March 22, 2018
4th Floor Rackham Building (915 E. Washington St., Ann Arbor)
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Light breakfast items and coffee
Bio: Dr. Raju Namburu is an internationally recognized expert in computational sciences and Chief of the Computational Sciences Division, Computational and Information Sciences Directorate at the US Army Research Laboratory (ARL) where he is also the Director for ARL’s Department of Defense Supercomputing Resource Center. Dr. Namburu is the founding Director for the Mobile Network Modeling Institute, and Cooperative Agreement Manager for the Army High Performance Computing Research Center. ARL’s Computational Sciences Division conducts basic and applied research in computer science, computational science, and applied mathematics. Dr. Namburu joined ARL in 1999 and was one of the key architects in establishing the computational sciences research program at ARL. Dr. Namburu has more than 100 publications in various journals and refereed papers in international conferences and symposiums in the areas of computational sciences, computational mechanics, scalable algorithms, network modeling and high performance computing. His awards include the Department of the Army Superior Civil Service Award; Army Research Development and Achievement Award 1997, 2001, 2009; and the Army Science best paper awards at the 1998, 2000, and 2002 Army Science Conference. Dr. Namburu is also a Fellow of ASME as well as a member of USACM, and IACM. Dr. Raju Namburu received his Ph.D. in Mechanical Engineering from the University of Minnesota. His research and development activities include computational sciences, computational mechanics, interdisciplinary thermal-structural-fluid applications, computational electro-magnetics, network modeling, multi-scale computational methods, and high performance computing.
Cardiovascular disease is the leading cause of death worldwide, with nearly 1 in 4 deaths caused by heart disease alone. In children, congenital heart disease affects 1 in 100 infants, and is the leading cause of infant mortality in the US. Patient-specific modeling based on medical image data increasingly enables personalized medicine and individualized treatment planning in cardiovascular disease patients, providing key links between the mechanical environment and subsequent disease progression. We will discuss recent methodological advances in cardiovascular simulations, including (1) fluid-structure interaction methods as a step towards whole-heart modeling (2) unified finite element methods for fluid and solid mechanics with realistic biological tissue models. Clinical application of these methods will be demonstrated in: 1) coronary blood flow simulations for assessment of vein graft failure in coronary bypass graft patients, and 2) assessment of mechanobiologic forces during cardiac development in a zebrafish model. We will also provide an overview of our open source SimVascular project, which makes our tools available to the scientific community (www.simvascular.org). Finally, we will provide an outlook on recent successes and challenges of translating modeling tools to the clinic.
Bio: Alison Marsden is an associate professor and Wall Center scholar in the departments of Pediatrics, Bioengineering, and, by courtesy, Mechanical Engineering at Stanford University. From 2007-2015 she was a faculty member in the Mechanical and Aerospace Engineering Department at the University of California San Diego. She graduated with a bachelor’s degree in Mechanical Engineering from Princeton University in 1998, and a PhD in Mechanical Engineering from Stanford in 2005 working with Prof. Parviz Moin. She was a postdoctoral fellow at Stanford University in Bioengineering and Pediatric Cardiology from 2005-07 working with Charles Taylor and Jeffrey Feinstein. She was the recipient of a Burroughs Wellcome Fund Career Award at the Scientific Interface in 2007, an NSF CAREER award in 2011, and was a recent member of an international Leducq Foundation Network of Excellence. She received the UCSD graduate student association faculty mentor award in 2014 and MAE department teaching award at UCSD in 2015, and was recently elected as a fellow of AIMBE. She has published over 90 peer reviewed journal papers, and has received funding from the NSF, NIH, and numerous private foundations. She is an associate editor of the Journal of Biomechanical Engineering and PLOS Computational Biology. Her research focuses on the development of numerical methods for cardiovascular blood flow simulation, medical device design, application of optimization to large-scale fluid mechanics simulations, and application of engineering tools to impact patient care in cardiovascular surgery and congenital heart disease.
The construction of a comprehensive and accurate Tree of Life has been a major goal of the biological sciences for more than a century. While there have been many breakthroughs in the sequencing and collection of data, several major computational challenges have made the task of reconstructing phylogenies of enormous scale (e.g., using many taxa or many gene regions) difficult. In this talk, I will discuss these difficulties as well as how newly developed tools have addressed these and allowed us to construct larger phylogenies. I will also discuss how these broader views have led to new biological insights.
