temporal sequences using R

• Designed, implemented and evaluated a tree-based algorithm on Apache Hadoop for frequent pattern mining using Java

• Designed, Implemented an algorithm to mine interesting Trip Patterns from Large Scale Geo- tagged Photos using Java

• Installed and configured Apache Hadoop on the prototype server.

• Utilized MapReduce, HDFS, Mahout, Spark.

• Developed MapReduce programs to implement data mining algorithms.

• Worked on debugging, performance tuning of MapReduce Jobs. Research Scientist IBM • Markham, Ontario Canada March 2014 to December 2014

• Assisted in the research, design and develop novel data mining algorithms based on IBM Platform Symphony, Apache Hadoop and Apache Spark.

• Integrated parent-child in Symphony into Hadoop-MapReduce, solving load balancing problem in frequent pattern mining algorithm.

• Write maintainable and extensible code in a team environment.

• Collaborate closely with other team members to plan, design and develop robust solutions.

• Prepare detailed reports concerning project specifications and activities. Publications

• Adetokunbo Makanju, Zahra Farzanyar, Aijun An, Nick Cercone, Zane Zhenhua Hu, Yonggang Hu: Deep Parallelization of Parallel FP-Growth Using Parent-Child MapReduce. IEEE International Conference on Big Data 2016.

• Zahra Farzanyar, Mohammad Reza Kangavari: Distributed frequent item sets mining over P2P networks. Journal of computing and Informatics, Vol. 34, 201*-****-****, V 2015-Aug-11.

• Zahra Farzanyar, Nick Cercone. Trip Pattern Mining Using Large Scale Geo tagged Photos. International Conference on Computer and Information Science and Technology Ottawa, Ontario, Canada, May 11 12, 2015.

340 Greenbriar ln, Ballwin,

MO 63011 Zahra Farzanyar

Email: ac6rkf@r.postjobfree.com

Mobile: 314-***-****

Edited: 2016-04-05 Zahra Farzanyar Page 2 of 1

• Zahra Farzanyar, Nick Cercone. Accelerating Frequent Itemsets Mining on the Cloud: A MapReduce Based Approach. In 2013 IEEE 13th International Conference on Data Mining Workshops, pp. 592-598. IEEE, 2013.

• Zahra Farzanyar, Nick Cercone. (2013, August). Efficient Mining of Frequent itemsets in Social Network Data based on MapReduce Framework. In Proceedings of the 2013 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013) (pp. 1183- 1188). IEEE Computer Society.

• Zahra Farzanyar, Mohammad Reza Kangavari, Nick Cercone. P2P-FISM: Mining (recently) frequent item sets from distributed data streams over P2P network. Information Processing Letters (2013). http://dx.doi.org/10.1016/j.ipl.2013.07.016.

• Zahra Farzanyar, Mohammadreza Kangavari, Nick Cercone: Max-FISM: Mining (recently) maximal frequent itemsets over data streams using the sliding window model. Computers & Mathematics with Applications 64(6): 1706-1718, 2012.

• Zahra Farzanyar, MohammadrezaKangavari. Efficient mining of fuzzy association rules from the preprocessed dataset. Journal of computing and Informatics, Vol. 31, pages 1001–1017, 2012.

• Zahra Farzanyar, Mohammadreza Kangavari, Naser Mozayani. Efficient Algorithm for Mobile Group Pattern Mining. Accepted for ISPA 2009.

• Zahra Farzanyar, Mohammadreza Kangavari, Sattar Hashemi. An Efficient Distributed Algorithm for Mining Association Rules. ISPA 2006: 383-393, LNCS.

• Zahra Farzanyar, Mohammadreza Kangavari, Sattar Hashemi. A New Algorithm for Mining Fuzzy Association Rules in the Large Databases Based on Ontology. In Sixth IEEE International Conference on Data Mining-Workshops (ICDMW'06), pp. 65-69. IEEE, 2006.

• Zahra Farzanyar, Mohammadreza Kangavari, Sattar Hashemi. Effect of Similar Behaving Attributes in Mining of Fuzzy Association Rules in the Large Databases. ICCSA (1) 2006: 1100- 1109, LNCS.

Technical Skills

• Operating Systems: Linux (Ubuntu, CentOS), Windows, Mac OS.

• Hadoop ECO System: MapReduce, HDFS, Mahout, Spark.

• Programming languages: C, C++, Java and Python.

• IDE: Eclipse.

• Scripting Languages: UNIX, Python.

Education

Ph.D. • Computer Science • 2012 Iran University of Science & Technology (IUST) • Tehran, Iran M.Sc. • Software Engineering • 2005 Azad University • Tehran, Iran B.Sc. • Computer Engineering • 2001 Azad University • Tehran, Iran Online Courses

Machine Learning • Stanford University

Data Science and Engineering with Spark • Berkeley University of California Text Mining and Analytics• University of Illinois at Urbana-Champaign Data Mining with Weka• University of Waikato

Developing Hadoop Applications • MapR Academy

Python Programing • DataQuest

SQL and Databases • DataQuest

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