I will be graduating soon and am on the job market. My application material is posted here.

Mohan Krishnamoorthy

PhD Candidate · Research Assistant

Volgebau School of Engineering · George Mason University

I am currently working on my doctrol dissertation investigating architectures that model and optimize stochastic closed-form arithmetic simulation models of manufacturing processes with work-in-process inventories over multiple intervals. I have been a Research Assistant (RA) in GMU since Jan 2013 and a Guest Researcher at National Institute of Standards and Technology since 2015. Before that, I worked at Los Alamos National Laboratory for 4 years. I also have a Masters in Computer Science from Rochester Institute of Technology and a Bachelors in computer engineering from Mumbai University.

Curriculum Vitae

Ph.D. Reseach Synopsis & Research Statement

in PDF

Selected Journal and Conference Publications

For the full list please visit Google Scholar.

  1. M. Krishnamoorthy, A. Brodsky, and D. Menascé. Stochastic Decision Optimization based on Deterministic Approximations of Processes described as Closed-form Arithmetic Simulation. Journal of Decision Systems, May 2018. Web   Paper
  2. A. Brodsky, M. Krishnamoorthy, M. O. Nachawati, and W.Z. Bernstein, D Menascé. Manufacturing and Contract Service Networks: Composition, Optimization and Tradeoff Analysis based on a Reusable Repository of Performance Models. In Proceedings of the 2017 IEEE International Conference on Big Data, December 2017. Web   Paper
  3. A. Brodsky, M. Krishnamoorthy, W. Z. Bernstein, M. O. Nachawati. A System and Architecture for Reusable Abstractions of Manufacturing Processes. In the IEEE International Conference on Big Data (Big Data) 2016, Washington DC, 5-8 Dec. 2016. Web   Paper
  4. A. Brodsky, G. Shao, M. Krishnamoorthy, A. Narayanan, D Menascé, R. Ak. Analysis and Optimization in Smart Manufacturing based on a Reusable Knowledge Base for Process Performance Models. International Journal of Advanced Manufacturing Technology, April 2016. Web   Paper
  5. M. Krishnamoorthy, A. Brodsky, D. Menascé. Modular Modeling & Optimization of Temporal Manufacturing Processes with Inventories. Hawaii International Conference on System Sciences (HICSS-49) 2016 proceedings, Kauai, HI. 5-8 Jan. 2016. Web   Paper
  6. A. Brodsky, G. Shao, M. Krishnamoorthy, A. Narayanan, D Menascé, R. Ak. Analysis and Optimization in Smart Manufacturing based on a Reusable Knowledge Base for Process Performance Models. IEEE International Conference on Big Data (Big Data) 2015, Santa Clara, CA, 29 Oct.-1 Nov. 2015. Web   Paper
  7. D. Menascé, M. Krishnamoorthy, A. Brodsky. Autonomic Smart Manufacturing. Journal of Decision Systems, Special Issue on Integrated Decision Support Systems (eds. I. Linden, J. Linden, S. Liu.), June 2015. Web   Paper
  8. A. Brodsky, M. Krishnamoorthy, D. Menascé, G. Shao, S. Rachuri. Toward Smart Manufacturing Using Decision Analytics. IEEE International Conference on Big Data (BigData), 27-30 Oct. 2014. Web   Paper
  9. M. Krishnamoorthy, A. Brodsky, D. Menascé. Optimizing Stochastic Temporal Manufacturing Processes with Inventories: An Efficient Heuristic Algorithm Based on Deterministic Approximations. Operations Research and Computing: Algorithms and Software for Analytics, Proc. INFORMS Computing Society Conf., Richmond, VA, January 11-13, 2015. Web   Paper
  10. M. Krishnamoorthy, A. Brodsky, D. Menascé. Temporal Manufacturing Query Language (tMQL) for Domain Specific Composition, What-if Analysis, and Optimization of Manufacturing Processes With Inventories. Technical Report. Department of Computer Science, George Mason University, Fairfax, VA, 22030, Tech. Rep. GMU-CS-TR-2014-3, 2014. [Online]. Also presented at INFORMS Computing Society Conf., Richmond, VA, January 11-13, 2015. Paper
  11. J. Brodin, M. Krishnamoorthy, G. Athreya, W. Fischer, P. Hraber, C. Gleasner, L. Green, B. Korber, T. Leitner. A multiple-alignment based primer design algorithm for genetically highly variable DNA targets. In BMC bioinformatics Journal, August 2013. Web   Paper
  12. M. Krishnamoorthy, P.Patel, M. Dimitrijevic, J. Dietrich, M. Green, C. Macken. Tree pruner: An efficient tool for selecting data from a biased genetic database. in BMC bioinformatics Journal, January 2011. Web   Paper


