Carnegie Mellon University

CEE Graduate Seminar Series

Spring 2018

All seminars will be held in Gates 4401 from 10:30-11:50 unless otherwise noted.

All seminars are open to the campus community. The use of electronic devices is prohibited during seminar. 

Bringing Air Quality Models into Policy and Systems Analysis


Fine particulate matter (PM) is arguably the most important air pollutant. It consists of sub-micron-sized airborne particles, from diverse sources, composed of both organic and inorganic compounds. Exposure to fine PM is associated with approximately 100,000 premature deaths annually in the United States and several million globally.

Decisions regarding energy, transportation, and agricultural systems involve complex tradeoffs between societal needs, public health, global climate, and other considerations. In many cases, air pollution considerations are paramount; in cost-benefit calculations, premature mortality from fine PM often exceeds climate and other environmental damages. Yet, the health effects of air pollution are often neglected in these analyses because they are hard to quantify. Models must account for a complex set of atmospheric chemical reactions that form PM and for its spatial distribution, which determines human exposure.

This seminar will discuss this challenge, show some examples where state-of-the-art air quality models have been used to inform such decisions, and describe work my group has done to give decision-makers simple but accurate tools to account for air pollution health effects.


Peter Adams is a Professor in the Civil and Environmental Engineering Department and the Engineering and Public Policy Department at Carnegie Mellon University. Adams’s research largely focuses on development of chemical transport models and their application to decision-making, especially related to fine particulate matter. Adams also has extensive expertise in the simulation of aerosol microphysical processes, ultrafine particles and the formation of cloud condensation nuclei in global climate models. Areas of research have also included the effects of climate change on air quality, short-lived climate forcers, atmospheric ammonia and particulate matter formation from livestock operations, and the simulation organic particulate matter.

Adams currently serves on the EPA’s Clean Air Scientific Advisory Committee Particulate Matter Review Panel. He was selected for a Fulbright grant to collaborate with researchers at the Institute of Atmospheric Sciences and Climate in Bologna, has been a Visiting Senior Research Scientist at the National Aeronautics and Space Administration’s Goddard Space Flight Center, and received the Sheldon K. Friedlander Award for outstanding doctoral thesis from the American Association for Aerosol Research.

He has previously served on the Commonwealth of Pennsylvania’s Air Quality Technical Advisory Committee and the Allegheny County Health Department’s Air Toxics New Guidelines Proposal Committee, served as a consultant to the California Air Resources Board, and served in various capacities the American Association for Aerosol Research.

His research is supported primarily by the Environmental Protection Agency, the National Science Foundation, the National Aeronautics and Space Administration, the Department of Energy, and the Department of Defense. Adams received his BS degree in Chemical Engineering, summa cum laude, from Cornell University. He was awarded a Hertz Foundation Applied Science Fellowship for graduate study and received MS and PhD degrees in Chemical Engineering from the California Institute of Technology. He also holds an associated faculty position in the Chemical Engineering department at Carnegie Mellon.

Electric Vehicles in the Smart Grid: Optimization & Control


The rapid electrification of the transportation fleet imposes unprecedented demands on the electric grid. If controlled, however, these electric vehicles (EVs) provide an immense opportunity for smart grid services that enable renewable penetration and increased reliability.

In this talk we discuss paradigms for aggregating and optimally controlling EV charging. Specifically, we discuss (i) distributed optimization of large-scale EV fleets, (ii) aggregate modeling via partial differential equations, (iii) and plug-and-play model predictive control.

The talk closes with future perspectives for EVs in the Smart Grid, and a short description of new project-based courses on Design of Cyber-Physical Systems taught at UC Berkeley.


Peggy Johnson is currently the Dean of the Schreyer Honors College and a Professor of Civil Engineering at Penn State University, where she had been a faculty member since 1996. As the Dean of the Schreyer Honors College, she oversees Honors Scholars, representing the top 2% of Penn State students across all disciplines. From 2006 to 2015, she was the Head of the Civil and Environmental Engineering Department at Penn State. She has served on the CEE faculty at Penn State since 1996.

Prior to coming to Penn State, she served on the faculty of the Civil and Environmental Engineering Department at the University of Maryland. Over her nearly three decades as a Professor in Civil Engineering, she has conducted research and taught classes in the areas of hydraulic engineering, bridge scour, stream restoration, reliability analyses, and river mechanics. Now, as the Dean, she teaches courses on leadership.

She has published numerous papers in peer-reviewed journals on bridge scour, stream restoration, uncertainty in hydraulics, bridge scour, and stream restoration, and the probability of bridge failure due to scour. She has conducted work on the stability and vulnerability of stream channel designs at bridges. Her method for assessing stream stability at bridge-stream intersections is incorporated as part of the Federal Highway Administration’s manual on stream stability at bridges (HEC-20).

