Jeffrey Jestes 50 S. Central Campus Drive, School of Computing Salt Lake City, UT, USA 84112 Voice: (801) 648-9790 Fax: (801) 581-5843 E-mail: jestes@cs.utah.edu Webpage: http://www.cs.utah.edu/~jestes Job Objective: Software Developer, Research Engineer, Data Analyst, Cluster Administrator Education: • Ph.D. in Computer Science, University of Utah, July 2013 (GPA 4.00 / 4.00) - Focus: Data Management and Analysis - Dissertation: Efficient Summarization Techniques for Massive Data • B.S. Computer Science, Florida State University, April 2008 (GPA 4.00 / 4.00) Work/Research Experience: • University of Utah Datagroup Cluster Administrator & Big Data Analyst August 2011 - July 2013 - Description: Deployed and administered MapReduce cluster. Lead studies on data analytics on real world BIG data including meteorological and sensor data. • Microsoft Research: eXtreme Computing Group Research Intern May 2012 - August 2012 - Description: Developed automated text classifiers and parsers using machine learning techniques, from web data, improving accuracy over the state of the art up to 30%. • Microsoft Research: eXtreme Computing Group Research Intern June 2011 - September 2011 - Description: Developed light-weight distributed transaction services for cloud-based applications which offer guarantees stronger than eventual consistency. • Florida State University Datagroup Cluster Administrator & Big Data Analyst January 2010 - August 2011 - Description: Deployed and administered MapReduce cluster. Performed data analytics on real world BIG data including IP traces, sensor data, and meteorological data. Programming Languages: - C++ (expert) - Java, C#, C, SQL (proficient) - Perl, Python (prior experience) Programming Platforms: - Linux - Hadoop MapReduce - Microsoft SQL Server Projects: • Data Analytics & Summaries for Massive Data - Skills: C++, Java, Hadoop MapReduce, Perl, TPIE - Description: Developed techniques to accelerate data analytics through summaries considering CPU, I/O, and communication costs resulting in decreased summary construction time and data analytics by more than 10 times. - Project webpage: http://www.cs.utah.edu/~jestes/datasummaries • Ranking and Monitoring Probabilistic Data - Skills: C, C++, Perl, SQL, Microsoft SQL Server - Proposed algorithms to rank and monitor data with uncertainties, such as noise, in both centralized and parallel & distributed settings improving CPU, I/O, and communication more than 10 times over baselines. - Project webpage: http://www.cs.utah.edu/~jestes/distprob Employment Authorization: Citizen of the United States of America Journal Publications: 1. Semantics of Ranking Queries for Probabilistic Data, by J. Jestes, G. Cor- mode, F. Li, K. Yi, Vol. 23, No. 12, pages 1903-1917, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2011. Conference Publications: 1. Quality and Efficiency for Kernel Density Estimates in Large Data, by Y. Zheng, J. Jestes, J. Phillips, F. Li, In Proceedings of 32nd ACM SIGMOD International Conference on Management of Data (SIGMOD 2013 Accep- tance Rate: 20%), pages TBA, NYC, NY, June 2013. 2. Ranking Large Temporal Data, by J. Jestes, J. Phillips, F. Li, M. Tang, In Proceedings of 38th International Conference on Very Large Databases (VLDB 2012 Acceptance Rate: 20.3%), PVLDB 5(11): 1412-1423, Istanbul, Turkey, August, 2012. 3. Building Wavelet Histograms on Large Data in MapReduce, by J. Jestes, K. Yi, F. Li, In Proceedings of 38th International Conference on Very Large Databases (VLDB 2012 Acceptance Rate: 20.3%), PVLDB 5(2): 109-120, Istanbul, Turkey, August, 2012. 4. Efficient Parallel kNN Joins for Large Data in MapReduce, by C. Zhang, F. Li, J. Jestes, In Proceedings of 15th International Conference on Extending Database Technology (EDBT 2012 Acceptance Rate: 22.5%), pages 38-49, Berlin, Germany, March, 2012. 5. Efficient Threshold Monitoring for Distributed Probabilistic Data, by M. Tang, F.Li, J. Phillips, J. Jestes, In Proceedings of 28th IEEE International Conference on Data Engineering (ICDE 2012 Acceptance Rate: 20%), pages 1120-1131, Washington DC. April 2012. 6. Probabilistic String Similarity Joins, by J. Jestes, F. Li, Z. Yan, K. Yi, In Proceedings of 29th ACM SIGMOD International Conference on Manage- ment of Data (SIGMOD 2010 Acceptance Rate: 20.8%), pages 327-338, Indianapolis, Indiana, June 2010. 7. Ranking Distributed Probabilistic Data, by F. Li, K. Yi, J. Jestes, In Pro- ceedings of 28th ACM SIGMOD International Conference on Management of Data (SIGMOD 2009 Acceptance rate: 15.9%), pages 361-374, Providence, Rhode Island, June 2009. Achievements & Awards: • Best Presentation, 2011 Computer Science Research Conference Building Wavelet Histograms on Large Data in MapReduce Florida State University, Tallahassee, FL, USA April 2011 • Full Instructor of Object Oriented Programming in C++ Florida State University, Tallahassee, FL, USA May 2010 - August 2010 • GAANN Fellowship, U.S. Department of Education Florida State University, Tallahassee, FL, USA August 2008 - August 2010 • Member Phi Betta Kappa, Alpha of Florida Florida State University, Tallahassee, FL, USA Fall 2008 • Graduated with Honors, Summa Cum Laude with a 4.00 GPA (out of 4.00) Florida State University, Tallahassee, FL, USA August 2004 - April 2008 • Member National Society of Collegiate Scholars Florida State University, Tallahassee, FL, USA Fall 2006 References: Available upon request.