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Yi Lu Murphey

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Dr. Yi Lu Murphey is an Associate Dean for Graduate Education and Research, a Professor of the ECE(Electrical and Computer Engineering) department and the director of the Intelligent Systems Lab at the University of Michigan, Dearborn. She received a M.S. degree in computer science from Wayne State University, Detroit, Michigan, in 1983, and a Ph.D degree with a major in Computer Engineering and a minor in Control Engineering from the University of Michigan, Ann Arbor, Michigan, in 1989. Her current research interests are in the areas of machine learning, pattern recognition, computer vision and intelligent systems with applications to automated and connected vehicles, optimal vehicle power management, data analytics, and robotic vision systems. She has authored over 130 publications in refereed journals and conference proceedings. She is an editor for the Journal of Pattern Recognition, a senior life member of AAAI and a fellow of IEEE.

Romesh Saigal

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Professor Saigal has held faculty positions at the Haas School of Business, Berkeley and the department of Industrial Engineering and Management Sciences at Northwestern University, has been a researcher at the Bell Telephone Laboratories and numerous short term visiting positions. He currently teaches courses in Financial Engineering. In the recent past he taught courses in optimization, and Management Science. His current research involves data based studies of operational problems in the areas of Finance, Transportation, Renewable Energy and Healthcare, with an emphasis on the management and pricing of risks. This involves the use of data analytics, optimization, stochastic processes and financial engineering tools. His earlier research involved theoretical investigation into interior point methods, large scale optimization and software development for mathematical programming. He is an author of two books on optimization and large set of publications in top refereed journals. He has been an associate editor of Management Science and is a member of SIAM, AMS and AAAS. He has served as the Director of the interdisciplinary Financial Engineering Program and as the Director of Interdisciplinary Professional Programs (now Integrative Design + Systems) at the College of Engineering.

Ding Zhao

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Ding Zhao, PhD, is Assistant Research Scientist in the department of Mechanical Engineering, College of Engineering with a secondary appointment in the Robotics Institute at The University of Michigan, Ann Arbor.

Dr. Zhao’s research interests include autonomous vehicles, intelligent/connected transportation, traffic safety, human-machine interaction, rare events analysis, dynamics and control, machine learning, and big data analysis

 

Peter Adriaens

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My research focus is on the development and application of machine learning tools to large scale financial and unstructured (textual) data to extract, quantify and predict risk profiles and investment grade rating of private and public companies.  Example datasets include social media and financial aggregators such as Bloomberg, Pitchbook, and Privco.

Mark Allison

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Mark Allison, PhD, is Assistant Professor of Computer Science in the department of Computer Science, Engineering and Physics at the University of Michigan-Flint.

Dr. Allison’s research pertains to the autonomic control of complex cyberphysical systems utilizing software models as first class artifacts. Domains being explored are microgrid energy management and unmanned aerial vehicles (UAVs) in swarms.

 

Greg Rybarczyk

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Using GIS, visual analytics, and spatiotemporal modeling, Dr. Rybarczyk examines the utility of Big Data for gaining insight into the causal mechanisms that influence travel patterns and urban dynamics. In particular, his research sets out to provide a fuller understanding of “what” and “where” micro-scale conditions affect human sentiment and hence wayfinding ability, movement patterns, and travel mode-choices.

Recent works:

Rybarczyk, G. and S. Banerjee. (2015) Visualizing active travel sentiment in an urban context, Journal of Transport and Health, 2(2): 30

Rybarczyk, G., S. Banerjee, and M. Starking-Szymanski, and R. Shaker. (2018) “Travel and us: The impact of mode share on sentiment using geosocial media data and GIS” Journal of Location-Based Services (forthcoming)

Necmiye Ozay

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Necmiye Ozay, PhD, is Assistant Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.

Prof. Ozay and her team develop the scientific foundations and associated algorithmic tools for compactly representing and analyzing heterogeneous data streams from sensor/information-rich networked dynamical systems. They take a unified dynamics-based and data-driven approach for the design of passive and active monitors for anomaly detection in such systems. Dynamical models naturally capture temporal (i.e., causal) relations within data streams. Moreover, one can use hybrid and networked dynamical models to capture, respectively, logical relations and interactions between different data sources. They study structural properties of networks and dynamics to understand fundamental limitations of anomaly detection from data. By recasting information extraction problem as a networked hybrid system identification problem, they bring to bear tools from computer science, system and control theory and convex optimization to efficiently and rigorously analyze and organize information. The applications include diagnostics, anomaly and change detection in critical infrastructure such as building management systems, transportation and energy networks.

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://mingxugroup.org/

Henry Liu

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Henry Liu, PhD, is Professor of Civil and Environmental Engineering in the College of Engineering and holds a secondary appointment of Research Professor in the U-M Transportation Research Institute (UMTRI). Prof. Liu is also Director of the Center for Connected and Automated Transportation (Regional 5 UTC).

Prof. Liu’s research is in the areas of traffic network monitoring, modeling and control. His recent work has focused on traffic flow modeling and simulation, traffic signal control and optimization, traffic management under network disruptions and equilibrium traffic assignment. Prof. Liu is the co-founder and chairman of the advisory board for SMART Signal Technologies. The SMART Signal (Systematic Monitoring of Arterial Road Traffic Signals) is a real-time arterial performance monitoring system that uses traffic data from existing signal systems. SMART Signal simultaneously collects event-based high-resolution traffic data from multiple intersections and generates real-time signal performance measures, including arterial travel time, number of stops, queue length, intersection delay and level of service. “Traffic engineers can use this information to improve traffic flow on roads controlled by traffic lights—reducing congestion and saving drivers both time and fuel. SMART Signal could also give drivers a more accurate prediction of travel times by accounting for time spent waiting at traffic lights,” states the University of Minnesota Intelligent Transportation Systems website. The system is now deployed at more than 100 intersections on major arterial corridors in Minnesota and Pasadena, California. Arterial corridors are roads where many cars can move from urban centers to high-capacity freeways.