13 citations · 24 across the 8 of their papers we have counts for
10 papers
Structured Learning on Mapper Representations
George Babus, Farzana Nasrin
Modern machine learning (ML) methods are highly effective for prediction tasks, but many commonly used representations reduce complex data to fixed dimensional embeddings that may…
Stochastic Dynamics on Persistence Diagram Space via Reinforcement Learning
Farzana Nasrin
Persistence diagrams (PDs) provide stable and interpretable summaries of multiscale topological structure. While substantial progress has been made in the statistical analysis of P…
Bayesian Inference for Left-Truncated Log-Logistic Distributions for Time-to-event Data Analysis
Fahad Mostafa, Md Rejuan Haque, Md Mostafijur Rahman +1
Parameter estimation is a foundational step in statistical modeling, enabling us to extract knowledge from data and apply it effectively. Bayesian estimation of parameters incorpor…
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael Bronstein +19
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new fron…
Genomics Data Analysis via Spectral Shape and Topology
Erik J. Amézquita, Farzana Nasrin, Kathleen M. Storey +1
Mapper, a topological algorithm, is frequently used as an exploratory tool to build a graphical representation of data. This representation can help to gain a better understanding…
A Random Persistence Diagram Generator
Theodore Papamarkou, Farzana Nasrin, Austin Lawson +3
Topological data analysis (TDA) studies the shape patterns of data. Persistent homology is a widely used method in TDA that summarizes homological features of data at multiple scal…
Materials Fingerprinting Classification
Adam Spannaus, Kody J. H. Law, Piotr Luszczek +6
Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional c…
Bayesian Topological Learning for Classifying the Structure of Biological Networks
Vasileios Maroulas, Cassie Putman Micucci, Farzana Nasrin
Actin cytoskeleton networks generate local topological signatures due to the natural variations in the number, size, and shape of holes of the networks. Persistent homology is a me…
Bayesian Topological Learning for Brain State Classification
Farzana Nasrin, Christopher Oballe, David L. Boothe +1
Investigation of human brain states through electroencephalograph (EEG) signals is a crucial step in human-machine communications. However, classifying and analyzing EEG signals ar…
A Bayesian Framework for Persistent Homology
Vasileios Maroulas, Farzana Nasrin, Christopher Oballe
Persistence diagrams offer a way to summarize topological and geometric properties latent in datasets. While several methods have been developed that utilize persistence diagrams i…