Bryan R. Goldsmith

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Bryan R. Goldsmith, PhD, is Assistant Professor in the department of Chemical Engineering within the College of Engineering at the University of Michigan, Ann Arbor.

Prof. Goldsmith’s research group utilizes first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., compressed sensing, kernel ridge regression, and subgroup discovery) to extract insights of catalysts and materials for sustainable chemical and energy production and to help create a platform for their design. For example, the group has exploited subgroup discovery as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-science data.  They also have been using compressed sensing techniques to find physically meaningful models that predict the properties of perovskite (ABX3) compounds.

Prof. Goldsmith’s areas of research encompass energy research, materials science, nanotechnology, physics, and catalysis.

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2).

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2).

 

Sriram Chandrasekaran

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Sriram Chandrasekaran, PhD, is Assistant Professor of Biomedical Engineering in the College of Engineering at the University of Michigan, Ann Arbor.

Dr. Chandrasekaran’s Systems Biology lab develops computer models of biological processes to understand them holistically. Sriram is interested in deciphering how thousands of proteins work together at the microscopic level to orchestrate complex processes like embryonic development or cognition, and how this complex network breaks down in diseases like cancer. Systems biology software and algorithms developed by his lab are highlighted below and are available at http://www.sriramlab.org/software/.

– INDIGO (INferring Drug Interactions using chemoGenomics and Orthology) algorithm predicts how antibiotics prescribed in combinations will inhibit bacterial growth. INDIGO leverages genomics and drug-interaction data in the model organism – E. coli, to facilitate the discovery of effective combination therapies in less-studied pathogens, such as M. tuberculosis. (Ref: Chandrasekaran et al. Molecular Systems Biology 2016)

– GEMINI (Gene Expression and Metabolism Integrated for Network Inference) is a network curation tool. It allows rapid assessment of regulatory interactions predicted by high-throughput approaches by integrating them with a metabolic network (Ref: Chandrasekaran and Price, PloS Computational Biology 2013)

– ASTRIX (Analyzing Subsets of Transcriptional Regulators Influencing eXpression) uses gene expression data to identify regulatory interactions between transcription factors and their target genes. (Ref: Chandrasekaran et al. PNAS 2011)

– PROM (Probabilistic Regulation of Metabolism) enables the quantitative integration of regulatory and metabolic networks to build genome-scale integrated metabolic–regulatory models (Ref: Chandrasekaran and Price, PNAS 2010)

 

Research Overview: We develop computational algorithms that integrate omics measurements to create detailed genome-scale models of cellular networks. Some clinical applications of our algorithms include finding metabolic vulnerabilities in pathogens (M. tuberculosis) using PROM, and designing multi combination therapeutics for reducing antibiotic resistance using INDIGO.

Research Overview: We develop computational algorithms that integrate omics measurements to create detailed genome-scale models of cellular networks. Some clinical applications of our algorithms include finding metabolic vulnerabilities in pathogens (M. tuberculosis) using PROM, and designing multi combination therapeutics for reducing antibiotic resistance using INDIGO.

Suleyman Uludag

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My research spans security, privacy, and optimization of data collection particularly as applied to the Smart Grid, an augmented and enhanced paradigm for the conventional power grid. I am particularly interested in optimization approaches that take a notion of security and/or privacy into the modeling explicitly. At the intersection of the Intelligent Transportation Systems, Smart Grid, and Smart Cities, I am interested in data privacy and energy usage in smart parking lots. Protection of data and availability, especially under assault through a Denial-of-Service attacks, represents another dimension of my area of research interests. I am working on developing data privacy-aware bidding applications for the Smart Grid Demand Response systems without relying on trusted third parties. Finally, I am interested in educational and pedagogical research about teaching computer science, Smart Grid, cyber security, and data privacy.

This figure shows the data collection model I used in developing a practical and secure Machine-to-Machine data collection protocol for the Smart Grid.

This figure shows the data collection model I used in developing a practical and secure
Machine-to-Machine data collection protocol for the Smart Grid.

Wencong Su

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In the next-generation power systems (Smart Grid), a large number of distributed energy devices (e.g., distributed generators, distributed energy storage, loads, smart meters) are connected to each other in an internet-like structure. Incorporating millions of new energy devices will require wide-ranging transformation of the nation’s aging electrical grid infrastructure. The key challenge is to efficiently manage a great amount of devices through distributed intelligence. The distributed grid intelligence (DGI) agent is the brain of distributed energy devices. DGI enables every single energy device to not only have a certain intelligence to achieve optimal management locally, but also coordinate with others to achieve a common goal. The massive volume of real-time data collected by DGI will help the grid operators gain a better understanding of a large-scale and highly dynamic power systems. In conventional power systems, the system operation is performed using purely centralized data storage and processing approaches. However, as the number of DGIs increases to more than hundreds of thousands, it is rather intuitive that the state-of-the-art centralized information processing architecture will no longer be sustainable under such big data explosion. The ongoing research work illustrates how advanced ideas from IT industry and power industry can be combined in a unique way. The proposed high-availability distributed file system and data processing framework can be easily tailored to support other data-intensive applications in a large-scale and complex power grids. For example, the proposed DGI nodes can be embedded into any distributed generators (e.g., roof-top PV panel), distributed energy storage devices (e.g., electric vehicle), and loads (e.g., smart home) in a future residential distribution system. If implemented successfully, we can translate Smart Grid with high-volume, high-velocity, and high-variety data to a completely distributed cyber-physical system architecture. In addition, the proposed work can be easily extended to support other cyber-physical system applications (e.g., intelligent transportation system).

