MICDE Seminar: Grace Gu, Assistant Professor, Mechanical Engineering, University of California- Berkeley
2020-10-20 15:00 - 16:00 (America/Detroit) | Zoom Event
About Grace Gu: Grace X. Gu is an Assistant Professor of Mechanical Engineering at the University of California, Berkeley. She received her Ph.D. and MS in Mechanical Engineering from the Massachusetts Institute of Technology and her BS in Mechanical Engineering from the University of Michigan, Ann Arbor. Her current research focuses on creating new materials with superior properties for mechanical, biological, and energy applications using multiphysics modeling, artificial intelligence, and high-throughput computing, as well as developing intelligent additive manufacturing technologies to realize complex material designs previously impossible. Gu is the recipient of several awards, including the 3M Non-Tenured Faculty Award, MIT Tech Review Innovators Under 35, Johnson & Johnson Women in STEM2D Scholars Award, Royal Society of Chemistry Materials Horizons Outstanding Paper Prize, and SME Outstanding Young Manufacturing Engineer Award.

METAMATERIALS DESIGN AND MANUFACTURING: LEARNING FROM BIOLOGY AND ARTIFICIAL INTELLIGENCE
After billions of years of evolution, it is no surprise that biological materials are treated as an invaluable source of inspiration in the search for new materials. Additionally, developments in computation spurred the fourth paradigm of materials discovery and design using artificial intelligence. Our research aims to advance design and manufacturing processes to create the next generation of high-performance engineering and biological materials by harnessing techniques integrating artificial intelligence, multiphysics modeling, and multiscale experimental characterization. This work combines computational methods and algorithms to investigate design principles and mechanisms embedded in materials with superior properties, including bioinspired materials. Additionally, we develop and implement deep learning algorithms to detect and resolve problems in current additive manufacturing technologies, allowing for automated quality assessment and the creation of functional and reliable structural materials. These advances will find applications in robotic devices, energy storage technologies, orthopedic implants, among many others. In the future, this algorithmically driven approach will enable materials-by-design of complex architectures, opening up new avenues of research on advanced materials with specific functions and desired properties.

The MICDE Fall 2020 and Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
To view the recording for this event, please complete this form and a link will be sent to you.
Questions? Email MICDE-events@umich.edu

MICDE Seminar: Denise Kirschner, Professor, Microbiology and Immunology, University of Michigan Medical School
2020-11-12 11:00 - 12:00 | Zoom Event
About Denise Kirschner: Dr. Kirschner received her Bachelors, Masters and PhD in applied mathematics from Tulane University. She did graduate work also at Los Alamos National Labs and a postdoctoral fellowship at Vanderbilt University joint with the departments of Mathematics and Infectious Diseases. Over the past 25 years Dr. Kirschner has focused on questions related to models of host-pathogen interactions in infectious diseases. Her main focus has been to build models of persistent infections (e.g. Helicobacter pylori and Mycobacterium tuberculosis and HIV-1). Her goal is to understand the complex dynamics involved, together with how perturbations to this interaction (via treatment with chemotherapies or immunotherapies) can lead to prolonged or permanent health. For the past 20 years, her research focus has been on building multi-scale models to describe the host immune response to M. tuberculosis at multiple spatial and time scales and in multiple physiological compartments including lung, lymph nodes and blood.
To date, she have worked and collaborated with experimentalists generating data on TB with mouse, non-human primate and human studies. Denise has over 150 publications in top journals describing this work that spans topics from methodological to biological advancement. Dr. Kirschner currently serves (and has for the past 17 years) as Editor-in-Chief of the Journal of Theoretical Biology. She serves as the founding co-director of The Center for Systems Biology at the University of Michigan, an interdisciplinary center at the University of Michigan aimed to facilitate research and training between wet-lab and theoretical scientists. In 2016 she was elected as President-elect of the Society for Mathematical Biology and has served as its president from 2017-2020. Denise’s passion for mentoring students, postdoctoral fellows and junior faculty has been a major focus of her career, and her key mission is to promote both mathematics and family values in the scientific community.

APPROACHES FOR STUDYING MULTISCALE COMPUTATIONAL MODELS:
Mycobacterium tuberculosis is a bacterium that infects 1/3 of the world today. While only 10% of infected individuals experience active tuberculosis disease, if left untreated infection results in death. The remainder of individuals harbor the bacteria in a clinically latent infection, and those individuals can experience reactivation of infection up to 10% per year. Our goal in a number of studies is to understand the role of the bacteria in initiating, sustaining and inhibiting the immune response during infection. Granulomas are a hallmark of tuberculosis infection arising within lungs of infected humans. Understanding the immune response that leads to formation of granulomas can help us better design therapies to control or clear infection. We use a hybrid multi-scale approach that is fine grained for spatial details to help uncover these dynamics paired with a coarse grained spatial model that allows us to capture the entire host dynamics. We use a combination of statistic and mathematical and engineering approaches to predict optimal treatments.

The MICDE Fall 2020 and Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Watch the full webinar here.
Questions? Email MICDE-events@umich.edu

MICDE / AIM Seminar: Baole Wen, Assistant Professor, Mathematics, University of Michigan
2020-11-20 15:00 - 16:00 | Zoom Event
About Baole Wen: Dr. Wen obtained a B.S. degree in Engineering Mechanics and a M.S. degree in Fluid Mechanics, respectively, from the Beijing University of Aeronautics and Astronautics. He was awarded a CEPS Graduate Fellowship & a Dissertation Year Fellowship and earned a Ph.D. in Applied Mathematics from University of New Hampshire in 2015. His Ph.D. research was focused on understanding the underlying flow and transport mechanisms governing the spatiotemporally-chaotic system of porous medium convection at large Rayleigh numbers. Upon graduation, he was awarded a Peter O’Donnell, Jr. Postdoctoral Fellowship through the Oden Institute for Computational Engineering and Sciences in the University of Texas at Austin. His primary research interests are fluid dynamics, mathematical modeling, scientific computing and dynamical systems theory. Recently, he is working with Dr. Charles Doering as a Postdoctoral Assistant Professor at University of Michigan on extreme behavior in fundamental models of fluid mechanics.
STEADY COHERENT STATES IN RAYLEIGH–B'{E}NARD CONVECTION: Buoyancy-driven flows are central to engineering heat transport, atmosphere and ocean dynamics, climate science, geodynamics, and stellar physics. Rayleigh–B’enard convection—the buoyancy driven flow in a fluid layer heated from below and cooled from above—is recognized as the simplest scenario in which to study such phenomena, and beyond its importance for applications this problem has served for a century as one of the primary paradigms of nonlinear physics, complex dynamics, pattern formation and turbulence. A central question about Rayleigh–B’enard convection is how the Nusselt number $Nu$ depends on the Rayleigh number $Ra$ and the Prandtl number $Pr$—i.e., how heat flux depends on imposed temperature gradient and the ratio of the fluid’s kinematic viscosity to its thermal diffusivity—as $Rarightarrowinfty$. Experiments and simulations have yet to rule out either `classical’ $Nu sim Ra^{1/3}$ or `ultimate’ $Nu sim Ra^{1/2}$ asymptotic scaling. Here we provide clear quantitative evidence suggesting that the ultimate regime might not exist. Our tactic is to study relatively simple time-independent states called rolls and compare heat transport by these rolls with that of turbulent convection. These steady rolls are not typically seen in large-$Ra$ simulations or experiments because they are dynamically unstable. Nonetheless, they are part of the global attractor for the infinite-dimensional dynamical system defined by Rayleigh’s model, and recent results suggest that steady rolls may be one of the key coherent states comprising the `backbone’ of turbulent convection. By developing novel numerical methods, we compute steady rolls between no-slip boundaries for $Rale 10^{14}$ with $Pr=1$ and various horizontal periods. We find that rolls of the periods that maximize $Nu$ at each $Ra$ have classical $Nusim Ra^{1/3}$ scaling asymptotically, and they transport more heat than turbulent experiments or simulations at similar parameters. If turbulent heat transport continues to be dominated by steady transport asymptotically, it cannot achieve ultimate scaling.

