Michigan Institute for Computational Discovery and Engineering

Introduction to Stata
2020-02-21 09:30 - 16:00 (America/Detroit)
Audience: Those who have never used Stata before but wish to learn.

By the end of the workshop, participants will be able to:

Work with Stata, including using Do-files and using the help system
Get data into Stata and manage your data files
Establish familiarity with your data
Clean the data to prepare it for analysis
Check for basic errors in the data
Generate new variables or manipulate existing variables
Merge or reshape the data
Produce summary tables and descriptive statistics

Note: This workshop does NOT cover any statistical modeling.

Note: This workshop is based on Stata 15; it does not cover the new features in Stata 16.
See upcoming workshop “Stata 16 New Data Management Features” for that material.

MICDE Seminar: Osman Basaran, Professor, Chemical Engineering, Purdue University
2020-02-21 15:00 - 16:00 | 1084 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
Bio: Professor Osman Basaran is a Burton and Kathryn Gedge Professor of Chemical Engineering at Purdue University. He received his undergraduate degree at Massachusetts Institute of Technology and a PhD from the University of Minnesota. Prof. Basaran’s research involves the use of a balanced approach based on computation, theory, and experiment to attack a number of fundamental issues that lie at the heart of such practical problems.
High-accuracy simulation of free surface flows near finite-time pinch-off and coalescence singularities
Motivated by applications such as ink jet printing, drop-by-drop manufacturing, sprays, emulsions, and chemical separations, we study the dynamics of breakup and coalescence through high-accuracy simulation, theory, and experiment. In this talk, I will highlight our group’s work on accurately capturing the fluid dynamics that takes place in the vicinity of finite-time singularities. The free surface flow algorithms and solvers that we develop and use rely on a sharp interface representation of phase boundaries. In the simulations, we are able to analyze situations that involve disparate length scales that differ by up to seven orders of magnitude (commercial codes can handle about 2-3 orders and custom codes can capture at most 3-4 orders of magnitude disparity in length scales). The primary focus of the talk will be on simulations of the breakup of surfactant-covered filaments where I will pay special attention to the pinch-off singularity. I will also summarize some of our recent work on the pre- and post-coalescence singularities that arise when two drops or bubbles are driven together and made to merge into one.
This seminar is co-sponsored by the Applied & Interdisciplinary Mathematics program. Prof. Basaran is being hosted by Prof. Deegan (Physics). If you would like to meet with Prof. Basaran during his visit, please send an email to micde-events@umich.edu. If you are an MICDE student or an AIM student and you’re interested in having lunch with Prof. Basaran during his visit, please RSVP by Thursday, February 20, 2020.

Multilevel models in Python
2020-02-24 15:00 - 17:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
Multilevel modeling is the state-of-the-art approach for handling data with complex dependence structure in a regression analysis. This workshop will discuss fitting multilevel models in Python using the Statsmodels package. We will discuss the motivation and main use cases for multilevel modeling, and illustrate by example how to fit linear and generalized linear mixed models.
Participants should bring a laptop if they want to work with the examples during the presentation, but this is optional.

Complex Systems Seminar: David Goluskin, Assistant Professor, Mathematics and Statistics, University of Victoria
2020-02-25 11:30 - 13:00 | Weiser Hall, Room 747, 500 Church St, Ann Arbor, MI, 48109, United States
Bio: David Goluskin is an Assistant Professor in the Department of Mathematics and Statistics at the University of Victoria. Goluskin received his undergraduate degrees from the University of Colorado, Boulder, a master’s from Columbia University, and a PhD in Applied Mathematics from Columbia University. His research is in the broad area of applied nonlinear dynamics and incorporates both computation and analysis. Much of Professor Goluskin’s work concerns fluid dynamics, but he also studies simpler ordinary and partial differential equations.
Studying dynamics using computational polynomial optimization
Many complex systems are governed by nonlinear ODEs or PDEs that cannot be solved exactly. Various properties of such solutions can be inferred by constructing auxiliary functions that satisfying suitable inequalities. The most familiar example is the construction of Lyapunov functions to infer stability of particular states, but similar approaches can produce many other types of mathematical statements, including for systems with chaotic or otherwise complicated behavior. Such statements include estimates of time-averaged quantities and extreme transient behavior, approximation of nonlinear stability properties, and design of controls. In many cases, the search for the auxiliary function that implies the strongest mathematical statement can be posed as a convex optimization problem. Such problems can be studied analytically or computationally, but in most cases computation is needed to find solutions that are close to optimal. Of particular use are computational methods of polynomial optimization, where the optimization constraints include polynomial inequalities. This talk will provide an overview of different ways in which auxiliary functions can be used to study nonlinear ODEs and PDEs, as well as how polynomial optimization can be used to implement these methods computationally. Methods will be illustrated using applications to various complex systems.

