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.