The MICDE Fall 2020 and Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
This event will be a joint seminar with the University of Michigan Applied Interdisciplinary Mathematics.
Questions? Email MICDE-events@umich.edu
Join the webinar via the Zoom details below:
https://umich.zoom.us/j/96450383843
Meeting ID: 964 5038 3843
Passcode: [REDACTED]

MICDE / IOE Seminar: Salar Fattahi, Assistant Professor, Industrial & Operations Engineering, University of Michigan
2020-12-03 15:00 - 16:00 | Zoom Event
About Salar Fattahi: Dr. Salar Fattahi is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan. He received his M.S. and Ph.D. degrees in Industrial Engineering and Operations Research from UC Berkeley. He received a M.S. degree from Columbia University, and a B.S. degree from Sharif University of Technology, Iran, both in Electrical Engineering. Salar’s research lies at the intersection of optimization, data analytics, and control theory. He was the recipient of several awards, including the 2020 INFORMS ENRE Best Student Paper Award, 2018 INFORMS Data Mining Best Paper Award and 2020 Power & Energy Society General Meeting Best-of-the-Best Paper Award. He was also a finalist for the 2018 American Control Conference Best Paper Award.
Webinar: LARGE-SCALE INFERENCE OF TIME-VARYING MARKOV RANDOM FIELDS: BRIDGING THE GAP BETWEEN STATISTICAL AND COMPUTATIONAL EFFICIENCIES
Contemporary systems are comprised of a massive number of interconnected components that interact according to a hierarchy of complex, dynamic, and unknown topologies. For example, with billions of neurons and hundreds of thousands of voxels, the human brain is considered as one of the most complex physiological networks, whose structure remains as a long-standing mystery. As another example, the emergence of self-driving cars has only accentuated the need for the development of real-time and reliable methods for detecting moving objects, whose temporal locations are captured through a dynamically-evolving 3D network. Nonetheless, the vast amounts of parameters to be estimated, caused both by the large number of components and the time-varying nature of the systems, are currently the major bottlenecks in our ability to successfully solve such inference problems.
The temporal behavior of today’s interconnected systems can be captured via time-varying Markov random fields (MRF). A popular approach to achieve this goal is based on the so-called maximum-likelihood estimation (MLE): to find a probabilistic graphical model, based on which the observed data is most probable to occur. The MLE-based methods suffer from several fundamental drawbacks which render them impractical in realistic settings. First, they often suffer from notoriously high computational cost in the massive problems, where the number of variables to be inferred is in the order of millions, or more. Second, they fail to efficiently incorporate prior structural information into their estimation procedure. With the goal of bridging this knowledge gap, the aim of this work is to revisit the standard MLE as the “Holy Grail” of the inference methods for graphical models, and precisely pinpoint and remedy the scenarios where it fails. A recurring theme in our proposed approach is a class of efficiently-solvable mixed-integer optimization problems that is used in lieu of the regularized MLE for the inference of time-varying MRFs. Our proposed optimization problems enjoy strong statistical and computational guarantees, while being amenable to a wide class of graphical models with different side information, such as sparsity, smoothness, etc.

The MICDE Fall 2020 and Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
This event will be a joint seminar with the University of Michigan College of Engineering’s Industrial Operations & Engineering department.
Questions? Email MICDE-events@umich.edu
Connect via this Zoom link: https://umich.zoom.us/j/96516676892#success

MICDE Seminar: Yang Liu, Research Scientist, Scalable Solvers Group of the Computational Research Division at the Lawrence Berkeley National Laboratory
2021-01-19 16:00 - 17:00 | Zoom Event, MI, United States
About Dr. Liu: Yang Liu is a research scientist in the Scalable Solvers Group of the Computational Research Division at the Lawrence Berkeley National Laboratory, in Berkeley, California. Dr. Liu received the Ph.D. degree in electrical engineering from the University of Michigan in 2015. From 2015 to 2017, he worked as a postdoctoral fellow at the Radiation Laboratory, University of Michigan. From 2017 to 2019, he worked as a postdoctoral fellow at the Lawrence Berkeley National Laboratory. His main research interest is in numerical linear and multi-linear algebras (including sparse solvers, randomized low-rank, butterfly and tensor algebras), computational electromagnetics (including fast iterative time-domain integral equation solvers, fast direct integral and differential equation solvers, and multi-physics
modeling), scalable machine learning algorithms, and high-performance scientific computing. Dr. Liu authored and co-authored the Sergei A. Schelkunoff Transactions Prize Paper, APS 2018, second place student paper, ACES 2012, and the first place student paper, FEM 2014.
FAST, DIRECT INTEGRAL DIFFERENTIAL EQUATION SOLVERS FOR ELECTROMAGNETIC ACOUSTIC, AND ELASTIC APPLICATIONS AT ALL FREQUENCY RANGES: Large-scale and full-wave modeling for acoustic and elastic inversion applications, analysis and synthesis of electromagnetic systems for traditional and emerging RF, microwave, terahertz applications rely on efficient numerical tools. Integral equation (i.e., method of moment) and differential equation (e.g., finite-difference, finite-element, and finite-volume) formulations lead to dense and sparse linear systems, respectively. These linear systems can be solved by either iterative or direct solvers. Iterative solvers, despite their success in constructing well-conditioned formulations and fast multipole-type algorithms, remain inefficient for systems that are inherently ill-conditioned and/or require multiple right-hand sides. This is particularly true for design automation, inverse scattering, and other coupled systems where iterative solvers often require forbiddingly high iteration time. Direct solvers, in stark contrast, can attain reliable solutions in a predictable time. However, exact direct solvers typically require O(N 3 ) and O(N 2 ) computational costs for dense and sparse systems of size N, respectively. Fast direct solvers, on the other hand, rely on the fact that off-diagonal blocks of the well-ordered linear systems can be compressed by numerical linear algebra tools including low-rank and butterfly decompositions. When further embedded in hierarchical matrix frameworks, such as H-matrix, hierarchically off-diagonal low-rank (HODLR), and hierarchically semi-separable (HSS) formats, these direct solvers and preconditioners can achieve quasi-linear complexities for construction, factorization and solution for the discretized systems across all frequency ranges. We will review the development of these solvers in the past two decades, with an emphasis on their butterfly-based variants and distributed-memory parallelization for high-frequency problems. An open source package integrating most techniques reviewed, called ButterflyPACK, will also be introduced.

Watch the full webinar.
Note: You can register after the webinar has started.

MICDE Seminar: Tianle Yuan, Associate Research Scientist, University of Maryland, Baltimore County, JCET, NASA Goddard Space Flight Center
2021-01-26 15:00 - 16:00 | Zoom Event
About Dr. Tianle Yuan: Dr. Yuan received his B.S. in Geophysics and Computer Science from Peking University, his Ph.D. from the University of Maryland, College Park in 2008. After graduation, he became affiliated with the Joint Center for Earth Systems Technologies (JCET) at the University of Maryland Baltimore County (UMBC) and NASA Goddard Space Flight Center (GSFC) as an Associate Research Scientist. His research interests include cloud and aerosol climate feedback, aerosol-cloud interactions, remote sensing, cloud physics, and application of ML/Deep Learning in Earth science. In deep learning applications, Dr. Yuan published a few papers in modeling sub-grid clouds, global scale clouds, hurricane prediction, finding ship-tracks, and supervised and unsupervised cloud morphology classifications.
ARTIFICIAL INTELLIGENCE-BASED CLOUD DISTRIBUTOR (AI-CD): MODELING CLOUDS AT DIFFERENT SCALES
Here we introduce the artificial intelligence-based cloud distributor (AI-CD) approach to generate cloud fields across different scales and cloud types. We show that generative adversarial nets (GANs) can not only generate realistic cloud fields with corresponding meteorological variables, but also capture known physical relationship between cloud fields and meteorological variables such as sea surface temperature, atmospheric stability, and relative humidity etc. We demonstrate that this approach works across a large range of spatial scales: from individual grid points (sub-grid process modeling), multiple grids, to global scale. In addition, the AI-CD approach is stochastic in nature. We suggest the AI-CD approach can be used as a data-drive framework for stochastic cloud parameterization.

The MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Register to immediately receive Zoom details. Note: you may register after the event has started.
Questions? Email MICDE-events@umich.edu

Using GPUs with Python
2021-01-27 14:00 - 17:00 | MI
To view registration information for the February 24, 2021 session of this workshop, visit the event page.

Python is the Lingua Franca of Data today and is being increasingly used in scientific computations. This workshop introduces Python GPU tools for porting and writing code that runs on GPUs. The primary tools, Numba and CuPy, are presented with examples. Back by popular demand, this workshop is presented by Kristopher Keipert of NVIDIA.
This event is open to students, faculty, and staff within the University of Michigan community. A Jupyter notebook is used along with a set of lecture slides. The workshop will use online tools, so there is no need to install any software ahead of time.
This event is brought to you by the Michigan Institute for Computational Discovery and Engineering, and Consulting for Statistics, Computing & Analytics Research at the University of Michigan in partnership with NVIDIA.
Space is limited, register today!

MICDE / MIDAS Seminar: Ivo Dinov, Professor, Nursing, Computational Medicine & Bioinformatics
2021-02-04 11:00 - 12:00 | Zoom Event
Bio: Dr. Ivo D. Dinov directs the Statistics Online Computational Resource (SOCR), co-directs the multi-institutional Probability Distributome Project, and is an associate director for education of the Michigan Institute for Data Science (MIDAS).
Dr. Dinov is an expert in mathematical modeling, statistical analysis, computational processing and visualization of Big Data. He is involved in longitudinal morphometric studies of human development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s and Parkinson’s diseases). Dr. Dinov is developing, validating and disseminating novel technology-enhanced pedagogical approaches for scientific education and active learning.
DATA SCIENCE, TIME COMPLEXITY, AND SPACEKIME ANALYTICS
Many observable processes demand managing, harmonizing, modeling, analyzing, interpreting, and visualizing of large and complex information. There is a substantial need to develop, validate, productize, and support novel mathematical techniques, advanced statistical computing algorithms, transdisciplinary tools, and effective artificial intelligence applications. Spacekime analytics is a new technique for modeling high-dimensional longitudinal data. This approach relies on extending the notions of time, events, particles, and wavefunctions to complex-time (kime), complex-events (kevents), data, and inference-functions. We will illustrate how the kime-magnitude (longitudinal time order) and kime-direction (phase) affect the subsequent predictive analytics and the induced scientific inference.
The mathematical foundation of spacekime calculus reveal various statistical implications including inferential uncertainty and a Bayesian formulation of spacekime analytics. Complexifying time allows the lifting of all commonly observed processes from the classical 4D Minkowski spacetime to a 5D spacekime manifold, where a number of interesting mathematical problems arise. Direct data science applications of spacekime analytics will be demonstrated using simulated data and clinical observations (e.g., structural and functional MRI).

The MICDE Fall 2020 and Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Watch the recorded webinar.
Questions? Email MICDE-events@umich.edu

MICDE / Mechanical Engineering Seminar: Ceila Reina, Assistant Professor, Mechanical Engineering and Applied Mechanics, University of Pennsylvania
2021-02-09 16:00 - 17:00 | Zoom Event
Bio: Celia Reina is the William K. Gemmill Term Assistant Professor in Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. She joined in 2014 after holding the Lawrence Postdoctoral Fellowship at Lawrence Livermore National Laboratory and the HCM postdoctoral Fellowship at the Hausdorff Center of Mathematics in Bonn, Germany. Dr. Reina received her PhD from the California Institute of Technology in Aerospace Engineering in 2011, under the supervision of Prof. Michael Ortiz, following a B.S. in Mechanical Engineering from the University of Seville in Spain, and a Master in Structural Dynamics from Ecole Centrale Paris in France. She is the 2017 recipient of the Eshelby Mechanics Award for Young Faculty, she is a member of the TTA on Nanotechnology and Lower Scale Phenomena at the USACM, and she currently serves as the recording secretary for the Applied Mechanics Division of the ASME.
CONTINUUM MECHANICS OF NON-EQUILIBRIUM PHENOMENA: A JOURNEY THROUGH SPACE AND TIME SCALES: The fascinating diversity of material behavior at the macroscopic scale can only emerge from the underlying atomistic or particle behavior. Yet, the direct connection between these two scales remains an extremely challenging quest, particularly in the context of non-equilibrium phenomena. In this talk, we will discuss several advances in this direction, in the context of plasticity, thermoelasticity, diffusion and viscous dissipation. In all these cases, the importance of fluctuations in the effective response will become apparent. More precisely, these will provide crucial information for the material description and evolution at the continuum scale, where the behavior is modeled as deterministic and free of fluctuations.

The MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
This event will be a joint seminar with the University of Michigan College of Engineering’s Mechanical Engineering department.
Questions? Email MICDE-events@umich.edu

Ph.D Seminar: Saibal De, Applied and Interdisciplinary Mathematics & Scientific Computing
2021-02-11 16:00 - 16:30 | Zoom Event, MI, United States
Bio: Saibal De is a 5th year PhD candidate in Applied and Interdisciplinary Mathematics. His research involves using high-performance computing and novel algorithms for accelerating physics-based simulation frameworks, and developing faithful reduced-order models of black-box high-fidelity simulations.
TENSOR METHODS FOR DATA COMPRESSION: With the advancement of computing software and hardware, physics-based simulations have gained notoriety in many scientific and industrial applications due to their highly accurate prediction capabilities. However, in addition to being computationally expensive, even a single of these high-fidelity simulations produce massive amounts of data. Storing and processing all these data thus requires novel approaches. In this talk, I will present how we can use tensor factorization methods for compressing scientific data, leading to dramatic savings in disk-space usage. Towards the end of the talk, I’ll also touch upon how we can potentially construct reduced-order models out of these compressed datasets.

This event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

MICDE Seminar: Emma Lejeune, Assistant Professor, Mechanical Engineering, Boston University
2021-02-16 15:00 - 16:00 | Zoom Event
Bio: Emma Lejeune is an Assistant Professor in the Mechanical Engineering Department at Boston University. She received her PhD from Stanford University in September 2018, and was a Peter O’Donnell, Jr. postdoctoral research fellow at the Oden Institute at the University of Texas at Austin until 2020 when she joined the faculty at BU. At BU, Emma has received the David R. Dalton Career Development Professorship, a Computational Science and Engineering Junior Faculty Fellowship, and the Haythornthwaite Research Initiation Grant from the ASME Applied Mechanics Division. Current areas of research involve integrating data-driven and physics based computational models, and characterizing and predicting the mechanical behavior of heterogeneous materials and biological systems.
MODELING HETEROGENEOUS MATERIALS: BENCHMARK DATASETS, METAMODELS, AND EXPERIMENTAL CHARACTERIZATION:
Biological systems are spatially heterogeneous across scales. To effectively model biological materials we need new tools to quantify and capture this heterogeneity. In this talk, we will first discuss our recent work on simulating spatially heterogeneous materials. Specifically, we will discuss our recent work in developing and exploring benchmark datasets of spatially heterogeneous materials simulated with the finite element method. These datasets are useful primarily for constructing metamodels, or computationally cheap models of models, that map defined model inputs to defined model outputs. By nature, a given metamodel will be tailored to a specific dataset. However, the most pragmatic metamodel type and structure will often be general to larger classes of problems. At present, the most pragmatic metamodel selection for predicting the mechanical behavior of spatially heterogeneous materials — specifically simulations of heterogenous materials — has not been thoroughly explored. Drawing inspiration from the benchmark datasets available to the computer vision research community, we introduce a benchmark data set (Mechanical MNIST https://open.bu.edu/handle/2144/39371) for constructing metamodels of heterogeneous material undergoing large deformation. We then show a few examples of problems that we have explored thus far with this dataset. Looking forward, we anticipate that disseminating benchmark datasets will enable the broader community of researchers to develop improved metamodeling techniques for capturing the behavior of spatially heterogeneous materials that will surpass the baseline performance that we show here. Finally, to conclude the talk, we will change gears and briefly discuss some of our recent work on creating new tools for characterizing cell behavior using concepts from kinematics and spatial statistics. Looking forward, we are interested in the natural synergy between advances in methods for both simulating and characterizing heterogeneous materials.

The MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Dr. Lejeune will be hosted by Professor Krishna Garikipati, MICDE Director.
Watch the full webinar.
Questions? Email MICDE-events@umich.edu

Ph.D Seminar: Matthew Duschenes & Yi Zhu
2021-02-18 16:00 - 17:00 | Zoom Event, MI, United States
MATTHEW DUSCHENES, GRADUATE STUDENT, APPLIED PHYSICS & SCIENTIFIC COMPUTING
Bio: I am in my third year of the Applied Physics & Scientific Computing Ph.D. programs, after completing a master’s in theoretical physics in my home country of Canada. As a member of Dr. Krishna Garikipati’s Computational Physics group, I am currently working on data driven modelling and am collaborating with several groups on applying these graph theoretic approaches to various systems of interest.
GRAPH THEORETIC APPROACHES FOR PHYSICAL SYSTEMS: Numerical analyses of physical systems are conventionally performed using direct numerical simulations, that have proven highly successful, yielding high fidelity solutions to very high dimensional problems, such as boundary value problems with upwards of tens of millions of degrees of freedom. However, there is always a balance to be met between the desire for higher accuracy and additional physics to be modeled, and the complexity, interpret-ability and ease of representation of such solutions. To aid in this dilemma, I will be introducing a novel graph theoretic approach, allowing for lower dimensional, reduced order models to be produced, given small amounts of high fidelity data. In this talk I will explain how such an approach allows for an intuitive representation of the states of a systems, and how it is possible to use a non-local calculus, allowing for rigorous operators and equations to be defined on the graph. I will then be discussing some implementation details, and convey the generality, validity, and future applications of this framework through some example results from collaborations.
YI ZHU, GRADUATE STUDENT, CIVIL AND ENVIRONMENTAL ENGINEERING & SCIENTIFIC COMPUTING
Bio: Yi is a 3rd year PhD candidate in Civil and Environmental Engineering & Scientific Computation. His research focuses on simulation, design, and fabrication of active origami systems for engineering devices, and is particularly focused on micro-scale shape morphing systems inspired by origami.
SIMULATION AND DESIGN OF MICRO-ORIGAMI SYSTEMS: In this talk, we will introduce some recent advancement in the simulation and the design of micro-origami systems. We will discuss the micro-origami structures we fabricated and the rapid simulation framework we developed to capture the behaviors of these active origami. We will focus on the simulation framework and demonstrate how we can capture the thermo-mechanically coupled folding behavior and contacts between origami panels effectively and rapidly. Finally, we will introduce some ongoing work on extracting origami design principle with interpretable machine learning, which demonstrates how we can use the simulation framework to create better origami design.

This event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

Using GPUs with Python
2021-02-24 14:00 - 17:00 | Your Desktop
Python is the Lingua Franca of Data today and is being increasingly used in scientific computations. This workshop introduces Python GPU tools for porting and writing code that runs on GPUs. The primary tools, Numba and CuPy, are presented with examples. Back by popular demand, this workshop is presented by Kristopher Keipert of NVIDIA.
This event is open to students, faculty, and staff within the University of Michigan community. A Jupyter notebook is used along with a set of lecture slides. The workshop will use online tools, so there is no need to install any software ahead of time.
This event is brought to you by the Michigan Institute for Computational Discovery and Engineering, and Consulting for Statistics, Computing & Analytics Research at the University of Michigan in partnership with NVIDIA.
Space is limited, register today!

Ph.D. Seminar: Anil Yildirim & Jiale Tan
2021-02-25 16:00 - 17:00 | Zoom Event, MI, United States
ANIL YILDIRIM, GRADUATE STUDENT, AEROSPACE ENGINEERING & SCIENTIFIC COMPUTING
Bio: Anil Yildirim is a PhD candidate in Aerospace Engineering and Scientific Computing. His research focuses on the development and application of robust computational tools in the context of multidisciplinary design optimization for aircraft configurations.
ROBUST AND HIGH-PERFORMANCE TOOLS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION: The development of future sustainable aircraft heavily relies on the design and integration of advanced propulsion systems. However, the design of these systems are challenging due to the tightly coupled interactions between the aerodynamic and the propulsion disciplines. My research focuses on enabling these advanced technologies using aeropropulsive design optimization, in which the aerodynamic and propulsion system designs are optimized in a coupled manner. In this process, I use multiple robust and high-performance computational tools including the computational fluid dynamics (CFD) solver we have been developing in the MDO Lab at the University of Michigan. In this talk, I will cover some recent advancements in the field of CFD-based aeropropulsive design optimization and the computational methodologies we have been using for this work.

JIALE TAN, GRADUATE STUDENT, EPIDEMIOLOGY & SCIENTIFIC COMPUTING
Bio: Jiale is a second year Phd student working with Prof. Rafael Meza in Epidemiology. His interest is to apply computational skills to public health challenges so that he can develop and apply modeling techniques for infectious and noninfectious diseases, including for viral infections like HIV and HCV, and eventually use them for modeling non-communicable diseases that disproportionately affect global health like cancer.
MARKOV MULTISTATE TRANSITION MODEL ON ELECTRONIC NICOTINE DELIVERY SYSTEMS AND TRADITIONAL CIGARETTES: Electronic nicotine delivery systems (ENDS) have dramatically changed the landscape of tobacco products patterns in the USA since 2011. The impact of ENDS use on traditional cigarettes smoking remains a topic of considerable debate. A Markov multistate transition model was used to estimate transition rates (Hazard rate) between ENDS and cigarette use states (25 use states); never user, non-current experimental user, non-current regular user, current experimental user, and current regular user for each product. A 25×25 transition matrix was generated from this model. Parallel computations using 150 processors was used to estimate the transition rates. The Population Assessment of Tobacco and Health study, which includes longitudinal data from 11,475 youth of ages 12 to 24 years from 2013-2018 was used to calibrate the model. The hazard estimates show the patterns of ENDS and cigarette use experimentation and transition to regular use. Next steps will assess the impact of different sociodemographic covariates (age, sex, race, education, household income) on the estimated transition rates.

This event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
This webinar was not recorded for public distribution.
Questions? Email MICDE-events@umich.edu

MICDE Seminar: Santo Fortunato, Director of the Indiana University Network Science Institute (IUNI), Professor, School of Informatics, Computing, and Engineering (SICE), Indiana University at Bloomington
2021-03-01 13:00 - 14:00 | Zoom Event, MI, United States
About Dr. Fortunato: Santo Fortunato is the Director of the Indiana University Network Science Institute (IUNI) and a faculty at Luddy School of Informatics, Computing and Engineering. Previously he was professor of complex systems at the Department of Computer Science of Aalto University, Finland. Prof. Fortunato got his PhD in Theoretical Particle Physics at the University of Bielefeld In Germany. He then moved to the field of complex systems, via a postdoctoral appointment at Luddy School of Informatics, Computing and Engineering of Indiana University. His current focus areas are network science, especially community detection in graphs, computational social science, science of science, climate change. His research has been published in leading journals, including Nature, Science, PNAS, Physical Review Letters, Reviews of Modern Physics, Physics Reports and has collected over 33,000 citations (Google Scholar). His review article Community detection in graphs (Physics Reports 486, 75-174, 2010) is one of the best known and most cited papers in network science. He received the Young Scientist Award for Socio- and Econophysics 2011, a prize given by the German Physical Society, for his outstanding contributions to the physics of social systems. He is the Founding Chair of the International Conference on Computational Social Science (IC2S2) and Chair of Networks 2021, the first merger of the NetSci and the Sunbelt conferences, possibly the largest ever event in network science.
COMMUNITY DETECTION IN NETWORKS: Complex systems typically display a modular structure, as modules are easier to assemble than the individual units of the system, and more resilient to failures. In the network representation of complex systems, modules, or communities, appear as subgraphs whose nodes have an appreciably larger probability to get connected to each other than to other nodes of the network. In this talk I will discuss three main issues in this area. I will address the limits of the most popular class of clustering algorithms, those based on the optimization of a global quality function, like modularity maximization. Testing algorithms is probably the single most important issue of network community detection, as it implicitly involves the concept of community, which is ill-defined. I will discuss the importance of using realistic benchmark graphs with built-in community structure. Finally, I will introduce an increasingly popular post-processing technique that allows to “average” the results of stochastic clustering algorithms, improving their quality: consensus clustering.