Building software projects with Make: beyond basics
2020-02-26 10:00 - 12:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
In this workshop we will use Make to manage build dependency in a multi-file, multi-language software project. We will learn how to use Make functions, automatically generate dependencies, and inquire the operating system about available packages and libraries. Also, we will briefly review alternative build dependency managers. At the end of the workshop you will be able to understand and write Makefiles for managing dependencies in complex software projects.
Participants will need to have laptops with WiFi connection if they wish to follow the hands-on exercises. A basic knowledge of Unix-like operating systems would be helpful in following and understanding the material, but is not required.

R III: Modeling
2020-02-28 13:00 - 16:00
This workshop will be heavy on conceptual understanding of basic regression modeling, but with demonstration of activities both essential and tangential to good modeling practice. GLM, model interpretation, model comparison, model debugging, model criticism and more will be covered.

Prereq: Some experience using R is required (R I, preferably R II workshops), as well as exposure to basic statistical analysis would be beneficial.

Content: http://m-clark.github.io/data-processing-and-visualization/

MICDE Seminar: Sarah D. Olson, Associate Professor, Mathematical Sciences, Worcester Polytechnic Institute
2020-02-28 15:00 - 16:00 | 1084 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
Bio:Sarah Olson is an Associate Professor in the Department of Mathematical Sciences at Worcester Polytechnic Institute. Olson received her undergraduate degrees in Mathematics and Biology from Providence College, a master’s from the University of Rhode Island in Mathematics, and a PhD in Biomathematics from North Carolina State University. She has worked in the general areas of fluid dynamics, scientific computing, and mathematical biology.
Sperm Navigation in Complex Environments
Microorganisms can swim in a variety of environments, interacting with chemicals and other proteins in the fluid. In this talk, we will highlight recent computational methods and results for swimming efficiency and hydrodynamic interactions of swimmers in different fluid environments. Sperm are modeled via a centerline representation where forces are solved for using elastic rod theory. The method of regularized Stokeslets is used to solve the fluid-structure interaction where emergent swimming speeds can be compared to asymptotic analysis. In the case of fluids with extra proteins or cells that may act as friction, swimming speeds may be enhanced and attraction may not occur.
This seminar is co-sponsored by the Applied & Interdisciplinary Mathematics program. Prof. Olson is being hosted by Prof. Alben (MATH). If you would like to meet with her during her visit, please send an email to micde-events@umich.edu. If you are an MICDE student or a MATH student and you would like to join Professor Olson for lunch during her visit, please RVSP by Feb. 27.

Introduction to Python's NumPy library
2020-03-03 10:00 - 14:00
This workshop will introduce you to the NumPy library in Python, which is useful in scientific computing. We will cover NumPy’s n-dimensional array object and associated functions in depth, along with related linear algebra and random number capabilities. Some familiarity with Python is expected. Computers will be available to complete exercises.

Visualization of spatial data
2020-03-05 14:00 - 17:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
This workshop will cover basic concepts and tools available in QGIS and R for visualizing spatial data. We will cover vector data but will also touch upon the visualization of raster and spatial network data.
Participants should have some familiarity with R, but exposure to QGIS is not required.

R by Example: Functional Programming with data.table
2020-03-06 14:30 - 16:00
In the R by Example series of workshops, we’ll discuss example analyses in R as a vehicle for learning commonly used tools and programming patterns. The “Functional Programming with dplyr” workshop will initially focus on analyzing winter home temperatures in the US using data from the Residential Energy Consumption Survey (https://www.eia.gov/consumption/residential/). We’ll use the data.table package for data manipulation, and then demonstrate how to encapsulate the basic pattern within a function. Such functional programming allows us to repeatedly apply this pattern to answer other questions about this data. By using a function, we make our code more concise and easier to understand. This workshop is geared towards intermediate to advanced R users, or as a follow-up to the “Analyzing RECS using data.table” workshop.

