Zhongming Liu

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My research is at the intersection of neuroscience and artificial intelligence. My group uses neuroscience or brain-inspired principles to design models and algorithms for computer vision and language processing. In turn, we uses neural network models to test hypotheses in neuroscience and explain or predict human perception and behaviors. My group also develops and uses machine learning algorithms to improve the acquisition and analysis of medical images, including functional magnetic resonance imaging of the brain and magnetic resonance imaging of the gut.

We use brain-inspired neural networks models to predict and decode brain activity in humans processing information from naturalistic audiovisual stimuli.

Harrison Crandall

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Harrison Crandall is a Web Developer and Social Media Assistant at MIDAS. He is a Senior at the University of Michigan, with a passion for Front-end Computer Science and Data Science. Harrison lives in Larchmont, New York and in 2015 he founded a company named Larchmont Web Design to create websites for local businesses. Prior to working at MIDAS he also worked as a Web Developer at an advertising agency in Norwalk, CT.

S. Sandeep Pradhan

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My research interest include information theory, coding theory, distributed data processing, quantum information theory, quantum field theory.

Lei Ying

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His research is broadly in the interplay of complex stochastic systems and big-data, including large-scale communication/computing systems for big-data processing, private data marketplaces, and large-scale graph mining.

Satish Narayanasamy

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Satish Narayanasamy, Ph.D., is Associate Professor in the Electrical Engineering and Computer Science department in the College of Engineering at the University of Michigan, Ann Arbor. Satish’s interests are working at the intersection of computer architecture, software systems and program analysis. His current interests include concurrency, security, customized architectures and tools for mobile and web applications, machine learning assisted program analysis, and tools for teaching at scale.

Eric Michielssen

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Eric Michielssen, PhD, is Professor of Electrical Engineering and Computer Science, Director of the Michigan Institute for Computational Discovery and Engineering, and Associate Vice President for Advanced Research Computing. His research interests include all aspects of theoretical, applied, and computational electromagnetics, with emphasis on the development of fast (primarily) integral-equation-based techniques for analyzing electromagnetic phenomena. His group studies fast multipole methods for analyzing static and high frequency electronic and optical devices, fast direct solvers for scattering analysis, and butterfly algorithms for compressing matrices that arise in the integral equation solution of large-scale electromagnetic problems. Furthermore, the group works on plane-wave-time-domain algorithms that extend fast multipole concepts to the time domain, and develop time-domain versions of pre-corrected FFT/adaptive integral methods.  Collectively, these algorithms allow the integral equation analysis of time-harmonic and transient electromagnetic phenomena in large-scale linear and nonlinear surface scatterers, antennas, and circuits.  Recently, the group developed powerful Calderon multiplicative preconditioners for accelerating time domain integral equation solvers applied to the analysis of multiscale phenomena, and used the above analysis techniques to develop new closed-loop and multi-objective optimization tools for synthesizing electromagnetic devices, as well as to assist in uncertainty quantification studies relating to electromagnetic compatibility and bioelectromagnetic problems.

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.

Mingyan Liu

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Mingyan Liu, PhD, is Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.

Prof. Liu’s research interest lies in optimal resource allocation, sequential decision theory, online and machine learning, performance modeling, analysis, and design of large-scale, decentralized, stochastic and networked systems, using tools including stochastic control, optimization, game theory and mechanism design. Her most recent research activities involve sequential learning, modeling and mining of large scale Internet measurement data concerning cyber security, and incentive mechanisms for inter-dependent security games. Within this context, her research group is actively working on the following directions.

1. Cyber security incident forecast. The goal is to predict an organization’s likelihood of having a cyber security incident in the near future using a variety of externally collected Internet measurement data, some of which capture active maliciousness (e.g., spam and phishing/malware activities) while others capture more latent factors (e.g., misconfiguration and mismanagement). While machine learning techniques have been extensively used for detection in the cyber security literature, using them for prediction has rarely been done. This is the first study on the prediction of broad categories of security incidents on an organizational level. Our work to date shows that with the right choice of feature set, highly accurate predictions can be achieved with a forecasting window of 6-12 months. Given the increasing amount of high profile security incidents (Target, Home Depot, JP Morgan Chase, and Anthem, just to name a few) and the amount of social and economic cost they inflict, this work will have a major impact on cyber security risk management.

