Warren Lecture Series

The Warren Lecture Series is made possible by a generous, renewing gift by Alice Warren Gaarden. The series brings in researchers and speakers from around the world.

Recordings are available on the Warren Lecture Series YouTube channel. You can search our extensive archive using the categories in the pull down menu.

Upcoming presentations

*Presentations during the Spring 2021 semester will be online via Zoom.

  • James Hambleton, Civil and Environmental Engineering, Northwestern University.  February 12, 2021 

  • Nikolaos Geroliminis, EPFL Lausanne, Switzerland.  April 16, 2021 

  • John Gulliver, Civil, Environmental, and Geo- Engineering, University of Minnesota.  April 30, 2021 


Impacts of Automation in Future Public Transport Systems

Constantinos Antoniou
Civil, Geo and Environmental Engineering, Technical University of Munich

ABSTRACT: Antoniou looks at some recent and ongoing research on expected impacts of automation in transforming public transport systems. He describes an integrated framework for a scenario-based impact assessment. Topics include an analysis of multiple waves of survey data collected from users of an automated bus deployment in Stockholm, as well as the development of an analytical model that provides insights into how automation may affect the design and operation of future public transport systems, applied to networks in Germany and Chile.

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Bottom-Up and Top-Down Approaches to the PFAS Problem: From Molecular Models to Policy Frameworks

Carla A. Ng
Civil and Environmental Engineering, University of Pittsburgh

Per- and polyfluorinated alkyl substances (PFAS) are a diverse group of chemicals used in a dizzying array of applications. Public concern is growing over ubiquitous human exposure to PFAS, and the recognition that some PFAS are bioaccumulative and can be toxic even at extremely low concentrations. This concern has prompted policy action at state, federal, and international levels. Yet the development of sound policy and decisions around PFAS is complicated by lack of data on most members of the broad class of chemicals and by the practical difficulties around a substance-by-substance approach to evaluating these chemicals, particularly given their unique properties and behavior in environmental and biological systems. Ng highlights several initiatives from her research group and collaborative work to tackle the PFAS problem at two levels. At the molecular level, she is developing integrative modeling strategies to predict the behavior and potential hazard of diverse PFAS using computational approaches that help to overcome limitations of traditional testing and increase throughput. At the policy framework level, she is collaborating with a team of international academic and regulatory scientists and policy analysts to develop scientifically sound strategies to eliminate hazardous PFAS from products and processes.

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Level Excursion Analysis of Probabilistic Quasibrittle Fracture

Jia-Liang Le
Civil, Environmental, and Geo- Engineering, University of Minnesota

ABSTRACT: It is widely acknowledged that no structure can be designed to be risk free, and therefore, reliability analysis plays a central role in the design of engineering structures. The recent focus has been placed on structures made of brittle heterogenous (a.k.a. quasibrittle) materials, such as ceramics, composites, concrete, and many more at the microscale. Le and his team recently developed a level excursion model for analyzing the probabilistic failure of quasibrittle structures, in which the structural failure probability is calculated as a first passage probability. The main feature of the model is that it captures both the spatial randomness of local material resistance and the random stress field induced by microstructures. The model represents a continuum generalization of the classical weakest-link model, which recovers the Weibull distribution as an asymptotic distribution function. In this talk, Le discusses two applications of this model.

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Modeling and Optimization of Transportation Networks with Emerging Technologies

Alireza Khani
Civil, Environmental, and Geo- Engineering, University of Minnesota

ABSTRACT: Transportation systems are confronting disruptive changes due to the emergence of new technologies such as shared mobility and autonomous vehicles. Traditional methods of modeling transportation systems do not capture the characteristics of new modes or the behavior of users in response to new modes. Therefore, new and customized methods are needed for i) analyzing human mobility patterns from new data sources, ii) controlling the operation of on-demand mobility services, and iii) optimizing long term planning of transportation infrastructures considering future technologies. In this seminar, Khani presents optimization methods to solve selected problems from the three research needs. He and his team develop and apply these methods in the context of multimodal transportation systems to increase benefits to society.

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When Topology Met Phononics: Wave Manipulation on Edges and Interfaces

Stefano Gonella
Civil, Environmental, and Geo- Engineering, University of Minnesota

ABSTRACT: Elastic metamaterials are structural materials that owe their unique wave manipulation capabilities to their complex internal architecture. Topological metamaterials are special metamaterials whose behavior is directly controlled by the topology of their phonon band structure. In this talk, we present two problems in topological phononics that deal with the propagation of waves at edges and interfaces. The first problem regards a class of metamaterials known as topological Maxwell lattices. In the second part of the presentation, we demonstrate the existence of valley-Hall edge states in the in-plane dynamics of honeycomb lattices with bi-valued strut thickness.