Survival analysis in Python
2020-03-09 15:00 - 17:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
Survival analysis is used when working with data that may be censored, as often is the case in studies of human subjects with incomplete follow-up. The presence of censoring makes most forms of regression and other standard statistical analyses inappropriate. A body of specialized techniques for analyzing this type of data has been developed, including methods for estimating and comparing marginal survival functions, and regression methods including the widely-utilized Cox proportional hazards model. This workshop will briefly review the key principles of survival analysis, then illustrate by example how various survival analysis methods can be carried out using Python with the Statsmodels package.
Participants should bring a laptop if they want to work with the examples during the presentation, but this is optional.

Introduction to SAS: Simple Inference Procedures
2020-03-10 08:30 - 15:30
Prerequisites: Participant should have some familiarity with introductory statistics and be able to load data into and perform basic data manipulations in SAS.
In this one-day, six-hour workshop we will discuss the basics of using SAS for statistical inference and modeling. The workshop is held in a computer lab and will alternate between instructor presentations and attendee work sessions. After this course the attendee will be able to perform, in SAS, basic statistical inference procedures (hypothesis tests, confidence intervals) for a variety of data scenarios such as one-sample, independent-samples, and paired-sample t-tests; chi-square test of independence of two categorical variables; correlation between two interval variables; ANOVA; simple and multiple linear regression; and simple and multiple logistic regression. Good statistical practice will be demonstrated but this workshop is not designed to teach statistics.

Regular Expressions
2020-03-13 14:00 - 16:30
Regular expressions are perfectly suited for people who like puzzles. Regular expressions are a sequence of characters used to define a search pattern. They are commonly used to do “find” and “find and replace” string operations. They are also used to validate strings like phone numbers, passwords, etc. in data entry. Regular expression capabilities can be found in a variety of programming languages and software like ArcGIS, Java, Javascript, Matlab, Perl, PHP, Python, R, Visual Basic, etc. and some text editors.
The workshop will consist of hands-on example problems. Learn to search beyond “*.txt”. The tutorials will be conducted using Python. A basic programming background is helpful but not required for this workshop.

MICDE Seminar: Demetrios Papageorgiou, Professor, Applied Mathematics, Imperial College London
2020-03-13 15:00 - 16:00
POSTPONED UNTIL FURTHER NOTICE
Bio: Demetrious Papageorgiou is a Professor at Imperial College London. He is an applied mathematician that works on problems that arise in fluid dynamics. He is interested in systems involving immiscible fluids that are characterized by the presence of spatiotemporally evolving sharp interfaces.
Electric field effects in immiscible multilayer flows
Multilayer flows such as falling films and coating flows, or pressure-driven flows of immiscible fluids in channels and pipes, are fundamental in applications. Such flows are typically stable if they are slow enough (highly viscous). Such regimes arise in small-scale geometries (e.g. microfluidics), and electric fields can be used to drive the system out of equilibrium to produce patterning, mixing and phase separation.
I will begin with some experiments and direct numerical simulations (DNS) that show how electric fields can be utilized in their dual role of inducing instabilities or stability depending on geometry and orientation. I will then review the theoretical models underpinning such phenomena and will use asymptotic theories to derive and study reduced-dimension model equations that describe nonlinear interfacial waves in the presence of fields. Computations predict rich dynamics including spatiotemporal chaos and singularity formation. Some novel inertialess nonlinear interfacial instabilities will also be described – these arise due to flux functions of derived evolution equations changing type from hyperbolic to elliptic. Finally, I will present results on the use of electric fields and/or blowing suction in achieving feedback and optimal control of falling film flows. Comparisons with DNS will be made and these will be used beyond the range of validity of asymptotic models to predict phenomena such as electrostatic suppression of Rayleigh-Taylor instabilities, and electrostatically induced pumping in microchannels.
This seminar is co-sponsored by the Applied & Interdisciplinary Mathematics program. Prof. Papageorgiou is being hosted by Prof. Krasny (MATH).

