This year, the 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB) – the flagship conference of the ACM SIGBio – will be held in Houston, TX, September 3-6, 2023. ACM-BCB is being organized for the fourteenth year, building upon the success of the first thirteen meetings in various locations, including Boston, Niagara Falls, Chicago, Orlando, Washington DC, Newport Beach, Atlanta, and Seattle.

As global interest in renewable energy continues to increase, there has been a pressing need for developing novel energy storage devices based on organic electrode materials that can overcome the shortcomings of the current lithium-ion batteries. One critical challenge for this quest is to find materials whose redox potential (RP) meets specific design targets.

In our recent study below, we proposed a computational framework for addressing this challenge:

Hyun-Myung Woo, Omar Allam, Junhe Chen, Seung Soon Jang, Byung- Jun Yoon, “Optimal high-throughput virtual screening pipeline for efficient selection of redox- active organic materials,” iScience (2023), doi: https://doi.org/10.1016/j.isci.2022.105735

Given a high-fidelity model for estimating the RP of a given material, we showed how a set of surrogate models with different accuracy and complexity may be designed to construct a highly accurate and efficient HTVS pipeline. The performance of the screening campaigns based on the constructed HTVS pipeline can be optimized by designing the optimal screening policy, which enables rapid screening of organic materials that satisfy the desired criteria. We demonstrated that the proposed HTVS pipeline construction and operation strategies can substantially enhance the overall screening throughput.

Further details of this study can be found in iScience.

The paper “Looking back on forward-looking COVID models”, in which Dr. Byung-Jun Yoon is a co-author, has been featured on the cover of the July 2022 issue of Patterns, a premium open access journal from Cell Press. This paper is an outcome of Dr. Yoon’s ongoing collaboration with COVID ACT NOW (CAN) – the COVID-focused U.S initiative of Act Now Coalition – and the Massachusetts General Hospital (MGH).

In this work, we presented the epidemiological model by Covid Act Now (CAN) and evaluated its performance by back-testing against historical data. For comparison, similar analyses were performed for several other COVID models and the obtained results were compared. It was found that all models generally captured the potential magnitude and directionality of the pandemic in the short term. While there are limitations to epidemiological models, understanding these limitations enables these models to be utilized as tools for “data-driven decision-making” in viral outbreaks.

The cover image is entitled “Face the Music”. The American idiom “face the music” means to accept consequences. It is thought to originate from an exhortation to face one’s stage fright. The sound waves in this image were created by superimposing Covid Act Now trend graphs representing cases, hospitalizations, ICU hospitalizations, and deaths in the United States since March 2020. This visualization represents the path of accepting the consequences of our actions and facing our fears in order to navigate the COVID pandemic: to “face the music.”

The paper can be accessed at the link below:

Paul Chong, Byung-Jun Yoon, Debbie Lai, Michael Carlson, Jarone Lee, Shuhan He, “Looking Back on Forward-Looking COVID Models,” Patterns, volume 3, issue 7, 100492, July 08, 2022, https://doi.org/10.1016/j.patter.2022.100492.

According to the National Spinal Cord Injury Statistical Center, approximately 18,000 new spinal cord injuries (SCI) occur each year in the United States. Spinal cord injuries often lead to serious constipation or incontinence, which can lead to decreased quality of life and may even be life-threatening. After a spinal cord injury, 41% of patients rated bowel dysfunction as a severe life-limiting problem.

Craig H. Neilsen Foundation recently announced that they will fund a new research project that aims to develop an optimal electrical stimulation method via reinforcement learning (RL) to help bowel dysfunction of spinal cord injury patients. In this project, Dr. Byung-Jun Yoon will collaborate with Dr. Hangue Park (PI, Texas A&M Electrical and Computer Engineering) and Dr. Cedric Geoffroy (Texas A&M College of Medicine) to develop a closed-loop stimulation scheme, which ultimately aims to improve the quality of life for SCI patients as well as their caregivers.