Bio: Stephen Smith received a B.A. in Liberal Arts from the Sarah Lawrence College in 2003 and a M. S. in Evolutionary Biology from Yale in 2005. He continued with his Ph.D. studies under the supervision of Prof. Michael Donoghue at Yale University, graduating in 2008. He was a postdoctoral fellow in the national Evolutionary Synthesis Center, at Duke University, and an iPlant postdoctoral researcher at Brown University where he developed tools for constructing large phylogenetic datasets, comparative analyses, and studied the utility of new sequencing technologies for phylogenetics.
Prof Smith joined the University of Michigan in 2012 where he currently is an assistant professor in the department of Ecology and Evolutionary Biology, and an assistant curator of Biodiversity Informatics at the University of Michigan Herbarium, and the University of Michigan Museum of Zoology. He was named the Young Botanist of the Year by the Botanical Society of America in 2003, and the Scientist to Watch by the magazine The Scientist in 2010. He has collaborated in the development of several scientific software packages in phylogenetics and co-authored more than 50 publications.
One of the first major breakthroughs in scientific computing occurred just after World War II when a group of mathematicians and scientists came together to create the world’s first numerical weather prediction on one of the world’s earliest computers. Perhaps the most important mathematical lessons learned from this endeavour was that there is an intimate relationship between the underlying mathematical structure of the governing equations and their numerical approximation. A new grand challenge is on our doorstep, the challenge of next generation computers, which have been designed in new ways to address physical limitations in the manufacture of transistors and energy consumption. To run well on these new computer architectures, new computational models will be required to exploit on the order of hundred-million-way parallelism. This degree of parallelism far exceeds anything possible even in today’s most sophisticated models. In this talk I will discuss one of the mathematical issues that leads to computational limitations for many different types of physical phenomenon including climate and weather prediction models, magnetohydrodynamics, wave propagation and more – oscillatory stiffness in the PDEs that leads to time scale separation. I will discuss the historical context of the first mathematical discoveries of how nonlinear phenomenon give rise to low-frequency solutions and its relationship to fast singular limits studied in PDE’s analysis and numerical analysis.
I will also introduce a parareal-type method where we have used a new strategy to approximate the long-time, low frequency dynamics to drive a locally accurate solution. I will show that under certain regularity constraints this method has superlinear convergence in the asymptotic limit, and sketch the ideas behind a new proof of convergence, one that relies on the role of near-resonances inherent in the PDEs, for the case when the time-scale separation is finite.
Finally, I will close by describing potential research directions where mathematics and statistics could provide solutions for the types of computing problems we will face into the future.
Bio: Beth Wingate is a Professor of Mathematics at the University of Exeter. Her research interests are mainly in fluid mechanics, mathematics, and numerics for high performance computing. Her recent research is focused on physics of the Arctic Ocean, Asymptotic Parallel-in-Time methods for climate modeling and High Performance Computing, and the fluid mechanics of the slow/fast manifolds.
She did her PhD work at the University of Michigan studying numerics, waves and ocean fluid dynamics. She was one of the first to graduate from the Ph.D. in Scientific Computing joint program. She has developed and used spectral element methods including the investigation of near optimal interpolation on triangles with Mark Taylor. She spent many years at the Los Alamos National Laboratory in New Mexico, USA. While there she studied such topics as the LANS-alpha model and overflows. She and her collaborators derived new equations for wave-mean flow in the weak stratification and fast rotation limit which has lead to new ideas such as novel investigations of fluctuations from the slow manifold (with Jared Whitehead) and an asymptotic parallel-in-time method for highly oscillatory PDEs on next generation heterogeneous computing architectures (with Terry Haut).
She has published poetry in literary journals such as the Cafe Luna Review, Iowa Review, Prairie Schooner, Natural Bridge, and others. Her work has also appeared in anthologies such as “Looking Back to Place” published by Old School Books.
Students and post-docs will be available to talk to you about their posters from 12:30 – 2:15 p.m.
MICDE is part of U-M Advanced Research Computing (ARC). Information about our three partner units, Advanced Research Computing – Technological Services (ARC-TS), Consulting for Statistics, Computing and Analytics Research (CSCAR), and the Michigan Institute for Data Science (MIDAS) will be available.
Please RSVP if you are planning on attending lunch.
Cleve Moler is known internationally as one of the founding fathers of numerical analysis and scientific computing. He helped Argonne National Laboratory create public domain software to do computer analyses with matrices, leading to the development of LINPACK and EISPACK software. While teaching at Michigan, he created software for students to do simple calculations on Michigan’s central mainframe computer. That software eventually evolved into the Matrix Laboratory or MatLab. In this talk, Dr. Moler will show how MATLAB has evolved over more than 30 years from a simple matrix calculator to a powerful technical computing environment. He will demonstrate several examples of MATLAB applications and conclude with a discussion of current developments, including parallel computation on multicore, multicomputer, and cloud systems.