CANCELLED - MICDE/AIM Seminar: Lyudmyla Barannyk, Associate Professor, Mathematics, University of Idaho
2020-03-13 15:00 - 16:00
Bio: Lyudmyla Barannyk is an Associate Professor in the Department of Mathematics at the University of Idaho. Barannyk received a masters in Applied Mathematics from the New Jersey Institute of Technology and a PhD in Mathematics Sciences from the New Jersey Institute of Technology and Rutgers the State University of New Jersery. She is currently a visiting Associate Professor of Mathematics at the University of Michigan.
Modeling of the solid-liquid phase change in materials with internal heat generation
We study a simple model for the evolution of the solid-liquid interface during melting and solidification (Stefan problem) of a material with constant internal heat generation and prescribed heat flux at the boundary in the cylindrical geometry. The problem is motivated by the need to control the behavior of nuclear fuel rods in a potential meltdown scenario. The equations are solved by splitting them into transient and steady-state components and then using separation of variables. This results in an ordinary differential equation for the interface that involves infinite series. The initial value problem is solved numerically, and solutions are compared to the previously published quasi-static solutions. We show that when the internal heat generation and boundary heat flux are close in value, the motion of the phase change front takes longer to reach steady-state than when the values are farther apart. As the difference between the internal heat generation and boundary heat flux increases, the transient solutions become more dominant and the phase change front does not reach steady-state before the outer boundary or centerline is reached. Hence the difference between the internal heat generation and boundary heat flux can be used to control the motion and speed of the solid-liquid interface. Limitations of the present model and possible future extensions will be discussed.

This is joint work with Sidney Williams (Georgia Tech), Irene Ogidan (University of Idaho), John Crepeau (University of Idaho), and Alexey Sakhnov (Kutateladze Institute of Thermophysics, Novosibirsk, Russia).

Go for data processing Part 1
2020-03-16 15:00 - 17:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
This is a two-session workshop on the use of Go for data processing. Go is an open source language developed for general-purpose programming. It is not more difficult to learn and use than a high-level scripting language like Python, but it is strongly typed, statically compiled, and provides native support for concurrency, leading to much better performance for many common tasks. In this series of workshops, we introduce Go as a tool for data processing. No prior exposure to Go is expected, but participants should have some programming background. Free and open source tools for Go are available for all common platforms.
Participants should bring a laptop if they want to work with the examples during the presentation, but this is optional.

Machine Learning in R
2020-03-19 14:00 - 16:00
In this workshop, we’ll first discuss core machine learning concepts such as: choosing loss functions and evaluation metrics; splitting the data into training, validation, and testing sets; and cross-validation patterns for tuning hyper-parameters. Next, we’ll apply these concepts to train models for identifying isolated letters from speech (https://archive.ics.uci.edu/ml/datasets/isolet).
Specifically, we’ll apply the elastic net (a generalization of ridge and lasso regression), random forests, and gradient boosting to this task. We’ll briefly discuss each model/method but our primary focus will be on understanding the core functionality of the related R packages (glmnet, randomForests, xgboost) and tuning associated hyper-parameters.

POSTPONED - MICDE/AIM Seminar: John Harlim, Professor, Mathematics and Meteorology, Penn State University
2020-03-20 15:00 - 16:00
Bio: John Harlim is a Professor in the Department of Mathematics and the Department of Meteorology and Atmospheric Sciences. Harlim received his undergraduate degree in Mathematics from the Universitas Padjadaran (Indonesia), a master’s from the University of Guelph in Applied Mathematics, and a PhD in Applied Mathematics and Scientific Computation from the University of Maryland at College Park. His research interests in applied mathematics include parameter estimation, machine learning, manifold learning, operator estimation, data assimilation.
Learning Missing Dynamics through Data
The recent success of machine learning has drawn tremendous interest in applied mathematics and scientific computations. In this talk, I would address the classical closure problem that is also known as model error, missing dynamics, or reduced-order-modeling in various community. Particularly, I will discuss a general framework to compensate for the model error. The proposed framework reformulates the model error problem into a supervised learning task to approximate a very high-dimensional target function involving the Mori-Zwanzig representation of projected dynamical systems. Connection to traditional parametric approaches will be clarified as specifying the appropriate hypothesis space for the target function. Theoretical convergence and numerical demonstration on modeling problems arising from PDE’s will be discussed.

Go for data processing Part 2
2020-03-23 15:00 - 17:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States

Introduction to Deep Neural Networks with Keras/TensorFlow
2020-03-24 10:00 - 12:00
Deep Neural Networks (DNNs) are used as a machine learning method for both regression and classification problems. Keras is a high-level, Python interface running on top of multiple neural network libraries, including the popular library TensorFlow. In this workshop, participants will learn how to quickly use the Keras interface to perform nonlinear regression and classification with standard fully-connected DNNs, as well as image classification using Convolutional Neural Networks (CNNs). We will also look at regularization techniques and how to deal with under- and over-fitting. All examples will use Python; some familiarity with Python is recommended. Computers will be available to complete exercises. We will run the models using Google Colab, which requires a Google account.

