The goal of this project is the creation of a crucial building block of the research on AI and Architecture – a database of 3D models necessary to successfully run Artificial Neural Networks in 3D. This database is part of the first stepping-stones for the research at the AR2IL (Architecture and Artificial Intelligence Laboratory), an interdisciplinary Laboratory between Architecture (represented by Taubman College of Architecture of Urban Planning), Michigan Robotics, and the CS Department of the University of Michigan. A Laboratory dedicated to research specializing in the development of applications of Artificial Intelligence in the field of Architecture and Urban Planning. This area of inquiry has experienced an explosive growth in recent years (triggered in part by research conducted at UoM), as evidenced for example by the growth in papers dedicated to AI applications in architecture, as well as in the investment of the industry in this area. The research funded by this proposal would secure the leading position of Taubman College and the University of Michigan in the field of AI and Architecture. This proposal would also address the current lack of 3D databases that are specifically designed for Architecture applications.
Dr. Farzaneh’s research interest centers on the application of computer science, in particular, machine learning, signal processing, and computer vision, to develop clinical decision support systems and solve medical problems.
Dr. Brian Lin has 12 years of experience in automotive research at UMTRI after his Ph.D. His current research is focused on mining naturalistic driving data, evaluating driver assistance systems, modeling driver performance and behavior, and estimating driver distraction and workload, using statistical methods, classification, clustering, and survival analysis. His most recent work includes classifying human driver’s decision for a discretionary lane change and traversal at unsignalized intersections, driver’s response to lead vehicle’s movement, and subjective acceptance on automated lane change feature. Dr. Lin also has much experience applying data analytic methods to evaluate automotive system prototypes, including auto-braking, lane departure, driver-state monitoring, electronic head units, car-following and curve-assist systems on level-2 automation, and lane-change and intersection assist on L3 automation on public roads, test tracks, or driving simulators. He is also familiar with the human factors methods to investigate driver distraction, workload, and human-machine interaction with in-vehicle technologies and safety features. He serves as a peer reviewer for Applied Ergonomics, Behavior Research Methods, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Vehicles and Transportation Research Part F.
My lab researches how the human brain processes social and affective information and how these processes are affected in psychiatric disorders, especially schizophrenia and bipolar disorder. We use behavioral, electrophysiological (EEG), neuroimaging (functional MRI), eye tracking, brain stimulation (TMS, tACS), and computational methods in our studies. One main focus of our work is building and validating computational models based on intensive, high-dimensional subject-level behavior and brain data to explain clinical phenomena, parse mechanisms, and predict patient outcome. The goal is to improve diagnostic and prognostic assessment, and to develop personalized treatments.
I am an Assistant Professor in the School for Environment and Sustainability at the University of Michigan and am part of the Sustainable Food Systems Initiative. My research examines the impacts of environmental change on agricultural production, and how farmers may adapt to reduce negative impacts. I also examine ways that we can sustainably enhance agricultural production. To do this work, I combine remote sensing and geospatial analyses with household-level and census datasets to examine farmer decision-making and agricultural production across large spatial and temporal scales.
Bilal Butt is an Associate Professor in the School for Environment and Sustainability at the University of Michigan. His research is concerned with understanding the drivers and effects of violent conflicts over natural resources. He places a large emphasis on empirical fieldwork to understand the lived geographies of the interactions between people and the environment in ecologically heterogeneous regions. He combines various geospatial technologies with historical and ecological dynamics of dryland environments to understand how differential power relations between agencies, states, and other actors come together to influence the etiology of resource conflicts. He is also interested in the ways that scientific and technical appraisals of indigenous peoples and environments have misread the landscape, leading to orientalist approaches to development programs. He has also had a long history of engagement on questions of environmental conflicts, particularly around wildlife poaching, land grabs, and green energy. Dr. Butt received the National Science Foundations Career Award and is a recipient of the Superior Teaching Award from the University of Michigan. He has published in diverse journals such as the Annals of the Association of American Geographers, Remote Sensing of Environment, Journal of Applied Ecology and Humanity. He teaches courses on Conservation and Development, Political Ecology, Environmental Security and Conflict, Environmental Governance, and Preparing for International Fieldwork.
Dr. Hadjiyski research interests include computer-aided diagnosis, artificial intelligence (AI), machine learning, predictive models, image processing and analysis, medical imaging, and control systems. His current research involves design of decision support systems for detection and diagnosis of cancer in different organs and quantitative analysis of integrated multimodality radiomics, histopathology and molecular biomarkers for treatment response monitoring using AI and machine learning techniques. He also studies the effect of the decision support systems on the physicians’ clinical performance.
Uncertainty quantification and decision making are increasingly demanded with the development of future technology in engineering and transportation systems. Among the uncertainty quantification problems, Dr. Wenbo Sun is particularly interested in statistical modelling of engineering system responses with considering the high dimensionality and complicated correlation structure, as well as quantifying the uncertainty from a variety of sources simultaneously, such as the inexactness of large-scale computer experiments, process variations, and measurement noises. He is also interested in data-driven decision making that is robust to the uncertainty. Specifically, he delivers methodologies for anomaly detection and system design optimization, which can be applied to manufacturing process monitoring, distracted driving detection, out-of-distribution object identification, vehicle safety design optimization, etc.
Broadly, I study legal decision making, including decisions related to crime and employment. I typically use large social science data bases, but also collect my own data using technology or surveys.
My research focuses on building infrastructure for public health and health science research organizations to take advantage of cloud computing, strong software engineering practices, and MLOps (machine learning operations). By equipping biomedical research groups with tools that facilitate automation, better documentation, and portable code, we can improve the reproducibility and rigor of science while scaling up the kind of data collection and analysis possible.
Research topics include:
1. Open source software and cloud infrastructure for research,
2. Software development practices and conventions that work for academic units, like labs or research centers, and
3. The organizational factors that encourage best practices in reproducibility, data management, and transparency
The practice of science is a tug of war between competing incentives: the drive to do a lot fast, and the need to generate reproducible work. As data grows in size, code increases in complexity and the number of collaborators and institutions involved goes up, it becomes harder to preserve all the “artifacts” needed to understand and recreate your own work. Technical AND cultural solutions will be needed to keep data-centric research rigorous, shareable, and transparent to the broader scientific community.