Watch the full webinar recording.
The MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

PhD Seminar: K G & Ryan Sandberg
2021-03-04 16:00 - 17:00 | Zoom Event, MI, United States
K G, PSYCHOLOGY & SCIENTIFIC COMPUTING
Bio: K is a 4th year PhD candidate in Psychology and Scientific Computing. He has a Bachelors and a Masters degree in Biomedical Engineering and a Masters in Psychology. He works in the multisensory perception lab with Dr. David Brang and studies how multisensory integration occurs in the human brain and their mechanisms.
EFFECTS OF VISUAL SPEECH ON AUDITORY SPEECH PERCEPTION: For quite some time now, the notion of different regions in the brain being highly interconnected instead of being segregated into modules has been widely discussed. There are numerous studies that provide evidence for such an effect where distinct regions in the brain responsible for different functionalities work together to create a unified sense of reality. A case in point would be audio-visual integration, where a person’s auditory stimuli/input is modulated by visual stimuli. One such example is the McGurk effect where the auditory component of one sound, paired with the visual component of another sound leads to the perception of a third sound. How does this effect happen and what are the ways in which the brain handles integration of these different senses? My research explores questions such as whether the brain integrates information from two different senses in a third, unrelated region of the brain or whether the sense of integration is just an illusion created by the modulatory effect of one sense on another. In this talk, I would provide evidence indicating a modulatory effect of visual stimuli on auditory speech perception. Results from complimentary data obtained using two different imaging modalities including intracranial electrocortocographic recordings and functional magnetic resonance imaging would be discussed.

RYAN SANDBERG, GRADUATE STUDENT, APPLIED AND INTERDISCIPLINARY MATHEMATICS & SCIENTIFIC COMPUTING
Bio: I work with Robert Krasny in math and Alec Thomas in NERS on numerical methods in plasma physics, incorporating tree codes and particle methods in plasma simulation. I also study plasma-based electron and photon acceleration.
FARRSIGHT: A FORWARD ADAPTIVELY REFINED AND REGULARIZED SEMI-LAGRANGIAN INTEGRAL GPU- AND HEIRARCHICAL TREECODE-ACCELERATED METHOD FOR THE VLASOV-POISSON SYSTEM: We present a new forward semi-Lagrangian particle method for the Vlasov-Poisson (VP) system. Recently developed methods for the VP system include deformable particles and high-order or discontinuous-Galerkin Eulerian methods. In contrast to these, we do not use any operator splitting and obtain the electric field by summing regularized pairwise particle interactions using a GPU-accelerated tree-code. We remesh and use adaptive mesh refinement to maintain an efficient representation of phase space. We benchmark on several standard test cases including Landau damping and the two-stream instability. We also compare the multi-threaded and single-GPU performance of the method.

This event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Register via Zoom to immediately receive login details for this event. Note: You may register and join after the event has started.
Questions? Email MICDE-events@umich.edu

MICDE Seminar: Warren B. Mori, Professor, Physics and Astronomy, Electrical and Computer Engineering, University of California, Los Angeles
2021-03-11 14:00 - 15:00 | Zoom Event, MI, United States
WATCH THE RECORDING HERE.
Bio: Warren B. Mori is a Distinguished Professor in the departments of Physics and Astronomy and of Electrical and Computer Engineering a UCLA. He received his BS from UC Berkeley in 1981, and his M.S. and Ph.D. from UCLA in 1984 and 1987, respectively. He has been at UCLA from 1981 until today. He served as the Director of the UCLA Institute for Digital Research and Education from 2006 until 2021. His current research interests are in advanced computing, particle-in-cell simulations of plasmas, basic plasma physics, high intensity laser and beam plasma interactions, plasma based accelerators and light sources, nonlinear optics of plasmas, inertial fusion science, and high energy density science. He is the coauthor of more than 400 publications on a variety of topics in plasma and computational physics. He is a fellow of both APS (1997) and IEEE (2009) and is a current member of both societies. In 1987 he received the International Center for Theoretical Physics Medal for Excellence in Nonlinear Plasma Physics by a Young Researcher was a recipient of the Advanced Accelerator Concepts Prize in 2016 for, “ his leadership and pioneering contributions in theory and particle-in-cell code simulations of plasma based particle acceleration.” In 2020 he received the APS James Clerk Maxwell prize for, “leadership in and pioneering contributions to the theory and kinetic simulations of nonlinear processes in plasma-based acceleration and relativistically intense laser and beam plasma interactions.
PLASMA BASED ACCELERATION AND THE ROLE OF HIGH FIDELITY SIMULATIONS IN ITS DEVELOPMENT
Particle accelerators are critical components of high energy physics colliders and x-ray free electron lasers (XFELs), which are complex and expensive tools for scientific discovery. To reduce the size and cost of these tools there is active research aimed at finding new technologies for compact accelerators. One such possibility is the use of plasma waves which phase velocities near the speed of light that can be excited as wakefields behind intense lasers and particle beams as they traverse tenuous plasmas. These ideas are the basis for the field of plasma based acceleration (PBA). In this talk I will describe how PBA works, and how high fidelity computer simulations have and are playing a critical role in its development. I will also describe the simulation methods and their associated algorithms. Last, I will offer some perspectives for the future of plasma based acceleration and the simulation methods that will critical role in this future. Work supported by DOE and NSF.

This seminar is co-presented by the Michigan Institute for Computational Discovery & Engineering and the Michigan Institute for Plasma Science and Engineering. Dr. Mori will be hosted by Professor Alec Thomas, Professor of Nuclear Engineering and Radiological Sciences, Electrical Engineering and Computer Science, and Physics.
Register for this event via Zoom to receive an email with the link and passcode to connect. Note: You may register after the event has started.
The MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

PhD Seminar: Anna Redgrave and Agnit Mukhopadhyay
2021-03-11 16:00 - 17:00 | Zoom Event, MI, United States
ANNA REDGRAVE, GRADUATE STUDENT, ECOLOGY AND EVOLUTIONARY BIOLOGY & SCIENTIFIC COMPUTING
Bio: Anna Redgrave began her science career as an undergrad, master’s student, and lab technician studying developmental biology in zebrafish. She became fascinated by how complicated developmental systems are, and joined the Wittkopp lab at U-M for her PhD to investigate one mechanism of complicating developmental systems: gene duplication.
REGULATORY DIVERGENCE OF DUPLICATED GENES: Gene duplication has long been studied as a mechanism of evolution at the genetic level. Duplicated genes introduce redundant protein-coding sequence, allowing duplicates to acquire novel functions while preserving existing functions. Gene duplication, however, also provides a substrate for non-protein coding, regulatory sequence evolution. Genes are duplicated with varying levels of their native regulatory sequence intact. This prompts the question: how does the degree to which duplication preserves native regulatory sequence affect future evolutionary paths? Here, I investigate this question by comparing the expression profiles of duplicate genes across many environments in two diverging species of yeast.
AGNIT MUKHOPADHYAY, GRADUATE STUDENT, CLIMATE AND SPACE SCIENCES AND ENGINEERING & SCIENTIFIC COMPUTING
Bio: Agnit is a NASA Earth & Space Sciences Fellow at the Climate and Space Sciences and Engineering department at the University of Michigan, with a background in Aerospace Engineering. He is co-advised by Drs. Michael Liemohn and Daniel Welling to quantify the nonlinear coupling between the Earth’s atmosphere and it’s near-plasma environment. He loves working with numerical models to assess and predict the impact of extreme natural events on life and technology.
QUANTIFYING THE IMPACT OF THE AURORA ON SPACE WEATHER: Conjuring a captivating vista of a colourful nightsky, the aurora borealis (Northern Lights) and australis (Southern Lights) are a byproduct of upper atmospheric ionization by charged particles (plasma) of solar origin. The near-constant drizzling of auroral plasma particles from outer space are excellent drivers of space weather activity caused by solar disruptions like flares and coronal mass ejections that can adversely affect man-made technology like GPS satellites, electrical power grids and oil pipelines. Using a combination of physics-based models, data regression tools, in-situ satellite and ground-based telemetry, we figure out what forms and drives the aurora, how these drivers modify the aurora’s electro-chemical atmospheric modification, and how this system could be predicted during extreme natural events.