Johnson has supervised the dissertations and theses of dozens of PhD, MS, and BS students. She is the Past-President and a Fellow of the ASCE Environmental and Water Resources Institute (EWRI), the largest institute within ASCE with more than 23,000 members. She received the ASCE Hans Albert Einstein award in 2016 for her contributions in the use of sediment transport for the evaluation and design of in-line control structures and stream restoration projects and the use of uncertainty and risk management for scour analyses. She also received the ASCE-EWRI Outstanding Woman of the Year award in 2012. In addition to winning several teaching awards, Johnson won the National Science Foundation Young Investigator award and in 1995, she won the NSF Presidential Faculty Fellow award.

She received a Master’s Degree in 1988 and a PhD in 1990, both from the Civil and Environmental Engineering department at the University of Maryland.

Green Infrastructure and Urban Stormwater Management


The management of stormwater runoff in urban environments remains one of the greatest environmental challenges of our time, not least because urban stormwater runoff is a primary cause of water-quality impairment in the United States today. Traditional engineering solutions for stormwater management, which include the construction of large underground detention basins, are becoming increasingly difficult to implement in many urban environments because of design and cost challenges, as well as land-area constraints. As a result, the identification of new approaches to manage urban stormwater runoff is becoming a priority for many U.S. cities. One approach that is rapidly gaining in popularity, involves the development of city-wide green infrastructure programs.

This talk will present results from a multi-year research program at Columbia University that has been investigating the stormwater management performance of a suite of green infrastructure types located in New York City. Data collection, modeling protocols and findings from the research program will be discussed, as well as the potential broader role of green infrastructure in promoting urban sustainability and resilience.


A leader in the field of water resources and urban sustainability, Patricia Culligan explores novel, interdisciplinary solutions to the challenges of urbanization, with a particular emphasis on the City of New York.  Her research investigates the opportunities for green infrastructure, social networks, and advanced measurement and sensing technologies to improve urban water, energy, and environmental management.

She is co-Director of a $12 million research network sponsored by the National Science Foundation (NSF) to develop new models for urban infrastructure to make cities cleaner, healthier, and more enjoyable places to live.  She is the founding associate director of Columbia University’s Data Science Center and the co-Director of the Earth Institute’s Urban Design Lab. In 2011, she was elected to the Board of Governors of the American Society of Civil Engineer's Geo-Institute. She is the author or co-author of more than 150 technical articles.

Culligan received her M.Phil. and Ph.D. from the University of Cambridge.

Patterns, Proxies and Predictions: Some perspectives in geologic modeling and calibration


Formulating models that are data-exact, reproduce target statistics and at the same time provide an assessment of uncertainty due to incomplete information is an important objective of stochastic spatial modeling. Ultimately, these models for uncertainty feed into models for decision-making and reservoir development.

In this talk, I will present some of my recent efforts to stochastically model complex reservoirs, develop fast transfer function proxies to represent flow and transport through such complex reservoirs and finally, perform real-time updating and feed-back control of reservoir processes using these fast proxies. I will conclude my talk with some thoughts on how these improved uncertainty quantification procedures can help critical questions such as the value of information and the timing of reservoir develop decisions.

Geological systems such as subsurface reservoir or aquifers in many cases exhibit complex patterns of spatial heterogeneity in the form of channels, sand lenses, crosscutting faults and/or natural fractures. We have developed a unique stochastic simulation technique in the spectral domain that utilizes polyspectra to reproduce complex spatial patterns of continuity. Some novel approaches to condition these spectral simulations to “hard” data observed along wells will also be presented.

In most reservoir modeling scenarios, the data available to model reservoir heterogeneity is sparse and there is significant prior geologic uncertainty. The practice of model calibration or history matching to update the prior depiction of reservoir heterogeneity is quite popular. In this talk I will present a unique model selection scheme that is a distinct departure from this traditional paradigm of history matching. Instead of using the observed dynamic data to drive an iterative model perturbation scheme, the talk will explore the use of proxy responses to group a suite of prior models into clusters exhibiting similar connectivity characteristics. Then, the cluster exhibiting a flow behavior closest to the observed data is selected using a Bayesian scheme. The resulting selected subset of models permit assessment of residual uncertainty persistent after the model calibration process.


Sanjay Srinivasan is a professor of petroleum and natural gas engineering at the Pennsylvania State University and holds the John and Willie Leone Family chair in Energy and Mineral Engineering. He is also the Department Head for the John and Willie Leone Family Department of Energy and Mineral Engineering.

Srinivasan’s primary research focus is in the area of petroleum reservoir characterization and improved management of reservoir recovery processes. Some of the algorithms and methods that he has pioneered have been applied for early appraisal of ultra-deepwater plays in the Gulf of Mexico and for characterizing natural fracture networks in conventional as well as unconventional reservoirs. He has also partnered with researchers at the UT Institute of Geophysics and the Bureau of Economic Geology to develop novel schemes for integrating seismic data in reservoir models.