Yixin Wang works in the fields of Bayesian statistics, machine learning, and causal inference, with applications to recommender systems, text data, and genetics. She also works on algorithmic fairness and reinforcement learning, often via connections to causality. Her research centers around developing practical and trustworthy machine learning algorithms for large datasets that can enhance scientific understandings and inform daily decision-making. Her research interests lie in the intersection of theory and applications.
A major focus of the MLiNS lab is to combine stimulated Raman histology (SRH), a rapid label-free, optical imaging method, with deep learning and computer vision techniques to discover the molecular, cellular, and microanatomic features of skull base and malignant brain tumors. We are using SRH in our operating rooms to improve the speed and accuracy of brain tumor diagnosis. Our group has focused on deep learning-based computer vision methods for automated image interpretation, intraoperative diagnosis, and tumor margin delineation. Our work culminated in a multicenter, prospective, clinical trial, which demonstrated that AI interpretation of SRH images was equivalent in diagnostic accuracy to pathologist interpretation of conventional histology. We were able to show, for the first time, that a deep neural network is able to learn recognizable and interpretable histologic image features (e.g. tumor cellularity, nuclear morphology, infiltrative growth pattern, etc) in order to make a diagnosis. Our future work is directed at going beyond human-level interpretation towards identifying molecular/genetic features, single-cell classification, and predicting patient prognosis.
My research concentrates on the area of bioinformatics, proteomics, and data integration. I am particularly interested in mass spectrometry-based proteomics, software development for proteomics, cancer proteogenomics, and transcriptomics. The computational methods and tools previously developed by my colleagues and me, such as PepExplorer, MSFragger, Philosopher, and PatternLab for Proteomics, are among the most referred proteome informatics tools and are used by hundreds of laboratories worldwide.
I am also a Proteogenomics Data Analysis Center (UM-PGDAC) member as part of the NCI’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative for processing and analyzing hundreds of cancer proteomics samples. UM-PGDAC develops advanced computational infrastructure for comprehensive and global characterization of genomics, transcriptomics, and proteomics data collected from several human tumor cohorts using NCI-provided biospecimens. Since 2019 I have been working as a bioinformatics data analyst with the University of Michigan Proteomics Resource Facility, which provides state-of-the-art capabilities in proteomics to the University of Michigan investigators, including Rogel Cancer Center investigators as Proteomics Shared Resource.
My research interest lies in applying data science for actionable transformation of human health from the bench to bedside. Current research focus areas include cutting edge single-cell sequencing informatics and genomics; precision medicine through integration of multi-omics data types; novel modeling and computational methods for biomarker research; public health genomics. I apply my biomedical informatics and analytical expertise to study diseases such as cancers, as well the impact of pregnancy/early life complications on later life diseases.
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.
Our research aims to address fundamental problems in both biomedical research and computer science by developing new tools tailored to rapidly emerging single-cell omic technologies. Broadly, we seek to understand what genes define the complement of cell types and cell states within healthy tissue, how cells differentiate to their final fates, and how dysregulation of genes within specific cell types contributes to human disease. As computational method developers, we seek to both employ and advance the methods of machine learning, particularly for unsupervised analysis of high-dimensional data. We have particular expertise in manifold learning, matrix factorization, and deep learning approaches.
Dr. Douville is a critical care anesthesiologist with an investigative background in bioinformatics and perioperative outcomes research. He studies techniques for utilizing health care data, including genotype, to deliver personalized medicine in the perioperative period and intensive care unit. His research background has focused on ways technology can assist health care delivery to improve patient outcomes. This began designing microfluidic chips capable of recreating fluid mechanics of atelectatic alveoli and monitoring the resulting barrier breakdown real-time. His interest in bioinformatics was sparked when he observed how methodology designed for tissue engineering could be modified to the nano-scale to enable genomic analysis. Additionally, his engineering training provided the framework to apply data-driven modeling techniques, such as finite element analysis, to complex biological systems.
Shobita Parthasarathy studies the governance of emerging science and technology as well as the politics of evidence and expertise in policymaking, in comparative and international perspective. She has a long-standing interest in the use and regulation of genomic and genetic data. Her first two books, Building Genetic Medicine: Breast Cancer, Technology, and the Comparative Politics of Health Care (MIT Press, 2007) and Patent Politics: Life Forms, Markets, and the Public Interest in the United States and Europe, (University of Chicago Press, 2017) cover these themes. Using comparative and qualitative interpretive research methods, she studies the the ethics, politics, and economics of data collection and interpretation. This includes concerns about consent and intellectual property in genomic databases, the social implications of commodifying data, the use of personal data in determining access to social services and health care, and the use of data for social justice and public good.
Her current research focuses on the politics of inclusive innovation in international development, with a focus in India. She is interested in how political culture and ideology shape what counts as inclusive “innovation”, and in the implications for social and political order—particularly the “empowerment” of poor girls and women.
I develop probabilistic and statistical models to analyze genetic and genomic data. We use these methods to study evolution, natural selection, and human history. Recently, I have been interested in applying these techniques to study viral epidemics (e.g., HIV) and cancer.