For more information, please visit:

https://engineering.tamu.edu/news/2022/06/ecen-electrical-stimulation-could-help-bowel-dysfunction-after-spinal-cord-injury.html

This week’s ACM Tech News has featured our recent work on Transfer Learning for Bayesian Error Estimation (TL-BEE):

https://technews.acm.org/archives.cfm?fo=2022-03-mar/mar-11-2022.html

This study has also received spotlight on the Department of Energy (DOE), Office of Science website:

https://www.energy.gov/science/office-science

The work has been recently published in Cell Press Patterns, and the full article can be accessed at the link below:

Omar Maddouri, Xiaoning Qian, Francis J. Alexander, Edward R. Dougherty, Byung-Jun Yoon, “Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning,” Patterns, 2022, DOI: https://doi.org/10.1016/j.patter.2021.100428

Recent work by Omar Maddouri, currently a Ph.D. candidate in the BioMLSP lab, on transfer learning for Bayesian error estimation has been featured in an article entitled “Doctoral student offers new insight into machine-learning error estimation”, which has been published on the Texas A&M College of Engineering website.

The article can be found at: https://engineering.tamu.edu/news/2022/03/ecen-doctoral-student-offers-new-insight-into-machine-learning-error-estimation.html

Our recent study on Bayesian error estimation via optimal Bayesian transfer learning has been published in Patterns, a premium open access journal from Cell Press that publishes ground-breaking original research across the full breadth of data science.

Omar Maddouri, Xiaoning Qian, Francis J. Alexander, Edward R. Dougherty, Byung-Jun Yoon, “Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning,” Patterns, DOI:https://doi.org/10.1016/j.patter.2021.100428.

Identifying robust diagnostic/prognostic biomarkers from gene expression data that can lead to accurate and reproducible predictions is a challenging problem.

In our recent paper “Deep graph representations embed network information for robust disease marker identification”, we show that deep graph representations using graph convolutional networks (GCNs) can identify effective markers that significantly improve the predictive performance and their reproducibility across different datasets and platforms.

Omar Maddouri, Xiaoning Qian, Byung-Jun Yoon, “Deep graph representations embed network information for robust disease marker identification,” Bioinformatics, btab772, https://doi.org/10.1093/bioinformatics/btab772.

We are happy to announce that our NeurIPS 2021 paper entitled “Efficient Active Learning for Gaussian Process Classification by Error Reduction” is now available in OpenReview.net: https://openreview.net/pdf?id=UK15Hj9qX6I

Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian, “Efficient Active Learning for Gaussian Process Classification by Error Reduction,” Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS 2021), Dec. 6 – 14, 2021.

We are happy to announce the availability of a new post-doc position in areas relevant to Scientific Machine Learning (SciML).

The position resides in the Applied Mathematics Group of the Computational Science Initiative (CSI) at Brookhaven National Laboratory (BNL), and the post-doctoral researcher will work with Dr. Byung-Jun Yoon and Dr. Nathan Urban on a project focused on scientific data reduction.

We invite outstanding candidates to apply for a post-doctoral research associate position in applied mathematics, machine learning, and scientific computing. This position offers a unique opportunity to conduct research in emerging interdisciplinary research problems at the intersection of applied mathematics, machine learning, and high-performance computing (HPC) with applications in diverse scientific domains of interest to BNL and the Department of Energy (DOE).

Topics of specific interest include:

• optimal decision-guided data reduction
• feature extraction/engineering in high-dimensional compositional workflows that involve machine learning (ML) models
• Bayesian inference and uncertainty quantification in scientific ML models
• learning/optimization of low-dimensional latent feature spaces for ML surrogates.

The position includes access to world-class HPC resources, such as the BNL Institutional Cluster and DOE leadership computing facilities. Access to these platforms will allow computing at scale and will ensure that the successful candidate will have the necessary resources to solve challenging DOE problems of interest.

This program provides full support for a period of two years at CSI with possible extension. Candidates must have received a doctorate (Ph.D.) in applied mathematics, statistics, computer science, or a related field (e.g., mathematics, engineering, operations research, physics) within the past five years. This post-doc position presents a unique chance to conduct interdisciplinary collaborative research in BNL programs with a highly competitive salary.

For further information, please visit: [post-doc position announcement]

For inquiries, contact Dr. Byung-Jun Yoon or Dr. Nathan Urban.