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.

Jason Owen-Smith

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Professor Owen-Smith conducts research on the collective dynamics of large scale networks and their implications for scientific and technological innovation and surgical care. He is the executive director of the Institution for Research on Innovation and Science (IRIS, http://iris.isr.umich.edu).  IRIS is a national consortium of research universities who share data and support infrastructure designed to support research to understand, explain, and eventually improve the public value of academic research and research training.

One year snapshot of the collaboration network of a single large research university campus. Nodes are individuals employed on sponsored project grants, ties represent copayment on the same grant account in the same year. Ties are valued to reflect the number of grants in common. Node size is proportional to a simple measure of betweenness centrality and node color represents the results of a simple (walktrip) community finding algorithm. The image was created in Gephi.

One year snapshot of the collaboration network of a single large research university campus. Nodes are individuals employed on sponsored project grants, ties represent copayment on the same grant account in the same year. Ties are valued to reflect the number of grants in common. Node size is proportional to a simple measure of betweenness centrality and node color represents the results of a simple (walktrip) community finding algorithm. The image was created in Gephi.

Yi-Su Chen

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My current data science research interest lies in the broad area of supply chain and its management.   I am particularly interested in using longitudinal data set to identify early signals (or warning) and to draw causal inferences pertaining to supply chain security and product quality and safety.   I am also interested in developing experiments to capture the behavioral side of decision makings to be complementary to secondary data analysis.   Industry setting wise, I have based my research on the auto industry, and will expand my auto-industry centered research into a broader, transportation industry oriented context.   I am also interested in food and agricultural products, pharmaceutical, and medical devices industries where product quality and safety have significant implications to human life and society as a whole.

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/

Luis E. Ortiz

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Luis Ortiz, PhD, is Assistant Professor of Computer and Information Science, College of Engineering and Computer Science, The University of Michigan, Dearborn

The study of large complex systems of structured strategic interaction, such as economic, social, biological, financial, or large computer networks, provides substantial opportunities for fundamental computational and scientific contributions. Luis’ research focuses on problems emerging from the study of systems involving the interaction of a large number of “entities,” which is my way of abstractly and generally capturing individuals, institutions, corporations, biological organisms, or even the individual chemical components of which they are made (e.g., proteins and DNA). Current technology has facilitated the collection and public availability of vasts amounts of data, particularly capturing system behavior at fine levels of granularity. In Luis’ group, they study behavioral data of strategic nature at big data levels. One of their main objectives is to develop computational tools for data science, and in particular learning large-population models from such big sources of behavioral data that we can later use to study, analyze, predict and alter future system behavior at a variety of scales, and thus improve the overall efficiency of real-world complex systems (e.g., the smart grid, social and political networks, independent security and defense systems, and microfinance markets, to name a few).

Vijay Subramanian

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Professor Subramanian is interested in a variety of stochastic modeling, decision and control theoretic, and applied probability questions concerned with networks. Examples include analysis of random graphs, analysis of processes like cascades on random graphs, network economics, analysis of e-commerce systems, mean-field games, network games, telecommunication networks, load-balancing in large server farms, and information assimilation, aggregation and flow in networks especially with strategic users.

Danai Koutra

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The GEMS (Graph Exploration and Mining at Scale) Lab develops new, fast and principled methods for mining and making sense of large-scale data. Within data mining, we focus particularly on interconnected or graph data, which are ubiquitous. Some examples include social networks, brain graphs or connectomes, traffic networks, computer networks, phonecall and email communication networks, and more. We leverage ideas from a diverse set of fields, including matrix algebra, graph theory, information theory, machine learning, optimization, statistics, databases, and social science.

