Author Archives: Byung-Jun Yoon

About Byung-Jun Yoon

I am an Associate Professor at Texas A&M University, Department of Electrical & Computer Engineering, and a Scientist at Brookhaven National Laboratory, Computational Science Initiative. My research is focused on machine learning and signal processing theories, models, and algorithms for various scientific applications, primarily bioinformatics and computational biology.

TRIMER – a modeling and simulation framework for integrated analysis of transcription regulation and metabolic regulation

Our recent collaborative work on integrated modeling of transcription regulation and metabolic regulation has been published in iScience, an open access interdisciplinary journal from Cell Press.

Puhua Niu, Maria J. Soto, Byung-Jun Yoon, Edward R. Dougherty, Francis J. Alexander, Ian Blaby, Xiaoning Qian, “TRIMER: Transcription Regulation Integrated with MEtabolic Regulation,” iScience, doi:10.1016/j.isci.2021.103218.

In this work, we have developed a modeling and simulation pipeline enabling the analysis of Transcription Regulation Integrated with MEtabolic Regulation: TRIMER. TRIMER utilizes a Bayesian network (BN) inferred from transcriptome data to model the transcription factor regulatory network (TRN). TRIMER then infers the probabilities of the gene states relevant to the metabolism of interest, and predicts the metabolic fluxes and their changes that result from the deletion of one or more transcription factors. In this study, we demonstrate that TRIMER yields accurate predictions of how the knockout of one or more TFs affect the metabolic outcomes, outperforming existing state-of-the-art approaches based on both simulated as well as real experimental data from gene knockout experiments.

Overview of the TRIMER modeling and simulation pipeline.

iScience is a new open-access journal from Cell Press that provides a platform for original research in the life, physical, and earth sciences. The primary criterion for publication in iScience is a significant contribution to a relevant field combined with robust results and underlying methodology. The advances appearing in iScience include both fundamental and applied investigations across this interdisciplinary range of topic areas.

Dr. Yoon’s DOE-funded project on scientific data reduction featured on TAMU ECE website

Dr. Yoon’s project on “Objective-Driven Data Reduction for Scientific Workflows” recently funded by the U.S. Department of Energy (DOE), Advanced Scientific Computing Research (ASCR), has been featured in a recent article posted on the Texas A&M Department of Electrical and Computer Engineering website.

The full article can be accessed at the link below:

DOE-funded research project to efficiently reduce massive scientific data

DOE funds research on “Objective-based Data Reduction for Scientific Workflows” to tame massive data sets to advance scientific discovery

The following news release was issued by the U.S. Department of Energy. It announces funding for nine projects that will address management and processing of massive data sets produced by scientific observatories, experimental facilities, and supercomputers that span the DOE national laboratory complex.

As part of this program, Brookhaven Lab was awarded $2.4 million in funding over three years. Dr. Byung-Jun Yoon of Brookhaven National Lab‘s Computational Science Initiative will lead the project with Texas A&M University and University of Illinois Urbana-Champaign as partners. Dr. Yoon and his team’s work will involve using novel theoretical strategies to develop practical algorithms anchored in scientific objectives – i.e., focused specifically on the goals of interest. These algorithms would bypass scientifically irrelevant information, which could considerably reduce overall data generated by complex experimental or computational systems and streamline any required processing.

Further information and the full announcement can be found at the link below:

New York Scientific Data Summit (NYSDS 2021) will be held virtually on Oct 26-29, 2021

The seventh annual New York Scientific Data Summit, NYSDS 2021, is a multi-day interactive virtual event that will focus on Applied Mathematics, HPC, and AI challenges in diverse areas, such as climate science, medical research, critical infrastructure and other cross-cutting research topics.

Register online now at There is no registration fee, but registration is required for attendance. Registration deadline is Thursday, October 21, 2021. The virtual event will be held on Tuesday, October 26 through Friday, October 29, 2021.

NYSDS will provide a great opportunity to connect with researchers, developers, and end-users to inform efforts that will drive data research forward with new ideas and partnerships.

Dr. Yoon to co-organize COVID-19 themed Data Hackathon event at the 2021 IEEE Healthcare Summit (IHS)

Dr. Yoon is co-organizing a Data Hackathon event at the 2021 IEEE Healthcare Summit (IHS). The Data Hackathon focuses on COVID-19 Datasets with the goal of demonstrating the potentials of Biomedical Health Informatics (BHI) and Artificial Intelligence (AI) to combat pandemics.

The Data Hackathon features a wide range of datasets and challenges, which include:

Individuals or teams interested in participating in the Hackathon should register by Sep. 15, 2021 and submit their report and code by Sep. 26, 2021. Many awards sponsored by IEEE and other sponsoring institutions will be given to the top performing participants.

For more information about the Data Hackathon, please visit:

To learn more about the IEEE Healthcare Summit, please visit:

IEEE COVID-19 Data Hackathon Event will be held at 2021 IEEE Healthcare Summit (IHS)

We are happy to announce that a Data Hackathon event will be organized at the First IEEE Healthcare Summit (IHS) – a virtual event that will be held on Oct. 4-7, 2021.

The main focus of the Data Hackathon will be on integrating the latest advances in Biomedical Health Informatics and AI (artificial intelligence) to combat COVID-19 pandemics. Multiple challenges based on various datasets – including single cell RNA sequencing data, CT imaging data, audio data, public health data, NLP (natural language processing) data, and others – will be provided, where participants can choose the challenge and dataset based on their interest and expertise.

