Salvador Aguinaga's Projects
Machine intelligence
BigData Utilities
Works not yet published
Sense of Hearing: Screening and Assessment
Resources Limited Open Source Medical Tools
Documentation on X
Scientific literature Bibtex List
Notes and Opinion on Complex Systems in Biology
Bluetooth low energy Sensors Technology Software Development Kit (Android version)
Exploring human behavior: Modeling human navigation of information networks
Code release for the paper "Modeling Graphs with Vertex Replacement Grammars" by Sikdar et al.
Explorations into COVID and related infectious diseases
Access a current copy of my CV and/or Resume
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
SCI Challenge Project
Frienso iOS Nu
✍️Fusuma makes slides with Markdown easily.
GraMi is a novel framework for frequent subgraph mining in a single large graph, GraMi outperforms existing techniques by 2 orders of magnitudes. GraMi supports finding frequent subgraphs as well as frequent patterns, Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Also, GraMi supports user-defined structural and semantic constraints over the results, as well as approximate results. For more details, check our paper: Mohammed Elseidy, Ehab Abdelhamid, Spiros Skiadopoulos, and Panos Kalnis. GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph. PVLDB, 7(7):517-528, 2014.
GraphChi's C++ version. Big Data - small machine.
A framework for large-scale machine learning and graph computation.
Graph transformations, graph rewrites
Hyperedge Replacement Grammars Network Model
Exploring the limits of HRG for network modeling
Port of HTML5UP's Strata theme to Hugo
Hyperedge Replacement Grammars Generalized
Integrated Network Decomposition & Dynamic programming for Graph Optimization problems
Infinity Mirror Graph Test
Infinity Mirror Graph Comparison Test
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.