Data Scientist

Home: 214-***-**** ~ Mobile : 678-***-****

ANJUM CHIDA *****.*****@*****.***

**** ********** *****, ***** **- 75013

Objective

To work in a dynamically growing environment as a Data Scientist that demands my knowledge and

experience in designing complex machine learning algorithm studying huge amount of

structured/unstructured data. To design and implement highly complex statistical models and learning

algorithms that studies high dimensional data.

Educational Background

Ph.D. in Computer Science (2007-2012)

Georgia State University, GA

GPA 4.00/4.00

Master in Computer Science (2006 - 2007)

Georgia State University, GA

GPA 4.00/4.00

Bachelor of Engineering (Computer Science & Engineering (1999 - 2004)

University of Madras, Tamil Nadu, India

GPA 8.20/10.0

Research Works

Current Work: Protein Tertiary Model Assessment Using Granular Machine Learning

Techniques

In this research, we attempt to solve the problem of protein structure model assessment using machine

learning techniques and information from both sequence and structure of the protein. The goal is to

generate a machine that understands structures from PDB and when given a new model, predicts

whether it belongs to the same class as the PDB structures or not (correct or incorrect protein models).

In the study, we show two such machines (SVM and fuzzy decision trees); results appear promising for

further analysis. For the purpose of reducing computational overhead multiprocessor environment and

basic feature selection method is used in one method. The prediction accuracy using improved fuzzy

decision tree is above 80% and results are better when compared with other machine learning

technique.

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Master’s Thesis: Protein Secondary Structure Prediction Using Support Vector Machines,

Neural Networks and Genetic Algorithms.

Bioinformatics techniques to protein secondary structure prediction mostly depend on the information

available in amino acid sequence. Support vector machines (SVM) have shown strong generalization

ability in a number of application areas, including protein structure prediction. In this study, a new

sliding window scheme is introduced with multiple windows to form the protein data for training and

testing SVM. Orthogonal encoding scheme coupled with BLOSUM62 matrix is used to make the

prediction. First the prediction of binary classifiers using multiple windows is compared with single

window scheme, the results shows single window not to be good in all cases. Two new classifiers are

introduced for effective tertiary classification. This new classifiers use neural networks and genetic

algorithms to optimize the accuracy of the tertiary classifier. The accuracy level of the new architectures

are determined and compared with other studies. Both the tertiary classifiers are better than most

available techniques.

Publications

Anjum Chida, R. W. Harrison and Y.-Q. Zha g, E ha ed E odi g ith I p o ed Fuzzy De isio T ee

Testi g Usi g CA“P Te plates, “pe ial Issue o Bioi fo atics, IEEE Computational Intelligence Magazine,

vol. 8, no. 4, pp. 55-60, 2012.

Anjum Chida (A. Reyaz-Ahmed), R. W. Harrison and Y.-Q. Zha g, Protein Model Assessment via Machine

Lea i g Te h i ues, International Journal of Functional Informatics and Personalized Medicine, 2011.

Anjum Chida (A. Reyaz-Ahmed), N. Abu-halaweh, R. W. Harrison and Y.-Q. Zha g, P otei Model Assess e t

ia I p o ed Fuzzy De isio T ee, P o . of BIOCOMP 2010, Las Vegas, July 12-15, 2010.

Anjum Chida (A. Reyaz-Ahmed), R. W. Harrison and Y.-Q. Zha g, 3D P otei Model Assess e t Usi g

Geo et i a d Biologi al Featu es, P o eedi gs of SEDM 2010, Chengdu, June 23-25, 2010.

Anjum Chida (A. Reyaz-Ahmed), Y.-Q. Zhang, and R. W. Harrison, Granular Decision Tree and Evolutionary

Neural SVM for Protein Secondary Structure Prediction, International Journal of Computational Intelligence

Systems, v.2, n.2, p.343-352, Dec. 2009.

Anjum Chida (A. Reyaz-Ahmed) and Y.-Q. Zha g, A Ne “VM-Based Decision Fusion Method Using Multiple

Granula Wi do s fo P otei “e o da y “t u tu e P edi tio, P o . of the Third International Conference

on Rough Sets and Knowledge Technology (RSKT2008), Chengdu, China, May 17-19, 2008.

Anjum Chida (A. Reyaz-Ahmed) and Y.-Q. Zha g, P otei “e o da y “t u tu e Prediction Using Genetic

Neu al “uppo t Ve to Ma hi es, P o . of IEEE 7th International Conference on Bioinformatics and

Bioengineering, pp. 1355-1359, Boston, USA, Oct. 14-17, 2007.

Anjum Chida(A. Reyaz-Ahmed), Barrett, N., Zhang, Y-Q., D. A. Washburn, Patte Re og itio Usi g “uppo t

Ve to Ma hi es, The A ual eeti g of the “o iety fo Co pute s i Psy hology, Housto, U“A,

November, 2006. (Poster)

** Please Note

My published work on my maiden name Reyaz-Ahmed, Recently I changed my last name to y hus a d’s last a e Chida.

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Teaching Experience

Primary Instructor

CSC 3210 - COMPUTER ORGANIZATION & PROGRAMMING. Computer structure and machine

language, addressing techniques, macros, file I/O, program segmentation, and linkage.

Teaching Assistant

CSC 8810 - COMPUTATIONAL INTELLIGENCE. Introduction to computational intelligence techniques

and their applications. Major topics include soft computing, granular computing, knowledge

discovery and data mining, distributed intelligent agents, etc.

CSC 8320 - ADVANCED OPERATING SYSTEMS. Advanced operating systems concepts and

mechanisms. Topics may include process synchronization, process deadlock, distributed operating

systems, atomicity, commitment, recovery, fault-tolerance, distributed leader election, distributed

manual exclusion algorithm, and concurrency control.

CSC 6210 - COMPUTER ARCHITECTURE. Logic design, combinatorial and sequential circuits, input-

output devices, memory, processors, controllers, parallel architectures, bit-slicing, reduced

instruction sets.

Technical Expertise

Operating System

UNIX, LINUX, Windows 95/98/NT/2000/XP

Packages Visual Basics, Visual C++, ASP & HTML, CSS, SQL Server, XHTML,

XPath, XQueries, XML, Make, gcc, gdb, Vim/Vi, and LaTeX.

Programming Languages C, C++, JAVA,VB, C# & PL/SQL, Python, Prolog

Machine Learning Algorithms Support Vector Machines, Decision Trees, Neural Networks, Genetic

Algorithms, cluster algorithms etc.

Strengths

Strong analytical, organizational and problem solving skills.

Ability to think critically and engage in complex scenarios

Strong research oriented proficiency.

Awards

Georgia State 2CI Fellowship for year 2011 for excellence in bioinformatics research.

Molecular Bases for Disease Fellowship for years 2007, 2008, 2009 &2010.

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