PhD Research at IIT Kanpur
Department of Management Sciences (DoMS) — Advancing the frontier of Agentic AI Orchestration and Decentralized Data Governance through rigorous empirical inquiry.
RQ1: Autonomous Schema Mapping
Cascading agentic routing mitigating LLM FinOps latency by 97.05% (ACM SIGMOD)
Abstract
Modern enterprise data systems struggle to ingest high-velocity data streams from heterogeneous sources due to schema mismatches and semantic inconsistencies. While generative Large Language Models (LLMs) offer high alignment accuracy, their direct integration into streaming data pipelines introduces prohibitive API costs, high latency, and stochastic behaviors, creating a structural "FinOps Computational Gap." This paper presents a three-tier Cascading Agentic Router architecture that automates semantic schema mapping while minimizing operational expenditure. Evaluated against a synthetic Consumer Packaged Goods (CPG) transaction stream containing 15.04% "Omnibus Chaos" Ingestion noise, the router achieved an Overall F1-Score of 0.9955 and demonstrated a 97.05% FinOps cost reduction compared to direct LLM processing, establishing a scalable paradigm for compliant and cost-effective enterprise data mesh integration.
Regulatory Shield
WhitepapersDORA Compliance Framework
Digital Operational Resilience Act
Strategic implementation guide for financial institutions navigating ICT risk management requirements.
EHDS Data Governance
European Health Data Space
Architectural patterns for sovereign health data exchange under EU regulatory frameworks.
EU AI Act Readiness
High-Risk AI Systems Compliance
Technical conformity assessment methodology for Article 6 classification systems.
Deep Dive Papers
Technical ResearchThe Sovereign Validator
Technical ArchitectureDeterministic Governance for Autonomous Compliance
A formal methodology for achieving provable regulatory conformity through rule-based inference engines and immutable audit trails.
Abstract
The Sovereign Validator introduces a paradigm shift in compliance automation by eliminating probabilistic decision-making in favor of deterministic rule execution. Unlike traditional AI-driven compliance tools that rely on statistical inference, our architecture guarantees reproducible, auditable outcomes through formal verification of regulatory predicates.
Core Principles
Every compliance check produces identical outputs for identical inputs
Cryptographically signed decision logs for regulatory review
Mathematical proofs of regulatory predicate satisfaction
"Deterministic governance eliminates the regulatory uncertainty inherent in probabilistic AI systems, providing financial institutions with provable compliance guarantees."
— Abhishek Khaparde, PhD Candidate, IIT Kanpur DoMS
Live Research Pipeline
In-Flight TrackerRQ1: Agentic Schema Mapping
Under Peer Review (SIGMOD)Cascading agentic routing mitigating LLM FinOps latency by 97.05%.
RQ2: Socio-Technical SEM
Under Peer Review (MISQ)SEM and Gaussian Copula analysis of Agentic Safety Risk in legacy ERPs.
RQ3: ADWIN Concept Drift
Under Peer Review (KDD)Streaming anomaly detection with bounded memory footprint and 0.7147 FNR.
PhD Research Flow
Agentic PipelineAutomated knowledge synthesis pipeline transforming raw PDF documents into a structured, queryable knowledge graph using a swarm of AI agents.
Academic Affiliations
CredentialsDepartment of Management Sciences
PhD Candidate — Enterprise Data Ecosystems & Agentic AI Orchestration
Researching the intersection of deterministic compliance systems and sustainable intelligence frameworks. Focus areas include sovereign AI governance, regulatory automation, and circular economy data architectures.