Abstract: This talk is divided into three parts. 1) An overview of the Modeling & Simulation group at Novartis and challenges in the drug development industry. 2) An application of a mathematical model for solid tumor growth for assisting with dose selection of a cancer drug. 3) A survey of some open questions in the field of solid tumor growth modeling.

Andy's background is in engineering and mathematics. He earned his BS and MS in Mechanical Engineering from MIT, his PhD in Applied Mathematics from University of Michigan, and completed a postdoctoral fellowship at the Institute for Mathematics and its Applications. Since 2009, he has worked as a biological modeler in the Modeling and Simulation group at Novartis in Cambridge, MA.

The state and observation equations of space objects are nonlinear and therefore it is hard to estimate the conditional probability density of the space object trajectory states given EO/IR, radar or other nonlinear observations. Moreover, space object trajectories can suddenly change due to abrupt changes in the parameters affecting a perturbing force or due to unaccounted forces. Such trajectory changes can lead to the loss of existing tracks and may cause collisions with vital operating space objects such as weather or communication satellites. In this talk, Scientific Systems Company, Inc. (SSCI) and Lockheed Martin Corporation (LMCO) joint work on algorithms and methods for Space Situational Awareness (SSA) is presented including problem formulation, derivation of mathematical methods for modeling and estimation of space-based object states, derivation of observation models, estimation algorithm derivation, numerical implementations, simulation testbed, and simulation results. The presented estimation methods will aid in preventing the occurrence of collisions in space and also provide warnings for collision avoidance.

Aleksandar Zatezalo is a Senior Research Engineer at Scientific Systems Company, Inc (SSCI), located in Woburn, Massachusetts. Dr. Zatezalo received a Ph.D. in Mathematics from the University of Minnesota, Minneapolis in 1998 under the supervision of Nicolai Vladimirovic Krylov. From 1998 to 2000, he worked on Research and Development (R&D) projects for Lockheed Martin Corporation (LMCO) as a IMA postdoctoral member. In 2000/2001 academic year, he worked for Siemens AG Corporate Technologies in Munich as an industrial postdoctoral fellow. After habilitation, he held the academic position of Docent at the University of Rijeka, Croatia, where he was lecturing in Linear Algebra, Logic, Theory of Probability, and Statistics. From February to November of 2003, he was a visitor Research Associate at the Florida State University. Since then, he has been working at SSCI on R&D projects for the U.S. Air Force, Missile Defense Agency, DARPA, NASA, and the Navy. Dr. Zatezalo’s experience includes target tracking and classification, multisensor-multitarget tracking and estimation, game theory applications, nonlinear filtering, information fusion, sensor management situational awareness, space situation awareness, image-based estimation, information fusion, high precision navigation, and cold atom interferometry.

Over the last decade, there has been an increased interest in the development of ocean models using finite elements or finite volumes on unstructured meshes. The latter offer many compelling features such as the ability to conform to complex coastlines and bathymetry and to allow for the mesh resolution to vary in space and time. Numerical ocean models based on these techniques have the possibility to simultaneously resolve both small- and large-scale processes. While the potential of such models is large, much remains to be done before they can become operational. A step-by-step conversion from existing structured-grid models to unstructured-mesh models is unlikely to occur due to intrinsic algorithmic differences. New models must therefore be designed from scratch. When resorted to naively, finite elements are not ideally suited to represent the ocean dynamics, where wave phenomena, advective processes and conservation laws must be modeled with caution in order to preserve the physical integrity of numerical solutions. This presentation will focus on a selection of these processes and will outline what has been recently done to overcome the difficulties encountered. Illustrations of these concepts will be shown and include some idealized test cases in simple geometries, the propagation of slow Rossby waves in the Gulf of Mexico, barotropic and baroclinic instabilities, barotropic tidal flow and flow in the Great Barrier Reef.

Laurent White obtained a Diploma in mathematical engineering from Universite catholique de Louvain (Belgium) in 2002 and a Master of Science in civil engineering from the University of Texas at Austin in 2003. He then returned to Belgium to complete a PhD in computational science at Universite catholique de Louvain where his research focused on applying novel finite-element technique to ocean modeling. Upon completion of his PhD in 2007, he joined NOAA(*)'s Geophysical Fluid Dynamics Laboratory at Princeton University as a postdoc where he continued to apply his expertise in numerical methods to issues pertaining to ocean modeling. In 2009, Laurent joined ExxonMobil's Corporate Strategic Research Lab (NJ) where he is currently working towards improving the computational algorithms used for seismic imaging and reservoir simulation.

(*) National Oceanic and Atmospheric Administration

Reductions in bladder compliance are common in spinal cord injured patients with neurogenic bladder and are associated with multiple adverse outcomes ranging from bothersome lower urinary tract symptoms to dangerously high detrusor storage pressures with the potential for renal failure. Cystometry is the current gold standard for assessing bladder compliance, but this test is invasive, expensive, time intensive, can be extremely uncomfortable, and carry a risk of clinical side effects. A noninvasive method for bladder compliance assessment is therefore of great interest. To address this problem, we have developed a new ultrasonic method based on acoustic radiation force to evaluate bladder compliance in neurogenic patients. This method is based on propagation of Lamb waves in the bladder wall. This method is noninvasive, quantitative, rapid, and requires minimal training. Here we will discuss theoretical, technical, and clinical aspects of this new method.

