Peter Adriaens

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Peter Adriaens, PhD, is Professor of Civil and Environmental Engineering, College of Engineering, Professor of Environment and Sustainability, School for Environment and Sustainability and Professor of Entrepreneurship, Stephen M Ross School of Business, at the University of Michigan, Ann Arbor.

Prof. Adriaens’ research focuses on the use of data science to uncover trends and features in a range of financial (‘fintech’) applications relevant to economic development and investments aimed at catalyzing sustainable growth, including:
1. Network mapping to query relations in financial networks using visualization techniques
2. Trend and features prediction of value capture and investment grade in startup business models, using machine learning, natural language processing, and decision tools
3. Asset risk pricing of stocks exposed to water risk in their supply chains, using statistical methods, and portfolio theory predictions
4. Financial risk modeling of multi-asset investment funds to drive low carbon economies, leveraging network mapping, and machine learning.


Structure of financial data-driven industry ecosystems following relational network mapping and network theory application.

Structure of financial data-driven industry ecosystems following relational network mapping and network theory application.

Perry Samson

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Perry Samson, PhD, is the Arthur F Thurnau Professor of Climate and Space Sciences and Engineering, College of Engineering and Professor of Information, School of Information, at the University of Michigan, Ann Arbor.

Prof. Samson has developed LectureTools with NSF support in response to a need to increase opportunities for student participation in larger lecture courses. It was subsequently spun off campus using NSF SBIR funding and was acquired by Echo360 which has incorporated it into its Active Learning Platform (ALP).  ALP collects data on how students behave before, during and after class including how many slides they view, how many notes they type, how many questions they answer and how many gradable questions they get correct as well as what question they pose and how often do they indicate confusion.

These unique data are used to understand how student participation is related to exam grades and to build models to forecast which students will have trouble in class far earlier in the semester.  His goal is to combine data from ALP with other data sets to ascertain which, if any, participation data allows the best prediction of student success.

Jeremy M G Taylor

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Jeremy Taylor, PhD, is the Pharmacia Research Professor of Biostatistics in the School of Public Health and Professor in the Department of Radiation Oncology in the School of Medicine at the University of Michigan, Ann Arbor. He is the director of the University of Michigan Cancer Center Biostatistics Unit and director of the Cancer/Biostatistics training program. He received his B.A. in Mathematics from Cambridge University and his Ph.D. in Statistics from UC Berkeley. He was on the faculty at UCLA from 1983 to 1998, when he moved to the University of Michigan. He has had visiting positions at the Medical Research Council, Cambridge, England; the University of Adelaide; INSERM, Bordeaux and CSIRO, Sydney, Australia. He is a previously winner of the Mortimer Spiegelman Award from the American Public Health Association and the Michael Fry Award from the Radiation Research Society. He has worked in various areas of Statistics and Biostatistics, including Box-Cox transformations, longitudinal and survival analysis, cure models, missing data, smoothing methods, clinical trial design, surrogate and auxiliary variables. He has been heavily involved in collaborations in the areas of radiation oncology, cancer research and bioinformatics.

I have broad interests and expertise in developing statistical methodology and applying it in biomedical research, particularly in cancer research. I have undertaken research  in power transformations, longitudinal modeling, survival analysis particularly cure models, missing data methods, causal inference and in modeling radiation oncology related data.  Recent interests, specifically related to cancer, are in statistical methods for genomic data, statistical methods for evaluating cancer biomarkers, surrogate endpoints, phase I trial design, statistical methods for personalized medicine and prognostic and predictive model validation.  I strive to develop principled methods that will lead to valid interpretations of the complex data that is collected in biomedical research.

William Currie

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William Currie, PhD, is Professor in the School of Environment and Sustainability at the University of Michigan, Ann Arbor. Prof. Currie is interested in interdisciplinary approaches to the study of the environment and the development of sustainability science. His research and scholarly interests include ecosystem ecology, biogeochemistry including carbon and nutrient cycling, physics and energetics, landscapes and coupled human-natural systems, land conservation and management, biofuels and food security, computational modeling and simulation, synthesis using models, and philosophical foundations of modeling.

Prof. Currie has a background in ecosystem ecology, biogeochemistry (nutrient and carbon cycling), energetics, systems dynamics modeling and individual-based / agent-based modeling. He is interested in using our current understanding in these fields to investigate ecosystem change and dynamics in coupled human-environment systems.

Prof. Currie studies the linkages among carbon, nutrient, and water cycling and energy flows and transformations in terrestrial ecosystems and human-environment systems.  He is interested in using our current understanding of ecosystems to explore creative, new understanding of the two-way interactions in human-environment systems.  He works at scales from field plots to landscapes, collaborating with other researchers and students to integrate understanding and build models for synthesis.  The goal of this research is to contribute to the developing field of sustainability science using an approach that grows out of ecosystem science.


Roderick Little

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Roderick Joseph Little, PhD, is the Richard D. Remington Distinguished University Professor of Biostatistics, Professor of Statistics, Research Professor, Institute for Social Research, and Senior Fellow, Michigan Society of Fellows, at the University of Michigan, Ann Arbor.

Prof. Little’s primary research interest is the analysis of data sets with missing values. Many statistical techniques are designed for complete, rectangular data sets, but in practice biostatistical data sets contain missing values, either by design or accident. As detailed in my book with Rubin, initial statistical approaches were relatively ad-hoc, such as discarding incomplete cases or substituting means, but modern methods are increasingly based on models for the data and missing-data mechanism, using likelihood-based inferential techniques.

Another interest is the analysis of data collected by complex sampling designs involving stratification and clustering of units. Since working as a statistician for the World Fertility Survey, I have been interested in the development of model-based methods for survey analysis that are robust to misspecification, reasonably efficient, and capable of implementation in applied settings. Statistics is philosophically fascinating and diverse in application. My inferential philosophy is model-based and Bayesian, although the effects of model misspecification need careful attention. My applied interests are broad, including mental health, demography, environmental statistics, biology, economics and the social sciences as well as biostatistics.