Bio: Cleve B. Moler is a mathematician and computer programmer specializing in numerical analysis. He is the original author of MATLAB and one of the founders of MathWorks (R). He is currently chairman and chief mathematician of the company, as well as a member of the National Academy of Engineering and past president of the Society for Industrial and Applied Mathematics, among many accomplishments.
Cleve Moler received his bachelor’s degree from California Institute of Technology in 1961, and a Ph.D. from Stanford University, both in mathematics. Following graduation, he received a one-year fellowship to research computer mathematics at the Swiss Federal Institute of Technology in Zurich. In 1966 he returned to the U.S. to teach mathematics and computer science for more than 20 years, starting at the University of Michigan’s department of mathematics. In 1972 he was recruited to the University of New Mexico’s Computer Science department, where he spent 13 years, and served as chair of C.S. from 1980 to 1985. He has received honorary degrees from Linköping University, Sweden, from the University of Waterloo, and the Technical University of Denmark. Moler left academia in 1985 to work for different companies, including Ardent Computing, Intel Hypercube and MathWorks.
Viome is a “wellness as a service” company that uses AI systems to understand the biological ecosystem inside each of us, and improve our wellness through a personalized diet and lifestyle plan. Viome applies statistical AI (machine learning) and symbolic AI (knowledge representation & reasoning) to create a high-resolution model of individual microbiomes, then uses concepts from modern medical science to recommend nutrition and lifestyle regimes personalized for individuals. In this talk, I will describe some of the technical challenges in this space and the approaches we’re using to address them.
Bio: Guruduth Banavar is the Chief Technology Officer at Viome, a company pioneering artificial learning engines that are designed to analyze the human Microbiome. Banavar received a bachelor in engineering from Bangalore University in 1989, a master in computer science from Arizona State University in 1991, and a Ph. D. in computer science from the University of Utah in 1995, where he focused on language technologies. He joined the IBM research team in 1995, where his numerous accomplishments led him to be named founding vice president of the Cognitive Computing Group. In that position, he lead IBM’s strategy and a worldwide team responsible for creating a range of Watson AI technologies and solutions. As the Chief Science Officer of Cognitive Computing, he created the Cognitive Horizons Network, a collaboration with top AI universities, for advancing IBM’s scientific ecosystem. He has also spent considerable energy on the social and economic aspects of AI.
Brain machine interfaces or neural prosthetics have the potential to restore movement to people with paralysis or amputation, bridging gaps in the nervous system with an artificial device. Microelectrode arrays can record from hundreds of individual neurons in motor cortex, and machine learning can be used to generate useful control signals from this neural activity. Performance can already surpass the current state of the art in assistive technology in terms of controlling the endpoint of computer cursors or prosthetic hands. The natural next step in this progression is to control more complex movements at the level of individual fingers. Our lab has approached this problem in three different ways. For people with upper limb amputation, we acquire signals from individual peripheral nerve branches using small muscle grafts to amplify the signal. After a successful study in animals, human study participants have recently been able to control individual fingers online using acute electrodes within these grafts. For spinal cord injury, where no peripheral signals are available, we implant Utah arrays into finger areas of motor cortex, and have successfully decoded finger flexion and extension with correlations above 0.8. Decoding “spiking band” activity at much lower sampling rates, we recently showed that power consumption of an implantable device could be reduced by 89% compared to existing broadband approaches, and fit within the specification of existing systems for upper limb functional electrical stimulation. Finally, finger control is ultimately limited by the number of independent electrodes that can be placed within cortex or the nerves, and this is in turn limited by the extent of glial scarring surrounding an electrode. Therefore, we developed an electrode array based on 8 um carbon fibers, no bigger than the neurons themselves. We were able to insert arrays with 3x the density of the Utah array by temporarily shortening the fibers for penetration of the top cortical layers. This enabled chronic recording of single units with no apparent contiguous scarring over time. The long-term goal of this work is to make neural interfaces for the restoration of hand movement a clinical reality for everyone who has lost the use of their hands.
Bio: Cynthia A. Chestek received the B.S. and M.S. degrees in electrical engineering from Case Western Reserve University in 2005 and the Ph.D. degree in electrical engineering from Stanford University in 2010. From 2010 to 2012, she was a Research Associate at the Stanford Department of Neurosurgery with the Braingate 2 clinical trial. In 2012 she became an assistant professor of Biomedical Engineering at the University of Michigan, Ann Arbor, MI, where she runs the Cortical Neural Prosthetics Lab. She is the author of 32 full-length scientific articles. Her research interests include high-density interfaces to the nervous system for the control of multiple degree of freedom hand and finger movements.