I build data science tools to address challenges in medicine and clinical care. Specifically, I apply signal processing, image processing and machine learning techniques, including deep convolutional and recurrent neural networks and natural language processing, to aid diagnosis, prognosis and treatment of patients with acute and chronic conditions. In addition, I conduct research on novel approaches to represent clinical data and combine supervised and unsupervised methods to improve model performance and reduce the labeling burden. Another active area of my research is design, implementation and utilization of novel wearable devices for non-invasive patient monitoring in hospital and at home. This includes integration of the information that is measured by wearables with the data available in the electronic health records, including medical codes, waveforms and images, among others. Another area of my research involves linear, non-linear and discrete optimization and queuing theory to build new solutions for healthcare logistic planning, including stochastic approximation methods to model complex systems such as dispatch policies for emergency systems with multi-server dispatches, variable server load, multiple priority levels, etc.
My research is to support more people learn in effective ways. I draw techniques and theories from Human-Computer Interaction, Learning Sciences, and Artificial Intelligence to develop computational methods and systems to support scalable teaching and learning. There are several directions in my research that draw on data science techniques and also contribute to interdisciplinary data science research, 1) data-driven authoring techniques of intelligent tutoring systems, with application domains in UX education and data science education 2) AI-augmented instructional design and the use Human-AI collaborative techniques in instructional design.
I am a Research Fellow in the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan. My research is currently supported by a NSF project, Developing Evidence-based Data Sharing and Archiving Policies, where I am analyzing curation activities, automatically detecting data citations, and contributing to metrics for tracking the impact of data reuse. I hold a Ph.D. in Geography from UC Santa Barbara and I have expertise in GIScience, spatial information science, and urban planning. My interests also include the Semantic Web, innovative GIS education, and the science of science. I have experience deploying geospatial applications, designing linked data models, and developing visualizations to support data discovery.
Prof. Huang is specialized in satellite remote sensing, atmospheric radiation, and climate modeling. Optimization, pattern analysis, and dimensional reduction are extensively used in his research for explaining observed spectrally resolved infrared spectra, estimating geophysical parameters from such hyperspectral observations, and deducing human influence on the climate in the presence of natural variability of the climate system. His group has also developed a deep-learning model to make a data-driven solar forecast model for use in the renewable energy sector.
Tayo Fabusuyi is an assistant research scientist in the Human Factors Group at UMTRI. His research interests are in Urban Systems and Operations Research, specifically designing and implementing initiatives that support sustainable and resilient communities with a focus on efficiency and equity issues. Drawing on both quantitative and qualitative data, his research develops and applies hard and soft Operations Research methods to urban systems issues in a manner that emphasizes theory driven solutions with demonstrated value-added. A central theme of his research activities is the use of demand side interventions, via information and pricing strategies in influencing the public’s travel behavior with the objective of achieving more beneficial societal outcomes. Informed by the proliferation of big data and the influence of transportation in the urban sphere, these research activities are categorized broadly into three overlapping and interdependent areas – intelligent transportation systems (ITS), emerging mobility services and urban futures. Before joining the research faculty at UMTRI, Dr. Fabusuyi was a Planning Economist at the African Development Bank and an adjunct Economics faculty member at Carnegie Mellon University, where he received his Ph.D. in Engineering and Public Policy.
Biodiversity in nature can be puzzlingly high in the light of competition between species, which arguably should eventually result in a single winner. The coexistence mechanisms that allow for this biodiversity shape the dynamics of communities and ecosystems. My research focuses on understanding the mechanisms of competitive coexistence, how competition influences community structure and diversity, and what insights observed patterns of community structure might provide about competitive coexistence.
I am interested in the use and development of data science approaches to draw insights regarding coexistence mechanisms from the structural patterns of ecological communities with respect to species’ functional traits, relative abundance, spatial distribution, and phylogenetic relatedness, through as community dynamics proceed. I am also interested in the use of Maximum Likelihood and Bayesian approaches for fitting demographic models to forest census data sets, demographic models that can then be used to quantitatively assess the role of different competitive coexistence mechanisms.
The current goal of our research is to learn enough about the physiology and ecology of microbes and microbial communities in the gut that we are able to engineer the gut microbiome to improve human health. The first target of our engineering is the production of butyrate – a common fermentation product of some gut microbes that is essential for human health. Butyrate is the preferred energy source for mitochondria in the epithelial cells lining the gut and it also regulates their gene expression.
One of the most effective ways to influence the composition and metabolism of the gut microbiota is through diet. In an interventional study, we have tracked responses in the composition and fermentative metabolism of the gut microtiota in >800 healthy individuals. Emerging patterns suggest several configurations of the microbiome that can result in increased production of butyrate acid. We have isolated the microbes that form an anaerobic food web to convert dietary fiber to butyrate and continue to make discoveries about their physiology and interactions. Based on these results, we have initiated a clinical trial in which we are hoping to prevent the development of Graft versus Host Disease following bone marrow transplants by managing butyrate production by the gut microbiota.
We are also beginning to track hundreds of other metabolites from the gut microbiome that may influence human health. We use metagenomes and metabolomes to identify patterns that link the microbiota with their metabolites and then test those models in human organoids and gnotobiotic mice colonized with synthetic communities of microbes. This blend of wet-lab research in basic microbiology, data science and in ecology is moving us closer to engineering the gut microbiome to improve human health.
In the area of multi-scale modeling of manufacturing processes: (a) Models for understanding the mechanisms of forming and joining of lightweight materials. This new understanding enables the development of advanced processes which remove limitations of current state-of-the-art capabilities that exhibit limited formability of high strength lightweight alloys, and limited reproducibility of joining quality; (b) Innovative multi-scale finite element models for ultrasonic welding of battery tabs (resulting in models adopted by GM for designing and manufacturing batteries for the Chevy Volt), and multi-scale models for ultrasonic welding of short carbon fiber composites (resulting in models adopted by GM for designing and manufacturing assemblies made of carbon fiber composites with metallic parts); (c) Data-driven algorithms of prediction geometrical and microstructural integrity of the incremental formed parts. Machine learning is used for developing fast and robust methods to be integrated into the designing process and replace finite element simulations.
My work lies in the learning, control, and design of autonomous systems with an emphasis on connected automated vehicles (CAVs). I have been committed to developing robust autonomous vehicles, augmented reality (AR) technology, and V2X systems at Mcity. The highlights include: (1) a robust self-driving algorithm/software stack enabling high-level CAVs; (2) a data-and-AI-driven sensor-level augmented reality (AR) system for efficient safe CAV tests. These systems have been deployed on the Mcity CAV fleet and Mcity testing track for daily operations. I am interested in using big naturalistic human-driving data to train motion planning and control algorithms of self-driving cars, so the automated cars could behave with better roadmanship and thus higher acceptance. I am also interested in data-driven low-uncertainty learning algorithms for object detection, tracking, and fusion, in order to build the perception system of safety-critical autonomous systems.
The Aguilar group is focused understanding transcriptional and epigenetic mechanisms of skeletal muscle stem cells in diverse contexts such as regeneration after injury and aging. We focus on this area because there are little to no therapies for skeletal muscle after injury or aging. We use various types of in-vivo and in-vitro models in combination with genomic assays and high-throughput sequencing to study these molecular mechanisms.