Mostafa Fatemi received his PhD degree in Electrical Engineering from Purdue University. Currently, he is a Professor of Biomedical Engineering at the Department of Physiology and Biomedical Engineering of Mayo Clinic College of Medicine in Rochester, MN. At the Mayo Clinic Dr. Fatemi is also a member of the Mayo Clinic Cancer Center, Cancer Imaging Program, Prostate Cancer Program, and the Center for Translational Science Activities. He is also a faculty member of the University of Minnesota Rochester Biomedical Informatics and Computational Biology graduate program. Dr. Fatemi has published extensively in the field of medical ultrasound with over 150 articles, including over 80 peer-reviewed papers, one book, and ten book chapters. Dr. Fatemi is the inventor of vibro-acoustography, an imaging technology based on acoustic properties of biological tissues, and holds 9 patents in the field. Dr. Fatemi has presented invited or keynote talks at a number of national and international meetings and has authored several invited papers in the field of ultrasound. Dr. Fatemi has served on the editorial boards of several journals, including IEEE Transactions on Medical Imaging. Dr. Fatemi’s past and current research have been funded by various federal, state, and private funding agencies, including DoD-CDMRP Breast Cancer Research Program, NIH, NSF, Susan G. Komen Breast Cancer Foundation, and Minnesota Partnership Program. Dr. Fatemi holds the Fellow status in the following professional institutions: IEEE, Acoustical Society of America, American Institute of Ultrasound in Medicine, and American Institute of Medical and Biological Engineering.

Implantable medical devices, in particular, implantable cardioverter defibrillators (ICD), provide lifesaving therapies to the patients with ventricular tachyarrhythmia/ ventricular fibrillation (VT/VF). The ICD therapies are in the form of anti-tachycardia pacing (ATP) or high voltage shocks. The remote monitoring systems for ICDs collect large quantities of data that present opportunities and challenges to patient management and clinical research. One of the challenges of this industry is inappropriate shocks for non-ventricular arrhythmias (non VT/VF). The presentation will go into some detail to discuss the challenges and their solution with respect to reducing inappropriate shocks.

Deepa Mahajan works as a Principal Research Scientist at Boston Scientific, CRV. She is responsible for the initiations, design, development, execution, and implementation of scientific research projects. She is with Boston Scientific for over 4 years now. She holds PhD in Applied Mathematics with a focus on Numerical Analysis, minor in Statistics from University of Minnesota in 2007. She has applied for over 15 patents and presented her work at a leading international cardiovascular meeting. She serves in the Editorial Advisory board of Medical Device and Diagnostic Industry (MD&DI).

Many of Boeing's products, including the new 787, make effective use of composite materials in their construction. These advanced materials offer superior weight and strength characteristics and are one of the principal reasons the 787 has become the fastest selling jetliner in aviation history. Building geometric models which enable the manufacture of these materials is non-trivial, however, and many ad hoc approaches are widely used. This talk will focus on a relatively new approach to the problem which makes use of a convolution operation to build empirical geometry models for these parts and the tools used to construct them. Insights into both the application and the underlying mathematics involved in the geometric modeling will be offered.

Tom Grandine is a specialist in advanced geometric design and numerical analysis. His areas of expertise include curve and surface modeling, numerical approximation, splines, and multidisciplinary design optimization. He has extensive experience in computational methods for both engineering and manufacturing applications.

Next generation energy and aerospace systems such as smart grids, building systems, communication networks and UAV swarms give rise to high dimensional mathematical models with complex interconnections and dynamic interactions. This makes the task of designing and analyzing such systems particularly challenging.

In this work, we will develop decentralized solutions for analysis and uncertainty quantification of large networks. Motivated by standard problems and approaches in continuous spaces, we will construct scalable algorithms for graph clustering. It will be demonstrated that graph clustering aids in the development of scalable methods for simulating and propagating uncertainty through high dimensional differential algebraic equations (DAEs). In particular, our scalable algorithms are iterative schemes that rely on graph partitioning for finding "weak connections" that accelerate convergence. These algorithms will be demonstrated on models of aircraft communication networks, building systems and social networks.

Tuhin Sahai is a Staff Research Scientist at the United Technologies Research Center (UTRC), broadly interested in the design, analysis and uncertainty quantification of complex systems. Of particular interest are large networks of continuous and discrete dynamical systems that tend to be analytically and computationally intractable. Typical application areas include energy efficient buildings, smart grids, social networks, UAV swarms, next generation electrical and communication networks for aircraft, sensor networks and MEMS oscillator networks. At UTRC, Tuhin serves as a principal investigator for DARPA's GUARD-DOG program on scalable analysis of social networks in MapReduce. Tuhin's work on decentralized clustering received the 2012 Technical Excellence Award, the highest individual award at UTRC for contributions to Science and Engineering. Prior to joining UTRC, Tuhin earned his Ph.D. in January 2008 from Cornell University, where he was a McMullen Fellow and won the H.D. Block teaching award. Tuhin received his Masters and Bachelors in Aerospace Engineering from the Indian Institute of Technology, Bombay in 2002.