collaborators

6 papers

cs.LG2025

AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up

Sakhinana Sagar Srinivas, Shivam Gupta, Venkataramana Runkana

Recent advances in generative AI have accelerated the discovery of novel chemicals and materials. However, scaling these discoveries to industrial production remains a major bottle…

cs.LG2025

Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework

Sakhinana Sagar Srinivas, Akash Das, Shivam Gupta +1

The growing use of foundation models (FMs) in real-world applications demands adaptive, reliable, and efficient strategies for dynamic markets. In the chemical industry, AI-discove…

cs.LG2024

Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design

Sakhinana Sagar Srinivas, Akash Das, Shivam Gupta +1

Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (PIDs) are critical tools for industrial process design, control, and safety. However, the generation of preci…

cs.LG2024

Joint Hypergraph Rewiring and Memory-Augmented Forecasting Techniques in Digital Twin Technology

Sagar Srinivas Sakhinana, Krishna Sai Sudhir Aripirala, Shivam Gupta +1

Digital Twin technology creates virtual replicas of physical objects, processes, or systems by replicating their properties, data, and behaviors. This advanced technology offers a…

cs.LG2024

Multi-Knowledge Fusion Network for Time Series Representation Learning

Sagar Srinivas Sakhinana, Shivam Gupta, Krishna Sai Sudhir Aripirala +1

Forecasting the behaviour of complex dynamical systems such as interconnected sensor networks characterized by high-dimensional multivariate time series(MTS) is of paramount import…

cs.LG2024

Multi-Source Knowledge-Based Hybrid Neural Framework for Time Series Representation Learning

Sagar Srinivas Sakhinana, Krishna Sai Sudhir Aripirala, Shivam Gupta +1

Accurately predicting the behavior of complex dynamical systems, characterized by high-dimensional multivariate time series(MTS) in interconnected sensor networks, is crucial for i…