6 citations · 21 across the 29 of their papers we have counts for
24 papers · 1 filter
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…
Towards Automated Patent Workflows: AI-Orchestrated Multi-Agent Framework for Intellectual Property Management and Analysis
Sakhinana Sagar Srinivas, Vijay Sri Vaikunth, Venkataramana Runkana
Patents are the currency of innovation, and like any currency, they need to be managed and protected (Gavin Potenza). Patents, as legal documents that secure intellectual property…
Sparks of Artificial General Intelligence(AGI) in Semiconductor Material Science: Early Explorations into the Next Frontier of Generative AI-Assisted Electron Micrograph Analysis
Sakhinana Sagar Srinivas, Geethan Sannidhi, Sreeja Gangasani +2
Characterizing materials with electron micrographs poses significant challenges for automated labeling due to the complex nature of nanomaterial structures. To address this, we int…
EMCNet : Graph-Nets for Electron Micrographs Classification
Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana
Characterization of materials via electron micrographs is an important and challenging task in several materials processing industries. Classification of electron micrographs is co…
Retrieval-Augmented Instruction Tuning for Automated Process Engineering Calculations : A Tool-Chaining Problem-Solving Framework with Attributable Reflection
Sagar Srinivas Sakhinana, Geethan Sannidhi, Venkataramana Runkana
The current technology landscape lacks a foundational AI model for solving process engineering calculations. In this work, we introduce a novel autonomous agent framework leveragin…
Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis
Sakhinana Sagar Srinivas, Geethan Sannidhi, Venkataramana Runkana
We present a novel framework for analyzing and interpreting electron microscopy images in semiconductor manufacturing using vision-language instruction tuning. The framework employ…
Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis
Sakhinana Sagar Srinivas, Chidaksh Ravuru, Geethan Sannidhi +1
Semiconductors, crucial to modern electronics, are generally under-researched in foundational models. It highlights the need for research to enhance the semiconductor device techno…
Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning
Sakhinana Sagar Srinivas, Venkataramana Runkana
In the field of chemistry, the objective is to create novel molecules with desired properties, facilitating accurate property predictions for applications such as material design a…
Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning
Sakhinana Sagar Srinivas, Chidaksh Ravuru, Geethan Sannidhi +1
Spatio-temporal forecasting plays a crucial role in various sectors such as transportation systems, logistics, and supply chain management. However, existing methods are limited by…
Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering
Sagar Srinivas Sakhinana, Geethan Sannidhi, Venkataramana Runkana
In the chemical and process industries, Process Flow Diagrams (PFDs) and Piping and Instrumentation Diagrams (P&IDs) are critical for design, construction, and maintenance. Recent…
Hierarchical Network Fusion for Multi-Modal Electron Micrograph Representation Learning with Foundational Large Language Models
Sakhinana Sagar Srinivas, Geethan Sannidhi, Venkataramana Runkana
Characterizing materials with electron micrographs is a crucial task in fields such as semiconductors and quantum materials. The complex hierarchical structure of micrographs often…
Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings
Sagar Srinivas Sakhinana, Geethan Sannidhi, Chidaksh Ravuru +1
Spatio-temporal forecasting is crucial in transportation, logistics, and supply chain management. However, current methods struggle with large, complex datasets. We propose a dynam…
Preliminary Investigations of a Multi-Faceted Robust and Synergistic Approach in Semiconductor Electron Micrograph Analysis: Integrating Vision Transformers with Large Language and Multimodal Models
Sakhinana Sagar Srinivas, Geethan Sannidhi, Sreeja Gangasani +2
Characterizing materials using electron micrographs is crucial in areas such as semiconductors and quantum materials. Traditional classification methods falter due to the intricate…
Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving
Sakhinana Sagar Srinivas, Vijay Sri Vaikunth, Venkataramana Runkana
We present the Process Engineering Operations Assistant (PEOA), an AI-driven framework designed to solve complex problems in the chemical and process industries. The framework empl…
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption
Sakhinana Sagar Srinivas, Chidaksh Ravuru, Geethan Sannidhi +1
Semiconductor imaging and analysis are critical yet understudied in deep learning, limiting our ability for precise control and optimization in semiconductor manufacturing. We intr…
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…
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…
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…
Hypergraph Learning based Recommender System for Anomaly Detection, Control and Optimization
Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana
Anomaly detection is fundamental yet, challenging problem with practical applications in industry. The current approaches neglect the higher-order dependencies within the networks…
Vision HgNN: An Electron-Micrograph is Worth Hypergraph of Hypernodes
Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Sreeja Gangasani +1
Material characterization using electron micrographs is a crucial but challenging task with applications in various fields, such as semiconductors, quantum materials, batteries, et…
Agentic Retrieval-Augmented Generation for Time Series Analysis
Chidaksh Ravuru, Sagar Srinivas Sakhinana, Venkataramana Runkana
Time series modeling is crucial for many applications, however, it faces challenges such as complex spatio-temporal dependencies and distribution shifts in learning from historical…
Retrieval-Augmented Generation Meets Data-Driven Tabula Rasa Approach for Temporal Knowledge Graph Forecasting
Geethan Sannidhi, Sagar Srinivas Sakhinana, Venkataramana Runkana
Pre-trained large language models (PLLMs) like OpenAI ChatGPT and Google Gemini face challenges such as inaccurate factual recall, hallucinations, biases, and future data leakage f…
Crossing New Frontiers: Knowledge-Augmented Large Language Model Prompting for Zero-Shot Text-Based De Novo Molecule Design
Sakhinana Sagar Srinivas, Venkataramana Runkana
Molecule design is a multifaceted approach that leverages computational methods and experiments to optimize molecular properties, fast-tracking new drug discoveries, innovative mat…
Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation
Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana
Battery life estimation is critical for optimizing battery performance and guaranteeing minimal degradation for better efficiency and reliability of battery-powered systems. The ex…