11 citations · 34 across the 22 of their papers we have counts for
29 papers
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…
Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection
Dorianis M. Perez, Maksim E. Eren, Bryan E. Kaiser
Malicious anomalous activity detection is a fundamental challenge for cyber security systems. Both tensor decomposition under statistical framework with CANDECOMP-PARAFAC alternati…
Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning
Cade W. Trotter, Maksim E. Eren, Justin C. Holmes +4
Radio-frequency (RF) monitoring is essential for space domain awareness, but it often generates large, variable, and sparsely populated datasets with few labels. These observations…
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…
Prompt Programming for Cultural Bias and Alignment of Large Language Models
Maksim Eren, Eric Michalak, Brian Cook +1
Culture shapes reasoning, values, prioritization, and strategic decision-making, yet large language models (LLMs) often exhibit cultural biases that misalign with target population…
Rethinking Science in the Age of Artificial Intelligence
Maksim E. Eren, Dorianis M. Perez
Artificial intelligence (AI) is reshaping how research is conceived, conducted, and communicated across fields from chemistry to biomedicine. This commentary examines how AI is tra…
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…
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs
Afia Anjum, Maksim E. Eren, Ismael Boureima +2
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing (NLP) tasks, such as question-answering,…
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…
Electrical Grid Anomaly Detection via Tensor Decomposition
Alexander Most, Maksim Eren, Nigel Lawrence +1
Supervisory Control and Data Acquisition (SCADA) systems often serve as the nervous system for substations within power grids. These systems facilitate real-time monitoring, data a…
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…
Semi-supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model Selection
Maksim E. Eren, Manish Bhattarai, Robert J. Joyce +3
Identification of the family to which a malware specimen belongs is essential in understanding the behavior of the malware and developing mitigation strategies. Solutions proposed…
MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware
Maksim E. Eren, Manish Bhattarai, Kim Rasmussen +2
Malware is one of the most dangerous and costly cyber threats to national security and a crucial factor in modern cyber-space. However, the adoption of machine learning (ML) based…
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?
Ran Liu, Maksim Eren, Charles Nicholas
With the increasing number and sophistication of malware attacks, malware detection systems based on machine learning (ML) grow in importance. At the same time, many popular ML mod…
SeNMFk-SPLIT: Large Corpora Topic Modeling by Semantic Non-negative Matrix Factorization with Automatic Model Selection
Maksim E. Eren, Nick Solovyev, Manish Bhattarai +3
As the amount of text data continues to grow, topic modeling is serving an important role in understanding the content hidden by the overwhelming quantity of documents. One popular…