My Research – Jenna Landy

This is a rolling list of my research projects. I’m broadly interested in computational approaches to understanding molecular biology and mutational processes in cancer. My work draws from unsupervised learning, multi-study and ensemble learning, deep learning, and Bayesian modeling and computation. Many of my current projects explore various aspects of mutational signatures analysis, which models mutational processes in cancer genomes using latent factors.

Some of these are tagged as in-progress, and this list will be updated regularly. Please reach out to me if our research interests align and you would like to chat!

bayesNMF: Fast Bayesian Poisson NMF with Automatically Learned Rank Applied to Mutational Signatures

bayesian computation

mutational signatures

genomics

Landy, Jenna M., Nishanth Basava, and Giovanni Parmigiani. “bayesNMF: Fast Bayesian Poisson NMF with Automatically Learned Rank Applied to Mutational Signatures.” arXiv…

Gridsemble: Selective Ensembling for False Discovery Rates

multiple hypothesis testing

genomics

Landy, Jenna M., and Parmigiani, Giovanni. “Gridsemble: Selective Ensembling for False Discovery Rates.” arXiv preprint arXiv:2401.12865 (2024).

A Transferable GNN for Perturb-Seq

IN PROGRESS

deep learning

genomics

CRISPR can be used to knock out a particular gene, or “perturb” it, so that it is no longer functional. Single cell RNA sequencing on perturbed and control cells…

Bayesian Causal Inference for Cancer Mutational Signatures

IN PROGRESS

mutational signatures

causal inference

bayesian computation

genomics

Mutational signatures analysis is a quickly growing field to model mutational processes in tumor genomes. Computationally-derived mutational signatures have been associated…

Deep Unfolding Bayesian Non-Negative Matrix Factorization

IN PROGRESS

deep learning

mutational signatures

genomics

Bayesian non-negative matrix factorization is a method used across fields including genomics, audio and signal processing, and neuroscience. The complexity of the posterior…

Part of Speech-Based Data Augmentation for Neural Machine Translation

deep learning

NLP

Data augmentation improves accuracy of ML models for natural language processing tasks, such as neural machine translation (NMT), by increasing the amount and variety of…

Studying Underlying Characteristics of Computing and Engineering Student Success (SUCCESS)

I was a statistical consultant research assistant on the SUCCESS project, a collaboration between Cal Poly San Luis Obispo and Purdue University. In this role, I analyzed…

Public GitHub Notebook Corpus Research

This was my independent research project as a data science intern at Amazon Web Services (AWS) in 2019. This work is available on Project Jupyter’s GitHub.