S. Sandeep Pradhan

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

Gregory S. Miller

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Greg’s research primarily investigates information flow in financial markets and the actions of agents in those markets – both consumers and producers of that information. His approach draws on theory from the social sciences (economics, psychology and sociology) combined with large data sets from diverse sources and a variety of data science approaches. Most projects combine data from across multiple sources, including commercial data bases, experimentally created data and extracting data from sources designed for other uses (commercial media, web scrapping, cellphone data etc.). In addition to a wide range of econometric and statistical methods, his work has included applying machine learning , textual analysis, mining social media, processes for missing data and combining mixed media.

Aaron A. King

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The long temporal and large spatial scales of ecological systems make controlled experimentation difficult and the amassing of informative data challenging and expensive. The resulting sparsity and noise are major impediments to scientific progress in ecology, which therefore depends on efficient use of data. In this context, it has in recent years been recognized that the onetime playthings of theoretical ecologists, mathematical models of ecological processes, are no longer exclusively the stuff of thought experiments, but have great utility in the context of causal inference. Specifically, because they embody scientific questions about ecological processes in sharpest form—making precise, quantitative, testable predictions—the rigorous confrontation of process-based models with data accelerates the development of ecological understanding. This is the central premise of my research program and the common thread of the work that goes on in my laboratory.

Harm Derksen

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Current research includes a project funded by Toyota that uses Markov Models and Machine Learning to predict heart arrhythmia, an NSF-funded project to detect Acute Respiratory Distress Syndrome (ARDS) from x-ray images and projects using tensor analysis on health care data (funded by the Department of Defense and National Science Foundation).

Lu Wei

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Lu Wei, DSc,  is Assistant Professor in the Department of Electrical and Computer Engineering at the University of Michigan, Dearborn.

Prof. Wei studies the analytical properties of interacting particle systems relevant to both classical and quantum information theory.

 

Charu Chandra

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My research interests are in developing inter-disciplinary knowledge in System Informatics, as the basis for study of complex system problems with the fusion of theory, computation, and application components adopted from Systems and Informatics fields. In this framework, a complex system such as the supply chain is posited as a System-of-Systems; i.e., a collection of individual business entities organized as a composite system with their resources and capabilities pooled to obtain an interoperable and synergistic system, possessing common and shared goals and objectives. Informatics facilitates coordination and integration in the system by processing and sharing information among supply chain entities for improved decision-making.

A common theme of my research is the basic foundation of universality of system and the realization that what makes it unique is its environment. This has enabled to categorize problems, designs, models, methodologies, and solution techniques at macro and micro levels and develop innovative solutions by coordinating these levels in an integrated environment.

My goal is to study the efficacy of the body of knowledge available in Systems Theory, Information Science, Artificial Intelligence & Knowledge Management, Management Science, Industrial Engineering and Operations Research fields; applied uniquely to issues and problems of complex systems in the manufacturing and service sectors.

Theoretical work investigated by me in this research thrust relates to:

  • Developing Generalized System Taxonomies and Ontologies for complex systems management.
  • Experimenting with Problem Taxonomies for design and modeling efficiencies in complex system networks.
  • Developing methodologies, frameworks and reference models for complex systems management.
  • Computation and application development focused on developing algorithms and software development for:
    • Supply chain information system and knowledge library using Web-based technology as a dissemination tool.
    • Integration with Enterprise Resource Planning modules in SAP software.
    • Supply chain management problem-solving through application of problem specific simulation and optimization.

My research has extended to application domains in healthcare, textiles, automotive, and defense sectors. Problems and issues addressed relate to health care management, operationalizing of sustainability, energy conservation, global logistics management, mega-disaster recovery, humanitarian needs management, and entrepreneurship management.

Currently, my application focus is on expanding the breadth and depth of inquiry in the healthcare domain. Among the topics being investigated are: (1) the organization and structure of health care enterprises; and (2) operations and strategies that relate to management of critical success factors, such as costs, quality, innovation and technology adoption by health care providers. Two significant topics that I have chosen to study with regard to care for elderly patients suffering from chronic congestive heart failure and hypertension are: (1) the design of patient-centered health care delivery to improve quality of care; and (2) managing enhanced care costs due to readmission of these patients.

Data science applications: Real-time data processing in supply chains, Knowledge portals for decision-making in supply chains, information sharing for optimizing patient-centered healthcare delivery

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.

Emanuel Gull

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Professor Gull works in the general area of computational condensed matter physics with a focus on the study of correlated electronic systems in and out of equilibrium. He is an expert on Monte Carlo methods for quantum systems and one of the developers of the diagrammatic ‘continuous-time’ quantum Monte Carlo methods. His recent work includes the study of the Hubbard model using large cluster dynamical mean field methods, the development of vertex function methods for optical (Raman and optical conductivity) probes, and the development of bold line diagrammatic algorithms for quantum impurities out of equilibrium. Professor Gull is involved in the development of open source computer programs for strongly correlated systems.

Quantum impurities are small confined quantum systems coupled to wide leads. An externally applied time-dependent magnetic field induces a change in the population of spins on the impurity, leading to time-dependent switching behavior. The system's equations of motion are determined by a many-body quantum field theory and solved using a diagrammatic Monte Carlo approach. The computations were performed at Columbia University and the University of Michigan.

Quantum impurities are small confined quantum systems coupled to wide leads. An externally applied time-dependent magnetic field induces a change in the population of spins on the impurity, leading to time-dependent switching behavior. The system’s equations of motion are determined by a many-body quantum field theory and solved using a diagrammatic Monte Carlo approach. The computations were performed at Columbia University and the University of Michigan.

Issam El Naqa

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Our lab’s research interests are in the areas of oncology bioinformatics, multimodality image analysis, and treatment outcome modeling. We operate at the interface of physics, biology, and engineering with the primary motivation to design and develop novel approaches to unravel cancer patients’ response to chemoradiotherapy treatment by integrating physical, biological, and imaging information into advanced mathematical models using combined top-bottom and bottom-top approaches that apply techniques of machine learning and complex systems analysis to first principles and evaluating their performance in clinical and preclinical data. These models could be then used to personalize cancer patients’ chemoradiotherapy treatment based on predicted benefit/risk and help understand the underlying biological response to disease. These research interests are divided into the following themes:

  • Bioinformatics: design and develop large-scale datamining methods and software tools to identify robust biomarkers (-omics) of chemoradiotherapy treatment outcomes from clinical and preclinical data.
  • Multimodality image-guided targeting and adaptive radiotherapy: design and develop hardware tools and software algorithms for multimodality image analysis and understanding, feature extraction for outcome prediction (radiomics), real-time treatment optimization and targeting.
  • Radiobiology: design and develop predictive models of tumor and normal tissue response to radiotherapy. Investigate the application of these methods to develop therapeutic interventions for protection of normal tissue toxicities.

Laura Balzano

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Professor Balzano and her students investigate problems in statistical signal processing, machine learning, 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 matrix factorization problems, such as PCA and many interesting variants such as sparse or structured principal components, orthogonality and non-negativity constraints, nonlinear variants such as low-dimensional algebraic variety models, heteroscedastic data, 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 application areas in sensor networks, power systems, control, and computer vision.