  1. M. Krishnamoorthy, A. Brodsky, and D. Menascé. Stochastic Optimization for Steady State Production Processes based on Deterministic Approximations. Under review. Abstract  
  2. A. Brodsky, M. O. Nachawati, M. Krishnamoorthy, W. Z. Bernstein, D. A. Menascé. Factory Optima: A Web-based System for Composition and Analysis of Manufacturing Service Networks based on a Reusable Model Repository. Under review. Abstract  


Position Institution Dates
Guest Researcher National Institute of Standards and Technology. Gaithersburgh, MD January 2015 - Present
Research Assisrant George Mason University. Fairfax, VA January 2013 - Present
Teaching Assistant George Mason Univerity. Fairfax, VA August 2012 - December 2012
Research Technologist Los Alamos National Laboratory. Los Alamos, NM May 2010 - June 2012
Research Assistant Los Alamos National Laboratory. Los Alamos, NM July 2008 - April 2010


Institution Degree Dates GPA
George Mason Univerity. Fairfax, VA PhD in Computer Science August 2012 - Present 3.96/4
Rochester Institute of Technology. Rochester, NY MS in Computer Science August 2007 - June 2010 3.69/4
Mumbai Univeristy. Mumbai, INDIA BE in Computer Engineering June 2003 - June 2007 3.6/4

Projects · Experise · Accomplishments


  • Stochastic optimization algorithms based on deterministic approximations (GMU) : 2014-Present
    • Purpose: Stochastic optimization algorithms that make use of the mathematical structure of the original problem are inefficient especially for real-world processes composed of complex process networks because they extract the mathematical structure using samples from a black-box simulation. The goal here is to improve the computation complexity and convergence of these algorithms for probabilistic models.
    • Contribution: Extracted the mathematical structure of the problem from a white-box simulation code analysis as part of a heuristic algorithm based on deterministic approximations to find the most optimal decision points for the system using statistics of the simulated probabilistic model.
    • Results: Experimental study on a 22-variable and 21-constraint real-world use case demonstrated that this approach significantly outperforms popular simulation-based optimization approaches.
    • See 1, 9 publications.
  • Framework for composition, analysis, and optimization of real-world processes (NIST) : 2016-2017
    • Purpose: To build a system of reusable process models in manufacturing such that it is easy to use, simple, and cost-effective so that the end user can perform multiple analysis and optimization operations on these models.
    • Contribution: Built a software framework and prototype using Generic Model Environment and cloud architectures that allowed hierarchical composition, visualization, and analysis of manufacturing systems consisting of real-world processes from a reusable model repository.
    • Results: Demonstrated the prototype system to compose an hierarchical model for a real-world supply chain use case and performed simulation, prediction, optimization, and trade-off analysis using Pareto optimal graphs on this model.
    • See 2, 3 in publications.
  • Reusable repository of process performance models (GMU, NIST) : 2015-2017
    • Purpose: To build a reusable repository of models for manufacturing so that analysis and optimization solutions need not be implemented de novo because it leads to cost and time intensive development of models and algorithms, which are difficult to modify, extend, and reuse.
    • Contribution: Designed and developed a reusable repository of mathematical models called performance models for manufacturing end-users with the goal of ease of use and reusability to compose and perform analysis and optimization on complex real-world hierarchical processes.
    • Results: This repository was used as the basis for a competition to crowdsource Reusable Abstractions of Manufacturing Processes (RAMP). For this competition, I also demonstrated the structure of a process performance model in an instructional webinar.
    • See 4-8, 10 in publications.
  • Scientific algorithm for Primer design (LANL) : 2011
    • Purpose: To build a tool for primer design, which is difficult to do for highly variable DNA sequences and for which experimental success requires attention to many interacting constraints.
    • Contribution: Designed and developed scalable scientific algorithm for primer design that included recursive generation of combinatorial bio-barcodes of specified length with design constraints and dimer risk filtration among the generated primer constructs in C and Perl.
    • Results: Primer design tool (v1.0) was included among the HIV analysis tools (see current tool at v2.0).
    • See 11 in publications.
  • Redesign of computing architecture to improve efficiency of scientific analysis (LANL) : 2010-2011
    • Purpose: To redesign scientific tools in order to ensure high performance and minimize compute time and file system usage.
    • Contribution: Designed and deployed five scientific tools using a Model-View-Controller (MVC) framework and web services using XML-RPC on the MVC model provided by Perl Catalyst and object oriented Moose libraries.
    • Results: The five tools were successfully deployed with an improvement of 35% in performance and 50% in file-system usage.
  • Masters Thesis: Compression and caching in distributed file system (RIT) : 2009-2010
    • Purpose: To perform research on compression and caching algorithms to improve data fetch time in a distributed system.
    • Contribution & Results: Implemented a distributed system using the Java NIO framework and reduced data and file fetch time by 14%.
  • Tree Viewer, Pruner, and Decorator (LANL) : 2009-2010
    • Purpose: To build tools that automate the selection and annotation of influenza genetic data by making the correct trade-off between speed and simplicity on the one hand and control over quality and contents of the data set on the other.
    • Contribution: Designed and developed the tree pruner and decorator tools to perform this selection and annotation for Influenza Sequence Database (ISD). This project was based on the open source project Archaeopteryx using Java Applets, AJAX, and REST web services with the JSON and phyloXML data formats.
    • Results: Pruner and Decorator tools were made available among influenza analysis tools and were also made open source.
    • See 12 in publications..
  • Database and Web architecture development (LANL) : 2008-2010
    • Purpose: Design a schema to accommodate millions of records in ISD and develop a website over ISD to serve influenza analysis tools.
    • Contribution & Results: Designed a new schema in PostgreSQL. Also, developed a website over ISD using Perl, Mason Perl, HTML, XML, Java Scripts and SQL. Further, redesign of the website using jQuery and AJAX request objects yielded 23% better performance.


