Talks and presentations

Modeling quantitative trait locus effects through gene regulatory networks

July 12, 2022

Chalk Talk, Computational Genomics Summer Institute, University of California Los Angeles, Los Angeles CA, USA

I attended the first short program of the Computational Genomics Summer Institute (CGSI) and gave a chalk talk describing a technique to simulate gene regulatory networks (GRNs) with realistic structure, function, and expression data.

Modeling quantitative trait locus effects through gene regulatory networks

June 23, 2022

Poster, National Library of Medicine Training Informatics Conference, University of Buffalo, Buffalo NY, USA

Preliminary description of methods to simulate gene regulatory networks (GRNs) with realistic structure, function, and expression data, with early results to connect the statistical genetic basis of complex traits in context of GRNs. Presented as a poster at the “National Library of Medicine Training Informatics Conference” (NLM-ITC) hosted by the University of Buffalo – more information about the training conference can be found here.

Simulating systemic effects of expression quantitative trait loci across gene regulatory networks

June 08, 2022

Poster, Population, Evolutionary, and Quantitative Genetics Conference, Asilomar Conference Grounds, Pacific Grove CA, USA

Preliminary description of methods to simulate gene regulatory networks (GRNs) with realistic structure, function, and expression data, with early results to connect the statistical genetic basis of complex traits in context of GRNs. Presented as a poster at the “Population, Evolutionary, and Quantitative Genetics Conference” (PEQG 2022), hosted by the Genetics Society of America (GSA).

Simulating realistic gene regulatory networks and expression data

November 15, 2021

Talk, Microsoft Research PhD Fellowship Research Showcase, Virtual (Microsoft Research)

Preliminary description of methods to simulate gene regulatory networks (GRNs) with realistic structure, function, and expression data, with early results with implications for GRN structure inference tasks and the statistical basis of complex trait heritability. Given as a talk to Microsoft Research via the “Microsoft Research PhD Fellowship Research Showcase”, which highlights research conducted by the Microsoft Research PhD Fellows.

A deep learning classifier for local ancestry inference

June 23, 2021

Poster, National Library of Medicine Training Informatics Conference, Virtual (University of Washington)

Preliminary results towards developing a deep convolutional neural network to predict local ancestry tracts in whole-genome sequence data. Presented as a poster at the “National Library of Medicine Training Informatics Conference” (NLM-ITC) hosted by the University of Washington – more information about the training conference can be found here. This project has been posted as an arXiv preprint.

A deep learning classifier for local ancestry inference

December 11, 2020

Poster, Conference on Neural Information Processing Systems (NeurIPS), Virtual

Preliminary results towards developing a deep convolutional neural network to predict local ancestry tracts in whole-genome sequence data. Presented as a poster at the “Learning Meaningful Representations of Life” (LMRL) workshop. Now posted as an arXiv preprint.

A deep learning classifier for local ancestry inference

October 27, 2020

Poster, Annual meeting of the American Society of Human Genetics (ASHG), Virtual

Preliminary results towards developing a deep convolutional neural network to predict local ancestry tracts in whole-genome sequence data. Selected as a reviewer’s choice abstract (scored by reviewers in top 10% of all poster abstracts), and presented as a Poster Talk. Now posted as an arXiv preprint.