16 citations · 65 across the 11 of their papers we have counts for
13 papers
Literature-based Discovery for Landscape Planning
David Marasco, Ilya Tyagin, Justin Sybrandt +2
This project demonstrates how medical corpus hypothesis generation, a knowledge discovery field of AI, can be used to derive new research angles for landscape and urban planners. T…
SmartChoices: Augmenting Software with Learned Implementations
Daniel Golovin, Gabor Bartok, Eric Chen +7
In many software systems, heuristics are used to make decisions - such as cache eviction, task scheduling, and information presentation - that have a significant impact on overall…
Accelerating COVID-19 research with graph mining and transformer-based learning
Ilya Tyagin, Ankit Kulshrestha, Justin Sybrandt +3
In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect…
Accelerating Text Mining Using Domain-Specific Stop Word Lists
Farah Alshanik, Amy Apon, Alexander Herzog +2
Text preprocessing is an essential step in text mining. Removing words that can negatively impact the quality of prediction algorithms or are not informative enough is a crucial st…
Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization
Fei Ding, Xiaohong Zhang, Justin Sybrandt +1
Graph representation learning based on graph neural networks (GNNs) can greatly improve the performance of downstream tasks, such as node and graph classification. However, the gen…
CBAG: Conditional Biomedical Abstract Generation
Justin Sybrandt, Ilya Safro
Biomedical research papers use significantly different language and jargon when compared to typical English text, which reduces the utility of pre-trained NLP models in this domain…
AGATHA: Automatic Graph-mining And Transformer based Hypothesis generation Approach
Justin Sybrandt, Ilya Tyagin, Michael Shtutman +1
Medical research is risky and expensive. Drug discovery, as an example, requires that researchers efficiently winnow thousands of potential targets to a small candidate set for mor…
Hypergraph Partitioning With Embeddings
Justin Sybrandt, Ruslan Shaydulin, Ilya Safro
Problems in scientific computing, such as distributing large sparse matrix operations, have analogous formulations as hypergraph partitioning problems. A hypergraph is a generaliza…
FOBE and HOBE: First- and High-Order Bipartite Embeddings
Justin Sybrandt, Ilya Safro
Typical graph embeddings may not capture type-specific bipartite graph features that arise in such areas as recommender systems, data visualization, and drug discovery. Machine lea…
Are Abstracts Enough for Hypothesis Generation?
Justin Sybrandt, Angelo Carrabba, Alexander Herzog +1
The potential for automatic hypothesis generation (HG) systems to improve research productivity keeps pace with the growing set of publicly available scientific information. But as…
Large-Scale Validation of Hypothesis Generation Systems via Candidate Ranking
Justin Sybrandt, Michael Shtutman, Ilya Safro
The first step of many research projects is to define and rank a short list of candidates for study. In the modern rapidity of scientific progress, some turn to automated hypothesi…
To Agile, or not to Agile: A Comparison of Software Development Methodologies
Ruslan Shaydulin, Justin Sybrandt
Since the Agile Manifesto, many organizations have explored agile development methods to replace traditional waterfall development. Interestingly, waterfall remains the most widely…
MOLIERE: Automatic Biomedical Hypothesis Generation System
Justin Sybrandt, Michael Shtutman, Ilya Safro
Hypothesis generation is becoming a crucial time-saving technique which allows biomedical researchers to quickly discover implicit connections between important concepts. Typically…