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Plagiarism, Plagiarisma, Plagiarmania

Nancy Sims
Libraries, University of Minnesota

Citation, credit, authorship, ownership—at first pass most of these seem like fairly straightforward topics. But beyond a cursory look, these issues are rich and complex, with surprising and subtle nuances across academic disciplines, creative communities, cultural groups, and legal jurisdictions. Technologies intended to address concerns in these areas often fail on numerous edge cases, although they are also sometimes useful. Attorney and librarian Nancy Sims explores some of the tensions inherent in academic understandings of credit and plagiarism.

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Nonlinear Bayesian Inference for High-dimensional Systems

Peter Jan van Leeuwen
Atmosphereic Science, Colorado State University

ABSTRACT: In the geosciences, Bayesian inference is known as data assimilation, and many powerful methodologies have been developed, for instance for weather prediction. At present, weather prediction centers try to find useful solutions to the data-assimilation problem in a state space of size 10 billion or more. These methods are all based on linearizations of the problem, while with ever increasing model resolution and more sophisticated observations the problem has become highly nonlinear. Hence, there is a call for fully nonlinear methods. A new possibility has been developed based on so-called particle flows. Van Leeuwen demonstrates the usefulness of this new approach in simple toy problems and in high-dimensional ocean systems discussing theoretical and practical issues. Finally, van Leeuwen discusses how the present-day weather prediction scheme can be adapted to transform weather prediction from an approximate linearized solution to a fully nonlinear solution of the Bayesian Inference problem.

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Machine Learning and Computer Vision for Processing-Property Analyses

Samantha Daly
Mechanical Engineering, University of California Santa Barbara

ABSTRACT: Microstructure influences deformation and failure mechanisms, such as twinning, slip, grain boundary sliding, and multi-crack systems. This influence includes complex stochastic and deterministic factors whose interactions are currently under active debate. Daly discusses the application of machine learning and computer vision to microscale displacement data for the high-throughput segmentation and identification of deformation mechanisms, and their evolution under load across mm-scale fields of view. Twinning in magnesium is an example. Daly presents a recently developed experimental approach to obtain high-resolution, large FOV microscale deformation maps. Also discussed is the analysis of deformation twinning in Mg WE43 over thousands of grains in each individual test, including the relative activity of specific variants automatically identified from microscale strain fields. The newly developed experimental and analytical approaches are length-scale independent and material agnostic. The approaches can be modified to identify a range of deformation and failure mechanisms.

(recording not available)

Transportation Systems Resilience: Models and Algorithms

Elise Miller-Hooks
Infrastructure Engineering, George Mason University

ABSTRACT: Miller describes mathematical and algorithmic approaches developed for quantifying and maximizing resilience of surface transportation systems and the societal functions they support. Transportation systems interconnect with other critical lifelines, such as power and water supplies. Together, these lifelines support societal activities. System users’ (for example, transit riders) abilities play an important role in their experience of these systems and services. Miller considers both the technical components of a system and the way that system enables its users to adapt. She discusses resilience in the context of our current environment and a developing environment that is intelligent and connected.

(recording not available)

Customized Machine Learning for Safer and Smarter Transportation Applications

Yinhai Wang
Civil and Environmental Engineering, University of Washington

ABSTRACT: Recent advances in sensing, networking, and computing technologies, have led more and more cities to launch smart city plans to improve quality of life, sustainability, efficiency, and productivity. Sensor networks are essential for smart cities, and many new transportation-related data and computational resources are expected within the Smart Cities environment. However, classic traffic analysis methods are not designed to analyze and process the big-data sets generated from sensors. To take full advantage of these data sets, new methods and tools are needed. Machine learning methods have been increasingly utilized in transportation applications, but most have been developed in other fields and might not fit well. Customizations of conventional machine learning methods are highly desirable. Wang introduces a couple research efforts made at the University of Washington Smart Transportation Applications and Research Laboratory (STAR Lab) that produced customized machine learning methods for safer and smarter transportation applications. The superb performance of these customized machine learning methods clearly indicates the value of such artificial intelligence methods.

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Special video: 2020 Spring Graduation

CEGE 2020 Graduation Celebration and Order of the Engineer

Class of 2020 Spring Photo Montage