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: https://healthcaresummit.ieee.org

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: https://sites.google.com/umn.edu/kgmlworkshop/

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 http://www.sigbio.org

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: https://objectiveUQ.org

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.

The final version of our paper entitled “Physics-constrained Automatic Feature Engineering for Predictive Modeling in Materials Science” recently presented at the 35th AAAI Conference on Artificial Intelligence (AAAI-21) is now accessible at the link below:

Ziyu Xiang, Mingzhou Fan, Guillermo Vázquez Tovar, William Trehem, Byung-Jun Yoon, Xiaofeng Qian, Raymundo Arroyave, Xiaoning Qian, “Physics-constrained Automatic Feature Engineering for Predictive Modeling in Materials Science“, 35th AAAI Conference on artificial intelligence (AAAI-21), Feb. 2-9, 2021.

Automatic Feature Engineering (AFE) aims to extract useful knowledge for interpretable predictions given data for the machine learning tasks of interest. In this work, we presented a novel AFE scheme that effectively extracts relationships from data that can be interpreted based on functional formulas to discover their “physical meaning” or “new hypotheses”. Here we focused on materials science applications, where interpretable predictive modeling may enhance our understanding of materials systems and also guide the discovery of new materials.

Typically, it is computationally prohibitive to exhaustively explore all potential relationships to identify interpretable and predictive features. We overcome this challenge by designing an AFE strategy that efficiently explores a feature generation tree (FGT) using a deep Q-network (DQN) for scalable and efficient exploration of the feature space in an automated manner. We demonstrate that our proposed DQN-based AFE strategy yields promising results when benchmarked against existing AFE methods based on several materials science datasets.

We are happy to announce that our paper entitled “Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations,” has now been published in IEEE Access.

Youngjoon Hong, Bongsuk Kwon, and Byung-Jun Yoon, “Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations,” IEEE Access, doi: 10.1109/ACCESS.2021.3071038.

In this work, we present a general optimal experimental design (OED) strategy for an uncertain system that is described by coupled ordinary differential equations (ODEs), whose parameters are not completely known. As a vehicle to develop the OED strategy, we focus on non-homogeneous Kuramoto models in this study as the primary example. The proposed OED strategy quantifies the objective uncertainty of the Kuramoto model based on the mean objective cost of uncertainty (MOCU), where the optimal experiment can be identified by predicting the one in a given experimental design space that is expected to maximally reduce the MOCU.

Our study highlights the importance of quantifying the operational impact of the potential experiments in selecting the optimal experiment and it demonstrates that the MOCU-based OED scheme enables us to minimize the objective cost (i.e., cost of robust control in the application considered in this paper) of the uncertain Kuramoto model with the fewest experiments compared to other alternatives.

This work was performed in collaboration with Prof. Youngjoon Hong (Department of Mathematics, Sungkyunkwan University) and Prof. Bongsuk Kwon (Department of Mathematical Sciences, Ulsan National Institute of Science and Technology).

The Brookhaven National Laboratory (BNL), where Dr. Byung-Jun Yoon is working as a Scientist (via joint appointment), has officially joined the ATOM consortium for Accelerating Therapeutics for Opportunities in Medicine (ATOM).

“At Brookhaven, we are excited to apply our team’s work developing and using optimization algorithms directly to ATOM’s diverse computational data-driven modeling efforts,” said Francis J. “Frank” Alexander, deputy director of the Computational Science Initiative. “Often, mathematical models and systems of interest to ATOM cancer therapy problems are uncertain and under-characterized due to their extremely complex nature. At Brookhaven, our artificial intelligence, machine learning and applied mathematics work aims to unravel complexities to design computational and laboratory experiments that achieve discovery goals in the most efficient manner. We believe these efforts will have significant applications in ATOM that can greatly benefit and enhance the program’s impact. We look forward to contributing as part of the collaboration.”

Further information is available on the official ATOM website at the link below:
https://atomscience.org/news-releases/2021/3/24/atom-consortium-welcomes-three-doe-national-laboratories-to-accelerate-drug-discovery

We are happy to announce that our AISTATS 2021 paper entitled “Bayesian Active Learning by Soft Mean Objective Cost of Uncertainty” is now available in the Proceedings of Machine Learning Research, which can be accessed at the following link:

Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian, “Bayesian Active Learning by Soft Mean Objective Cost of Uncertainty,” 24th International Conference on Artificial Intelligence and Statistics (AISTATS), April 13 – 15, 2021.

In this paper, we suggest a strictly concave approximation of MOCU – referred to as “Soft MOCU” – that can be used to define an acquisition function for Bayesian active learning with a theoretical convergence guarantee. We show in this study that the Soft MOCU based Bayesian active learning outperforms other existing methods, with the important benefit of theoretical guarantee of convergence to the optimal classifier.

We are happy to announce that our ICLR 2021 paper below can now be accessed online on OpenReview.net:

Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian, “Uncertainty-aware Active Learning for Optimal Bayesian Classifier,” 9th International Conference on Learning Representations (ICLR), May 4-8, 2021.

In this paper, we propose an acquisition function for active learning of a Bayesian classifier based on a weighted form of MOCU (mean objective cost of uncertainty). By quantifying the uncertainty that directly affects the classification error, the proposed method avoids the shortcoming of the previous expected Loss Reduction (ELR) methods by avoiding their myopic behavior. Unlike existing ELR methods, which may get stuck before reaching the optimal classifier, the proposed weighted-MOCU based strategy provides the critical advantage that the resulting Bayesian active learning algorithm guarantees convergence to the optimal classifier of the true model. We demonstrate its performance with both synthetic and real-world datasets.

The Applied Mathematics Group of the Computational Science Initiative (CSI) at Brookhaven National Laboratory (BNL) invites exceptional candidates to apply for the Amalie Emmy Noether Fellowship in applied mathematics and scientific computing.

This fellowship offers a unique opportunity to conduct research in a broad set of fields, including reduced order modeling, uncertainty quantification and scalable computational statistics for Bayesian inference, optimization and control for decision making under uncertainty, scientific machine learning, high-dimensional inverse problems, multiscale modeling, integrated computational modeling frameworks, data science for streaming or “in-situ” (within simulation) analytics in high performance computing (HPC), and numerical methods. The methods and fundamental advances made in the course of this research will further the progress of applications of interest to BNL and the Department of Energy (DOE). Examples of such applications might include: data-driven uncertainty quantification and hybrid process-based/data-driven modeling for climate prediction and resilience planning, optimal experimental and simulation design for drug discovery and materials science, and large-scale data processing for particle accelerator experiments. The followship 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. Candidates must have received a doctorate (Ph.D.) in applied mathematics or a related field (e.g., mathematics, physics, engineering, statistics, operations research, or computer science) within the past five years. This fellowship presents a unique chance to conduct interdisciplinary collaborative research in BNL programs with a strongly competitive salary. Recipients will be allowed to select a direct mentor from a list of CSI staff scientists. This mentor will help the recipient define and pursue their own research agenda during their appointment.

For more information, visit: https://jobs.bnl.gov/job/upton/post-doc-applied-math-scientific-computing/3437/3147419072