Lawrence Seiford

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Lawrence Seiford, PhD, is Professor of Industrial and Operations Engineering in the College of Engineering and the Goff Smith Co-Director of the Tauber Institute for Global Operations at the University of Michigan, Ann Arbor.

Prof. Seiford’s research interests include:

  • Analytics
    Data Analytics
  • Applications
    Healthcare Quality Improvement
    Banking & Finance
    Manufacturing
    Service Systems
  • Industrial Operations
    Distribution & Logistics
    Inventory Control
    Production Scheduling
    Supply-Chain Management
  • Operations Research Tools
    Data Envelopment Analysis
    Game Theory
    Math Modeling
    Performance Measurement
    Productivity And Efficiency Analysis
  • Quality and Applied Statistics
    Statistical Quality Control
    Exploratory Data Analysis
    Visualization
  • Risk Management
    Risk Analysis

Edward G. Happ

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Edward G. Happ is an Executive Fellow at the University of Michigan School of Information, where he is teaching and conducting research. He is also the Co-Founder and former Chairman of NetHope (www.nethope.org), a U.S. based consortium of 50+ leading international relief, development and conservation nonprofits focused on information and communications technology (ICT) and collaboration.

 

Hyun-soo Ahn

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Hyun-soo Ahn is an Associate Professor of Operations and Management Science at the Michigan Business School. He joined Michigan in 2003 from the department of Industrial Engineering and Operations Research at UC Berkeley. In his research, Hyun-soo develops and analyzes mathematical models related to supply chain management, dynamic pricing and revenue management, workforce agility, and resource allocation. He is also interested in modeling the customer’s behavior (such as subscription) and how it affects the firm’s profit. He has worked with more than 20 companies and his research has been funded by several organizations including National Science Foundation. His papers appear in leading journals in the field, including Operations Research, M&SOM, and Journal of Applied Probability.

At Ross, he teaches supply chain analytics and business statistics to MBAs, Exec. MBAs, MSCM, and BBAs. He has won a number of teaching awards voted by students, including 2012 Exec MBA teaching excellence award, 2012 Global MBA teaching excellence award, and 2006 BBA teaching excellence award.

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

Romesh P. Nalliah

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Dr. Nalliah’s expertise in research focuses on process evaluation. He has studied healthcare delivery processes, educational processes and healthcare outcomes. Dr. Nalliah’s research studies were the first time nationwide data was used to highlight hospital resource utilization for managing dental caries, pulpal lesions, periapical lesions and general oral conditions in the United States. Dr. Nalliah is internationally recognized as a pioneer in the field of nationwide hospital dataset research for dental conditions and has numerous publications in peer reviewed journals.

Dr. Nalliah’s interest for future research is to expand experience in various areas of public health but not forget his connection to dentistry. Dr. Nalliah has conducted research related to gun violence, facial fractures, spinal fusion, oral cancer, dental conditions, educational debt, mental health, suicide, sports injuries, poisoning and the characteristics of patients discharged against medical advice. National recognition of his expertise in these broader topics of medicine have given rise to opportunities to lecture to medical residents, nurse practitioners, students in medical, pharmacy and nursing programs about oral health. This is his passion- that his research should inform an evolution of dental and health education curriculum and practice.

Dr. Nalliah’s passion in research is improving healthcare delivery systems and he’s interested in improving processes, minimizing inefficiencies, reducing healthcare bottlenecks, increasing quality, and increase task sharing which will lead to a patient-centered, coherent healthcare system. Dr. Nalliah’s research has identified systems constraints and his goal is to influence policy and planning to break those constraints and improve healthcare delivery.

Jason Owen-Smith

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Our data architecture combines naturally-occurring data from research grant inputs with scientific outputs including publications, citations, dissertations, and patents, as well as with biographic data on researchers scraped from the web and in databases. These data integrate with STAR METRICS administrative data on grant purchases and employment, which can in turn be linked to Longitudinal Employer-Household Dynamics (LEHD) Census data enabling individuals to be traced as they move across employers and start businesses. These data are then linked using cutting edge disambiguation/name-entity resolution, web scraping and entity extraction. This IRIS methodology is advancing the underlying computational sciences and creating more useful data for broader applications.

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.

Peter Lenk

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Peter Lenk, PhD, is Professor of Technology and Operations, Stephen M Ross School of Business, at the University of Michigan, Ann Arbor.

Prof. Lenk develops Bayesian models that disaggregate data to address individuals.  He also studies Bayesian nonparametric methods and currently consider shape constraints.  Prof. Lenk teaches and uses data mining methods such as recursive partition and neural networks.

Pascal Van Hentenryck

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Pascal Van Hentenryck, Phd, is the Seth Bonder Collegiate Professor of Industrial and Operations Engineering, Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.

His 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. He applies 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.

Muzammil M. Hussain

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Muzammil M. Hussain is an Assistant Professor of Communication Studies, and Faculty Associate in the Institute for Social Research at the University of Michigan.

Dr. Hussain’s interdisciplinary research is at the intersections of global communication, comparative politics, and complexity studies. At Michigan, Professor Hussain teaches courses on research methods, digital politics, and global innovation. His published books include “Democracy’s Fourth Wave? Digital Media and the Arab Spring” (Oxford University Press, 2013), a cross-national comparative study of how digital media and information technologies have supported the opening-up of closed societies in the MENA, and “State Power 2.0: Authoritarian Entrenchment and Political Engagement Worldwide” (Ashgate Publishing, 2013), an international collection detailing how governments, both democracies and dictatorships, are working to close-down digital systems and environments around the world. He has authored numerous research articles, book chapters, and industry reports examining global ICT politics, innovation, and policy, including pieces in The Journal of Democracy, The Journal of International Affairs, The Brookings Institution’s Issues in Technology and Innovation, The InterMedia Institute’s Development Research Series, International Studies Review, International Journal of Middle East Affairs, The Communication Review, Policy and Internet, and Journalism: Theory, Practice, and Criticism.

Twitter: @m_m_hussain.

Puneet Manchanda

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My interest is in using econometrics, especially Bayesian econometrics, and machine learning methods to infer causality. I tend to work with mostly parametric models of firm and consumer behavior to assess the effectiveness of firm actions. My work spans a variety of industries such as pharmaceuticals, e-commerce, gaming and hi-technology.