Oct 12
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Birhanu Eshete

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I study cybercrime using data-driven methods to analyze, characterize, and measure the infrastructure and modus operandi used by criminal activities on the Internet. In particular, I focus on collection, analysis, and semantic characterization of cyber threat intelligence that comes in many shapes and forms (e.g., natural language, network traffic, system audit logs). The ultimate goal is to learn insights that will inform decisions on building robust defense against online criminal activities that involve threats such as ransomware, exploit kits, and botnets. To achieve these goals, I find graph theory and analytics, machine learning (deep learning), longitudinal analysis, and causality inference to be the natural methods. I also study the training and deployment of cyber threat classification/prediction systems in adversarial settings.

From behavioral fingerprinting and detection of cybercrime toolkits to analytics and detection of online cyber threats; from semantic characterization of cyber threat intelligence to detection and forensics of advanced cyber attacks, machine learning, graph theory and analytics, graph isomorphism, and causal inference serve as the core ingredients to build robust defense against cyber threats.

Oct 12
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Jin Lu

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Dr. Jin Lu is an Assistant Professor of Computer and Information Science at the University of Michigan, Dearborn.
His major research interests include machine learning, data mining, optimization, matrix analysis, biomedical informatics, and health informatics. Two main directions are being pursued:
(1) Large-scale machine learning problems with data heterogeneity. Data heterogeneity is common across many high-impact application domains, ranging from recommendation system to Computer Vision, Bioinformatics and Health-informatics. Such heterogeneity can be present in a variety of forms, including (a) sample heterogeneity, where multiple resources of data samples are available as side information; (b) task heterogeneity, where multiple related learning tasks can be jointly learned to improve the overall performance; (c) view heterogeneity, where complementary information is available from various sources. My research interests focus on building efficient machine learning methods from such data heterogeneity, aiming to improve the learning model by making the best use of all data resources.
(2) Machine learning methods with provable guarantees. Machine learning has been substantially developed and has demonstrated great success in various domains. Despite its practical success, many of the applications involve solving NP-hard problems based on heuristics. It is challenging to analyze whether a heuristic scheme has any theoretical guarantee. My research interest is to employ granular data structure, e.g. sample clusters or features describing an aspect of the sample, to design new theoretically-sound models and algorithms for machine learning problems.

Mar 28
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Fred Feng

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Dr. Feng’s research involves conducting and using naturalistic observational studies to better understand the interactions between motorists and other road users including bicyclists and pedestrians. The goal is to use an evidence-based, data-driven approach that improves bicycling and walking safety and ultimately makes them viable mobility options. A naturalistic study is a valuable and unique research method that provides continuous, high-time-resolution, rich, and objective data about how people drive/ride/walk for their everyday trips in the real world. It also faces challenges from the sheer volume of the data, and as with all observational studies, there are potential confounding factors compared to a randomized laboratory experiment. Data analytic methods can be developed to interpret the behavioral data, make meaningful inferences, and get actionable insights.

9.9.2020 MIDAS Faculty Research Pitch Video.

Using naturalistic driving data to examine the interactions between motorists and bicyclists

Mar 11
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Shan Bao

Shan Bao

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My research interests are to improve safety associated with motor-vehicle transportation by addressing both active safety (increased crash avoidance) and passive safety (increased crash protection) issues through the development and application of a wide range of research methodologies. These methodologies are targeted at developing a better understanding and modeling of driver behavior, including physical and cognitive attributes, driver decision-making processes and human intention prediction. I am currently interested in applying data science to study the following topics:
*Driver state detection and prediction;

*Improve user intersection with automated vehicle technologies;

*Communication and interaction between vehicle and vulnerable road users

*Driving style classification

*Human factors issues associated with connected and automated vehicle technologies

Where do drivers look when they are not paying attention to the road

 

Apr 04
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Antonios Koumpias

Antonios M. Koumpias

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Antonios M. Koumpias, Ph.D., is Assistant Professor of Economics in the department of Social Sciences at the University of Michigan, Dearborn. Prof. Koumpias is an applied microeconomist with research interests in public economics, with an emphasis on behavioral tax compliance, and health economics. In his research, he employs quasi-experimental methods to disentangle the causal impact of policy interventions that occur at the aggregate (e.g. states) or the individual (e.g. taxpayers) level in a comparative case study setting. Namely, he relies on regression discontinuity designs, regression kink designs, matching methods, and synthetic control methods to perform program evaluation that estimates the causal treatment effect of the policy in question. Examples include the use of a regression discontinuity design to estimate the impact of a tax compliance reminders on payments of overdue income tax liabilities in Greece, matching methods to measure the influence of mass media campaigns in Pakistan on income tax filing and the synthetic control method to evaluate the long-term effect of state Medicaid expansions on mortality.

Evolution of Annual Changes in All-cause Childless Adult Mortality in New York State following 2001 State Medicaid Expansion

Jan 17
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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.

Mar 08
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Keshav Pokhrel

Keshav Pokhrel

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Keshav Pokhrel, PhD, is Assistant Professor of Statistics at the University of Michigan, Dearborn.

Prof. Pokhrel’s research interests include the epidemiology of cancer, time series forecasting, quantile regression and functional data analysis. The skewed and non-normal data are increasingly more frequent than ever before. The data in the extreme ends are of their own importance. Hence the importance of quantile regression. The availability of the information is increasingly functional. My current work is gearing towards functional data analysis techniques such as principal differential analysis which can estimate a system of differential equations to reveal the dynamics of real data.

Mar 08
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Yi-Su Chen

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.

Mar 08
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Di Ma

Di Ma

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Di Ma is currently an Associate Professor in the Computer and Information Science (CIS) Department, College of Engineering and Computer Science (CECS), at the University of Michigan-Dearborn. She is also serving as the Interim Associate Dean for Graduate Education and Research and the director of the Cybersecurity Center for Education, Research, and Outreach, CECS. Dr. Ma received her PhD degree from the University of California, Irvine, in 2009. She is the recipient of the Trevor O. Jones Outstanding Paper Award from the Society of Automobile Engineers (SAE) in 2019, the Distinguished Research Award from CECS in 2017, and the Tan Kah Kee Young Inventor Award in 2004. She is broadly interested in the general area of security, privacy, and applied cryptography. Her research spans a wide range of topics, including connected and autonomous vehicle security, smartphone and mobile device security, RFID and sensor security, data privacy, and so on. Her research is supported by NSF, NHTSA, AFOSR, Intel, Ford, and Research in Motion. She was with IBM Almaden Research Center in 2008 and the Institute for Infocomm Research, Singapore in 2000-2005

Mar 08
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Charu Chandra

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