Hyun Min Kang

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Hyun Min Kang is an Associate Professor in the Department of Biostatistics. He received his Ph.D. in Computer Science from University of California, San Diego in 2009 and joined the University of Michigan faculty in the same year. Prior to his doctoral studies, he worked as a research fellow at the Genome Research Center for Diabetes and Endocrine Disease in the Seoul National University Hospital for a year and a half, after completing his Bachelors and Masters degree in Electrical Engineering at Seoul National University. His research interest lies in big data genome science. Methodologically, his primary focus is on developing statistical methods and computational tools for large-scale genetic studies. Scientifically, his research aims to understand the etiology of complex disease traits, including type 2 diabetes, bipolar disorder, cardiovascular diseases, and glomerular diseases.

Jinseok Kim

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Jinseok Kim, Ph.D., is Research Assistant Professor in the Institute for Social Research at the University of Michigan, Ann Arbor.  Prof. Kim works on resolving named entity ambiguity in large-scale scholarly data (publication, patent, and funding records) in digital libraries. Especially, his current research is focused on developing methods for disambiguating author and affiliation names at a digital library scale using various supervised machine learning approaches trained on automatically labeled data . Disambiguated data from multiple sources will be integrated to be analyzed for insights into research production, scientific collaboration, funding evaluation, and research policy at a national level.

Amal Alhosban

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Amal Alhosban, is an Associate Professor of Computer Science at the University of Michigan Flint campus. She received her Ph.D. in Computer Science at Wayne State University in 2013. Her research focuses on Semantic Web and Fault Management and Wireless Network.

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.

Z. Morley Mao

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Z. Morley Mao, PhD, is Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor campus.

Murali Mani

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Murali Mani, PhD, is Professor of Computer Science at the University of Michigan, Flint.

The significant research problems Prof. Mani is investigating include the following: big data management, big data analytics and visualization, provenance, query processing of encrypted data, event stream processing, XML stream processing. data modeling using XML schemas, and effective computer science education. In addition, he has worked in industry on clickstream analytics (2015), and on web search engines (1999-2000). Prof. Mani’s significant publications are listed on DBLP at: http://dblp.uni-trier.de/pers/hd/m/Mani:Murali.

9.9.2020 MIDAS Faculty Research Pitch Video.

Illustrating how our SMART system effectively integrates big data processing and data visualization to enable big data visualization. The left side shows a typical data visualization scenario, where the different analysts are using their different visualization systems. These visualization systems can provide interactive visualizations but cannot handle the complexities of big data. They interact with a distributed data processing system that can handle the complexities of big data. The SMART system improves the user experience by carefully sending additional data to the visualization system in response to a request from an analyst so that future visualization requests can be answered directly by the visualization system without accessing the data processing system.

Illustrating how our SMART system effectively integrates big data processing and data visualization to enable big data visualization. The left side shows a typical data visualization scenario, where the different analysts are using their different visualization systems. These visualization systems can provide interactive visualizations but cannot handle the complexities of big data. They interact with a distributed data processing system that can handle the complexities of big data. The SMART system improves the user experience by carefully sending additional data to the visualization system in response to a request from an analyst so that future visualization requests can be answered directly by the visualization system without accessing the data processing system.

 

Mark Allison

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Mark Allison, PhD, is Associate Professor of Computer Science in the department of Computer Science, Engineering and Physics at the University of Michigan-Flint.

Dr. Allison’s research pertains to the autonomic control of complex cyberphysical systems utilizing software models as first class artifacts. Domains being explored are microgrid energy management and unmanned aerial vehicles (UAVs) in swarms.

 

Matthew Kay

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Matthew Kay, PhD, is Assistant Professor of Information, School of Information and Assistant Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.

Prof. Kay’s research includes work on communicating uncertainty, usable statistics, and personal informatics. People are increasingly exposed to sensing and prediction in their daily lives (“how many steps did I take today?”, “how long until my bus shows up?”, “how much do I weigh?”). Uncertainty is both inherent to these systems and usually poorly communicated. To build understandable data presentations, we must study how people interpret their data and what goals they have for it, which informs the way that we should communicate results from our models, which in turn determines what models we must use in the first place. Prof. Kay tackles these problems using a multi-faceted approach, including qualitative and quantitative analysis of behavior, building and evaluating interactive systems, and designing and testing visualization techniques. His work draws on approaches from human-computer interaction, information visualization, and statistics to build information visualizations that people can more easily understand along with the models to back those visualizations.

 

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.

Emily Mower Provost

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Research in the CHAI lab focuses on emotion modeling (classification and perception) and assistive technology (bipolar disorder and aphasia).

Behavioral Signal Processing Approach to Modeling Human-centered Data

Behavioral Signal Processing Approach to Modeling Human-centered Data