  • Programming Languages & Libraries: Java (expert), C (proficient), C++ (proficient), LaTex (proficient), SQL (proficient), NoSQL (proficient), Ruby (familiar), Perl (proficient), Python (competent), Shell script (familiar), R (proficient), JavaScript (familiar), jQuery (familiar), XQuery (competent), JSONiq (proficient).
  • Technical Skills: Data analytics, Analytical Modeling, Data Science, Algorithm Design, Decision Optimization, Operations Research, Decision Systems, Model Simulation & Prediction, Database Management Systems, Software Development Life Cycle.
  • Operating Systems: Linux, Windows 7/8/10, MAC OS/X.
  • Version control: GIT, Repo, SVN.
  • Functional abilities: Software Architecture, Object Oriented Programming, Distributed Business and Scientific Applications, Software Development and Testing, Data Mining and Analytics.
  • Tools: Docker, Eclipse, Emacs and VI editors, Oxygen XML editor, Rational Rose, Microsoft Visio, Microsoft Office, Microsoft Visual C++, Microsoft Visual Studio, gedit, Atom.
  • Mathematical modeling & Optimization Solvers: OPL, AMPL, CPLEX, Gurobi, MINOS, SNOPT, LGO, Coin OR, BARON, BONMIN.
  • Internet Technology: Amazon AWS, Azure, Hadoop, OpenStack, Apache Spark.


Awards & Accomplishments Date                
Outstanding PhD Student Award from Computer Science Department at George Mason University, Fairfax, VA. 2018
Travel Grant to attend the IEEE International Conference on Big Data. 2014-2017
Travel Grant to attend the International Conference on Tools for Artificial Intelligence, Boston, MA. 2017
Helped write three proposals to NIST and DFW airport, two of which were successfully funded. 2014-2017
Travel Grant to attend the INFORMS Computing Society Conference, Richmond, VA. 2015
Research Grant from National Institute of Standards and Technology, Gaithersburgh, MD. 2015-2017
Graduate Research assistantship from George Mason University, Fairfax, VA. 2013-Present
Invited to join multiple honor societies for being an outstanding student. 2012-2017
Dean Fellowship Award from George Mason University, Fairfax, VA. 2012-2013
Research Assistantship from Los Alamos National Laboratory, Los Alamos, NM. 2008-2010
Certificate of excellence for creating a budget database back end in SQL Server and web user interface for monitoring quarterly budgets, Mumbai, India. 2007

Contact Information

PhD Candidate in Computer Science · Research Assistant
Volgenau School of Engineering
George Mason University
4400 University Drive, MS 4A5
Fairfax, Virginia 22030-4444
Phone: 703-989-6434
Email: mkrishn4 (at) or mxk4903 (at)


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