III: Small: Statistical Inference through Data-Collection and Expert-Knowledge Incorporation

Recent technological advances have provided opportunities for studying a wide range of complex systems in science and engineering, including biological and environmental processes, cyber-physical systems, and social systems. Accurate modeling of these systems can help characterize their temporal behavior, diagnose and forecast abnormalities, and predict and control their future behavior. For instance, advances in single-cell high-throughput technology have recently provided the opportunity for the deep system-level understanding of genomic systems and the discovery of new effective solutions for early diagnosis, prognosis, and treatment of chronic diseases. However, the lack of access to targeted data and the difficulty of acquiring such data pose significant challenges to accurately modeling complex systems. This project aims to develop algorithms that advance data collection and incorporate user and expert knowledge into the modeling process. Furthermore, the research will support the cross-disciplinary development of a diverse cohort of PhD and undergraduate students at Northeastern University and promote diversity in research through multiple initiatives. This project aims to advance inference of dynamical systems by developing statistical methods that leverage advanced techniques such as reinforcement learning, Bayesian statistics, and machine learning. The technical aims of the project are divided into two thrusts. The first thrust develops statistical learning techniques that effectively capture and quantify valuable expert insights into the inference process. These methods quantify the expert knowledge incorporated within data without requiring expert oversight. The second thrust develops approaches to systematize data collection for causal and accurate inference of complex systems and processes. The proposed approaches leverage the system and data structures, as well as the associated uncertainty, to achieve effective data collection. The project employs the proposed methods in genomics applications, including topology inference in gene regulatory networks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.