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.
Our recent study on Bayesian error estimation via optimal Bayesian transfer learning has been published in Patterns, a premium open access journal published by Cell Press.
Our NeurIPS 2021 paper entitled “Efficient Active Learning for Gaussian Process Classification by Error Reduction” is now available online at the following link: https://openreview.net/pdf?id=UK15Hj9qX6I
In this paper, we investigate active learning scenarios for Gaussian Process Classification (GPC), where we develop computationally efficient algorithms for EER (expected error reduction)-based active learning with GPC. In particular, we consider EER as the reduction of the Mean Objective Cost of Uncertainty (MOCU), where the learning objective of GPC is to reduce the classification error.
Our experiments clearly demonstrate the computational efficiency of the proposed approach and performance evaluation of our algorithms on both synthetic and real-world datasets show that they significantly outperform existing state-of-the-art algorithms in terms of sampling efficiency.
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:
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.
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 paper entitled “Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations,” has been published in IEEE Access and is now accessible in the link below:
In this work, a general optimal experimental design (OED) strategy is proposed for an uncertain system that is described by coupled ordinary differential equations (ODEs), whose parameters are not completely known. As a vehicle for developing the OED strategy, this study focuses on non-homogeneous Kuramoto oscillator models, where the objective is the robust control of a given uncertain Kuramoto model to achieve global frequency synchronization.
Illustrative overview of the proposed optimal experimental design (OED) framework.
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 best experiment in the design space that is expected to maximally reduce the MOCU.
This study highlights the importance of quantifying the operational impact of the potential experiments in designing the optimal experiment and it demonstrates that the MOCU-based OED scheme enables one to minimize the cost of robust control of a uncertain Kuramoto model with the fewest experiments compared to other alternatives.
The proposed scheme is fairly general and it can be applied to any uncertain complex system represented by coupled ODEs.
In this paper, a strictly concave approximation of MOCU – referred to as “Soft MOCU” – is proposed, which can be used to define an acquisition function for Bayesian active learning with a theoretical convergence guarantee. This study shows that the Soft MOCU based Bayesian active learning outperforms other existing methods, with the important additional benefit of theoretical guarantee of convergence to the optimal classifier.
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.
We are happy to announce the opportunity to apply for 2021 NSF Math Sciences Graduate Internship (MSGI) to work on the research project entitled: Uncertainty-Aware Data-Driven Models for Optimal Learning and Robust Decision Making Under Uncertainty. (Mentors: Drs. Nathan Urban & Byung-Jun Yoon)
This project aims to develop Scientific ML techniques that enable objective-driven uncertainty quantification (UQ) for data-driven models. We will focus on developing theories and algorithms that can ultimately lead to an automated learning procedure of effective surrogates for complex systems that can be used for making optimal decisions robust to system uncertainties and surrogate approximation errors. These goals will be attained based on a Bayesian ML paradigm, in which we integrate scientific prior knowledge on the system and the available data to obtain a prior directly characterizing the scientific uncertainty in the physical system, quantify the uncertainty relative to the objective, develop optimal operators robust to the uncertainty, and design strategies that can optimally reduce the uncertainty and thereby directly contribute to the attainment of the objective. Potential applications of this methodology will be discussed with the student, but may focus on biological and biomedical discovery science. Detailed information of this project can be found in the project catalog at the following link (search for reference code: BNL-URBAN1): https://orise.orau.gov/nsf-msgi/project-catalog.html
The NSF Mathematical Sciences Graduate Internship (MSGI) program is aimed at students who are interested in understanding the application of advanced mathematical and statistical techniques to “real world” problems, regardless of whether you plan to pursue an academic or nonacademic career. Internship activities will vary based on the assigned research project and hosting facility. As part of your application, you will identify your top 3 research projects from the 2021 NSF MSGI Project Catalog: https://orise.orau.gov/nsf-msgi/project-catalog.html
We are happy to announce that we have launched a new website for objective uncertainty quantification (UQ). This website will be used to present the latest research findings, papers, resources, and other developments relevant to objective-UQ based on mean objective cost of uncertainty (MOCU).
Objective-UQ 