Dhany Saputra. Summary. Experience. Doctoral Candidate in Bioinformatics

Transcription

1 Dhany Saputra Doctoral Candidate in Bioinformatics Summary I'm writing a PhD thesis at the Center for Biological Sequence Analysis, Technical University of Denmark. I have experiences in: - Bioinformatics: Biological Sequence Analysis, Computational Biology, Epidemiology, Microbial Pathogenesis, Next Generation Sequencing, Species Identification, Metagenomics, Unix and Parallel Computation - Web and Application Development in a wide variety of business applications - Flexible Programming Languages, including, but not limited to: ASP, PHP, Java, Matlab, JSP-Servlet, HTML-JavaScript-CSS,.NET, C/C++, Prolog, and Python - Flexible Database System, including, but not limited to: Oracle, MySQL, and MS-SQL Server - Building a fast, efficient algorithm for many systems. - Client-server architecture. - Data mining, Machine Learning, Pattern Recognition, Artificial Intelligence Experience Doctoral Candidate at DTU September Present (2 years 8 months) - Rapid Taxonomy Identification of Single Isolates - Metagenomics Community & Richness Profiling Teaching Assistant at PETRONAS University of Technology July June 2008 (2 years) - System Analysis and Design - Introduction to Computer and Information System - Artificial Intelligence - Algorithm and Data Structure - Data Mining and Knowledge Discovery - Deliver training of Moodle for UTP Lecturers - Deliver training of Java programming for secondary school students training camp Programmer, Teaching Assistant at Department of Information System, Institut Teknologi Sepuluh Nopember Surabaya July December 2005 (2 years 6 months) Projects: - Monography Information System of Regency Government of Sampang, East Java Page1

2 - Treasury Information System of Regency Government of Malang, East Java - Project Realization Information System of Regency Government of Sampang, East Java Teaching Assistant: - Database System I - Algorithm and Programming - Data Structure Skills & Expertise Machine Learning Algorithms Artificial Intelligence Databases Data Mining Pattern Recognition Web Development Sequence Analysis PHP Java MySQL Python Programming Linux C Matlab JavaScript HTML SQL Data Analysis Research Genomics Prediction Scientific Computing Bioinformatics Metagenomics Molecular Epidemiology NGS Languages Indonesian English Dutch French Danish (Native or bilingual proficiency) (Full professional proficiency) Page2

3 Malay (Professional working proficiency) Publications Analisis Kinerja Algoritma PrefixSpan dan AprioriAll dalam Penggalian Pola Sekuensial Proc. Seminar Nasional Aplikasi Teknologi Informasi 2006 Authors: Dhany Saputra, Rully Soelaiman An Efficient Data Structure for General Tree- Like Framework in Mining Sequential Patterns Using MEMISP Proceeding of 5th International Conference on Information Technology in Asia 2007 (CITA 07) July 9, 2007 Mining Sequential Patterns Using I-PrefixSpan Proceeding of World Academy of Science, Engineering and Technology November 2007 Mining Sequential Patterns Using I-PrefixSpan International Journal of Computer Science and Engineering 2008 (IJCSE 08) 2008 Separator Database Technique for Mining Sequential Patterns Using PrefixSpan with Pseudoprojection Proceeding of National Postgraduate Conference 2008 (NPC 08) December 2007 Sequential Pattern Mining using PrefixSpan with Pseudoprojection and Separator Database Proceeding of IEEE International Symposium on Information Technology 2008 August 2008 Sequential pattern mining is a branch of data mining science that solves inter-transaction pattern mining problems. A comprehensive performance study has been reported that PrefixSpan, one of its algorithms, outperforms GSP, SPADE, as well as FreeSpan in most cases, and PrefixSpan integrated with pseudoprojection technique is the fastest among those tested algorithms. Nevertheless, Pseudoprojection technique, which requires maintaining and visiting the in-memory sequence database frequently until all patterns are found, consumes a considerable amount of memory and induces the algorithm to undertake redundant and unnecessary checks to this copy of original database into memory when the candidate patterns are examined. In this paper, we propose Separator Database to improve PrefixSpan with pseudoprojection through early removal of uneconomical in-memory sequence database. The experimental results show that Separator Database improves PrefixSpan with pseudoprojection. Future research includes exploring the use of Separator Database in PrefixSpan with pseudoprojection to improve mining constrained sequential patterns. Education Technical University of Denmark PhD, Biological Sequence Analysis, Page3

4 Katholieke Universiteit Leuven Predoctoral, Pattern Set Mining and Sequence Mining, Universiti Teknologi Petronas Master of Science, Data Mining and Artificial Intelligence, Sepuluh Nopember Institute of Technology, Indonesia Sarjana Komputer, Decision Support System and Business Intelligence, Honors and Awards - Silver Medal and 2nd Runner Up Best Poster for Engineering Design Exhibition UTP, Cumlaude charter from Institut Teknologi Sepuluh Nopember Surabaya Interests songwriting, badminton, swimming Page4

5 Dhany Saputra Doctoral Candidate in Bioinformatics Contact Dhany on LinkedIn Page5