Hernán López-Fernández

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I am interested in the evolutionary processes that originate “mega-diverse” biotic assemblages and the role of ecology in shaping the evolution of diversity. My program studies the evolution of Neotropical freshwater fishes, the most diverse freshwater fish fauna on earth, with an estimate exceeding 7,000 species. My lab combines molecular phylogenetics and phylogeny-based comparative methods to integrate ecology, functional morphology, life histories and geography into analyses of macroevolutionary patterns of freshwater fish diversification. We are also comparing patterns of diversification across major Neotropical fish clades. Relying on fieldwork and natural history collections, we use methods that span

Andrea Thomer

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Andrea Thomer is an assistant professor of information at the University of Michigan School of Information. She conducts research in the areas of data curation, museum informatics, earth science and biodiversity informatics, information organization, and computer supported cooperative work. She is especially interested in how people use and create data and metadata; the impact of information organization on information use; issues of data provenance, reproducibility, and integration; and long-term data curation and infrastructure sustainability. She is studying a number of these issues through the “Migrating Research Data Collections” project – a recently awarded Laura Bush 21st Century Librarianship Early Career Research Grant from the Institute of Museum and Library Services. Dr. Thomer received her doctorate in Library and Information Science from the School of Information Sciences at the University of Illinois at Urbana‐Champaign in 2017.

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.

Christopher Brooks

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The basis of my work is to make the often invisible traces created by interactions students have with learning technologies available to instructors, technology solutions, and students themselves. This often requires the creation of new novel educational technologies which are designed from the beginning with detailed tracking of user activities. Coupled with machine learning and data mining techniques (e.g. classification, regression, and clustering methods), clickstream data from these technologies is used to build predictive models of student success and to better understand how technology affords benefits in teaching and learning. I’m interested in broadly scaled teaching and learning through Massive Open Online Courses (MOOCs), how predictive models can be used to understand student success, and the analysis of educational discourse and student writing.

Jerome P. Lynch

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Jerome P. Lynch, PhD, is Professor and Donald Malloure Department Chair of the Civil and Environmental Engineering Department in the College of Engineering in the University of Michigan, Ann Arbor.

Prof. Lynch’s group works at the forefront of deploying large-scale sensor networks to the built environment for monitoring and control of civil infrastructure systems including bridges, roads, rail networks, and pipelines; this research portfolio falls within the broader class of cyber-physical systems (CPS). To maximize the benefit of the massive data sets, they collect from operational infrastructure systems, and undertake research in the area of relational and NoSQL database systems, cloud-based analytics, and data visualization technologies. In addition, their algorithmic work is focused on the use of statistical signal processing, pattern classification, machine learning, and model inversion/updating techniques to automate the interrogation sensor data collected. The ultimate aim of Prof. Lynch’s work is to harness the full potential of data science to provide system users with real-time, actionable information obtained from the raw sensor data collected.

A permanent wireless monitoring system was installed in 2011 on the New Carquinez Suspension Bridge (Vallejo, CA). The system continuously collects data pertaining to the bridge environment and the behavior of the bridge to load; our data science research is instrumental in unlocking the value of structural monitoring data through data-driven interrogation.

A permanent wireless monitoring system was installed in 2011 on the New Carquinez Suspension Bridge (Vallejo, CA). The system continuously collects data pertaining to the bridge environment and the behavior of the bridge to load; our data science research is instrumental in unlocking the value of structural monitoring data through data-driven interrogation.

H. V. Jagadish

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Research summary: My area of research in the past has been data management, which has since evolved into data science. I’ve worked in many aspects of data science over the course of my career, particularly the integration of data across multiple sources and the usability of database systems by non-technical users. Currently, the bulk of my research focuses on equity issues in data science and AI and, more broadly, issues of ethics and fairness in these fields that are becoming ever more pervasive. I have over 200 major papers, an H-index of 96, and 38 patents. I am also a fellow of the ACM, “The First Society in Computing,” (since 2003), and of the AAAS (since 2018).  I’ve served on the board of the Computing Research Association (2009-2018). I’ve been an Associate Editor for the ACM Transactions on Database Systems (1992-1995), Program Chair of the ACM SIGMOD annual conference (1996), Program Chair of the ISMB conference (2005), a trustee of the VLDB (Very Large DataBase) foundation (2004-2009), Founding Editor-in-Chief of the Proceedings of the VLDB Endowment (2008-2014), and Program Chair of the VLDB Conference (2014). Since 2016, I’ve been Editor of the Morgan & Claypool Synthesis Lecture Series on Data Management. I have received the ACM SIGMOD Contributions Award in 2013 and the David E Liddle Research Excellence Award (at the University of Michigan) in 2008, among other awards.

Interesting projects: I direct the NSF Institute Framework for Integrative Data Equity Systems (FIDES). Equity matters as we increasingly use data and AI methods in so many aspects of our lives. These powerful tools can magnify existing inequities or inadvertently introduce inequities because of system design choices. Our work in this topic is to develop methods that can systematically identify inequities in the data and potentially also address them. 

I developed the first MOOC on Data Science Ethics more than 5 years ago and published it on EdX. Individual case study videos are independently available for download with a creative commons license to encourage teachers everywhere to incorporate these into their classes, picking and choosing only what they want. This MOOC has been so successful that it is now also carried on Coursera and FutureLearn.

My current position and the journey to it: As MIDAS Director, I am responsible for the overall direction and mission of MIDAS. It’s my role to make sure that MIDAS is working productively and strategically towards its stated mission as well as delivering value to the University of Michigan and our research community. I am also the Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science at the University of Michigan in Ann Arbor, which I joined in 1999.  Prior to that, I served as Head of the Database Research Department at AT&T Labs in Florham Park, NJ.

Why I’m passionate about my work: Data science has so much potential to do good things in so many aspects of life and society. I’m passionate about helping with that transformation and helping Michigan lead in that transformation while at the same time being cognizant of the potential risk and pitfalls. I want to help us get as much of the benefit of data science and AI as we can without suffering the harm they could bring if mismanaged.

Fun fact: MIDAS’ offices are located on the 6th floor of Weiser Hall.  I make it a point never to use the elevator: I always take the stairs.

Barzan Mozafari

Barzan Mozafari

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Building data-intensive systems that are more scalable, more robust, and more predictable. He draws from advanced statistical models to deliver practical database solutions to real-world problems. In particular, he adapts concepts and tools from applied statistics, optimization theory, and machine learning.

Long Nguyen

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I am broadly interested in statistical inference, which is informally defined as the process of turning data into prediction and understanding. I like to work with richly structured data, such as those extracted from texts, images and other spatiotemporal signals. In recent years I have gravitated toward a field in statistics known as Bayesian nonparametrics, which provides a fertile and powerful mathematical framework for the development of many computational and statistical modeling ideas. My motivation for all this came originally from an early interest in machine learning, which continues to be a major source of research interest. A primary focus of my group’s research in machine learning to develop more effective inference algorithms using stochastic, variational and geometric viewpoints.