2. Detect propagation in temporal data and its application to identifying phishing activities. Phishing activities propagate from one network to another in a highly regular fashion, a phenomenon known as fast-flux, though how the destination networks are chosen by the malicious campaign remains unknown. An interesting challenge arises as to whether one can use community detection methods to automatically extract those networks involved in a single phishing campaign; the ability to do so would be critical to forensic analysis. While there have been many results on detecting communities defined as subsets of relatively strongly connected entities, the phishing activity exhibits a unique propagating property that is better captured using an epidemic model. By using a combination of epidemic modeling and regression we can identify this type of propagating community with reasonable accuracy; we are working on alternative methods as well.

3. Data-driven modeling of organizational and end-user security posture. We are working to build models that accurately capture the cyber security postures of end-users as well as organizations, using large quantities of Internet measurement data. One domain is on how software vendors disclose security vulnerabilities in their products, how they deploy software upgrades and patches, and in turn, how end users install these patches; all these elements combined lead to a better understanding of the overall state of vulnerability of a given machine and how that relates to user behaviors. Another domain concerns the interconnectedness of today’s Internet which implies that what we see from one network is inevitably related to others. We use this connection to gain better insight into the conditions of not just a single network viewed in isolation, but multiple networks viewed together.

A predictive analytics approach to forecasting cyber security incidents. We start from Internet-scale measurement on the security postures of network entities. We also collect security incident reports to use as labels in a supervised learning framework. The collected data then goes through extensive processing and domain-specific feature extraction. Features are then used to train a classifier that generates predictions when we input new features, on the likelihood of a future incident for the entity associated with the input features. We are also actively seeking to understand the causal relationship among different features and the security interdependence among different network entities. Lastly, risk prediction helps us design better incentive mechanisms which is another facet of our research in this domain.

A predictive analytics approach to forecasting cyber security incidents. We start from Internet-scale measurement on the security postures of network entities. We also collect security incident reports to use as labels in a supervised learning framework. The collected data then goes through extensive processing and domain-specific feature extraction. Features are then used to train a classifier that generates predictions when we input new features, on the likelihood of a future incident for the entity associated with the input features. We are also actively seeking to understand the causal relationship among different features and the security interdependence among different network entities. Lastly, risk prediction helps us design better incentive mechanisms which is another facet of our research in this domain.

Jeff Fessler

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My research group develops models and algorithms for large-scale inverse problems, especially image reconstruction for X-ray CT and MRI.  The models include those based on sparsity using dictionaries learned from large-scale data sets.  Developing efficient and accurate methods for dictionary learning is a recent focus.

For a summary of how model-based image reconstruction methods lead to improved image quality and/or lower X-ray doses, see: http://web.eecs.umich.edu/~fessler/re

To see how model-based image reconstruction methods lead to improved image quality and/or lower X-ray doses, see: http://web.eecs.umich.edu/~fessler/rehttp://web.eecs.umich.edu/~fessler/result/ct/

Michael Cafarella

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Michael Cafarella, PhD, is Associate Professor of Electrical Engineering and Computer Science, College of Engineering and Faculty Associate, Survey Research Center, Institute for Social Research, at the University of Michigan, Ann Arbor.

Prof. Cafarella’s research focuses on data management problems that arise from extreme diversity in large data collections. Big data is not just big in terms of bytes, but also type (e.g., a single hard disk likely contains relations, text, images, and spreadsheets) and structure (e.g., a large corpus of relational databases may have millions of unique schemas). As a result, certain long-held assumptions — e.g., that the database schema is always known before writing a query — are no longer useful guides for building data management systems. As a result, my work focuses heavily on information extraction and data mining methods that can either improve the quality of existing information or work in spite of lower-quality information.

A peek inside a Michigan data center! My students and I visit whenever I am teaching EECS485, which teaches many modern data-intensive methods and their application to the Web.

A peek inside a Michigan data center! My students and I visit whenever I am teaching EECS485, which teaches many modern data-intensive methods and their application to the Web.