Michigan Institute for Computational Discovery and Engineering

MICDE Seminar: Amir Ali Ahmadi, Assistant Professor, Operations Research and Financial Engineering, Princeton University
2019-02-01 11:00 - 12:00 (America/Detroit) | 2717 IOE, 1205 BEAL AVE, ANN ARBOR, MI, 48109, United States
Bio: Amir Ali Ahmadi is an Assistant Professor at the Department of Operations Research and Financial Engineering at Princeton University and an Associated Faculty member of the Program in Applied and Computational Mathematics, the Department of Computer Science, the Department of Mechanical and Aerospace Engineering, and the Center for Statistics and Machine Learning. Amir Ali received his PhD in EECS from MIT and was a Goldstine Fellow at the IBM Watson Research Center prior to joining Princeton. His research interests are in optimization theory, computational aspects of dynamics and control, and algorithms and complexity. Amir Ali’s distinctions include the Sloan Fellowship in Computer Science, a MURI award from the AFOSR, the NSF CAREER Award, the AFOSR Young Investigator Award, the DARPA Faculty Award, the Google Faculty Award, the Howard B. Wentz Junior Faculty Award as well as the Innovation Award of Princeton University, the Goldstine Fellowship of IBM Research, and the Oberwolfach Fellowship of the NSF. His undergraduate course at Princeton (ORF 363, “Computing and Optimization’’) has received the 2017 Excellence in Teaching of Operations Research Award of the Institute for Industrial and Systems Engineers and the 2017 Phi Beta Kappa Award for Excellence in Undergraduate Teaching at Princeton University. Amir Ali is also the recipient of a number of best-paper awards, including the INFORMS Optimization Society’s Young Researchers Prize, the INFORMS Computing Society Prize (for best series of papers at the interface of operations research and computer science), the Best Conference Paper Award of the IEEE International Conference on Robotics and Automation, and the prize for one of two most outstanding papers published in the SIAM Journal on Control and Optimization in 2013-2015.
POLYNOMIAL OPTIMIZATION AND DYNAMICAL SYSTEMS
In recent years, there has been a surge of exciting research activity at the interface of optimization (in particular polynomial, semidefinite, and sum of squares optimization) and the theory of dynamical systems. In this talk, we focus on two of our current research directions that are at this interface. In part (i), we propose more scalable alternatives to sum of squares optimization and show how they impact verification problems in control and robotics, as well as some classic questions in polynomial optimization and statistics. Our new algorithms do not rely on semidefinite programming, but instead use linear programming, or second-order cone programming, or are altogether free of optimization. In particular, we present the first Positivstellensatz that certifies infeasibility of a set of polynomial inequalities simply by multiplying certain fixed polynomials together and checking nonnegativity of the coefficients of the resulting product.
In part (ii), we introduce a new class of optimization problems whose constraints are imposed by trajectories of a dynamical system. As a concrete example, we consider the problem of optimizing a linear function over the set of initial conditions that forever remain inside a given polyhedron under the action of a linear, or a switched linear, dynamical system. We present a hierarchy of linear and semidefinite programs that respectively lower and upper bound the optimal value of such problems to arbitrary accuracy.
This seminar is co-sponsored by the department of Industrial and Operations Engineering. Prof. Ahmadi is being hosted by Prof. Shen (IOE). If you would like to meet with him during his visit, please send an email to micde-events@umich.edu

Introduction to the Linux Command Line
2019-01-30 09:00 - 12:00 (America/Detroit) | East Hall B254, 530 Church St., Ann Arbor, MI, 48109, United States
This course will familiarize the student with the basics of accessing and interacting with Linux computers using the GNU/Linux operating system’s Bash shell, also generically referred to as “the command line”. Topics include: a brief overview of Linux, the Bash shell, navigating the file system, basic commands, shell redirection, permissions, processes, and the command environment. The workshop will also provide a quick introduction to nano a simple text editor that will be used in subsequent workshops to edit files.
INSTRUCTOR
Kenneth Weiss
IT Project Senior Manager
Medical School Information Services (MSIS)
Ken is a High Performance Computing Consultant in the Computational Research Consulting Division of MSIS at the University of Michigan. He works with a team of IT specialists to provide high performance computing support and training for the Medical School. Prior to this, he spent 21 years managing research computing, including an HPC cluster, for Dr. Charles Sing in the Human Genetics Department.
MATERIALS

Lecture Notes
Reference text: William E Shotts, Jr., “The Linux Command Line: A Complete Introduction,” No Starch Press, January 2012 .http://linuxcommand.org/tlcl.php
Download Creative Commons Licensed version at http://downloads.sourceforge.net/project/linuxcommand/TLCL/13.07/TLCL-13.07.pdf

COURSE PREPARATION
You must register at least three full days prior to the event so that we have time to insure you have proper UM credentials for the workshop. This allows enough time for you to get your account adjusted by ITS in case you do not have access to the Linux systems.

Regression analysis with Generalized Linear Models in Python
2019-01-29 14:00 - 16:00 (America/Detroit) | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
This workshop will cover fitting generalized linear models (GLMs) in Python, using the Statsmodels package. We will cover logistic regression, but the majority of the time we will focus on other GLMs including Poisson, negative binomial, and gamma regression. We will provide an overview of the underlying foundation for GLMs, focusing on the mean/variance relationship and the link function. Participants should have familiarity with linear regression and (ideally) with logistic regression, but prior exposure to other GLMs is not required.
Participants should bring a laptop if they want to work with the examples during the presentation, but this is optional.

Graduate Studies in Computational & Data Sciences Info Session - Central Campus
2019-01-28 16:00 - 17:00 UTC | Weiser Hall, 10th Floor, 500 Church Street, Ann Arbor, MI, 48109, United States
Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided

The Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
The Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan.
The Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:
1) Modeling — Understanding of core data science principles, assumptions and applications;
2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization;
3) Practice — Hands-on experience with real data, modeling tools, and technology resources.
The Graduate Certificate in Computational Neuroscience provides training in interdisciplinary computational neuroscience to graduate students in experimental neuroscience programs and to graduate students in quantitative science programs, such as physics, biophysics, mathematics and engineering. The curriculum includes required core computational neuroscience courses and coursework outside of the student’s home department research focus, i.e. quantitative coursework for students in experimental programs, and neuroscience coursework for students in quantitative programs.

Introduction to Stata
2019-01-25 09:30 - 16:00 (America/Detroit) | Modern Languages Building (MLB), Room 2001A
Topics:

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 is a full day workshop. To get the most out of it, please plan to stay for the entire class.

(Topics subject to change)

Graduate Studies in Computational & Data Sciences Info Session - North Campus
2019-01-24 16:30 - 17:30 UTC | Johnson Rooms, Lurie Engineering Center, 3rd Floor LEC 3213ABC, 1221 Beal Ave., Ann Arbor, MI, United States

Linear regression analysis in Python
2019-01-22 14:00 - 16:00 (America/Detroit) | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
This workshop will cover regression analysis using linear models and least squares in Python. We will discuss the goals and main use-cases for linear regression, and how to interpret a fitted linear model. We will then discuss methods for fitting more complex models with larger data sets, including the use of interactions, dummy-coding of categorical variables, and splines. Finally we will discuss some aspects of statistical inference and model selection for linear regression. Several case studies using open data sets will be used to illustrate the approaches.
Participants should bring a laptop if they want to work with the examples during the presentation, but this is optional.

MICDE Seminar: Mattia Gazzola, Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign
2019-01-18 15:00 - 16:00 (America/Detroit) | 1084 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
Bio: Mattia Gazzola is an Assistant Professor in the department of Mechanical and Science Engineering at the University of Illinois, Urbana-Champaign. Originally from Galliate, Italy, he obtained his B. Sc. in Energy Engineering and M. Sc. in Nuclear Engineering from the Politecnico di Milano. He then was granted a PhD. in Mechanical Engineering from ETH Zurich where he worked with Prof. Petros Koumoutsakos specializing in simulation, optimization and learning of artificial swimmers. His research interests include locomotion in fluids, where he combines theory, numerical simulations and AI to advance our understanding of the physical mechanisms involved. He is also interested in creating artificial animals – or cyborgs – to link neuro-dynamics, mechanics and complex controllable gaits-coupling sensory information to motor coordination and movement that leads to behavior. His research group develops numerical algorithms that allow the integration of AI with large scale simulations.
MODELING, SIMULATION AND CONTROL OF COMPLEX MUSCULOSKELETAL ARCHITECTURES
We introduce a modeling approach based on assemblies of Cosserat rods for the simulation and characterization of arbitrary muscoloskeletal architectures. The obtained solver is coupled to evolutionary optimization techniques for the rational design of soft artificial creatures characterized by different scales and operating across environments. A control approach for these distributed mechanical systems is outlined and demonstrated in simple settings. Applications range from slithering, swimming and flying biolocomotion strategies to bio-hybrid systems.
This is a joint seminar with the Applied and Interdisciplinary Mathematics seminar series. Prof. Gazzola is being hosted by Prof. Alben (Mathematics). If you would like to meet with him during his visit, please send an email to micde-events@umich.edu.

Intro to SQL
2019-01-18 09:30 (America/Detroit) | Modern Languages Building (MLB), Room 2001A
Ever want to know how to communicate with a database? You need to know SQL, a standard programming language for working with relational database management systems in data warehouses or just Microsoft Access. This workshop will cover the basic syntax of SQL. Material will focus mainly on how to query databases. A web-based tool will be used for the tutorial.

MICDE Seminar: Yuri Bazilevs, School of Engineering, Brown University
2019-01-11 15:00 - 16:00 (America/Detroit) | 2540 G.G. Brown (2350 Hayward St.), 2300 Hayward St, Ann Arbor, MI, 48109, United States
Bio: Yuri Bazilevs is the E. Paul Sorensen Chair in the School of Engineering at Brown University. He was previously a Professor and Vice Chair in the Structural Engineering Department at the University of California, San Diego. Yuri is the original developer of Isogeometric Analysis (IGA), a new computational methodology that aims to integrate engineering design (CAD) and simulation (FEM). For his research contributions Yuri received a number of awards and honors, including the 2018 ASCE Walter L. Huber Research Prize. He is included in the 2014-2018 lists of Highly Cited Researchers, both in the Engineering and Computer Science categories.
ISOGEOMETRIC METHODS FOR SOLIDS, STRUCTURES, AND FLUID-STRUCTURE INTERACTION: FROM EARLY RESULTS TO RECENT DEVELOPMENTS
This presentation is focused on Isogeometric Analysis (IGA) with applications to solids and structures, starting with early developments and results, and transitioning to more recent work. Novel IGA-based thin-shell formulations are discussed, and applications to progressive damage modeling in composite laminates due to low-velocity impact and their residual-strength prediction are shown. Fluid–structure interaction (FSI) employing IGA is also discussed, and a novel framework for air-blast-structure interaction (ABSI) based on an immersed approach coupling IGA and RKPM-based Meshfree methods is presented and verified on a set of challenging examples. The presentation is infused with examples that highlight effective uses of IGA in advanced engineering applications.
Prof. Bazilevs is being hosted by Prof. Garikipati (Mechanical Engineering). If you would like to meet him during his visit please send an email to micde-events@umich.edu. If you are an MICDE or ME student and would like to join Prof. Bazilevs for lunch please RVSP here by Wednesday, January 9.

MICDE Seminar: Aaron Frank, Chemistry and Biophysics, University of Michigan
2018-12-12 16:00 - 17:00 (America/Detroit) | 1210 Chemistry & Willard H Dow Laboratory, 930 University Ave., Ann Arbor, MI, 48109, United States
Bio: Aaron Frank is originally from Grenada, a small island in the Caribbean. After moving to the US in 2001, Aaron received his BA in chemistry from Brooklyn College in 2006, where he carried out research in the groups of Professors Charlene Forest, Shaneen Singh, and Alexander Greer. He then moved to Michigan to attend graduate school at the University of Michigan and then, with his Ph.D advisor Professor Ioan Andricioaei, moved to UC Irvine in 2008. Aaron received his Ph.D in chemistry in 2011. Following a 2 year stint at Nymirum Inc. — a small biotech company in Ann Arbor founded by a close collaborator, Professor Hashimi Al-Hashimi — he returned to the University of Michigan as a Presidential Postdoctoral Fellow where he was mentored by Professor Charles L. Brooks, III. Aaron is now an Assistant Professor at the University of Michigan in the Chemistry Department and the Biophysics Department.
DATA SCIENCE AT THE INTERFACE OF BIOLOGY, CHEMISTRY, AND PHYSICS
In this talk, I will describe examples of how my research group uses data science tools to tackle research problems that fall at the interface between Biology, Chemistry, and Physics. First, I will describe ongoing research focused on mapping the structure-landscape of functional ribonucleic acids (or RNAs). In this project, we combined machine learning and secondary structure modeling tools to predict the structure of RNAs conditioned on available NMR chemical shift data. This method now enables us to model individual conformational states, including previously invisible states of an RNA, based on its sequence and available chemical shift data. Second, I will describe ongoing research centered around decoding structure-kinetic relationships (SKRs) in sparse datasets. There is now immense interest in developing drugs that exhibit elevated residence times on their target. In this project, we used machine learning to encapsulate SKRs for CDK2, a prominent cancer target, from a dataset containing only fourteen (14) samples. I will describe our efforts to build and test CDK2-specific SKR models that take as input, the atomic structure of receptor-ligand complexes and output estimates of their residence times. Additionally, I will describe proof-of-concept studies that demonstrate the utility of our CDK2-specific SKR models as tools to help efficiently explore chemical space in search of novel chemical scaffolds that are enriched with high-residence time and potent inhibitors of CDK2.

MICDE Seminar: Youssef Marzouk, Department of Aeronautics and Astronautics, MIT
2018-12-03 16:00 - 17:00 (America/Detroit) | 107 Gorguze Family Laboratory, 2609 Draper Dr, Ann Arbor, MI, 48109, United States
Bio: Youssef Marzouk is an associate professor in the Department of Aeronautics and Astronautics at MIT, and co-director of the MIT Center for Computational Engineering. He is also director of MIT’s Aerospace Computational Design Laboratory.
His research interests lie at the intersection of physical modeling with statistical inference and computation. In particular, he develops methodologies for uncertainty quantification, inverse problems, large-scale Bayesian computation, and optimal experimental design in complex physical systems. His methodological work is motivated by a wide variety of engineering, environmental, and geophysics applications.
He received his SB, SM, and PhD degrees from MIT and spent several years at Sandia National Laboratories before joining the MIT faculty in 2009. He is a recipient of the Hertz Foundation Doctoral Thesis Prize (2004), the Sandia Laboratories Truman Fellowship (2004-2007), the US Department of Energy Early Career Research Award (2010), and the Junior Bose Award for Teaching Excellence from the MIT School of Engineering (2012). He is an Associate Fellow of the AIAA and currently serves on the editorial boards of the SIAM Journal on Scientific Computing, Advances in Computational Mathematics, and the SIAM/ASA Journal on Uncertainty Quantification. He is also an avid coffee drinker and classical pianist.
A TOUR OF TRANSPORT METHODS FOR BAYESIAN COMPUTATION
Bayesian inference provides a natural framework for quantifying uncertainty in parameter estimates and model predictions, and for combining heterogeneous sources of information. Characterizing the results of Bayesian inference—by simulating from the posterior distribution—often proceeds via Markov chain Monte Carlo or sequential Monte Carlo sampling, but remains computationally challenging for complex posteriors and large-scale models.
This talk will describe a broad framework for using measure transport in Bayesian computation. This framework seeks deterministic couplings of the posterior measure with a tractable “reference” measure (e.g., a standard Gaussian). Such couplings are induced by transport maps, and enable direct simulation from the desired measure simply by evaluating the transport map at samples from the reference. Approximate transports can also be used to “precondition” and accelerate standard Monte Carlo schemes. Within this framework, one can describe many useful notions of low-dimensional structure associated with inference: for instance, sparse or decomposable transports underpin modeling and computation with non-Gaussian Markov random fields, and low-rank transports arise frequently in inverse problems.
We will then describe recent work specializing transport maps to the problem of nonlinear filtering in high-dimensional state-space models. The idea is to transform a forecast ensemble into samples from the current filtering distribution via a sequence of nonlinear transport maps, computed via convex optimization. Construction of the maps is regularized by leveraging potential structure in the filtering problem—e.g., decay of correlations, approximate conditional independence, and local likelihoods—thus extending notions of localization to nonlinear updates. The proposed framework can be understood as a non-Gaussian generalization of the ensemble Kalman filter.
This is joint work with Alessio Spantini, Daniele Bigoni, Ricardo Baptista, and Matthew Parno.
Prof. Marzouk is being hosted by Prof. Duraisamy (Aerospace). If you would like to meet him during his visit please send an email to micde-events@umich.edu. If you are an MICDE student and would like to join Prof. Marzouk for lunch please RVSP here by Friday, November 30.

CANCELLED --MICDE Seminar: Ali Yilmaz, Electrical Engineering, University of Texas at Austin
2018-11-26 15:00 - 16:00 (America/Detroit) | 1311 EECS, 1301 Beal Ave., Ann Arbor, MI, 48109, United States
CANCELLED
Bio: Ali Yilmaz is an Associate Professor of Electrical and Computer Engineering and a core faculty member at the Institute for Computational Engineering and Sciences at the University of Texas at Austin.
Dr. Yilmaz received the Ph.D. degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2005. He spent 2005 to 2006 as a post-doctoral research associate with the Center for Computational Electromagnetics at the University of Illinois; in 2006, he joined the faculty of The University of Texas at Austin.
His research interests include computational electromagnetics (particularly fast frequency- and time-domain integral equation solvers), parallel algorithms, antenna and scattering analysis, bioelectromagnetics, geoelectromagnetics, and electronic packages. He has authored or co-authored over 170 papers in refereed journals and international conferences on these topics.
USING (SUPER) COMPUTERS JUDICIOUSLY FOR HIGHER FIDELITY ELECTROMAGNETIC ANALYSIS
Increasing the fidelity of the electromagnetic models generally increases the predictive power of the analyses based on the models. It also generally increases the results’ sensitivity to model features/parameters as well as the difficulty of constructing the models, accurately solving the governing equations, and interpreting the resulting data. Therefore, one should base the analysis on the lowest-fidelity model one can get away with or, equivalently, the highest-fidelity model one can afford. The sweet spot for the tradeoff, “the appropriate model”, has changed over time in part because past successes in simulation-based science and engineering have increased expectations/requirements from electromagnetic analysis and in part because tremendous improvements in computing infrastructure and advances in computational methods have increased the affordability of complex analysis. Finding the appropriate model requires understanding both the benefits and the costs of analysis when a lower- or higher-fidelity model is used; neither side of the ledger, however, is known beforehand (unless one is repeating previously established analyses). A possible approach to revealing these unknowns is to construct models by gradually increasing their fidelity, performing analysis at each fidelity level, and comparing the analysis results and costs to those from the previous steps. I will show examples of this “analysis-driven modeling” in bioelectromagnetics (using the AustinMan and AustinWoman human body models) and signal integrity (using an electronic package example) by employing parallel algorithms and advanced integral-equation solvers on leading-edge supercomputers.
The examples will highlight many of the challenges arising from this approach to modeling. An important one is that “the appropriate method” of analysis generally depends on the model, e.g., a method can outperform alternatives for low-fidelity models but underperform them for high-fidelity ones; indeed, inappropriate (but convenient) methods can not only inflate the cost side of the ledger but also deflate the benefit side, leading to misjudgment of the appropriate model fidelity. Thus, not surprisingly, the development of appropriate electromagnetic models and appropriate computational methods are tightly linked (aka “if all you have is a hammer, everything looks like a nail”). Unfortunately, evaluating computational methods to find the appropriate one for a given model is surprisingly difficult, even for unbiased experts, as method performances depend not just on the models but also on the computers, the software realizations of the methods, and the users/developers of the software. On the one hand, theoretical comparisons (e.g., of asymptotic complexities, error convergence rates, parallel scalability limits) are often incapable of factoring in the large impact of software and hardware infrastructure on the realized/observed performance of a computational method—a problem that has worsened as the traditional Dennard scaling of clock frequencies ended in the last decade. On the other hand, empirical comparisons are beset by the same problems that physical measurements face (including irreproducible and uncertain results), require many (potentially low-efficiency) computations, and suffer from the large number of alternative methods. I will discuss whether benchmark suites can improve the judicious use of computational methods for electromagnetic analysis and what the necessary ingredients for such benchmarks are.
Prof. Yilmaz is being hosted by Prof. Michielssen (EECS). If you would like to meet with him during his visit, please send an email to micde-events@umich.edu. If you are an MICDE student and would like to join Prof. Yilmaz for lunch, please fill out this form.

SC2 Alumni Seminar Series: Eric Harper, NRC Research Associate, AFRL
2018-11-09 13:00 - 14:00 (America/Detroit) | 2540 G.G. Brown (2350 Hayward St.), 2300 Hayward St, Ann Arbor, MI, 48109, United States
Bio: Dr. Eric Harper is a Postdoctoral Fellow at the Air Force Research Laboratories (AFRL) at Wright-Patterson Air Force Base (WPAFB) in Dayton, Ohio as part of the Air Force Science and Technology Fellowship Program (STFP). He is a member of the Optical Theory Group (OTG), simulating optical metamaterials to optimize their design using scientific computing techniques. He earned his B.S. in Chemical Engineering at the University of Dayton (2011) and his M.S. (2014) and Ph.D. at the University of Michigan (2017).
Machine Accelerated Nano-targeted Inhomogenous Structures
The ability for nanoscale materials to control the propagation of light is well-known, both in biological systems and synthetic applications. However, the possible “solution-space” to search for nanoscale designs is near-infinite, requiring advanced computational techniques to optimize structures for targeted device performance. Here we consider a subset of the infinite design space, a simple bilayer structure of nanocylinders, to demonstrate the capabilities of machine learning to accelerate the design process. We compare the performance of human-driven optimization to a genetic algorithm based optimization routine. We also consider potential machine-learning tools to further accelerate the design of these structures.
The SC2 is holding a Meet the Speaker lunch at noon. If you would like to attend, please RSVP here.

Women in HPC, Michigan Chapter's meeting
2018-11-05 11:30 - 13:00 (America/Detroit) | Weiser Hall, Room 455, 500 Church St, Ann Arbor, MI, 48109, United States
Please join the newly formed University of Michigan chapter of Women in HPC at our first meeting!

The agenda will include time for networking at the beginning of the session, and the following speakers:

Dr. Sharon Broude Geva, ARC and WHPC
Dr. Sharon Glotzer, Chemical Engineering
Dr. Christiane Jablonowski, Climate and Space Sciences and Engineering (CLaSP)
Dr. Monica Valluri, Astronomy
Dr. Jocelyn Stitt, Institute for Research on Women and Gender (IRWG)

Lunch will be provided. Please RSVP
For more about the U-M WHPC chapter, see arc.umich.edu/whpc.

MICDE/IOE Seminar: Juan Pablo Vielma, Sloan School of Management, MIT
2018-10-24 16:00 - 17:00 (America/Detroit) | 1680 IOE, 1205 BEAL AVE, Ann Arbor, MI, 48109, United States
Bio: Juan Pablo Vielma is the Richard S. Leghorn (1939) Career Development Associate Professor at MIT Sloan School of Management and is affiliated to MIT’s Operations Research Center. Dr. Vielma has a B.S. in Mathematical Engineering from University of Chile and a Ph.D. in Industrial Engineering from the Georgia Institute of Technology. His current research interests include the theory and practice of mixed-integer mathematical optimization and applications in natural resource management, marketing and statistics. In January of 2017 he was named by President Obama as one of the recipients of the Presidential Early Career Award for Scientists and Engineers (PECASE). Some of his other recognitions include the NSF CAREER Award, the INFORMS Computing Society Prize and a first prize in the INFORMS Junior Faculty Interest Group Paper Competition. He served as vice-chair of Integer and Discrete Optimization for the INFORMS Optimization Society and as chair of the INFORMS Section on Energy, Natural Resources, and the Environment. He is currently an associate editor for Operations Research and Operations Research Letters, a member of the NumFocus steering committee for JuMP, and the Faculty Director for the MIT-Chile program of MIT’s International Science and Technology Initiatives (MISTI).
Modeling power of mixed integer convex optimization problems and their effective solution with Julia and JuMP
More than 50 years of development have made mixed integer linear programming (MILP) an extremely successful tool. MILP’s modeling flexibility allows it describe a wide range of business, engineering and scientific problems, and, while MILP is NP-hard, many of these problems are routinely solved in practice thanks to state-of-the-art solvers that nearly double their machine-independent speeds every year. Inspired by this success, the last decade has seen a surge of activity on the solution and application of mixed integer convex programming (MICP), which extends MILP’s versatility by allowing the use of convex constraints in addition to linear inequalities. In this talk we cover various recent developments concerning theory, algorithms and computation for MICP. Solvers for MICP can be significantly more effective than those for more general non-convex optimization, so one of the questions we cover in this talk is what classes of non-convex constraints can be modeled through MICP. We also cover the solution of MICP problems through polyhedral approximation algorithms that exploit the power of extended formulations. Finally, we cover various topics concerning the modeling and computational solution of MICP problems using the Julia programming language and the JuMP modeling language for optimization. In Particular, we show how mixed integer optimal control problems where the variables are polynomials can be easily modeled and solved by seamlessly combining several Julia packages and JuMP extensions with the Julia-written MICP solver Pajarito.
This seminar is co-sponsored by the department of Industrial and Operations Engineering. Prof. Vielma is being hosted by Prof. Shen (IOE). If you would like to meet with him during his visit, please send an email to micde-events@umich.edu

Complex Systems Seminar: Giovanni Fantuzzi, Aeronautics, Imperial College London
2018-10-23 11:30 - 13:00 (America/Detroit) | Weiser Hall, Room 747, 500 Church St, Ann Arbor, MI, 48109, United States
Bio: Giovanni Fantuzzi is an EPSRC Doctoral Prize Fellow in the Department of Aeronautics, Imperial College London, from which he received an MEng degree in 2014 and a PhD in 2018. During his PhD he developed optimization-based methods and software for studying stability and time-averaged properties of dynamical systems, with applications to ﬂuid ﬂows. In 2015 he was awarded a Geophysical Fluid Dynamics Fellowship from Woods Hole Oceanographic Institution and was subsequently a Research Assistant at the University of Oxford, where he worked on fast algorithms for structured semideﬁnite programmes and sum-of-squares optimization. His current research spans ﬂuid dynamics and convex optimization, and he is especially interested in scalable convex approaches to hydrodynamic analysis.
Beyond numerical integration: studying nonlinear dynamics with polynomial optimization
Systems characterized by complex nonlinear dynamics lie at the heart of 21st century technology. Examples are turbulent flows in the transport and aviation industries, smart energy networks, and models of cell dynamics used in synthetic biology. Quantitative analysis of such systems using direct numerical simulations sometimes requires prohibitively large computational resources even when one is interested only in some average properties, such as mean power consumption, because all time and length scales across which the system evolves must be resolved. In addition, while numerical simulations offer detailed information starting from a specific initial state, they cannot provide safety-critical performance or stability guarantees that hold for all possible initial states. In this talk, I will describe an alternative approach to studying nonlinear systems with polynomial dynamics, which combines ideas from Lyapunov’s stability theory with recent numerical tools for polynomial optimization. In particular, I will present a range of examples that demonstrate how this optimization-based method enables the efficient algorithmic construction of stability certificates and the computation of rigorous bounds on performance-related system properties. Other applications, including optimal control and disturbance amplification analysis, will be discussed along with open problems and future research directions.

MICDE/Quantitative Biology Seminar: Padmini Rangamani, Mechanical and Aerospace Engineering, UC San Diego
2018-10-22 12:00 - 13:00 (America/Detroit) | 335 West Hall, 1085 S University, Ann Arbor, MI, 48109, United States
Bio: Padmini Rangamani is an associate professor in Mechanical Engineering at the University of California, San Diego. She joined the department in July 2014. Earlier, she was a UC Berkeley Chancellor’s Postdoctoral Fellow, where she worked on lipid bilayer mechanics. She obtained her Ph.D. in biological sciences from the Icahn School of Medicine at Mount Sinai. She received her B.S. and M.S. in Chemical Engineering from Osmania University (Hyderabad, India) and Georgia Institute of Technology respectively. She is the recipient of the ARO, AFOSR, and ONR Young Investigator Awards, and a Sloan Research Fellowship for Computational and Molecular Evolutionary Biology. She is also the lead PI for a MURI award on Bioinspired low energy information processing from the AFOSR.
GEOMETRIC PRINCIPLES OF SPATIO-TEMPORAL DYNAMICS OF SECOND MESSENGERS IN DENDRITIC SPINES
The ability of the brain to encode and store information depends on the plastic nature of the individual synapses. The increase and decrease in synaptic strength, mediated through the structural plasticity of the spine, are important for learning, memory, and cognitive function. Dendritic spines are small structures that contain the synapse. They come in a variety of shapes (stubby, thin, or mushroom-shaped) and a wide range of sizes that protrude from the dendrite. These spines are the regions where the postsynaptic biochemical machinery responds to the neurotransmitters. Spines are dynamic structures, changing in size, shape, and number during development and aging. While spines and synapses have inspired neuromorphic engineering, the biophysical events underlying synaptic and structural plasticity remain poorly understood.
Our current focus is on understanding the biophysical events underlying structural plasticity. I will discuss two recent efforts from my group — first, a systems biology approach to construct a mathematical model of biochemical signaling and actin-mediated transient spine expansion in response to calcium influx caused by NMDA receptor activation and second, a series of spatial models to study the role of spine geometry and organelle location within the spine for calcium and cyclic AMP signaling. I will conclude with some new efforts in using reconstructions from electron microscopy to inform computational domains. I will conclude with how geometry and mechanics plays an important role in our understanding of fundamental biological phenomena and some general ideas on bio-inspired engineering.
Prof. Rangamani is being hosted by Prof. Garikipati (Mechanical Engineering). If you would like to meet with her please send an email to micde-events@umich.edu. If you are an MICDE student and would like to join Prof. Rangamani for lunch please fill out this form

CNSECCS 2018 Symposium
2018-10-16 08:30 - 12:30, 2018-10-15 08:30 - 17:00 (America/Detroit) | Space 2435 North Quad, 105 S. State St., Ann Arbor, MI, 48109, United States
The Center for Network and Storage Enabled Collaborative Computation is hosting its second Symposium on October 15 and 16, 2018, exploring the themes the Center was founded on. The Center seeks to address the challenges of extracting scientific results collaboratively from large, distributed or diverse data.
For more information please visit https://indico.cern.ch/event/692449/registrations/39788/

Confirmed speakers:

Stefan Robila, Program Director, Office of Advanced Cyberinfrastructure, Research Core Program, National Science Foundation
Saul Youssef, Research Associate Professor, Boston University Department of Physics
Ilkay Altintas, Director, Center of Excellence in Workflows for Data Science, San Diego Supercomputer Center, University of California, San Diego
Allison Steiner, Professor, Department of Climate and Space Science and Engineering, University of Michigan
Chris Miller, Associate Professor, Astronomy and Physics, University of Michigan
Jesse Capecelatro, Assistant Professor, Mechanical Engineering, University of Michigan
Alberto Figueroa, Professor, Biomedical Engineering, University of Michigan
Ivo Dinov, Professor, Computational Medicine and Bioinformatics, School of Nursing; Associate Director, Education and Training of the Michigan Institute for Data Science (MIDAS), University of Michigan
Shawn McKee, Research Scientist, Department of Physics, U-M; Director, Center for Network and Storage Enabled Collaborative Computational Science (CNSECCS)

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Monday, October 15, 2018 @ 8:30 a.m. – 5:00 p.m.
Tuesday, October 16, 2018 @ 8:30 a.m. – 12:30 p.m.

MICDE Seminar: Nandini Ananth, Department of Chemistry and Chemical Biology, Cornell University
2018-10-09 16:00 - 17:00 (America/Detroit) | CHEM 1300, 930 N University Ave, Ann Arbor, MI, 48109, United States
Bio: Nandini Ananth is an associate professor in the department of Chemistry and Chemical Biology at Cornell University. She received her bachelor’s degree in Chemistry from Stella Maris College in Chennai, India, and a Masters in chemistry from the Indian Institute of Technology Madras. Nandini moved to the United States in the fall of 2003 to pursue doctoral research at the University of California, Berkeley in William Miller’s group, working on developing semiclassical methods to model quantum dynamical behavior in complex chemical reactions. Upon graduation, she accepted a position as postdoctoral scholar in Thomas Miller’s group at the California Institute of Technology, Pasadena, where her research focused on developing path-integral methods for the simulation of electronically nonadiabatic processes in the condensed phase. She joined the faculty of the department of Chemistry and Chemical Biology at Cornell University in the Fall of 2012, and during her time here has received the Cottrell Scholar Award, NSF CAREER Award, NSF EAGER Award, Sloan Research Fellowship, and Army Research Office’s Young Investigator Award.
Charge Transfer Dynamics, Excited State Energetics, and Organic Photovoltaics
Designing molecular materials for use as organic photovoltaics, molecular electronics, and photocatalysts is a multifaceted challenge requiring a detailed understanding of both the excited state energetics and the dynamics of charge and energy transfer. We address the dynamic challenge by developing new methods based on the path integral formulation of quantum mechanics that are uniquely suited to the simulation of photo-initiated excited state dynamics in the condensed phase. We then tackle the characterization of the excited state manifold in molecular systems using a combination of high-level electronic structure methods to accurately calculate excited state energies, normal mode analysis to quantify vibronic couplings, and novel orbital analyses to uncover structure-spectrum correlations.
In this talk, we focus on one target application: designing chromophores that exhibit ultrafast Singlet Fission (SF), a phenomenon that has the potential to significantly increase organic solar cell efficiency. We investigate SF in non-bonded and covalently bonded pentacene dimers: we uncover two distinct mechanistic pathways for ultrafast SF and we identify molecular geometries and bonding motifs that can be modified to enhance efficiency in each case. Finally, we combine the insights obtained from our theoretical investigations to generate a priori design principles for next-generation SF chromophores, and working with experimental collaborators, we verify them.
Prof. Ananth is being hosted by Prof. Geva (Chemistry). If you would like to meet her during her visit please send an email to micde-events@umich.edu

MICDE Seminar: Pavel Bochev, Center for Computing Research, Sandia National Laboratories
2018-10-05 15:00 - 16:00 (America/Detroit) | 1084 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
Bio: Pavel Bochev is a Distinguished Member of the Technical Staff at Sandia National Laboratories in Albuquerque where he works in the Center for Computing Research. He joined Sandia in 2000 after six years of teaching and research at the University of Texas at Arlington.

Pavel’s research interests include compatible discretizations for partial differential equations, optimization and control problems, and the development of new, property preserving heterogeneous numerical methods for complex applications relevant to the mission of the US Department of Energy and the National Nuclear Security Administration.

Pavel’s thesis was awarded the SIAM Student paper prize in 1994. In 2012 he was elected a Fellow of the Society for Industrial and Applied Mathematics. Pavel is a recipient of 2014 US Department of Energy’s E. O. Lawrence Medal in the category of “Computer, information and knowledge sciences”. This award honors U.S. scientists and engineers, at mid-career, for exceptional contributions in research and development supporting the Department of Energy and its mission to advance the national, economic and energy security of the United States. In 2017 Pavel was awarded the Thomas J.R. Hughes Medal by the U.S. Association for Computational Mechanics for his contributions to the field of numerical partial differential equations.

Pavel has authored and co-authored over 100 research papers, two books and several book chapters, and has given numerous plenary and invited lectures in the US and abroad. He served two terms as Editor-in-Chief of the SIAM Journal on Numerical Analysis and is currently member of the editorial board of SINUM.
Compatible Mesh-Free Methods
Particle and mesh-free methods offer significant computational advantages in settings where quality mesh generation required for many compatible PDE discretizations may be expensive or even intractable. At the same time, the lack of underlying geometric grid structure makes it more difficult to construct mesh-free methods mirroring the discrete vector calculus properties of mesh-based compatible and mimetic discretization methods. In this talk we survey ongoing efforts at Sandia National Laboratories to develop new classes of locally and globally compatible meshfree methods that attempt to recover some of the key properties of mimetic discretization methods.
We will present two examples of recently developed “mimetic”-like meshfree methods. The first one is motivated by classical staggered discretization methods. We use the local connectivity graph of a discretization particle to define locally compatible discrete operators. In particular, the edge-to-vertex connectivity matrix of the local graph provides a topological gradient, whereas a generalized moving least-squares (GMLS) reconstruction from the edge midpoints defines a divergence operator. The second method can be viewed as a meshfree analogue of a finite volume type scheme. In this method, the metric information that would be normally provided by the mesh, such as cell volumes and face areas, is reconstructed algebraically, without a mesh. This reconstruction process effectively creates virtual cells having virtual faces and ensures a local conservation property matching that of mesh-based finite volumes. In contrast to similar recent efforts our approach does not involve a solution of a global optimization problem to find the virtual cell volumes and faces areas. Instead, we determine the necessary metric information by solving a graph Laplacian problem that can be effectively preconditioned by algebraic multigrid.
Several numerical examples will illustrate the mimetic properties of the new meshfree schemes. The talk will also review some of the ongoing work to build a modern software toolkit for mesh-free and particle discretizations that leverages Sandia’s Trillinos library and performance tools such as Kokkos.
This is a joint seminar with the Applied and Interdisciplinary Mathematics program. Dr. Bochev is being hosted by Prof. Robert Krasny (Mathematics). If you would like to meet with him, please send an email to micde-events@umich.edu

MICDE Seminar: Sanjay Padhi, AWS Research and Technical Computing
2018-10-01 16:00 - 17:00 (America/Detroit) | 1670 Bob and Betty Beyster Building, 2260 Hayward Street, Ann Arbor, MI, 48109, United States
Bio: Dr. Sanjay Padhi, leads the AWS Research Initiatives including AWS’s federal initiatives with the National Science Foundation. He is a physicist and Adjunct Professor at Brown University. Dr. Padhi has more than 15 years of experience in large-scale distributed computing, Data Analytics and Machine Learning. He is the co-creator of the Workload Management System currently used for all the data processing and simulations by CMS, one of the largest experiments in the world at CERN, consisting of more than 180 institutions across 40 countries. He also co-founded the ZEUS Computing Grid project at Deutsches Elektronen-Synchrotron (DESY), Germany before joining CERN. Sanjay obtained his Ph.D from McGill University in High Energy Physics, co-author of more than 900 publications and is also currently appointed by the Dean of Faculty as an Adjunct Professor of Physics at Brown University.
Predictive Analytics using Amazon Web Services
One of the most explored features of Big Data is predictive analytics. Predictive analytics is a set of techniques that are fundamental to large organizations like Amazon. Methods such as Machine Learning are used in many aspects of life, including health care, education, financial modeling, and marketing. Analytics on Big Data has given rise to various “smart” projects, such as Connected Intersections, Smart Cities, and Smart Health. This talk will provide a range of such studies using predictive analytics including detailed overview of methods such as Machine Learning (ML) and Deep Learning using AWS. Fully managed Artificial Intelligence (AI) services to help researchers build, train and deploy ML models in various domains including Computer Vision and Natural Language Processing (NLP) will also be outlined. Supervised and unsupervised based learning frameworks and its implications in the fields of Scientific Computing, Medical Imaging, Cancer detection, Diabetic Retinopathy, and Voice-enabled solutions to improve management of chronic disease will be discussed. The AWS Research Initiative with funding agencies such as the National Science Foundation (NSF) in the domains related to the foundation and innovative tracks, as well as AWS Research Credit program will also be outlined.
If you would like to meet Dr. Sanjay Padhi on October 1, please send a request to micde-events@umich.edu

Graduate Studies in Computational & Data Sciences Info Session - Central Campus
2018-09-25 16:30 - 17:30 UTC | Weiser Hall, 10th Floor, 500 Church Street, Ann Arbor, MI, 48109, United States
Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided

The Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
The Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan.
The Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:
1) Modeling — Understanding of core data science principles, assumptions and applications;
2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization;
3) Practice — Hands-on experience with real data, modeling tools, and technology resources.

Graduate Studies in Computational & Data Sciences Info Session - North Campus
2018-09-24 16:30 - 17:30 UTC | Johnson Rooms, Lurie Engineering Center, 3rd Floor LEC 3213ABC, 1221 Beal Ave., Ann Arbor, MI, United States

AIM Seminar: Robert Krasny, Mathematics, University of Michigan
2018-09-14 15:00 - 16:00 (America/Detroit) | 1084 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
Two topics in computational fluid dynamics
1. The Lamb dipole is a steady propagating solution of the inviscid fluid equations with opposite-signed vorticity in a circular disk. We compare finite-difference solutions of the Navier-Stokes equation (NSE) and the linear diffusion equation (LDE) using the Lamb dipole as the initial condition. We find some expected and some unexpected results; among the latter is that the maximum core vorticity decreases at the same rate for the NSE and LDE, but at higher Reynolds numbers, convection enhances the viscous cancellation of opposite-signed vorticity.
(This is joint work with Ling Xu.)
2. We discuss a new implementation of the vortex method for the incompressible Euler equations. The vorticity is carried by Lagrangian particles and the velocity is recovered by a regularized Biot-Savart integral. The new work employs remeshing and adaptive refinement to resolve small-scale features in the vorticity as well as a treecode for efficiency. The method is demonstrated for vortex dynamics on a rotating sphere (with Peter Bosler) and axisymmetrization of an elliptical vortex (with Ling Xu).

MICDE/EEB Seminar: Murat Eren, Department of Medicine, University of Chicago
2018-09-13 16:00 - 17:00 (America/Detroit)
Bio: Dr. Murat Eren is an Assistant Professor in the department of Medicine and affiliated with the Marine Biological Laboratory at the University of Chicago. He received his B.S. from Canakkale Onsekiz Mart University in Turkey in 2002, and his PhD from the University of New Orleans in 2001, both in computer science. His research focuses on the diversity and functioning of microbial communities in environments ranging from the human gastrointestinal tract and oral cavity, to sewages, oceans, and soils. He designs algorithms and experiments to better understand microbes and their ecology. He pursues interesting ecological and evolutionary questions, with some particularly interesting insights from molecular data into what constitutes a population in the microbial world.
Insights into ecology and evolution of microbial populations through single-amino acid variants
Neither the mechanisms by which genomic heterogeneity emerges within naturally occurring microbial populations, nor how it drives the partitioning of ecological niches are well understood. Yet the increasing number of environmental metagenomes with astonishing depth of sequencing offer new opportunities to investigate evolutionary processes acting upon them, and link genomic variation to predicted tertiary structures of genes to gain biochemical insights.
MICDE is co-sponsoring this seminar with the department of Ecology and Evolutionary Biology. If you would like to meet Dr. Murat during his visit please send an email to micde-events@umich.edu

Data-Intensive Social Science Challenge Symposium
2018-09-13 09:00 - 14:00 (America/Detroit) | Weiser Hall, 10th Floor, 500 Church Street, Ann Arbor, MI, 48109, United States
Data-intensive social science is one of the research focus areas that MIDAS supports with its Challenge Awards. Our long-term goal is to support this research area more broadly, using the Challenge Award projects as the starting point to build a critical mass. This symposium offers a platform for all participants to explore collaboration opportunities and aims to attract more researchers to our hub. The two Challenge Award teams will give in-depth presentations, and all participants are encouraged to submit posters on research related to data-intensive social science.
Registration | Poster submission form (Due Monday, Sept. 10)
Preliminary Schedule:
9 am: Introduction
9:05 am to 11:35 pm: Challenge Award presentations

Computational Approaches for the Construction of Macroeconomic Data, Matthew Shapiro and team
A Social Science Collaboration for Research on Communication and Learning Based upon Big Data, Michael Traugott and the UM-Georgetown team

11:35 am to 1 pm: lunch, poster session and networking (Please fill out this form to submit a poster; deadline is Monday, September 10)
1 to 2 pm: Panel discussion: the future of data-intensive social science research at U-M

Martha Bailey, Professor, Economics, University of Michigan
Sara Heller, Assistant Professor, Economics, University of Michigan
Matt Shapiro, Professor, Economics, University of Michigan
Lisa Singh, Professor, Computer Science, Georgetown University
Mike Traugott, Professor Emeritus, Communication Studies, Political Science, University of Michigan

AIM Seminar: Alex Gorodetsky, Aerospace Engineering, University of Michigan
2018-09-07 15:00 - 16:00 (America/Detroit) | 1084 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
Low-rank tensor approaches for adaptive function approximation: algorithms and examples
In this talk, we present an adaptive method for approximating high-dimensional low-rank functions. Taking advantage of low-rank structure in approximation problems has been shown to prove advantageous for scaling numerical algorithms and computation to higher dimensions by mitigating the curse-of-dimensionality. The method we describe is an extension of the tensor-train cross approximation algorithm to the continuous case of multivariate functions that enables both global and local adaptivity. Our approach relies on a new adaptive algorithm for computing the CUR/skeleton decomposition of bivariate functions. We then extend this technique to the multidimensional case of the function-train decomposition. We demonstrate the benefits of our approach compared with the standard methodology that computes low-rank approximations by decomposing coefficients of tensor-product basis functions. We finish by demonstrating a wide range of applications that include machine learning, uncertainty quantification, stochastic optimal control, and Bayesian filtering.

Geostatistical modeling
2018-08-28 14:00 - 16:30 (America/Detroit)
Geostatistics deals with continuous variation over space and emphasizes the idea of spatial correlation via covariance. It is widely used for spatial interpolation. We will use ArcGIS and R to explore and develop an understanding of variogram and kriging and how they can be used for robust and unbiased interpolation of data over space. The workshop will also highlight computational aspects involved in implementing geostatistical models for relatively large data.