Register via Zoom to immediately receive login information. Note: You may register and join after the event has started.
This event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

MICDE Seminar: Udo von Toussaint, PD, Group Leader at the Max-Planck-Institute for Plasmaphysics in Garching, Divison Numerical Methods for Plasmaphysics
2021-03-18 11:00 - 12:00 | Zoom Event, MI, United States
WATCH THE RECORDING HERE.
Bio: Dr. Udo v. Toussaint earned his PhD in Physics at the University of Bayreuth in 2000. He then worked as a Postdoctoral Researcher at NASA Ames (RIACS), in Mountain View, CA from 2000-2002. Since 2003, Dr. von Toussaint has been a Scientist at the Max-Planck Institute for Plasmaphysics in Garching. Dr. von Toussaint is also editor of the ‘Entropy’ journal.
Besides plasma-wall interaction, his research interests are focussed on the design of optimal analysis and measurement strategies (Bayesian experimental design) for computer- and physics experiments. This encompasses modern concepts of uncertainty quantification (UQ) of complex computer codes (e.g. Plasma-wall simulations) as well as active-learning systems, which dynamically decide which action (e.g. measurement of a specific spectral line) might yield the most informative data based on the results from previous actions. This is addressed with Machine Learning techniques, e.g. Hidden Markov Models (HMM), neutral networks or bayesian acyclic graphs and complemented by numerical methods like Markov Chain Monte Carlo (MCMC), sequential optimization or polynomial chaos expansion.
A BAYESIAN APPROACH TO ARTIFICIAL NEURAL NETWORKS: Artificial Neural networks (ANN) are famous for their advantageous flexibility for problems when there is insufficient knowledge to set up a proper model. On the other hand, this flexibility can cause overfitting and can hamper the generalization and stability of ANNs. Many approaches to regularize ANNs have been suggested (e.g. L1- or L2-norm based regularization) but most of them are based on ad hoc arguments. Employing the principle of transformation invariance, a general prior for feed-forward networks can be derived. This regularization prior not only favours cell and layer pruning but enable also a consistent Bayesian approach: Relying on Occam’s razor we demonstrate (as a proof of concept) how an ANN can be applied even in the >absence< of available training data. The relation to the concept of automatic relevance detection will be discussed.

The MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Dr. von Toussaint will be hosted by Professor Xun Huan, Assistant Professor of Mechanical Engineering.
Register for this event via Zoom to receive an email with the link and passcode to connect. Note: You may register after the event has started.
Questions? Email MICDE-events@umich.edu

PhD Seminar: Vishwas Goel and Benjamin Yang
2021-03-18 16:00 - 17:00 | Zoom Event, MI, United States
VISHWAS GOEL, GRADUATE STUDENT, MATERIALS SCIENCE AND ENGINEERING & SCIENTIFIC COMPUTING
Bio: Vishwas is a third year Ph.D. student in the Thornton group, Department of Materials Science and Engineering. His research involves the simulations of the continuum level or microstructure level electrochemical dynamics of energy conversion/storage devices such as batteries, fuel cells, etc.
SIMULATION OF EIS IN SOFC CATHODES USING SMOOTHED BOUNDARY METHOD: Electrochemical impedance spectroscopy is the most commonly used technique for the in-situ characterization of solid oxide fuel cells (SOFC). In this presentation, I will discuss about a method for simulating the impedance behavior of a mixed conducting SOFC cathode with an experimentally determined microstructure. I will also share the key insights that we generated through our work.
BENJAMIN YANG, GRADUATE STUDENT, BIOMEDICAL ENGINEERING & SCIENTIFIC COMPUTING
Bio: Ben is a 4th year PhD student in Dr. Carlos Aguilar’s Lab. His research explores the molecular mechanisms that regulate cellular fate plasticity using microfluidics, cell-cell fusion, and single-cell sequencing techniques.
DECONSTRUCTING METASTATIC REGULATORS USING INTERSPECIES HETEROKARYONS: Tumor metastasis, the spread of cancer cells to sites beyond the primary tumor, is the primary contributor to morbidity in cancer patients. While each step of the metastatic cascade is well characterized, the molecular mechanisms responsible for initiating the cascade remain unclear, inhibiting the efficacy of therapeutic modalities. We revisit a century-old hypothesis that changes in metastatic potential are conferred to tumor cells through fusion with neighboring stromal cells by fusing human breast cancer cells with brain-resident mouse microglia and astrocytes. Our main objectives are to assess how aberrant fusion between malignant cells and stromal cells overrides transcriptional safeguards against metastatic progression and to explore how fusion modifies the mechanical phenotype of tumor hybrids. Achieving these goals will advance our understanding of the biological significance of fusion events in metastasis and delineate markers that can serve as therapeutic targets.

Register via Zoom to immediately receive login information. Note: You may register and join after the event has started.
This event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

AIM/MICDE Seminar: Daniel Lecoanet, Postdoctoral Fellow, Princeton Center for Theoretical Science, Astrophysical Sciences, Princeton University
2021-03-19 15:00 - 16:00 | Zoom Event, MI, United States
Bio: Dr. Lecoanet earned a Bachelor of Science degree in Mathematics from the University of Wisconsin at Madison, a Master’s degree in Applied Mathematics from the University of Cambridge, and a PhD in Physics from the University of California, Berkeley. He currently holds a joint postdoc position at the Princeton Center for Theoretical Science and as a Lyman Spitzer, Jr. fellow at the Department of Astrophysical Sciences. Dr. Lecoanet works primarily on Astrophysical and Geophysical Fluid Dynamics. He is a core developer for Dedalus.
PROBING THE CORES OF MASSIVE STARS THROUGH THEIR SURFACE: Stars are opaque, which makes it difficult to study their interiors. Recent space-based telescopes have led to the new field of asteroseismology: by measuring global oscillation modes of a star, you can infer its interior properties. Massive stars have convection in their cores which can generate waves, which might be detectable at the surface. In the first part of this talk, I will describe a heuristic way of estimating wave generation by convection, and compare it to high-resolution numerical simulations in Cartesian geometry. To make quantitative predictions to compare with observations, one must run simulations in spherical geometry. In the second part of my talk, I will present a new spectral algorithm for solving nearly arbitrary, tensorial PDEs in spherical coordinates. The challenge is to devise bases which respect regularity conditions at r=0, which depend on the rank of the tensor. The algorithm can be easily applied to the problem of wave generation by convection in stars, as well as a wide range of other problems in stellar astrophysics, core geophysics, and planetary sciences.

