Mosharaf Chowdhury is an Assistant Professor in Electrical Engineering and Computer Science, Computer Science and Engineering Division. Prof. Chowdhury works on topics in networked systems, networking, and big data. He is part of the Software Systems Laboratory, a multidisciplinary group conducting research in software systems. His research focus is on increasing application-infrastructure symbiosis across different layers of software and hardware stacks.
Michael Cafarella is an Associate Professor in the Department of Electrical Engineering and Computer Science, Computer Science Division. He was appointed the Morris Wellman Faculty Development Assistant Professor of Computer Science and Engineering, and a Sloan Research Fellow (2016). Prof. Cafarella studies databases, information extraction, data integration, and data mining. His projects span several areas of data management including systems and algorithms for “messy” data management, novel data-intensive applications, and data systems infrastructure.
In a strategic environment, agents face decisions where the outcomes depend on the behavior of other autonomous agents. The strategic reasoning group develops techniques for understanding and engineering complex multiagent environments, using concepts and methods from economics as well as computer science. Specifically, we apply game-theoretic principles to data from large-scale agent-based simulation, in an approach called empirical game-theoretic analysis (EGTA). EGTA combines simulation, machine learning, and other empirical methods to reason about the strategic issues in complex multiagent settings. We are particularly interested in domains characterized by dynamism, networks, and uncertainty, including applications in financial markets, information security, and sustainable transportation.
Barzan Mozafari is an Associate Professor of Electrical Engineering and Computer Science at the University of Michigan (Ann Arbor), where he is a member of the Michigan Database Group and the Software Systems Lab. Prior to that, he was a postdoctoral associate at Massachusetts Institute of Technology. He earned his Ph.D. in Computer Science from the University of California at Los Angeles. He is passionate about building large-scale data-intensive systems, with a particular interest in database-as-a-service clouds, distributed systems, and crowdsourcing. In his research, he draws on advanced mathematical models to deliver practical database solutions. He has won several awards and fellowships, including SIGMOD 2012 and EuroSys 2013’s best paper awards.
Most of his research and teaching involves parallel computing of some form: design of scalable algorithms and data structures; applications to numerous scientific problems such as a large multidisciplinary team modeling space weather or a small interdisciplinary group doing imputation on datasets of social preferences; and performance analysis, both experimental and analytical. These projects have used a variety of computer architectures, ranging from tens to hundreds of thousands of cores. He also works on algorithms for abstract fine-grain parallel computer models motivated by concerns such as time/number-of-processors/peak-power tradeoffs and the constraints imposed by the fact that computation is done in 2- or 3-dimensional space. Further, he develops serial algorithms for optimizing adaptive sampling problems such as adaptive clinical trials, algorithms for isotonic regression, and various other computer science problems.
Thomas Wenisch is a Professor in the Department of Electrical Engineering and Computer Science. His research interests center on computer architecture with particular emphasis on multiprocessor and multicore systems, data center architecture, architectural support for medical imaging, and performance evaluation methodology.
Dr. Markov is a Professor in the Electrical Engineering and Computer Science department. His research involves computational techniques for large-scale discrete and continuous optimization in electronic design automation; computational simulation of quantum information processing.