Manager Computer Science

MOHAMMAD SALIM AHMED

**** ******** ****., ***. ***, Dallas, TX 75252, USA

Website: http://www.salimahmed.com

Email: *************@*****.***

Tel: +1-949-***-****

OBJECTIVE

Full time position in my research elds of interest from September, 2012.

FIELDS OF INTEREST

Data Mining, Machine Learning, Cloud Computing, Natural Language Processing, Information Extraction.

VISA STATUS

Permanent Residency application is being processed. No sponsorship is required.

EDUCATIONAL QUALIFICATION

August 2012 PhD in Computer Science. Doctoral Thesis Proposal accepted on November 24, 2010.

Expected graduation in Summer, 2012.

University of Texas at Dallas (UTD), USA

December 2010 Masters in Computer Science. Courses include Advanced Machine Learning, Natural

Language Processing, Data Mining, Arti cial Intelligence, Advanced Operating System, Ma-

chine Learning, Advanced Algorithms

GPA : 3.83/4.0

University of Texas at Dallas (UTD), USA

June 2005 Bachelors in Computer Science and Engineering (CSE). Courses include Programming

Language, Algorithms, Data Structures, Database, Software Engineering, Simulation and

Modeling

GPA : 3.95/4.0

Bangladesh University of Engineering and Technology (BUET), Bangladesh

EXPERIENCE

Research Intern May 2011-January 2012

Siemens Corporate Research

Responsibility:

- Worked on geothermal data keyword prediction, considering it as a multi-label text classi cation problem.

- Implementation of a text pipeline using Spring Batch framework.

- Implementation of a Well Log Header Analysis pipeline for data extraction from scanned images.

- Work with Mahout (A Distributed Machine Learning library) and MULAN (WEKA based Multi-Label Machine

Learning library)

PhD Student August 2006-Current

The University of Texas at Dallas

Responsibility:

- Work as TA for courses including Advanced Algorithms and Java.

- Responsible for assisting research advisor in conducting problem solving as well as Implementation and testing

of innovative ideas and algorithms.

- My area of research is focused on multi-label text classi cation. Semi-supervised Impurity based Subspace

Clustering (SISC), a novel soft subspace clustering algorithm, is used to perform grouping of text documents

and then -NN approach is used to assign predicted class labels. Comparative study with other state-of-the-art

multi-label classi cation and subspace clustering algorithms show the improvements in class label prediction.

Lecturer, Department of CSE July 2005- June 2006

Bangladesh University of Engineering and Technology

Responsibility:

- Design, prepare and develop teaching materials, deliver lectures in class room and conduct lab sessions.

- Supporting students through advisory role supervising research activities.

SELECTED PUBLICATIONS

- Mohammad Salim Ahmed, Latifur Khan, Mandava Rajeswari, Using Correlation Based Subspace Cluster-

ing For Multi-label Text Data Classi cation, In Proceedings of 22nd International Conference on Tools with

Arti cial Intelligence (ICTAI 2010), October, 2010, Arras, France.

- Mohammad Salim Ahmed, Latifur Khan, Nikunj Oza, Mandava Rajeswari, Multi-Label ASRS Dataset

Classi cation Using Semi-Supervised Subspace Clustering, In Proceedings of Conference on Intelligent Data

Understanding (CIDU 2010) Invited for Journal Extension in Statistical Analysis and Data Mining journal

(Best of CIDU 2010), the o cial journal of ASA, October, 2010, Mountain View, CA.

- Tahseen Al-Khateeb, Mohammad Salim Ahmed, Mohammad Masud, Latifur Khan, A Data Intensive Multi-

chunk Ensemble Technique to Classify Stream Data Using Map-Reduce Framework, In Proceedings of SIAM

Data Mining 2010 Workshop on High Performance Analytics Algorithms, Implementations, and Applications,

April, 2010, Columbus, Ohio.

- Mohammad Salim Ahmed, Latifur Khan, SISC: A Text Classi cation Approach Using Semi Supervised

Subspace Clustering, In Proceedings of 3rd International Workshop on Domain Driven Data Mining (DDDM

2009) in conjunction with ICDM 2010, October, 2010, Miami, FL.

- Mohammad Salim Ahmed, Ehab Al-Shaer, Latifur Khan, A Novel Quantitative Approach For Measuring

Network Security, In Proceedings of INFOCOM 2008, pages 1957-1965, April, 2008, Phoenix, AZ.

- Mohammad Salim Ahmed, Ehab Al-Shaer, M. M. Taibah, Muhammad Abedin, Latifur Khan, Towards

autonomic risk-aware security con guration. In NOMS 2008, pages 722-725, April, 2008, Bahia, Brazil.

SELECTED PROJECTS

ROCONA Java

Risk based prOactive seCurity cOn guration maNAger (ROCONA) is a security measurement tool that utilizes the

vulnerability history of services and calculates risk towards the network. It uses the NVD (National Vulnerability

Database) to gather past history of vulnerabilities, a ected products/softwares and their scores to calculate Existing,

Historical and Probabilistic vulnerability score for a given computer network. It also considers how much external

attacks can propagate within the network. Two third party tools, AOL Active Security Center and Nessus were

used for validation purposes. Vulnerability history of Jan, 1988 - Apr, 2007 was used during the experiments.

SISC Java

Semi-supervised Impurity based Subspace Clustering (SISC) is a subspace clustering based classi cation method for

multi-label text data. Performance evaluation were performed on ASRS (Aviation Safety Reporting System) (10K

reports), Reuters ( 10K articles) and 20 Newsgroups ( 15K articles) data set. Benchmark algorithms included Sub-

space Clustering approaches like SCAD2 and Entropy Based K-Means as well as multi-label classi cation approaches

like MetaLabeler and Ensemble of Pruned Sets, both of which uses SVM as base classi ers.

TECHNICAL PROFILE

Programming languages Java, C/GCC/C++, Basic, Pascal, Assembly

Development tools Eclipse, SpringSource Tool Suite, Spring Batch, Hadoop, Microsoft Vi-

sual Studio.NET 2005, NetBeans, Borland JBuilder, WEKA, Mahout

Web technologies ASP.NET, CSS with IIS, JSON and SOLR

Database Oracle 10g, MySQL, PostgreSQL, Microsoft Access

Script JavaScript, HTML

Simulator software R, Matlab

Virtual Machines Cygwin, VMware

PROFESSIONAL AFFILIATION

- IEEE Student Member (2009-Current)

- ACM Student Member (2010-Current)

REFERENCES

Dr. Latifur Khan Fabian Moerchen, Ph.D.

Associate Professor of Computer Science Program Manager

UTD Database and Data Mining Laboratory Knowledge & Decision Systems

University of Texas at Dallas Siemens Corporate Research

email : *****@********.*** email : ******.********@*******.***

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