Building software projects: CMake is more than a build tool
2020-03-25 10:00 - 12:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
This workshop is a continuation of the previous workshop “Building software projects: use CMake to build the building plan”. In this workshop, we will see that CMake is not just a fancy Makefile generator: it can help us to test, reuse, and distribute our software! We will use CMake to build two interdependent multi-language projects, and demonstrate how to invoke unit tests after the build, how to make our code discoverable and reusable by other software developers, and how to create a distributable package. If you intend to distribute your software to other research groups, or if you expect that your project will grow beyond a few files of code and a few months of use — this workshop is for you!
Participants will need to have laptops with WiFi connection if they wish to follow the hands-on exercises. A basic knowledge of Unix-like operating systems would be helpful in following and understanding the material, but is not required.

POSTPONED - MICDE/EEB Seminar: Yun Song, Professor, Computer Science and Statistics, University of California, Berkeley
2020-03-26 16:00 - 17:00
Bio: Yun S. Song is a professor of EECS and Statistics. He received the BS degrees in mathematics and physics from MIT, and a PhD in physics from Stanford University. After his PhD, he spent a year at the Mathematical Institute at the University of Oxford, where he decided to change fields. He became a postdoctoral researcher in the Department of Statistics at Oxford, and started doing research in computational biology and mathematical population genetics. From 2004 to 2007, he was a postdoctoral researcher at UC Davis in the Department of Computer Science, and the Section of Evolution and Ecology.
The key parameters that govern translation efficiency
Translation of mRNA into protein is a fundamental biological process mediated by the flow of ribosomes on mRNA transcripts. With multiple factors that can potentially affect its efficiency, this transport process is highly complex and heterogeneous: different mRNAs can have different initiation rates, local elongation rates can vary substantially along the mRNA, and multiple ribosomes can simultaneously translate the same mRNA, potentially leading to interference. In this talk, I will present new theoretical results on a probabilistic model of mRNA translation which allowed us to identify the key parameters that govern the overall rate of protein synthesis, sensitivity to initiation rate changes, and efficiency of ribosome usage. I will then describe our ongoing study, which combines in vitro translation experiments with mathematical modeling, to elucidate the role of the 5′ UTR (particularly uAUGs and uORFs) in regulating translation initiation in eukaryotes.

Data Visualization With 3D Graphics Using Unity3D and C#
2020-03-30 13:00 - 16:00 | Your Desktop
This session will be held online, and presenters will be in touch with more information after you register.

Video game development is more accessible than ever before thanks to modern software tools, with many options free to download. These tools are also used to program more “serious” applications that require interactive 3D graphics, from mobile apps, virtual and augmented reality, computer vision and artificial intelligence, and real-time CGI film production.

Unity3D is a powerful and popular game engine for both hobbyist and professional projects, able to compile a ‘game’ to almost any computer platform, and free to download for non-commercial use. This workshop will show how you can use it to render data from research projects in a 3D interactive representation for user analysis and demonstration.

In this workshop, we introduce the Unity3D workspace, and prepare a demo that allows the user to load an example dataset and view it as a simple set of 3D representations. A basic familiarity with any computer programming language (C# will be used during the session) is recommended to get the most out of the workshop. To take part, users will be responsible to bring their own laptop with Unity3D (available for Windows, Macintosh and Linux) pre-installed. Additional project files will be provided to registered users ahead of the workshop date.

GIS analysis in R
2020-04-02 14:00 - 17:00 | Your Desktop
This session will be held online, and presenters will be in touch with more information after you register.

R is a popular open source programming environment for statistics and data science. However, it has also gradually become very powerful for GIS and spatial data science.
This workshop will help you learn about the tools and techniques available in R, primarily for vector data analysis. Participants should register with the Census and get a census API key (https://api.census.gov/data/key_signup.html).

Mediation analysis in Python
2020-04-06 15:00 - 17:00 | Your Desktop
This session will be held online, and presenters will be in touch with more information after you register.

Mediation analysis is a set of tools for exploring hypotheses about causal pathways, with a special focus on differentiating “direct” from “mediated” associations between an exposure and an outcome. Many approaches to mediation analysis are based on regression analysis. In this workshop, we will cover some of the basic ideas behind regression-based mediation analysis, and show how this type of analysis can be performed in Python using the Statsmodels package. All software tools covered in this workshop are free and open source.