At a high level, we enable single-source and multi-source data analysis by providing scalable methods for fusing data sources, relating and comparing them, and summarizing patterns in them. Our work has applications to exploration of scientific data (e.g., connectomics or brain graph analysis), anomaly detection, re-identification, and more. Some of our current research directions include:

*Scalable Network Discovery from non-Network Data*: Although graphs are ubiquitous, they are not always directly observed. Discovering and analyzing networks from non-network data is a task with applications in fields as diverse as neuroscience, genomics, energy, economics, and more. However, traditional network discovery approaches are computationally expensive. We are currently investigating network discovery methods (especially from time series) that are both fast and accurate.

*Graph similarity and Alignment with Representation Learning*: Graph similarity and alignment (or fusion) are core tasks for various data mining tasks, such as anomaly detection, classification, clustering, transfer learning, sense-making, de-identification, and more. We are exploring representation learning methods that can generalize across networks and can be used in such multi-source network settings.

*Scalable Graph Summarization and Interactive Analytics*: Recent advances in computing resources have made processing enormous amounts of data possible, but the human ability to quickly identify patterns in such data has not scaled accordingly. Thus, computational methods for condensing and simplifying data are becoming an important part of the data-driven decision making process. We are investigating ways of summarizing data in a domain-specific way, as well as leveraging such methods to support interactive visual analytics.

*Distributed Graph Methods*: Many mining tasks for large-scale graphs involve solving iterative equations efficiently. For example, classifying entities in a network setting with limited supervision, finding similar nodes, and evaluating the importance of a node in a graph, can all be expressed as linear systems that are solved iteratively. The need for faster methods due to the increase in the data that is generated has permeated all these applications, and many more. Our focus is on speeding up such methods for large-scale graphs both in sequential and distributed environments.

*User Modeling*: The large amounts of online user information (e.g., in social networks, online market places, streaming music and video services) have made possible the analysis of user behavior over time at a very large scale. Analyzing the user behavior can lead to better understanding of the user needs, better recommendations by service providers that lead to customer retention and user satisfaction, as well as detection of outlying behaviors and events (e.g., malicious actions or significant life events). Our current focus is on understanding career changes and predicting job transitions.

Elizaveta Levina

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Elizaveta (Liza) Levina and her group work on various questions arising in the statistical analysis of large and complex data, especially networks and graphs. Our current focus is on developing rigorous and computationally efficient statistical inference on realistic models for networks. Current directions include community detection problems in networks (overlapping communities, networks with additional information about the nodes and edges, estimating the number of communities), link prediction (networks with missing or noisy links, networks evolving over time), prediction with data connected by a network (e.g., the role of friendship networks in the spread of risky behaviors among teenagers), and statistical analysis of samples of networks with applications to brain imaging, especially fMRI data from studies of mental health).

Raj Rao Nadakuditi

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Raj Nadakuditi, PhD, is Associate Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.

Prof. Nadakuditi received his Masters and PhD in Electrical Engineering and Computer Science at MIT as part of the MIT/WHOI Joint Program in Ocean Science and Engineering. His work is at the interface of statistical signal processing and random matrix theory with applications such as sonar, radar, wireless communications and machine learning in mind.

Prof. Nadakuditi particularly enjoys using random matrix theory to address problems that arise in statistical signal processing. An important component of his work is applying it in real-world settings to tease out low-level signals from sensor, oceanographic, financial and econometric time/frequency measurements/time series. In addition to the satisfaction derived from transforming the theory into practice, real-world settings give us insight into how the underlying techniques can be refined and/or made more robust.

Gerald Davis

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My research is broadly concerned with corporate governance and the effects of finance on society. Recent writings examine how ideas about corporate social responsibility have evolved to meet changes in the structures and geographic footprint of multinational corporations; whether “shareholder capitalism” is still a viable model for economic development; how income inequality in an economy is related to corporate size and structure; why theories about organizations do (or do not) progress; how architecture shapes social networks and innovation in organizations; why stock markets spread to some countries and not others; and whether there exist viable organizational alternatives to shareholder-owned corporations in the United States. Recent publications are available at http://webuser.bus.umich.edu/gfdavis/articles.htm.

Ties Among the Fortune 1000 Corporate Boards

Ties Among the Fortune 1000 Corporate Boards