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Pragyansmita Nayak
CGI Federal Inc.
Fairfax, Virginia

Pragyansmita Nayak is a Technical and Data Architect in the Momentum Product Development Group at CGI Federal Inc. She has been employed at CGI since 2004. Momentum is a US federal financial accounting-based suite of products. She has led teams of developers and analysts and managed various technical leadership tasks at federal agencies such as DOI, GSA, FCC, EPA, VA, NRC, USAID, and USIP among others. She develops new courses and teaches training programs at CGI primarily involving technical architecture, code frameworks, and databases. Prior to CGI, she worked with Novell India Development Center, Wipro Global R&D, and IIT Madras ERNET Point of Presence (PoP) as a software developer.

She holds a PhD in Computational Sciences and Informatics (GMU Fairfax, VA) and a BS in Computer Science (BITS Pilani, India). Her thesis focused on the application of machine learning techniques for the estimation of the photomorphic redshift of the galaxies. The study used Sloan Digital Sky Survey (SDSS) data on which generalized linear model and Bayesian network algorithms were applied. She is a StartingBloc Social Innovation Fellow, Class of DC16. She is actively involved in activities of local meetups and organizations such as ACT-IAC, IEEE, ACM, and CGI Federal Women's Forum. She has been a reviewer with ACM Computing Reviews since 2002. She has presented her work as part of the BayesiaLab User Conference, Smart Data Online 2016, PyData DC 2016, and Splunk GovSummit 2016.

She is an avid hackathon participant--locally and internationally. She has worked on innovative solutions for the ICRC as part of the ThePort2016 Humanitarian and Social Cause hackathon at CERN, Geneva. Additionally, she has worked on a fair pay-enabling solution for HackThePayGap using MIDAAS APIs conducted by the Dept. of Commerce, White House, the Presidential Innovation Fellows, and Office of the DC Govt CTO; a juvenile justice reform application for AngelHack DC; an education pipeline improvement application for Women in Tech DC; and many other similar challenging problems requiring effective disruptive solutions. She recently won the AngelHack DC HPE Haven OnDemand (HOD) challenge. She also won the best graduate poster prize at the Mid-Atlantic Sigma Xi competition.

Software design and development, data science, and learning new technologies and programming languages are her passion. She is currently learning advanced concepts to further expand her knowledge of big data architectures, design thinking, deep learning, augmented/virtual reality, and quantum computing.

She was born and raised in the eastern coastal state of Odisha, India. She lives with her husband in Washington, DC. She loves to cook, color, knit and embroider in her spare time. She volunteers for the Loudoun County Election Board, Odyssey of the Mind, and Cookies for Childhood Cancer.

For more information on Pragyan's professional experience, please visit her LinkedIn profile and her Twitter profile.


Kangaroo: workload-aware processing of range data and range queries in Hadoop
Aly A., Elmeleegy H., Qi Y., Aref W.  WSDM 2016 (Proceedings of the 9th ACM International Conference on Web Search and Data Mining, San Francisco, CA,  Feb 22-25, 2016) 397-406, 2016. Type: Proceedings

Kangaroo is a set of algorithms that is aimed at optimizing the execution of range-based queries. It has been implemented for Hadoop-based systems. Specific examples of range-based data queries include time intervals, spatial ranges, and lifetime ...


A multi-dimensional comparison of toolkits for machine learning with big data
Richter A., Khoshgoftaar T., Landset S., Hasanin T.  IRI 2015 (Proceedings of the 2015 IEEE International Conference on Information Reuse and Integration, San Francisco, CA,  Aug 13-15, 2015) 1-8, 2015. Type: Proceedings

The authors of this paper provide a good primer on open-source machine learning (ML) tools. They touch on the tools that are actively under development and available for use and extension for the implementation of new algorithms. Resources are inc...


Discrete Bayesian network classifiers: a survey
Bielza C., Larrañaga P.  ACM Computing Surveys 47(1): 1-43, 2014. Type: Article

Bayesian networks (BN) are used to estimate the value of one of the attributes (termed as the predicted or class attribute) of the dataset utilizing the remaining attributes (termed as predictor attribute(s)). The naive Bayes (NB) model, introduce...


Towards big data Bayesian network learning -- an ensemble learning based approach
Tang Y., Wang Y., Cooper K., Li L.  BigData Congress (Proceedings of the 2014 IEEE International Congress on Big Data,355-357, 2014. Type: Proceedings

A Bayesian network (BN) is a directed probabilistic graph model that is used to model variable dependency relationships. Over 50 learning algorithms exist for BNs. This paper proposes a big data-focused BN model learning algorithm: the parallel en...


A refresher in data flow diagramming: an effective aid for analysts
Freeman L.  Communications of the ACM 46(9): 147-151, 2003. Type: Article

A method to improve the accuracy of the data flow diagram (DFD) developed in the requirements analysis phase of the software development life cycle (SDLC) is proposed. The system analyst develops a DFD in consultation with the user...


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