In uncertainty quantification, one has to often face the problem of efficient posterior integration for computing moments of quantities of interest (QoIs) in high dimension. One method to do this is dimension-adaptive sparse quadrature. I describe efforts towards scalable dimension-adaptive sparse quadrature via constructing a sparsely parameterized push forward transport map between the prior and the posterior. Along the way, I discuss the connection between sparsity and semilattices.
We developed small size vibrational labels capable of testing membrane mobility via two-dimensional infrared spectroscopy. Despite low polarity of the membrane interior, the vibrational label shows a significant inhomogeneous linewidth, which enables measuring accurately lipid membrane mobilities via time-resolved spectral diffusion technique. Because of its small size, the label can be anchored to a specific depth in a bilayer, assessing its local mobility.
The Ensemble Kalman filter (EnKF) developed by Evensen and co-workers in the 1990s has had enormous impact in the geosciences and various engineering disciplines. EnKF has been historically used for data assimilation problems, where the objective is to infer the state of a partially observed dynamic system from observational data. Motivated by algorithmic ideas in EnKF, Ensemble Kalman Inversion (EKI) is a computational framework that has been recently proposed for solving PDE-constrained inverse problems (i.e. to infer inputs from outputs of a PDE model) in a derivative-free fashion. In this talk I will introduce EKI from a framework that unifies both the Bayesian and the classical (deterministic) approach for inverse problems. I will present regularisation strategies for EKI that can improve accuracy and performance of large-scale inversions. I will further discuss recent parameterisations within EKI which enable to efficiently infer geometric features of the underlying (unknown) field. Numerical examples will be used to show the potential advantages of these parameterisations in various application areas including the non-destructive evaluation of composite materials as well as the geoelectrical characterisation of the subsurface.
Urban Air Mobility (UAM) is an envisioned air transportation concept, where intelligent flying machines could safely and efficiently transport passengers and cargo within urban areas by rising above traffic congestion on the ground. Companies such as Boeing, Airbus, Bell, Embraer, Joby, Zee Aero, Pipistrel, and Volocopter are working with their battery vendors to build and test electric vertical takeoff and landing (eVTOL) aircraft to ensure that vehicle safety and energy efficiency become an integral part of people’s daily commute. Furthermore, in order to make UAM profitable for operators and affordable for passengers, the flight operations must be able to scale, which means that the expected air traffic density will be extremely high. For example, as one of the industry leaders in UAM, Uber estimated more than 5,000 eVTOL flights per day in the city of Los Angeles for its future scaled Uber Air operations. The UAM community recognized a key challenge remaining unanswered to make UAM a reality: how can we design and build a real-time, trustworthy, safety-critical autonomous UAM ecosystem to enable large-scale flight operations in high-density, dynamic and complex urban airspace environments? In this talk the speaker will present preliminary studies to address this critical research challenge from areas in autonomy, control, real-time systems and safety. Our multidisciplinary approach is based on bridging guidance and control, reinforcement learning, and Markov decision process.
Peng Wei is an assistant professor in Iowa State University Aerospace Engineering Department, with courtesy appointments in Electrical and Computer Engineering Department and Computer Science Department. Prof. Wei is leading the Intelligent Aerospace Systems Lab (IASL). By contributing to the intersection of control, optimization, machine learning, and artificial intelligence, he designs autonomous and human-in-the-loop decision making systems for aeronautics, aviation and aerial robotics. Recent applications include: Air Traffic Control/Management (ATC/M), Airline Operations, UAS Traffic Management (UTM), eVTOL Urban Air Mobility (UAM) and Autonomous Drone Racing (ADR). Prof. Wei received his undergraduate degree in Information Science and Control Theory from Tsinghua University and a Ph.D. degree in Aerospace Engineering from Purdue University. He serves in several advisory boards at Airbus and NASA. He is an associate editor of AIAA Journal of Aerospace Information Systems.
Low-rank matrix approximations, such as partial spectral decompositions or principal component analysis (PCA), play a central role in data analysis and scientific computing. The talk will describe a set of randomized algorithms for efficiently computing such approximations. These techniques exploit modern computational architectures more fully than classical methods and enable many computations involving massive data sets.
The algorithms described are supported by a rigorous mathematical analysis that exploits recent work in random matrix theory. The talk will briefly review some of the key theoretical results.
As we have entered the precision medicine and big data science era, there are many unmet challenges on identifying the disease related information from large, heterogeneous data, and translating the findings for clinical use. Among these challenges, one is how to effectively identify driver mutations and genes in cancer genomes, especially those with the potential for druggable targets for the development of molecularly targeted cancer therapies. In this talk, I will introduce several informatics approaches, including SGDriver, AlloDriver, and KNMPx, to identifying cancer mutations and genes from large amount of somatic mutation data and our recently developed integrative network-based framework for identifying new druggable targets and anticancer indications from existing drugs.
Dr. Zhongming Zhao holds Chair Professor for Precision Health and is the founding director of the Center for Precision Health, the University of Texas Health Science Center at Houston (UTHealth). Before he joined UTHealth in 2016, he was Ingram Endowed Professor of Cancer Research, Professor (with tenure) in the Departments of Biomedical Informatics, Psychiatry, and Cancer Biology of Vanderbilt University Medical Center, Chief Bioinformatics Officer of the Vanderbilt-Ingram Cancer Center (VICC), Director of the VICC Bioinformatics Resource Center, and the Associate Director of the Vanderbilt Center for Quantitative Sciences. Dr. Zhao has unique, interdisciplinary training: he received his master’s degrees in Genetics (1996), Biomathematics (1998), Computer Science (2002), Ph.D. degree in Human and Molecular Genetics (2000), and Postdoctoral Fellow in Bioinformatics (2001-2003). Dr. Zhao has broad interest in bioinformatics, genomics, precision medicine, and systems biology and has co-authored >330 papers in these areas. Dr. Zhao is the founding president of The International Association for Intelligent Biology and Medicine (IAIBM, 2018-).