Fujisaki-Manome’s research program aims to improve predictability of hazardous weather, ice, and lake/ocean events in cold regions in order to support preparedness and resilience in coastal communities, as well as improve the usability of their forecast products by working with stakeholders. The main question Fujisaki-Manome’s research aims to address is: what are the impacts of interactions between ice and oceans / ice and lakes on larger scale phenomena, such as climate, weather, storm surges, and sea/lake ice melting? Fujisaki-Manome primarily uses numerical geophysical modeling and machine learning to address the research question; and scientific findings from the research feed back into the models and improve their predictability. Her work has focused on applications to the Great Lakes, the Alaska’s coasts, Arctic Ocean, and the Sea of Okhotsk.
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 centers on studying the interaction between abstract, theoretically sound probabilistic algorithms and human beings. One aspect of my research explores connections of Machine Learning to Crowdsourcing and Economics; focused in both cases on better understanding the aggregation process. As Machine Learning algorithms are used in making decisions that affect human lives, I am interested in evaluating the fairness of Machine Learning algorithms as well as exploring various paradigms of fairness. I study how these notions interact with more traditional performance metrics. My research in Computer Science Education focuses on developing and using evidence-based techniques in educating undergraduates in Machine Learning. To this end, I have developed a pilot summer program to introduce students to current Machine Learning research and enable them to make a more informed decision about what role they would like research to play in their future. I have also mentored (and continue to mentor) undergraduate students and work with students to produce publishable, and award-winning, undergraduate research.
I am interested in using digital health technology for the treatment of cardiovascular disease with a particular emphasis on its application to patients with heart failure. More specific, my interests include (1) using non-invasive sensors and digital health technology to improve the delivery of cardiovascular care and (2) optimizing treatment for patients with advanced systolic heart failure through novel statistical tools and risk-modeling
Alzheimer’s disease (AD) afflicts more than 5 million people in the United States and is gaining widespread attention. Over 400 clinical trials were run between 2002 and 2012, but only one trial has resulted in a marketable product. One of the most common explanations for these failures is likely the consideration of Alzheimer’s as a homogeneous disease. In many cases, individuals within the same group respond to a drug in different ways. Given the highly complex nature of AD, the likelihood of identifying a single drug to provide meaningful benefits to every patient is minimal. There is a pressing and unmet need to develop personalized treatment plans based on each patients’ omics profiles.
To solve this problem, my research focus is to develop a data-driven computational approach to predict drug responses for individuals with AD. This approach is based on the patients’ metabolomics and transcriptomics profile and publicly available drug databases. Transcriptomics and metabolomics are increasingly being used to corroborate our interpretation of the pathophysiological pathways underlying AD. Integration of metabolomics and transcriptomics will guide the development of precision medicine for AD. In particular, I used the metabolome and transcriptome profiles of Alzheimer’s patients from ADNI database. For each patient, I identify his/her dysregulated pathways from their metabolome profiles and his/her specific gene regulatory network from their transcriptome profiles. My preliminary data suggested that each patient with Alzheimer’s has distinct dysregulated pathways and gene regulatory network. Drug selection based on a patient’s specific metabolome and transcriptome profiles offers a tremendous opportunity for more targeted and effective disease treatment and it represents a critical innovation towards personalized medicine for AD. My long-term goal is to become an independent investigator in computational biology with a focus on translating omics data to bedside application. The overall objective of my research is to combine metabolomics and gene expression data with drug data using advanced machine learning algorithms to personalize medicine for AD.
My research interests are in the areas of brain-inspired machine intelligence and its applications such as mobile robots and autonomous vehicles. To achieve true machine intelligence, I have taken two different approaches: bottom-up data-driven and top-down theory-driven approach. For the bottom-up data-driven approach, I have investigated the neuronal structure of the brain to understand its function. The development of a high-throughput and high-resolution 3D tissue scanner was a keystone of this approach. This tissue scanner has a 3D virtual microscope that allows us to investigate the neuronal structure of a whole mammalian brain in a high resolution. The top-down theory-driven approach is to study what true machine intelligence is and how it can be implemented. True intelligence cannot be investigated without embracing the theory-driven approach such as self-awareness, embodiment, consciousness, and computational modeling. I have studied the internal dynamics of a neural system to investigate the self-awareness of a machine and model neural signal delay compensation. These two meet in the middle where machine intelligence is implemented for mechanical systems such as mobile robots and autonomous vehicles. I have a strong desire to bridge the bottom-up and top-down approaches that lead me to conduct research focusing on mobile robotics and autonomous vehicles to combine the data-driven and theory-driven approaches.
Xingyu Zhang is a Research Assistant Professor at the School of Nursing’s Applied Biostatistics Laboratory. He received his Ph.D. in Biomedical Science concentrated on biostatistics from the University of Auckland in 2016. Prior to joining the ABL, he was a postdoctoral research fellow in epidemiology and biostatistics at Emory University, and also a visiting research scholar in medical informatics at Georgia Institute of Technology. Dr. Zhang’s research focuses on healthcare outcomes with an emphasis on study design and statistical analysis. The methods he applied include multi-level analysis, time series analysis, infection early warning modeling, medical imaging analysis, feature extraction, pattern classification, neural networks, support vector machine, natural language processing, deep learning, survival analysis, meta-analysis, etc.
I am an assistant professor in Department of Industrial and Manufacturing Systems Engineering (IMSE) at the University of Michigan-Dearborn. Prior to joining UM-Dearborn, I was a research assistant professor and postdoctoral research scholar at Vanderbilt University. My research areas of interest are uncertainty quantification, Bayesian data analytics, big data analytics, machine learning, optimization under uncertainty, and applications of data analytics and machine learning in aerospace, mechanical and manufacturing systems, and material science. The goal of my research is to develop novel computational methods to design sustainable and reliable engineering systems by leveraging the rich information contained in the high-fidelity computational simulation models, experimental data, and big operational data and historical data.
Dr. Jin Lu is an Assistant Professor of Computer and Information Science at the University of Michigan, Dearborn.
His major research interests include machine learning, data mining, optimization, matrix analysis, biomedical informatics, and health informatics. Two main directions are being pursued:
(1) Large-scale machine learning problems with data heterogeneity. Data heterogeneity is common across many high-impact application domains, ranging from recommendation system to Computer Vision, Bioinformatics and Health-informatics. Such heterogeneity can be present in a variety of forms, including (a) sample heterogeneity, where multiple resources of data samples are available as side information; (b) task heterogeneity, where multiple related learning tasks can be jointly learned to improve the overall performance; (c) view heterogeneity, where complementary information is available from various sources. My research interests focus on building efficient machine learning methods from such data heterogeneity, aiming to improve the learning model by making the best use of all data resources.
(2) Machine learning methods with provable guarantees. Machine learning has been substantially developed and has demonstrated great success in various domains. Despite its practical success, many of the applications involve solving NP-hard problems based on heuristics. It is challenging to analyze whether a heuristic scheme has any theoretical guarantee. My research interest is to employ granular data structure, e.g. sample clusters or features describing an aspect of the sample, to design new theoretically-sound models and algorithms for machine learning problems.
My research interests are in the areas of machine learning, statistical network analysis, analysis of high-dimensional data, and their applications in health sciences, biology, finance and marketing.