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Danny Hoang

Headshot of Danny Hoang

I am currently a graduate researcher in the Intelligent Systems and Control Laboratory under Dr. Farhad Imani at the University of Connecticut. My research is in knowledge representation systems for advanced manufacturing systems incorporating knowledge graphs, multimodal large language models, and artificial intelligence.

Research

Peer-reviewed work and other publications.

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Hyperdimensional Computing for Explainable Information Fusion and Multi-Task Adaptation in Advanced Manufacturing

Information Fusion, 2025
This paper introduces MultiHd, a graph-based hyperdimensional computing framework that intrinsically integrates explainability, multi-task learning, and computational efficiency to overcome these limitations. By encoding multi-channel time series data into a structured graph, MultiHD captures interdependencies among signals using hyperdimensional representations, enabling computationally efficient parallel processing and rapid inference.

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Enabling Grounded Answers through Knowledge Graphs and Retrieval Augmented Generation

Ground Vehicle Systems Engineering and Technology Symposium, 2025
This paper presents GraphLLM, integrating knowledge graphs with LLMs to extract relations, curb hallucinations, and improve technical answers, achieving 25% gains on LLaMA, supporting precise decisions in advanced manufacturing.

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Hierarchical Representation and Interpretable Learning for Accelerated Quality Monitoring in Machining Process

CIRP Journal of Manufacturing Science and Technology, 2024
This research introduces a novel graph-based hyper-dimensional computing framework that not only assesses work-piece quality on-edge in 5-axis CNC machining, but also characterizes the key signals vital for evaluating quality from in-situ multichannel data.

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Multi-Task Brain-Inspired Learning for Interlinking Machining Dynamics With Parts Geometrical Deviations

International Manufacturing Science and Engineering Conference, 2024
We introduce MTaskHD, a novel multi-task framework, that leverages hyperdimensional computing (HDC) to effortlessly fuse data from various channels and process signals while characterizing quality within a multi-task manufacturing operation.

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Edge Cognitive Data Fusion: From In-Situ Sensing to Quality Characterization in Hybrid Manufacturing Process

International Manufacturing Science and Engineering Conference, 2023
This paper introduces hyperdimensional computing (HDC) to fuse load, current, torque, command speed, control differential, power, and contour deviation which provides robust, sample-efficient, and explainable learning of quality characterization.

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Data Fusion Cognitive Computing for Characterization of Mechanical Property in Friction Stir Welding Process

International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2023
This research introduces hyperdimensional cognitive computing (HCC) that mimics human brain functionalities to fuse power, torque, and force data to provide robust, sample-efficient, and explainable learning for process-property characterization in friction stir welding.

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Contact

Reach me at danny.hoang@uconn.edu