David C. Anastasiu, Assistant Professor
Dept. of Computer Engineering
College of Engineering
San Jose State University, USA

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BIO:

Prof. Anastasiu received his Ph.D. in Computer Science from University of Minnesota in 2016. His research interests fall broadly at the intersection of data mining, high performance computing, information retrieval, and cloud and distributed computing. Much of his work has been focused on scalable and efficient methods for analyzing sparse data. He has developed serial and parallel methods for identifying near neighbors, methods for characterizing how user behavior changes over time, and methods for personalized and collaborative presentation of Web search results. As a result of his algorithmic work in the area of Data Science, Prof. Anastasiu was awarded the competitive Next Generation Data Scientist (NGDS) Award at the 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA’2016). His work has been published in many top-tier conferences and journals, and he serves on the program committees of several IEEE and ACM conferences.

RESEARCH AREAS:

(1)Data mining
(2)High performance computing
(3)Information retrieval
(4)Cloud and distributed computing.

WORK EXPERIENCE:

(1) Data Mining Consultant, In-Depth, Inc, San Francisco, CA, USA. 05/2014 to 11/2014.
(2) Associate System Developer I/II, PPD, Inc., Austin, TX, USA. 09/2007 to 7 2010.

SELECTED RECENT PUBLICATIONS:

[1] David C. Anastasiu and George Karypis. Efficient identification of tanimoto nearest neighbors. In Proceedings of the 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA ’16, 2016. Best Research Paper Award.

[2] David C. Anastasiu, Evangelia Christakopoulou, Shaden Smith, Mohit Sharma, and George Karypis. Big data and recommender systems. Novática: Journal of the Spanish Computer Scientist Association, (240), October 2016.

[3] David C. Anastasiu and George Karypis. Fast parallel cosine k-nearest neighbor graph construction. In Proceedings of the 6th Workshop on Irregular Applications: Architectures and Algorithms, in conjunction with SC’16, IA3 2016, New York, NY, USA, 2016. ACM.

[4] David C. Anastasiu and George Karypis. Pl2ap: Fast parallel cosine similarity search. In Proceedings of the 5th Workshop on Irregular Applications: Architectures and Algorithms, in conjunction with SC’15, IA3 2015, pages 1–8, New York, NY, USA, 2015. ACM.

[5] David C. Anastasiu and George Karypis. L2knng: Fast exact k-nearest neighbor graph construction with l2-norm pruning. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM ’15, pages 791–800, New York, NY, USA, 2015. ACM.

[6] David C. Anastasiu, Al M. Rashid, Andrea Tagarelli, and George Karypis. Understanding computer usage evolution. In 31st IEEE International Conference on Data Engineering, ICDE 2015, pages 1549–1560, 2015.

[7] David C. Anastasiu and George Karypis. L2ap: Fast cosine similarity search with prefix l-2 norm bounds. In The 30th IEEE International Conference on Data Engineering, ICDE 2014, pages 784–795, 2014.

[8] David C. Anastasiu, Jeremy Iverson, Shaden Smith, and George Karypis. Big data frequent pattern mining. In Frequent Pattern Mining, pages 225–260. Springer International Publishing, Switzerland, 2014.

[9] David C. Anastasiu, Andrea Tagarelli, and George Karypis. Document clustering: The next frontier. In Data Clustering: Algorithms and Applications, pages 305–338. CRC Press, Boca Raton, FL, USA, 2013.

[10] David C. Anastasiu, Byron J. Gao, Xing Jiang, and George Karypis. A novel two-box search paradigm for query disambiguation. World Wide Web, 16(1):1–29, 2013.

[11] Byron J. Gao, David Buttler, David C. Anastasiu, Shuaiqiang Wang, Peng Zhang, and Joey Jan. User-centric organization of search results. IEEE Internet Computing, 17(3):52–59, May 2013.

[12] David C. Anastasiu, Byron J. Gao, and David Buttler. A framework for personalized and collaborative clustering of search results. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM ’11, pages 573–582, New York, NY, USA, 2011. ACM.

[13] David C. Anastasiu, Byron J. Gao, and David Buttler. Clusteringwiki: personalized and collaborative clustering of search results. In The 34th ACM SIGIR International Conference on Research and Development in Information Retrieval, SIGIR 2011, pages 1263–1264, 2011.

[14] Byron J. Gao, David C. Anastasiu, and Xing Jiang. Utilizing user-input contextual terms for query disambiguation. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING ’10, pages 329–337, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics.

[15] Byron J. Gao, Mingji Xia, Walter Cai, and David C. Anastasiu. The gardener’s problem for web information monitoring. In Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM ’09, pages 1525–1528, New York, NY, USA, 2009. ACM.

[16] Walter Cai, David C. Anastasiu, Mingji Xia, and Byron J. Gao. Olap for multicriteria maintenance scheduling. In The 5th International Conference on Data Mining, DMIN ’09, pages 35–41. CSREA Press, 2009.