Rajkumar Kuppuswami
Applied Materials, Manager, Data scientist
Austin, Texas, United States
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I’m a Data Scientist and Data Engineering professional with 13+ years of experience building scalable analytics platforms, data lake architectures, and AI/ML solutions across semiconductor manufacturing, supply chain, and global trade domains. My work spans predictive modeling, machine learning, big data processing (Spark, Hadoop, Databricks), and business intelligence (Tableau, Power BI), with a strong focus on turning complex data into actionable insights for leadership. I’ve led end-to-end initiatives such as demand–supply optimization, revenue forecasting, tariff impact analytics, compliance fraud detection, and centralized executive dashboards—often reducing cycle times by 40–70% and improving decision quality across cross-functional teams
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Supply Chain & Forecasting AI
Attendees will gain a practical understanding of how Artificial Intelligence can transform supply chain forecasting from static reporting into a proactive, decision-driven capability. They will learn how machine learning models such as LSTM, ARIMA hybrids, and ensemble methods can improve SKU- and location-level forecast accuracy while reducing inventory imbalance and expedite costs. The session will provide a clear roadmap for operationalizing AI within S&OP and MRP workflows, ensuring models move beyond experimentation into production environments. Participants will also explore scenario-based simulation techniques to manage disruptions such as supplier risk, tariff changes, and demand volatility. Finally, they will leave with actionable strategies to measure ROI, align AI initiatives with executive KPIs, and scale forecasting intelligence across global operations.
Developer & Data Science Tools
The exponential growth of multimodal data—including text, images, audio, video, and structured enterprise signals—demands advanced AI systems capable of not only analyzing data but also generating coherent, human-centric narratives. This paper presents a Multimodal Narrative Intelligence Engine (MNIE) that integrates creative reasoning with multimodal learning to transform complex data into structured, interpretable, and actionable insights.
The proposed framework combines large language models, vision-language architectures, and graph-based reasoning to enable cross-modal understanding and contextual synthesis. By leveraging prompt-based reasoning and reinforcement learning, the system enhances narrative coherence, factual grounding, and adaptability across diverse domains. Unlike traditional analytics systems that focus primarily on prediction accuracy, MNIE introduces a paradigm shift toward narrative intelligence, where insights are communicated through explainable, story-driven outputs that align with human cognition.
The engine is evaluated across applications such as supply chain optimization, healthcare analytics, financial risk assessment, and smart manufacturing, demonstrating improved interpretability, faster decision-making, and reduced cognitive complexity for stakeholders. Additionally, the framework incorporates responsible AI principles, including bias mitigation, privacy preservation, and human-in-the-loop validation, ensuring trustworthy and ethical deployment.
This work positions multimodal narrative intelligence as a critical advancement in next-generation AI systems, bridging the gap between data-driven analytics and human understanding, and enabling more intuitive, transparent, and impactful decision support.
Creative + AI Reasoning: Multimodal Narrative Intelligence Engine
The rapid growth of multimodal data—spanning text, images, audio, video, and structured signals—has created a need for intelligent systems capable of synthesizing information into coherent, human-understandable narratives. This paper introduces a Multimodal Narrative Intelligence Engine (MNIE), a novel AI framework that integrates creative reasoning with advanced multimodal learning to generate context-aware, interpretable, and actionable narratives. The proposed system combines large language models, vision-language transformers, and graph-based reasoning modules to fuse heterogeneous data streams and construct semantically rich storylines.
Unlike traditional analytics pipelines that focus on isolated predictions, MNIE emphasizes narrative intelligence—the ability to explain patterns, infer causal relationships, and communicate insights in a structured, story-driven format. The framework incorporates reinforcement learning and prompt-based reasoning to enhance coherence, factual grounding, and domain adaptability across applications such as supply chain intelligence, healthcare diagnostics, financial risk analysis, and smart manufacturing.
Experimental results demonstrate that MNIE improves interpretability and decision support by transforming complex data into concise narratives, reducing cognitive load for stakeholders while maintaining analytical rigor. Furthermore, the system introduces governance-aware mechanisms to mitigate bias, ensure data privacy, and support human-in-the-loop validation.
This work highlights the emerging paradigm of AI-driven narrative generation as a critical bridge between data-driven insights and human decision-making, paving the way for next-generation intelligent systems that are not only predictive but also explanatory and creative.
A Responsive Artificial Intelligence for the Education of Safe Sleep
Sudden Infant Death Syndrome (SIDS) remains a leading cause of post-neonatal mortality, often exacerbated by the rapid spread of health misinformation and cultural barriers to safe sleep practices. This paper proposes a novel framework for leveraging Artificial Intelligence (AI) avatars to deliver interactive, culturally responsive safe sleep education. By integrating generative synthetic media with community-specific health data, the proposed system aims to provide personalized, trustworthy, and accessible guidance to caregivers. We discuss the system architecture, including natural language processing (NLP) for misinformation detection and the use of embodied conversational agents (ECAs) to enhance user engagement. Early analysis indicates that AI avatars designed for specific cultures can greatly help people remember information and build trust in different communities, providing a way to effectively address the SIDS information crisis.
Supply Chain & Forecasting AI
The AI Supply-Chain Scenario Simulator is an intelligent decision-support system that enables supply-chain leaders to simulate disruption scenarios, forecast downstream impacts, and receive AI-generated mitigation recommendations in real time. The platform leverages advanced reasoning capabilities from Google Gemini to translate complex supply-chain data into explainable, business-ready insights.
Developer & Data Science Tools
The AI Data Engineering Copilot is an intelligent assistant designed to augment data engineers and data scientists across the entire data lifecycle—from ingestion and transformation to validation, optimization, and documentation. By leveraging advanced reasoning capabilities of Google Gemini, the copilot converts complex schemas, logs, and pipelines into actionable insights, optimized code, and business-friendly explanations.
Smart Research & Academic Assistant
The Smart Research & Academic Assistant is an AI-driven research support system designed to accelerate literature review, comparative analysis, and scholarly synthesis across large collections of academic papers, technical reports, and policy documents. Powered by Google Gemini, the assistant enables researchers to move beyond manual reading and summarization toward AI-guided insight discovery and research gap identification.
Creative + AI Reasoning: Multimodal Narrative Intelligence Engine
The Multimodal Narrative Intelligence Engine is a creative AI system that combines storytelling, reasoning, and interpretation to generate coherent, meaningful narratives from mixed inputs such as text prompts, images, diagrams, short videos, or structured data. Powered by Google Gemini, the system goes beyond content generation to explain why a narrative unfolds the way it does, making creativity transparent, interpretable, and adaptive.
AI Ethics & Bias Detection Tool (Powered by Google Gemini)
The AI Ethics & Bias Detection Tool is an intelligent governance platform designed to proactively identify ethical risks, bias, and compliance gaps in data, machine-learning models, and AI-driven decisions. Powered by advanced reasoning capabilities from Google Gemini, the system transforms complex technical and ethical assessments into explainable, auditable, and regulator-ready insights.
Rajkumar Kuppuswami
Applied Materials, Manager, Data scientist
Austin, Texas, United States
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