Teams/individuals interested in participating in the Data Hackathon should register by the registration deadline (Sep. 15, 2021). Many awards – sponsored by IEEE as well as other individual institutions sponsoring the event – will be given to top performing teams/individuals.

For further information and the latest updates about the Data Hackathon, please visit:

The first IEEE Healthcare Summit will be held virtually on Oct. 4-7, 2021

The first annual IEEE Healthcare Summit will be a virtual event held on 4-7 October 2021. This event aims to report progress made in the fight against COVID-19, and in better preparations for future pandemics, through the integration of Artificial Intelligence (AI) methods and tools with Biomedical and Health Informatics (BHI):

  • Translational bioinformatics: use genomics and proteomics to do SARS-CoV-2 subtyping, and build tools to develop targeted vaccine or drug, to do early screening to limit outbreak, and to perform evolution trajectory prediction;
  • Sensor informatics: use real-time wearable sensor data to monitor asymptomatic and mild-symptom home-based patients, to treat severe-symptom patients in hospitals, and COVID-related sequelae (e.g. neurological and cardiac disease);
  • Imaging informatics: use CT, X-rays, MRI, ultrasound, and other imaging modalities with RT-PCR data to improve diagnosis, prognosis, and monitoring of patients with COVID-19, or other infectious diseases in the event of future pandemics;
  • Clinical informatics: use multimodal data to find effective clinical care workflows for critically ill patients, to track the occurrence of COVID-related sequelae for long term follow-up, and to perform risk assessment and decision making;
  • Public health informatics: use epidemiological models to analyze outbreak data for supporting population health management, resource supply chains, and future care preparation;
  • Mental health informatics: collect and analyze mental health data during the pandemic to model behavior changes caused by the pandemic, and to evaluate the consequences of policies for future preparedness;
  • Mixed VR/AR and Robotics: integrate BHI with VR, AR, and robotics to effectively visualize omic, imaging, sensor, and population pandemic data, to train medical robots, and to assist public health policy making for preparedness; and
  • Fairness and ethics: use pandemic data to identify regional, racial, and ethnic disparities in infection and vaccination rates and the causal factors of the disparities, for the preparedness of future pandemics and fair resource allocation.

For further information regarding the first IEEE Healthcare Summit, please visit the following website:

Knowledge Guided Machine Learning (KGML2021) Workshop – Call for Posters

The 2nd Workshop on Knowledge Guided Machine Learning (KGML2021) is inviting researchers working in relevant fields to share their latest research findings, including work in progress.

KGML2021 is a workshop that brings together data scientists – researchers in data mining, machine learning, and statistics – and researchers from hydrology, atmospheric science, aquatic sciences, and translational biology to discuss challenges, opportunities, and early progress in bringing scientific knowledge to machine learning.

KGML2021 is a virtual conference from August 9-11 and welcomes submissions from researchers around the world.

For more information, please visit:

Dr. Yoon has been elected as the Treasurer of the ACM SIGBio for the term 2021 July – 2024 June

Dr. Byung-Jun Yoon has been elected as the Treasurer of the ACM Special Interest Group on Bioinformatics, Computational Biology, and Biomedical Informatics (SIGBio):

[See the 2021 ACM SIGBio Election Results]

The ACM SIGBio bridges computer science, mathematics, statistics with biology and biomedicine. The mission of ACM SIGBio is to improve our ability to develop advanced research, training, and outreach in Bioinformatics, Computational Biology, and Biomedical Informatics by stimulating interactions among researchers, educators and practitioners from related multi-disciplinary fields.

Dr. Yoon’s term as the Treasurer will be from July 1, 2021 until June 30, 2024. For more information about ACM SIGBio, please visit

How does model uncertainty affect multi-objective optimization?

Various real-world applications involve modeling complex systems with immense uncertainty and optimizing multiple objectives based on the uncertain model. Being able to quantify the impact of such model uncertainty on the operational objectives of interest is critical, for example, to design optimal experiments that can most effectively reduce the uncertainty that affect the objectives pertinent to the application at hand. In fact, such objective-based uncertainty quantification (objective-UQ) has been shown to be much more efficient for optimal experimental design (OED) compared to other approaches that do not explicitly aim at reducing the “uncertainty that actually matters”.

The concept of MOCU (mean objective cost of uncertainty) provides an effective means to quantify this objective uncertainty, but its original definition was limited to the case of single objective operations.

In our recent paper, we extend the original MOCU to propose the mean multi-objective cost of uncertainty (multi-objective MOCU), which can be used for objective-based quantification of uncertainty for complex uncertain systems considering multiple operational objectives:

Byung-Jun Yoon, Xiaoning Qian, Edward R. Dougherty, “Quantifying the multi-objective cost of uncertainty“, IEEE Access, vol.9, pp. 80351-80359, 2021, doi: 10.1109/ACCESS.2021.3085486.

Based on several examples, we illustrate the concept of multi-objective MOCU and demonstrate its efficacy in quantifying the operational impact of model uncertainty when there are multiple, possibly competing, objectives.

For more information regarding the concept of objective-UQ, optimal experimental design (OED), and other relevant resources, please visit:

The multi-objective MOCU quantifies the expected performance gap between the robust multi-objective operator that needs to be used to main good performance in the presence of model uncertainty and the optimal multi-objective operator for the true (but unknown) model.