This seminar is co-presented by Applied and Interdisciplinary Mathematics program, and the Michigan Institute for Computational Discovery & Engineering. Dr. Lecoanet will be hosted by Professor Charlie Doering, the Nicholas D. Kazarinoff Collegiate Professor of Complex Systems, Mathematics and Physics, and Director of the Center for the Study of Complex Systems.
Register for this event to receive Zoom login information.
The MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

PhD Seminar: Chanese Forte and Hyeon Joo
2021-03-25 16:00 - 17:00 | Zoom Event, MI, United States
CHANESE FORTE, GRADUATE STUDENT, ENVIRONMENTAL HEALTH SCIENCES & SCIENTIFIC COMPUTING
Bio: Chanese is a Dual PhD student pursuing a degree in the Environmental Health Sciences and Scientific Computing. Chanese’s research interests lie in chemical exposure in agriculture workers and cellular alteration.
ASCERTAINING PESTICIDE EXPOSURE AND BIOACTIVITY USING OPEN SOURCE DATA: Pesticides are known to be harmful chemicals to human health, however, they are still heavily used in agriculture. Using large publicly available datasets, this study aims to quantify pesticide exposure levels of the US general population in comparison to farmworkers. The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional study representative of the US population. NHANES was used to quantify pesticide exposure among US farmworkers and the general population who responded to NHANES. It compares and analyzes, using regression, the US pesticide exposure levels to the bioactivity of these same pesticides within the human body. By comparing population-level data with toxicological assay data in future projects, we hope to create a more overarching idea of how pesticides may be affecting the body and the human population level.
HYEON JOO, GRADUATE STUDENT, HEALTH INFRASTRUCTURES AND LEARNING SYSTEMS & SCIENTIFIC COMPUTING
Bio: Hyeon Joo is a second year PhD student in the Health Infrastructures and Learning Systems program of the Department of Health Learning Systems (Michigan Medical School). He completed his MS in Computer Science and Engineering, and Master of Health Informatics from the University of Michigan, Ann Arbor. His research focuses on developing and implementing computational data-driven algorithms, systems or tools to help users identify gaps and make informed decisions. He loves working in the field of health care as a data scientist and a software engineer.
EARLY PREDICTION OF HEART FAILURE USING ATTENTION MODELS USING EHR DATA: Heart Failure (HF) is a severe and progressive chronic condition affecting over 5.8 million patients with a 5-year mortality rate of 45-60% in the United States. Despite significant efforts and advanced HF management, diagnosing HF in the early stages remains challenging due to its syndromic nature and non-specific disease presentation. In this seminar, I will present a single attention recurrent network and a hierarchical attention convolutional neural networks to detect the early stage of HF at a tertiary hospital. I will also describe various methods of feature selection to reduce the computation time and improve the performance of the models. Lastly, I will present the challenges of adopting models in clinical practice which leads to my next research steps.

Register via Zoom to immediately receive login information. Note: You may register and join after the event has started.
This event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

NVidia GTC 2021 Conference
2021-04-12 - 2021-04-16 | MI
NVIDIA has their GTC technical conference April 12-16. It is free to attend and all online so no travel. Making you aware of this opportunity to hear from a global community of developers, researchers, engineers, and innovators who are delivering over a 1000 sessions, interactive panels, demos, and research posters. Registration is now open and the session catalog is published. Register HERE
Keynote
You will want to attend NVIDIA CEO Jensen Huang’s keynote. Traditionally NVIDIA uses Jensen’s keynote to make important announcements.

Keynote: April 12, 11:30am-12:30pm Eastern Time

Sessions
Sessions ranging from very technical developer and researcher-focused talks, to business and implementation focused topics from leaders in their field. Below are a few examples of the sessions available at GTC 2021. (abstracts and details in the Session Catalog)

Accelerating Ray Tracing for the IceCube Neutrino Observatory with CUDA

Benedikt Reidel, Computing Manager, University of Wisconsin-Madison and IceCube Particle Astrophysics Center

Using Molecular Simulations to Help Drive Pharmaceutical Drug Discovery

David Mobley, Professor University of California, Irvine

Convergence of AI and HPC to Solve Grand Challenge Science Problems

Rommie Amaro, Professor University of California, Irvine

Toward a One-Hour Genomic Workup

Tychele Turner, Assistant Professor Washington University in St Louis

GPU-Accelerated Quantum Chemistry and Molecular Dynamics

NVIDIA Engineers
Register today, it only take a minute or two!

Object Detection using Deep Learning with TorchVision
2021-04-13 10:00 - 12:00 | Your Desktop
Like many image processing problems, deep learning has brought many effective solutions to the task of object detection. The TorchVision library is part of the PyTorch project, and it offers well-established and successful methods for object detection (as well as many other problems). This workshop will demonstrate the process of preparing your own image dataset and training it using TorchVision with one of Google Colab’s free-to-use GPUs. The workshop will be done online via Zoom. Some experience with Python is helpful, but no previous experience with PyTorch is needed. Google Colab requires a Google account (e.g. your umich account).

MCAIM Colloquium: Sheperd S. Doeleman, Harvard University, Founding Director of the Event Horizon Telescope
2021-04-14 16:00 - 17:00 | Zoom Event, MI, United States
Talk Title: Black Hole Imaging: First Results and Future Vision
Abstract: In April 2017, the Event Horizon Telescope (EHT) carried out a global Very Long Baseline Interferometry (VLBI) observing campaign at a wavelength of 1mm that led to the first resolved image of a supermas- sive black hole. For the 6.5 billion solar mass black hole in the giant elliptical galaxy M87, the EHT estimated the spin orientation and constrained models of accretion on Schwarzschild radius scales. This work relied on two decades of technical advances in ultra-high resolution interferometry and theoretical General Relativistic Magnetohydrodynamic (GRMHD) simulations. This talk will review these advances and recent new EHT results.
We will also look to the next decade when a next-generation EHT (ngE-HT) that doubles the number of participating radio dishes in the VLBI net-work will enable time-lapse movies of M87 that link the black hole to the relativistic jet it powers. For SgrA*, the Galactic Center black hole that evolves on time scales 1000 times faster, ngEHT will produce real-time video.
Bio: Sheperd S. Doeleman is an Astrophysicist at the Center for Astrophysics | Harvard & Smithsonian and Founding Director of the Event Horizon Telescope (EHT), a synchronized global array of radio observatories designed to examine the nature of black holes. He is also a Harvard Senior Research Fellow and a Project Co-Leader of Harvard’s recently established Black Hole Initiative (BHI). The BHI is a first-of-its-kind interdisciplinary program at the University that brings together the disciplines of Astronomy, Physics, Mathematics, Philosophy, and History of Science to define and establish black hole science as a new field of study.
As one of the founding members of the BHI, Doeleman leads a team studying supermassive black holes with sufficient resolution to directly observe the event horizon itself. Using Very Long Baseline Interferometry (VLBI) methods, the EHT telescope networks observe astronomical radio sources at 1.3 millimeter (mm) wavelengths. These sources include the supermassive black holes at the centers of our own Milky Way, called Sagittarius A* (SgrA*), as well as in Messier 87 (M87), the supergiant elliptical galaxy in the constellation Virgo.
Doeleman is a Guggenheim Fellow (2012) and was the recipient of the DAAD German Academic Exchange grant for research at the Max Planck Institute für Radioastonomie. He serves as a peer reviewer for the Astrophysical Journal, Science, and Nature, among others. Doeleman leads and co-leads research programs supported by grants from the National Science Foundation, the National Radio Astronomy Observatory (NRAO) ALMA-NA Development Fund, the Smithsonian Astrophysical Observatory, the MIT International Science & Technology Initiatives (MISTI), the Gordon and Betty Moore Foundation, and the John Templeton Foundation. He has taught at MIT and mentors students and post-doctoral fellows at MIT and Harvard.
Doeleman received his B.A. from Reed College in 1986, and left soon after for a year in Antarctica where he conducted multiple space-science experiments at McMurdo Station on the Ross Ice Shelf. With an appreciation for the challenges and rewards of instrumental work in difficult circumstances, he returned to complete a Ph.D. in astrophysics at MIT. After visiting the Max Planck Institute as a recipient of the DAAD, he came back to MIT in 1995 for a postdoctoral fellowship, eventually serving as assistant director of the MIT Haystack Observatory.
Doeleman’s interests focus on problems in astrophysics that require ultra-high resolving power—the ability to observe fine details of cosmic objects. His research employs the technique of Very Long Baseline Interferometry (VLBI), in which widely separated radio dishes are combined to form an Earth-sized virtual telescope. He has used this technique to study the atmospheres of dying stars, as well as stars that are just being born. His group at MIT pioneered development of instrumentation that enables VLBI to achieve the greatest resolving power possible from the surface of the Earth. He carried out the first global experiments using these new systems that successfully measured the size of the supermassive black hole at the center of the Milky Way Galaxy and in the galaxy M87. He now directs the international Event Horizon Telescope project, whose goal is to image the event horizon of a black hole, the boundary where gravity is so strong that even light cannot escape. This project addresses several fundamental questions about the Universe: Do event horizons exist? Does Einstein’s theory of gravity hold near a black hole? How do black holes affect the evolution of galaxies?