Webinar: 2020 MICDE Catalyst Grants Showcase - Session I
2020-04-09 10:00 - 11:30
This webinar will showcase some of the game-changing research supported by our Catalyst Grants program.
This event was recorded and will be on the UM Youtube channel shortly.
Speakers

Robert Krasny
Professor of Applied Mathematics and Mathematics
University of Michigan
INTEGRAL EQUATION BASED METHODS FOR SCIENTIFIC COMPUTING
There are several different approaches to the numerical solution of partial differential equations. For example, finite-difference methods and finite-element methods discretize either the strong form or the weak form of the equation in real space, while spectral methods discretize the equation in reciprocal space. This project employs an alternative method which converts the differential equation into an integral equation by convolution with the Green’s function, followed by discretization and linear solution; the hope is that this approach is more amenable to adaptive refinement and parallelization than other methods. In the past, integral equation based methods were hindered by the difficulty of discretizing singular integrals and the cost of computing dense matrix-vector products, but these obstacles are being brought under control. We present our recent work in this area including (1) a GPU-accelerated barycentric treecode for long-range particle interactions, (2) applications in electrostatics, electronic structure, and vortex dynamics.

Vikram Gavini
Professor of Mechanical Engineering
University of Michigan
Long time-scale simulations using exponential time-propagators
High-fidelity long-time scale simulations have been a challenge in a wide range of areas, including time-dependent electronic structure calculations and molecular dynamics. In particular, time-dependent density functional theory (TDDFT) calculations are limited to time-scales of the order of hundred femtoseconds, and MD simulations (even those based on interatomic potentials) are routinely limited to time-scales of the order of nanoseconds. However, there is very rich material phenomena, both at the quantum and atomistic scale, that occurs at time-scales that are orders of magnitude larger than the currently accessible range. In this talk, I will present the ideas we have been exploring as part of the MICDE catalyst grant to enable long time-scale simulations on a class of time-dependent problems. In particular, we investigate the use of exponential time-propagators as an alternative to the finite-difference based time-discretization of the PDEs. The ideas will be presented for time-dependent density functional theory and elastodynamics—as a prototypical problem for molecular dynamics—along with numerical results demonstrating the viability and computational efficiency of the proposed ideas.
This is joint work with Bikash Kanungo and Paavai Pari.

Yulin Pan
Assistant Professor of Naval Architecture and Marine Engineering
University of Michigan
Real-Time Phase-resolved ocean wave forecast with data assimilation enabled by gpu-accelerated computation
The real-time phase-resolved prediction of ocean waves is crucial for the safety of offshore operations. With the development of the remote sensing technology, it is now possible to reconstruct the phase-resolved ocean surface from radar measurements in real time. Using the reconstructed ocean surface as initial condition, nonlinear wave models such as the high-order spectral (HOS) method can be applied to predict the evolution of the ocean waves. However, the computations reply heavily on large CPU clusters which are usually not available in the offshore onboard environment, and the prediction can deviate quickly from the true wave evolution due to the chaotic nature of the nonlinear wave equations. To address these problems, we develop a novel GPU-accelerated computational framework, which features the coupling of HOS and an ensemble Kalman filter (EnKF) to reduce the uncertainties in the prediction. The new framework algorithm is tested and validated using both synthetic and real wave data, and is shown promising in fundamentally improving the real-time prediction capability of ocean waves.

Webinar: 2020 MICDE Catalyst Grants Showcase - Session II
2020-04-09 13:00 - 14:30
This webinar will showcase some of the game-changing research supported by our Catalyst Grants program.
This event was recorded and will be on the UM Youtube channel shortly.
Speakers

Stephen Smith
Associate Professor of Ecology and Evolutionary Biology
University of Michigan
The Emergence of Biological Complexity and Evolutionary Innovation in Plant Genomes

Xun Huan
Assistant Professor of Mechanical Engineering
University of Michigan
Towards Bayesian Uncertainty Quantification in Deep Learning Models for Brain Tumor Segmentation
While the use of deep learning (DL) models in healthcare has grown rapidly in recent years, the uncertainty/confidence information in their predictions is often unavailable and unreported. A lack of such information can render decision-making dangerous, and prompt clinicians to hesitate in using and trusting these machine learning technologies. We propose to adopt principles and computational methods of uncertainty quantification for medical artificial intelligence applications, focusing on a problem of brain tumor segmentation from MRI scans. As a first step, we assess the robustness and sensitivity of two such DL models, U-Net and SqueezeU-Net, with respect to uncertainty in model weights, which may arise due to sparsity and noise in training data features as well as labels. We achieve this through Monte Carlo uncertainty propagation of noise injected on trained weight values. The resulting uncertainty of segmentation maps can then be presented and visualized through robustness maps and summarizing box-plots of the Dice coefficients, which can help indicate the regions where our models do not predict well and most susceptible to training noise. In our on-going work, we seek to compute the Bayesian posterior distributions for the weights directly from training data. However, performing a full-scale inference for the millions of weights in U-Net and SqueezeU-Net would be prohibitive. Instead, we develop a procedure to use sensitivity analysis to identify the most important subset of weights (or layers), and perform a targeted Bayesian inference on this lower-dimensional parameter space.

Monica Valluri
Research Professor of Astronomy
University of Michigan
Probing the nature of dark matter by modeling the Milky Way
Despite nearly four decades of research in astrophysics and particle physics, the nature of dark matter, the substance that comprises 85% of the matter in the universe, is unknown. The shape of the Milky Way’s dark matter distribution and the variation of this shape with radius are important probes of the nature of dark matter. Mapping the detailed formation history of the Milky Way, especially the number of satellites that were assimilated by our Galaxy and their masses and their time of infall will provide clues to the dark matter distribution in satellites as well as evidence for nearby streams and dark matter satellites. We are developing a multi-pronged approach to understanding the nature of dark matter with new dynamical tools, new simulations and analysis of large cosmological simuations. I will describe progress on our efforts to enhance the galactic dynamics package AGAMA (Vasiliev, 2019)by adding GPU acceleration for the potential and action solvers. I will provide an update on how we are using positions and velocities for old stars in the Milky Way’s halo to determine the three dimensional shape of the dark matter distribution and its variation with radius.I will describe new simulations of the evolution of satellites that merge with our Milky Way that can lead to insights into the fundamental nature of dark matter. Finally I will descibe the use of two cluster finding tools (a self organizing mapping and multi-dimensional density estimation), that when applied to action-space properties of stars in the Milky Way’s halo, can yield insights into the accretion history of our Galaxy. This concert of efforts will significantly advance our goal of understanding the fundamental nature of dark matter using the properties of stars in the Milky Way.

Statistical analysis with missing data in Python
2020-04-13 15:00 - 17:00 | Your Desktop
This session will be held online, and presenters will be in touch with more information after you register.

Missing data arise in many fields of research, and a large body of statistical tools has been developed to facilitate statistical analysis in the presence of missing data. Here we focus mainly on multiple imputation, which is a broadly-applicable approach for working with missing data. We will illustrate through several case studies how multiple imputation allows certain types of missing data to be rigorously accounted for, while preserving the flexibility to use a variety of familiar statistical tools to account for other aspects of the data. The analyses presented in this workshop will be performed in Python using the Statsmodels package. All software tools covered in this workshop are free and open source.

Introduction to Survey Design: Data Collection, Questionnaire Design and Response Processes-Lecture
2020-04-17 09:00 - 17:00 | Your Desktop
This session will be held online, and presenters will be in touch with more information after you register.

This lecture-format workshop will present an overview of available modes and methods of survey data collection as well as an introduction to the survey response process and implications for questionnaire design. Participants will gain an appreciation of the tradeoffs inherent in survey design decisions and how design can affect data quality and survey errors. Topics will include:
Survey errors, in particular measurement, coverage, and nonresponse error.
What to consider when selecting a data collection method for a particular research question.
Measurement (response) error and how to reduce it through question wording/format and questionnaire structure.
The role of the interviewer and interviewee effects.

Webinar: Transmission modeling of infectious diseases and the COVID-19 outbreak
2020-04-17 15:00 - 16:30 | BlueJeans Events
This seminar will focus on differential equation transmission modeling approaches to analyze the spread of infections diseases, and how Prof. Eisenberg and her colleagues are using them to model the current COVID-19 outbreak in the State of Michigan.Their current model is helping to forecast the numbers of laboratory-confirmed cases, fatalities, hospitalized patients, and hospital capacity issues (such as ICU beds needed), and examining how social distancing can impact the spread of the epidemic.
