CAREER: Knowledge-driven Analytics, Model Uncertainty, and Experiment Design Grant uri icon


  • The advancement of high-throughput high-content data acquisition techniques has pushed modern scientific research from traditional reductionism to constructionism for studying complex systems with a strong data-driven focus. However, there are still significant issues in big-data analytics, especially, regarding the reproducibility of data-driven research findings. To enable analytic methods from traditional reductionist scientific research to constructionist research with the help of rich data, an integrative Bayesian framework is proposed for systems prediction and intervention in high-dimensional network-based systems, which will provide ways to translate the unprecedented amount of heterogeneous data into reproducible scientific knowledge in a cumulative manner with novel concepts of objective-based uncertainty quantification and optimal experiment design based on that.The proposed knowledge-driven analytics has strong potential of transforming available diverse large-scale data for reproducible knowledge to drive life and materials science research. If successful, it can eventually lead to computational tools for more efficient experiment design to maximize the use of existing big data and speed up the process for effective disease therapeutics and new materials discovery. The interdisciplinary nature of this proposal promises to foster cross-fertilization of ideas between engineering, life science, and materials science through research and education. The broader impact of this proposal involves the integration of the proposed research with an educational plan: (1) to strengthen interactions with the collaborators in life and materials sciences. In addition to making all the tools and research outcomes from this project publicly available, the developed algorithms and tools, including necessary technical help, will be distributed to the collaborators for more effective collaboration; (2) to develop new courses interfacing engineering, mathematics, life and materials sciences and incorporate them into the engineering curriculum for education and research training of students at all levels, which will help the new generation of researchers to establish a broad and solid foundation for interdisciplinary research and prepare them with required skills to address real-world challenges. The courses will be available across campus to attract female and minority students who are interested in research in science and engineering; (3) to involve both undergraduate and graduate students in the research in this interdisciplinary field with the efforts to increasing the participation of underrepresented groups in science and engineering through the collaborations with the REU programs and other scholarship programs developed to increase diversity at Texas A&M University (TAMU).The scientific focus of this proposal is solving mathematical and computational problems that exist in high-dimensional network-based systems prediction and intervention with uncertain models. The following open problems will be addressed: (1) to develop a network-based Bayesian framework and methods for systematic analysis of heterogeneous data sets; (2) to define novel objective-based uncertainty quantification for assessing model uncertainty and data significance to enable cumulative analytics to improve systems understanding and optimize future experiment design; (3) to derive optimal Bayesian experiment design based on uncertainty quantification for different operational objectives, which can lead to the maximal use of existing data and effective future experiment design and systems intervention; and (4) to apply the developed methodologies for understanding and treating specific diseases, for example, cancer and type 1 diabetes; as well as designing experiments for new materials discovery in an efficient way, in collaboration with the collaborators in life and materials sciences, which will translate the knowledge to practical applications. This project will lay out the foundation for translating existing data into systems understanding of life, disease, and man-made systems to gain deeper insights and direct them to desirable systems behavior to benefit human society.

date/time interval

  • 2016 - 2021