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biology

Mentors and Regional Facilitators
Name Region Skills Interests
diana Trotman CAREERS
Diana Toups Dugas RMACC, SWEETER, Campus Champions
Elie Alhajjar ACCESS CSSN
Jeffrey Weekley Campus Champions
Jason Wells ACCESS CSSN, Campus Champions
Amy Koshoffer Campus Champions
shuai liu ACCESS CSSN
Nicholas Danes Campus Champions, MINES
Nicholas Panchy Campus Champions
Rob Harbert Northeast
Xiaoqin Huang ACCESS CSSN
William Lai ACCESS CSSN
Users
Name Roles Skills Interests
Aidan McCrillis
student facilitator
Chitral Samala
student facilitator
diana Trotman
student facilitator
rcf
mentee
Emma Strand
student facilitator
Eren Ada
student facilitator
Gregory Ezike
student facilitator
Chris Hemme
researcher/educator
Hening Cui
student facilitator
Ifeoma Ugwuanyi
student facilitator
John Acheampong
student facilitator
Kenneth Acosta
student facilitator
Vinayak Mathur
researcher/educator
Lenore Martin
researcher/educator
Xiaoluo Jiao
student facilitator
Sanguthevar Ra…
researcher/educator
William Feng
student facilitator
Zoe Reich
student facilitator
Projects
Project Title Project Institution Project Owner Tags Status
Develop a data portal for the organization of molecular and imaging data University of Rhode Island -- Bay Campus Gaurav Khanna archiving, aws, bioinformatics, biology, Cloud, Data Storage, docker, genomics In Progress
Computational pipelines for the analysis of plastic-degrading genes University of Rhode Island Gaurav Khanna bioinformatics, biology, workflow, workforce-development In Progress
Development of personalized healthy food incentives to improve diet and cardiovascular risk University of Rhode Island Gaurav Khanna biology In Progress
High throughput Python pipeline to identify Horizontal Gene Transfer Cabrini University Vinayak Mathur bioinformatics, biology, data-wrangling, genomics, github, python, workflow Halted
Metagenomic analysis to identify gene clusters associated with IgA production SUNY Upstate Medical University Joel Wilmore bioinformatics, biology, genomics, python, r, research-facilitation Finishing Up
Simulating 21st century boreal forests and fire with a state-of-the-art process-based model Cary Institute Winslow Hansen biology, cluster-support, dependencies, deployment, netcdf, performance-tuning, r Finishing Up
Assembly and Taxonomic Profiling of Metagenomic Sequences using Deep Learning University of Rhode Island Gaurav Khanna ai, bioinformatics, biology, deep-learning, gpu, hardware, machine-learning, neural-networks, python Complete
Host-symbiont population genomics - analyzing the intraspecific variability of methanogenic archaeal endosymbionts of genus Methanocorpusculum hosted by a marine anaerobic ciliate Metopus sp. (Metopida) University of Rhode Island -- Bay Campus Gaurav Khanna bioinformatics, biology, genomics Complete
Detecting Covid-19 Misinformation on Social Media Bryant University Gaurav Khanna ai, bash, batch-jobs, big-data, biology, cuda Complete
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Announcements

Title Date
Ookami Webinar 02/14/24
Open Call: Minisymposia for PASC24 10/05/23

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Engagements

Run Markov Chain Monte Carlo (MCMC) in Parallel for Evolutionary Study
Texas Tech University

My ongoing project is focused on using species trait value (as data matrices) and its corresponding phylogenetic relationship (as a distance matrix) to reconstruct the evolutionary history of the smoke-induced seed germination trait. The results of this project are expected to increase the predictability of which untested species could benefit from smoke treatment, which could promote germination success of native species in ecological restoration. This computational resources allocated for this project pull from the high-memory partition of our Ivy cluster of HPCC (Centos 8, Slurm 20.11, 1.5 TB memory/node, 20 core /node, 4 node). However, given that I have over 1300 species to analyze, using the maximum amount of resources to speed up the data analysis is a challenge for two reasons: (1) the ancestral state reconstruction (the evolutionary history of plant traits) needs to use the Markov Chain Monte Carlo (MCMC) in Bayesian statistics, which runs more than 10 million steps and, according to experienced evolutionary biologists, could take a traditional single core simulation up 6 months to run; and (2) my data contain over 1300 native species, with about 500 polymorphic points (phylogenetic uncertainty), which would need a large scale of random simulation to give statistical strength. For instance, if I use 100 simulations for each 500 uncertainty points, I would have 50,000 simulated trees. Based on my previous experience with simulations, I could design codes to parallel analyze 50,000 simulated trees but even with this parallelization the long run MCMC will still require 50000 cores to run for up to 6 months. Given this computational and evolutionary research challenge, my current work is focused on discovering a suitable parallelization methods for the MCMC steps. I hope to have some computational experts to discuss my project.

Status: In Progress

People with Expertise

Vinayak Mathur

Cabrini University

Programs

CAREERS

Roles

researcher/educator

Expertise

Ana Ayala Pazzi

Programs

ACCESS CSSN

Roles

student-facilitator

Placeholder headshot

Expertise

Eren Ada

University of Rhode Island

Programs

CAREERS

Roles

student-facilitator

Expertise

People with Interest

Tejasvi Munge

New Jersey Institute of Technology

Programs

CAREERS

Roles

student-facilitator

Placeholder headshot

Interests

Kaitlyn Varela

Programs

ACCESS CSSN

Roles

student-facilitator

Photo of Kaitlyn Varela

Interests

William Lai

Cornell University

Programs

ACCESS CSSN

Roles

mentor, researcher/educator, research computing facilitator, research software engineer, ci systems engineer, Consultant

William KM Lai

Interests