1 citations · 3 across the 11 of their papers we have counts for
16 papers
Trustworthy Domain-Specific AI for Structured Knowledge Retrieval and Reasoning
Ryan C. Barron
This dissertation presents a scalable architecture for transforming unstructured, domain-specific text into structured knowledge for retrieval and reasoning. It integrates semi-aut…
Historical Anchors for Scientific Forecasting on Human-Aware Knowledge Graphs
Siu Tin Wu, Ryan C. Barron, Phoebe Upton Sanderbeck +3
Predicting which scientific connections will form next can point researchers toward the most promising leads. One successful approach learns representations by sampling paths throu…
Generated Query Expansion Still Helps Strong Sparse Retrieval: A Controlled Study with SPLADE-v3
Ryan C. Barron, Cade W. Trotter, Maksim E. Eren +3
Scientific queries are often brief, while relevant papers use specialized vocabulary. Generated query expansion can bridge this mismatch, but earlier work suggests that its value s…
Identity or Prompt Noise? A Calibrated Invariance Audit of LLM Code Generation
Maksim E. Eren, Ryan Barron, Eric Michalak +2
Identity cues are irrelevant to a fixed programming specification, but raw counterfactual differences can arise from unequal samples and prompt wording. We audit 30.73 million exec…
Occupational Prompting Reveals Cultural Bias in Large Language Models
Maksim E. Eren, Andrea Brennen, Ryan C. Barron +1
Social roles shape expectations, priorities, and judgments, yet it remains unclear how large language models (LLMs) associate occupational identities with broader cultural value pa…
Topic Modeling and Link-Prediction for Material Property Discovery
Ryan C. Barron, Maksim E. Eren, Valentin Stanev +2
Link prediction infers missing or future relations between graph nodes, based on connection patterns. Scientific literature networks and knowledge graphs are typically large, spars…
Matrix Factorization for Inferring Associations and Missing Links
Ryan Barron, Maksim E. Eren, Duc P. Truong +4
Missing link prediction is a method for network analysis, with applications in recommender systems, biology, social sciences, cybersecurity, information retrieval, and Artificial I…
Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization
Ryan C. Barron, Maksim E. Eren, Olga M. Serafimova +2
Agentic Generative AI, powered by Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG), Knowledge Graphs (KGs), and Vector Stores (VSs), represents a transformati…
HEAL: Hierarchical Embedding Alignment Loss for Improved Retrieval and Representation Learning
Manish Bhattarai, Ryan Barron, Maksim Eren +8
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external document retrieval to provide domain-specific or up-to-date knowledge. The effect…
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization
Ryan C. Barron, Ves Grantcharov, Selma Wanna +8
Large Language Models (LLMs) are pre-trained on large-scale corpora and excel in numerous general natural language processing (NLP) tasks, such as question answering (QA). Despite…
TopicTag: Automatic Annotation of NMF Topic Models Using Chain of Thought and Prompt Tuning with LLMs
Selma Wanna, Ryan Barron, Nick Solovyev +4
Topic modeling is a technique for organizing and extracting themes from large collections of unstructured text. Non-negative matrix factorization (NMF) is a common unsupervised app…
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection
Ryan Barron, Maksim E. Eren, Manish Bhattarai +3
In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as non-negative matrix factorization (NMF), RESCAL, and K-Means clustering, users must sel…
Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization
Ryan Barron, Maksim E. Eren, Manish Bhattarai +6
Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document o…
Catch'em all: Classification of Rare, Prominent, and Novel Malware Families
Maksim E. Eren, Ryan Barron, Manish Bhattarai +5
National security is threatened by malware, which remains one of the most dangerous and costly cyber threats. As of last year, researchers reported 1.3 billion known malware specim…
Interactive Distillation of Large Single-Topic Corpora of Scientific Papers
Nicholas Solovyev, Ryan Barron, Manish Bhattarai +3
Highly specific datasets of scientific literature are important for both research and education. However, it is difficult to build such datasets at scale. A common approach is to b…
Robust Adversarial Defense by Tensor Factorization
Manish Bhattarai, Mehmet Cagri Kaymak, Ryan Barron +3
As machine learning techniques become increasingly prevalent in data analysis, the threat of adversarial attacks has surged, necessitating robust defense mechanisms. Among these de…