“Agents move through time; you abide in the eternal now.” Gautam Parab
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I Read Twelve Agent Codebases in a Weekend. The Dead Ones Taught Me the Most.
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Eleven days ago, a 55,000-star project shut itself down. I spent a weekend reading it and eleven of its neighbors to understand why — and what the survivors quietly agree on.
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Optimization of an Airport Security Selectee Lane System Using Simulation
Published in M.S. Thesis, University at Buffalo, State University of New York, 2011
M.S. thesis, Department of Industrial and Systems Engineering, University at Buffalo (SUNY). Simulation and stochastic modelling of airport security screening, and the basis for the subsequent European Journal of Operational Research paper.
Recommended citation: G. Parab. (2011). "Optimization of an Airport Security Selectee Lane System Using Simulation." M.S. Thesis, State University of New York at Buffalo. 56 pages. ProQuest 1488938.
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Simulation-Based Selectee Lane Queueing Design for Passenger Checkpoint Screening
Published in European Journal of Operational Research, 2012
A simulation-based queueing framework that assigns passengers from different risk classes to the often-underutilized selectee lane in real time, raising the probability of correctly identifying high-risk passengers by more than 4% over then-current practice with minimal added processing time.
Recommended citation: X. Nie, G. Parab, R. Batta, and L. Lin. (2012). "Simulation-Based Selectee Lane Queueing Design for Passenger Checkpoint Screening." European Journal of Operational Research. 219(1), 146–155.
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AI-Driven Personalization in Retail Analytics: Transforming Customer Experiences
Published in International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 2024
How machine learning and real-time behavioural data reshape retail personalization — from recommendation and dynamic pricing to inventory alignment — and what the underlying analytics architecture has to support.
Recommended citation: G. Parab. (2024). "AI-Driven Personalization in Retail Analytics: Transforming Customer Experiences." International Journal of Research in Computer Applications and Information Technology. 7(2), 2387–2396.
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AI in Healthcare Analytics: Transforming Patient Care and Operational Efficiency
Published in International Journal of Computer Engineering and Technology (IJCET), 2024
Applied AI across the healthcare analytics stack — predictive risk stratification, clinical decision support, and operational forecasting — with attention to the data quality, interoperability, and regulatory constraints specific to the domain.
Recommended citation: G. Parab. (2024). "AI in Healthcare Analytics: Transforming Patient Care and Operational Efficiency." International Journal of Computer Engineering and Technology. 15(6), 1539–1546.
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Agentic AI in Data Analytics: Transforming Autonomous Insights and Decision-Making
Published in International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), 2024
A framework for agentic AI in enterprise analytics: autonomous agents that plan, query, and act against governed data platforms rather than returning static reports — covering architecture, orchestration, and the guardrails autonomy requires.
Recommended citation: G. Parab. (2024). "Agentic AI in Data Analytics: Transforming Autonomous Insights and Decision-Making." International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 10(6), 1752–1759.
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Manufacturing 4.0: AI-Driven Analytics for Predictive Maintenance
Published in International Journal for Multidisciplinary Research (IJFMR), 2024
Predictive maintenance as an analytics problem: sensor and telemetry pipelines, failure-mode modelling, and the shift from scheduled servicing to condition-based intervention in Manufacturing 4.0 environments.
Recommended citation: G. Parab. (2024). "Manufacturing 4.0: AI-Driven Analytics for Predictive Maintenance." International Journal for Multidisciplinary Research. 6(6).
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Absolute Self-Governance in Multi-Agent Systems: Architectural Design and Empirical Observations
Published in Technical report, with open-source reference implementation on GitHub, 2026
An orchestration framework in which role-based LLM agents govern their own team composition — dynamic roster sizing, threshold-based TETD consensus, and role-augmented execution — with an open-source reference implementation and benchmarks that report the trade-offs honestly, including where a single agent wins.
Recommended citation: G. Parab. (2026). "Absolute Self-Governance in Multi-Agent Systems: Architectural Design and Empirical Observations." Technical report. github.com/gparab/absolute-self-governance.
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