Join Zoom Meeting: https://umich.zoom.us/j/98619190605
Meeting ID: 986 1919 0605
Passcode: [REDACTED]
This event is hosted by the Michigan Center for Applied and Interdisciplinary Mathematics.

Computing our way out of a pandemic: modeling in the face of COVID-19
2021-05-06 09:00 - 16:30 | Zoom Event, MI, United States
The COVID-19 pandemic has produced massive amounts of information that require an accurate analysis to predict outcomes and design solutions rapidly. It also has required experts from many different backgrounds to rally around in the quest for rapid responses in the race to save lives.
Many of the most prominent of these researchers are from Michigan, and a significant number of them are computational scientists who addressed questions such as: What measures should be taken to minimize contagion? Is it safe to ride a bus? How are supply and demand chains being affected?
This virtual symposium will bring together researchers from the State of Michigan to share their past and future insights into the pandemic.

View additional event details.
View event agenda.
This is a free Zoom event, open to the general public. Please register to attend.

Marc Henry de Frahan Webinar
2021-06-04 10:00 - 11:00 | Zoom Event, MI, United States
Title: Leveraging modeling hierarchies in the Exascale era: applications to combustion technologies
Abstract: As we approach the confluence of widespread use of machine learning techniques and simulations running at exascale, several important challenges will need to be addressed. In this talk, we explore some of these challenges, with a specific focus on combustion applications. We discuss a combustion simulation code, PeleC, and its performance characteristics on the fastest supercomputers available today. We look at leveraging the resulting high-fidelity simulations to construct data-driven models for lower-fidelity simulations. We then examine how to adapt reinforcement learning methods to explore a modeling hierarchy and determine adequate control strategies for combustion technologies.
Bio: Marc Henry de Frahan is a computational scientist at the National Renewable Energy Laboratory, where he works on improving next-generation wind and combustion processes. As part of the Exascale Computing Project, Marc develops high-fidelity turbulence models to enhance simulation accuracy and efficient numerical algorithms for future high-performance computing hardware architectures. In addition to traditional physics-based modeling, he is integrating deep neural networks into modeling and reinforcement learning into advanced control strategies. Marc obtained his PhD in Mechanical Engineering in 2016 from the University of Michigan.

Zoom information to connect:
Link: https://umich.zoom.us/j/98133041706
Passcode: [REDACTED]

MICDE & MIDAS Graduate Programs Info Session (Virtual)
2021-09-29 12:00 - 13:00 | Zoom Event, MI, United States
Join the MICDE and MIDAS teams for a 1-hour virtual information session to learn more about our computational and data science graduate program offerings, including: the Ph.D. in Scientific Computing, the Graduate Certificate in Computational Discovery & Engineering, the Graduate Certificate in Computational Neuroscience, and the Graduate Certificate in Data Science.
After a short presentation, each program’s faculty director and/or staff manager will be present to answer questions in a small group or 1:1 setting.

Watch the recording of this info session here.

MICDE & MIDAS Graduate Programs Information Tables (In-person)
2021-09-30 15:00 - 16:00 | MI
Meet 1:1 with MICDE and MIDAS graduate program faculty and staff managers to learn more about the institutes and the computational and data science graduate programs they offer, including: the Ph.D. in Scientific Computing, the Graduate Certificate in Computational Discovery & Engineering, the Graduate Certificate in Computational Neuroscience, and the Graduate Certificate in Data Science.
Advanced Research Computing will also be there giving information about their services, including the new free cluster allocations and ARC’s consulting services.

This event will be held in-person under the outdoor canopy tent located on the Ingalls Mall, across the street from the Rackham Graduate School building.
All attendees are required to wear masks.

Check out the recording of our virtual info session on Wednesday, September 29 here.

PhD Seminar: Christiana Mavroyiakoumou and Vishwas Goel
2021-10-28 16:00 - 17:00 | Zoom Event, MI, United States
Register via Zoom to immediately receive login information. Note: You may register and join after the event has started.

The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.
Featured Speakers:

CHRISTIANA MAVROYIAKOUMOU, PhD Candidate, Applied and Interdisciplinary Mathematics, and Scientific Computing
Bio: Christiana Mavroyiakoumou is a 5th year PhD candidate in Applied and Interdisciplinary Mathematics, working on extensible membrane flutter in inviscid flow using theoretical and computational tools. Her advisor is Professor Silas Alben at the Department of Mathematics.
DYNAMICS OF TETHERED MEMBRANES IN INVISCID FLOW: We study the dynamics of membranes (with stretching stiffness but zero bending stiffness) that shed vortex wakes in inviscid flows. Previous studies have focused on membranes with fixed ends, where only static deflection occurs. Here we consider instead membranes held by tethers with hinged ends, and find that a variety of unsteady large-amplitude motions, both periodic and chaotic, may occur. We characterize the dynamics over ranges of the key parameters: membrane mass density, stretching stiffness, pretension, and tether length. We find the region of instability and the small-amplitude behavior in a linearized model by solving a nonlinear eigenvalue problem. We also derive asymptotic scaling laws by considering a simplified model: an infinite periodic membrane. We find qualitative similarities among all three models in terms of the oscillation frequencies and membrane shapes at small and large values of the parameters.

VISHWAS GOEL, PhD Candidate, MATERIALS SCIENCE AND ENGINEERING, and Scientific Computing
Bio: I am a 4th Ph.D. student in the Department of Materials Science and Engineering. My research is focused on multi-scale modeling of electrochemical processes such as energy storage, energy conversion, and corrosion.
MODELING BASED OPTIMIZATION OF HOLE ARCHITECTURE FOR ENABLING FAST CHARGING IN LI-ION BATTERIES: For the widespread adoption of electric vehicles, we need Li-ion batteries (LIBs) that are energy and power dense. However, we cannot realize both these properties even in state-of-the-art commercial Li-ion batteries. This inability is caused by the electrode design used in LIBs. In such a design, to increase the energy density, one needs to increase the active material loading (either in terms of active material mass fraction or the electrode thickness). However, such a design proves to be highly tortuous for the transport of Li-ions in the electrolyte, which causes the electrode to exhibit poor fast charging performance.
In our previous work [1], we demonstrated that the rate performance of the energy-dense electrodes can be improved by employing 3D architectures such as highly ordered laser-patterned electrodes (HOLE). The architecture alleviates the electrolyte mass transport limitations by providing rapid mass transport via laser-ablated channels through the electrode thickness. In this study, we investigate how the geometric parameters of the HOLE design, such as inter-channel spacing and channel radius, affect the fast-charging performance of the HOLE graphite anodes with > 3 mAh/cm2 loading. We conduct this analysis using a fully parameterized continuum scale model based on the porous electrode theory. Our results show that for a constant volume retained (after the laser ablation), the smaller and closer channels exhibit better 4C charging performance than the larger and farther channels.
1. K.-H. Chen et al., J. Power Sources, 471, 228475 (2020) doi.org/10.1016/j.jpowsour.2020.228475

This event is part of MICDE’s Fall 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu
