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 firstname.lastname@example.org
Bio: Ann S. Almgren is an applied mathematician who works as a staff scientist and acting group leader of the Center for Computational Sciences and Engineering at the Lawrence Berkeley National Laboratory. Her research interests involve the computational simulation of problems in astrophysics including the behavior of supernovae and white dwarfs. She earned a bachelor’s degree in physics from Harvard University in 1984 and master’s and doctoral degrees in mechanical engineering from the University of California, Berkeley in 1987 and 1991 respectively. After visiting the Institute for Advanced Study, she joined the applied mathematics group of the Lawrence Livermore National Laboratory in 1992, and moved to the Lawrence Berkeley Lab in 1996.
In 2015 she became a fellow of the Society for Industrial and Applied Mathematics “for contributions to the development of numerical methods for fluid dynamics and applying them to large-scale scientific and engineering problems.”
 Biographical information taken from https://en.wikipedia.org/wiki/Ann_S._Almgren
Several University of Michigan researchers and research IT staff made presentations at the SC16 conference in Salt Lake City Nov. 13-17. Material from many of the talks is now available for viewing online:
- Shawn McKee (Physics) and Ben Meekhof (ARC-TS) presented a demonstration of the Open Storage Research Infrastructure (OSiRIS) project at the U-M booth. The demonstration extended the OSiRIS network from its participating institutions in Michigan to the conference center in Utah. Meekhof also presented at a”Birds of a Feather” session on Ceph in HPC environments. More information, including slides, is available on the OSiRIS website.
- Todd Raeker (ARC-TS) made a presentation on ConFlux, U-M’s new computational physics cluster, at the NVIDIA booth. Slides and video are available.
- Nilmini Abeyratne, a Ph.D student in computer science, presented her project “Low Design-Risk Checkpointing Storage Solution for Exascale Supercomputers” at the Doctoral Showcase. A summary, slides, and poster can be viewed on the SC16 website.
- Jeremy Hallum (ARC-TS) presented information on the Yottabyte Research Cloud at the U-M booth. His slides are available here.
Other U-M activity at the conference included Sharon Broude Geva, Director of Advanced Research Computing, participating in a panel titled “HPC Workforce Development: How Do We Find Them, Recruit Them, and Teach Them to Be Today’s Practitioners and Tomorrow’s Leaders?”; Quentin Stout (EECS) and Christiane Jablonowski (CLASP) teaching the “Parallel Computing 101” tutorial.
Bio: David M. Higdon is a professor in the Social Decision Analytics Laboratory at the Biocomplexity Institute of Virginia Tech. Previously, he spent 10 years as a scientist or group leader of the Statistical Sciences Group at Los Alamos National Laboratory. He is an expert in Bayesian statistical modeling of environmental and physical systems, combining physical observations with computer simulation models for prediction and inference. His research interests include space-time modeling; inverse problems in hydrology and imaging; statistical modeling in ecology, environmental science, and biology; multiscale models; parallel processing in posterior exploration; statistical computing; and Monte Carlo and simulation based methods. Dr. Higdon has served on several advisory groups concerned with statistical modeling and uncertainty quantification and co-chaired the NRC Committee on Mathematical Foundations of Validation, Verification, and Uncertainty Quantification. He is a fellow of the American Statistical Association. Dr. Higdon holds a B.A. and M.A. in mathematics from the University of California, San Diego, and a Ph.D. in statistics from the University of Washington.
This seminar is co-sponsored by U-M Industrial Operations & Engineering department
 Biographical information taken from https://www.bi.vt.edu/faculty/Dave-Higdon
Bio: Dr. Andrea Lodi is a professor in the department of Mathematical and Industrial Engineering at the Polytechnique Montreal. He is the Canada Excellence Research Chair in Data Science for Real-Time Decision-Making at Polytechnique Montréal, Canada’s main chair in operations research.
Before joining the Polytechnique, Lodi was a professor in operations research in the faculty of electrical and information engineering at Italy’s University of Bologna. He earned his doctorate in systems engineering from this same university in 2000.
Lodi is interested in developing new models and algorithms that would make it possible to process a large quantity of data from multiple sources both rapidly and effectively. Through his research, he is looking for solutions designed to improve the electricity market, rail transport logistics, and health-care planning.
Lodi’s innovative work has earned him several awards, including the Google Faculty Research Award in 2010 and the IBM Faculty Award in 2011. In 2005 and 2006, he was a fellow in the prestigious Herman Goldstine program at the IBM Thomas J. Watson Research Center in New York.
In addition to co-ordinating several large-scale European projects in operations research, Lodi has also acted as a consultant for the IBM CPLEX research and development team since 2006. He has authored more than 70 publications in top mathematical programming journals; and has served as associate editor for several of these journals.
This seminar is co-sponsored by the U-M Department of Industrial Operations & Engineering
 Biographical information taken from http://www.cerc.gc.ca/chairholders-titulaires/lodi-eng.aspx
Bio: Anthony Wachs is an assistant professor with a joint appointment in the departments of Mathematics and of Chemical and Biological Engineering at the University of British Columbia, Vancouver, Canada.
He received his B. Sc. and M. Sc. from the University Louis Pasteur of Strasbourg and his PhD from the Institut National Polytechnique of Grenoble in 2000. Right after, he was hired in 2001 as a Fluid Mechanics research engineer at IFP Energies nouvelles (IFPEN, at that time Institut Français du Pétrole) in Paris.
In 2009, he spent a one-year sabbatical at the nuclear research center of Cadarache in the south of France, where he worked for IRSN (the french national safety administration for nuclear energy). In 2010, he got his HDR (French Habilitation to Supervise Research) and was later promoted Scientific Advisor at IFPEN in Multiphase Flows and Scientific Computing. He then moved to IFPEN-Lyon where he supervised a group of researchers (including PhD and post-doc students) on the numerical simulation of reactive particulate flows (www.peligriff.com).
His main research interests are non-Newtonian Flows, Multiphase Flows and High Performance Computing. He collaborates extensively with academic groups in Canada, Brazil, France and Germany.
The Michigan Institute for Data Science (MIDAS) hosted Dr. Gary King of Harvard University for a talk titled “Big Data is Not About the Data!” on Friday, Oct. 3 as part of the MIDAS Seminar Series.
Video of the talk is now available for viewing online.
For a schedule of upcoming MIDAS Seminars, visit the seminar webpage.
Bio: Jonathan Freund is the Donald Biggar Willett Professor of Mechanical Science & Engineering and Aerospace at the University of Illinois at Urbana-Champaign. He is a Fellow of the American Physical Society, and a winner of the 2008 Frenkiel Prize from its Division of Fluid Dynamics where he currently serves as the division secretary/treasurer. He is an associate editor of Physical Review Fluids and on the editorial board of Annual Review of Fluid Mechanics. Computational science has been central to his research, which has included simulations of turbulent jet noise and its control, the dynamics of molecularly thin liquid films, nanostructure formation by ion-bombardment of semiconductor materials, and most recently the dynamics of red blood cells flowing in the narrow confines of the microcirculation. He co-directs the DOE-funded Center for Exascale Simulation of Plasma-Coupled Combustion at the University of Illinois.
Adjoint-based optimization for understanding and reducing flow noise
Advanced simulation tools, particularly large-eddy simulation techniques, are becoming capable of making quality predictions of jet noise for realistic nozzle geometries and at engineering relevant flow conditions. Increasing computer resources will be a key factor in improving these predictions still further. Quality prediction, however, is only a necessary condition for the use of such simulations in design optimization. Predictions do not of themselves lead to quieter designs. They must be interpreted or harnessed in some way that leads to design improvements. As yet, such simulations have not yielded any simplifying principals that offer general design guidance. The turbulence mechanisms leading to jet noise remain poorly described in their complexity. In this light, we have implemented and demonstrated an aeroacoustic adjoint-based optimization technique that automatically calculates gradients that point the direction in which to adjust controls in order to improve designs. This is done with only a single flow solutions and a solution of an adjoint system, which is solved at computational cost comparable to that for the flow. Optimization requires iterations, but having the gradient information provided via the adjoint accelerates convergence in a manner that is insensitive to the number of parameters to be optimized. The talk will review the formulation of the adjoint of the compressible flow equations for optimizing noise-reducing controls and present examples of its use. We will particularly focus on some mechanisms of flow noise that have been revealed via this approach.
This seminar is co-sponsored by U-M Aerospace Engineering
Bio: Jeremy Lichstein is an assistant professor of Biology at the University of Florida. Professor Lichstein got his Ph. D. from Princeton University and was a postdoctoral research fellow at Princeton’s department of Ecology and Evolutionary Biology. He was the recipient of the University of Florida Excellence Award for Assistant Professor, and was named a Florida Climate Institute Fellow for 2016-2017. His research interests are forest dynamics, biodiversity, carbon cycle and climate change.
Biodiversity and the changing Earth System: computational challenges and new answers to old questions
Terrestrial ecosystems currently offset roughly 25% of global annual anthropogenic fossil fuel emissions. However, the fate of this carbon sink is highly uncertain, in large part because global models diverge in their predictions of ecosystem responses to climate change, drought, and other perturbations. Although there is little agreement on how terrestrial ecosystems will respond to global change on decadal and longer time-scales, there is wide consensus that current global models are overly simplistic in their representation of important ecological processes. I will discuss our current understanding of how tree functional diversity is maintained in forests, the consequences of including more realistic levels of functional diversity in global models, and the computational challenges that need to be overcome in order to introduce ecological realism into the Earth System Models that the scientific and policy communities rely on for climate projections. A key result that is emerging from empirical and theoretical studies is that shifts in species composition across time or space (beta diversity) have different (and sometimes opposite) effects on ecosystem stability as local (alpha) diversity.
This seminar is co-sponsored by the U-M department of Ecology and Evolutionary Biology