Big Data Applications in Power Systems

Big Data Applications in Power Systems

Ming Xu

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My research focuses on developing and applying computational and data-enabled methodology in the broader area of sustainability. Main thrusts are as follows:

1. Human mobility dynamics. I am interested in mining large-scale real-world travel trajectory data to understand human mobility dynamics. This involves the processing and analyzing travel trajectory data, characterizing individual mobility patterns, and evaluating environmental impacts of transportation systems/technologies (e.g., electric vehicles, ride-sharing) based on individual mobility dynamics.

2. Global supply chains. Increasingly intensified international trade has created a connected global supply chain network. I am interested in understanding the structure of the global supply chain network and economic/environmental performance of nations.

3. Networked infrastructure systems. Many infrastructure systems (e.g., power grid, water supply infrastructure) are networked systems. I am interested in understanding the basic structural features of these systems and how they relate to the system-level properties (e.g., stability, resilience, sustainability).

A network visualization (force-directed graph) of the 2012 US economy using the industry-by-industry Input-Output Table (15 sectors) provided by BEA. Each node represents a sector. The size of the node represents the economic output of the sector. The size and darkness of links represent the value of exchanges of goods/services between sectors. An interactive version and other data visualizations are available at http://complexsustainability.snre.umich.edu/visualization

A network visualization (force-directed graph) of the 2012 US economy using the industry-by-industry Input-Output Table (15 sectors) provided by BEA. Each node represents a sector. The size of the node represents the economic output of the sector. The size and darkness of links represent the value of exchanges of goods/services between sectors. An interactive version and other data visualizations are available at complexsustainability.snre.umich.edu/visualization

Jie Shen

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My research interests are in the digital diagnosis of material damage based on sensors, computational science and numerical analysis with large-scale 3D computed tomography data: (1) Establishment of a multi-resolution transformation rule of material defects. (2) Design of an accurate digital diagnosis method for material damage. (3) Reconstruction of defects in material domains from X-ray CT data . (4) Parallel computation of materials damage. My team also conducted a series of studies for improving the quality of large-scale laser scanning data in reverse engineering and industrial inspection: (1) Detection and removal of non-isolated Outlier Data Clusters (2) Accurate correction of surface data noise of polygonal meshes (3) Denoising of two-dimensional geometric discontinuities.

Processing and Analysis of 3D Large-Scale Engineering Data

Processing and Analysis of 3D Large-Scale Engineering Data

Pascal Van Hentenryck

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Our research is concerned with evidence-based optimization, the idea of optimizing complex systems holistically, exploiting the unprecedented amount of available data. It is driven by an exciting convergence of ideas in big data, predictive analytics, and large-scale optimization (prescriptive analytics) that provide, for the first time, an opportunity to capture human dynamics, natural phenomena, and complex infrastructures in optimization models. We apply evidence-based optimization to challenging applications in environmental and social resilience, energy systems, marketing, social networks, and transportation. Key research topics include the integration of predictive (machine learning, simulation, stochastic approximation) and prescriptive analytics (optimization under uncertainty), as well as the integration of strategic, tactical, and operational models.

The video above is of a planned evacuation of 70,000 persons for a 1-100 year flood in the Hawkesbury-Nepean Region using both predictive and prescriptive analytics and large data sets for the terrain, the population, and the transportation network.

Viswanath Nagarajan

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My research is on the design of efficient (approximation) algorithms for combinatorial optimization problems. I work on deterministic optimization models, as well as richer models that handle uncertainty in data: stochastic, robust and online optimization. I am also interested in algorithms for scheduling and energy-efficiency in data centers.

Laura Balzano

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Professor Balzano and her students investigate problems in statistical signal processing and optimization, particularly dealing with large and messy data. Her applications typically have missing, corrupted, and uncalibrated data as well as heterogeneous data in terms of sensors, sensor quality, and scale in both time and space. Her theoretical interests involve classes of non-convex problems that include Principal Components Analysis (or the Singular Value Decomposition) and many interesting variants such as PCA with sparse or structured principal components, orthogonality and non-negativity constraints, and even categorical data or human preference data. She concentrates on fast gradient methods and related optimization methods that are scalable to real-time operation and massive data. Her work provides algorithmic and statistical guarantees for these algorithms on the aforementioned non-convex problems, and she focuses carefully on assumptions that are realistic for the relevant applications. She has worked in the areas of online algorithms, real-time computer vision, compressed sensing and matrix completion, network inference, and sensor networks.

Real-time dynamic background tracking and foreground separation. At time t = 101, the virtual camera slightly pans to right 20 pixels. We show how GRASTA quickly adapts to the new subspace by t = 125. The first row is the original video frame; the middle row is the tracked background; the bottom row is the separated foreground.

Real-time dynamic background tracking and foreground separation. At time t = 101, the virtual camera slightly pans to right 20 pixels. We show how GRASTA quickly adapts to the new subspace by t = 125. The first row is the original video frame; the middle row is the tracked background; the bottom row is the separated foreground.