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.
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.
Our team leads research on the Ethical, Legal, and Social Implications (ELSI) of learning health systems and related enterprises. Our research uses mixed methods to understand policies and practices that make data science methods (data collection and curation, AI, computable algorithms) trustworthy for patients, providers, and the public. Our work engages multiple stakeholders including providers and health systems, as well as the general public and minoritized communities on issues such as AI-enabled clinical decision support, data sharing and privacy, and consent for data use in precision oncology.
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 group investigates hypertension using a principally patient-oriented approach, with key aspects of our work being collaborative with data scientists. For example, I collaborated with Casey Greene, PhD, computational biologist, on a project using generative adversarial neural networks to create a privacy-preserving dataset derivative of the SPRINT hypertension clinical trial. The work incorporated concepts from the differential privacy field, and the intent is to make clinical trial data sharing more feasible.
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.
In his various roles, he has helped develop several educational programs in Innovation and Entrepreneurial Development (the only one of their kind in the world) for medical students, residents, and faculty as well as co-founding 4 start-up companies (including a consulting group, a pharmaceutical company, a device company, and a digital health startup) to improve the care of surgical patients and patients with cancer. He has given over 80 invited talks both nationally and internationally, written and published over 110 original scientific articles, 12 book chapters, as well as a textbook on “Success in Academic Surgery: Innovation and Entrepreneurship” published in 2019 by Springer-NATURE. His research is focused on drug development and nanoparticle drug delivery for cancer therapeutic development as well as evaluation of circulating tumor cells, tissue engineering for development of thyroid organoids, and evaluating the role of mixed reality technologies, AI and ML in surgical simulation, education and clinical care delivery as well as directing the Center for Surgical Innovation at Michigan. He has been externally funded for 13 consecutive years by donors and grants from Susan G. Komen Foundation, the American Cancer Society, and he currently has funding from three National Institute of Health R-01 grants through the National Cancer Institute. He has served on several grant study sections for the National Science Foundation, the National Institute of Health, the Department of Defense, and the Susan G. Komen Foundation. He also serves of several scientific journal editorial boards and has serves on committees and leadership roles in the Association for Academic Surgery, the Society of University Surgeons and the American Association of Endocrine Surgeons where he was the National Program Chair in 2013. For his innovation efforts, he was awarded a Distinguished Faculty Recognition Award by the University of Michigan in 2019. His clinical interests and national expertise are in the areas of Endocrine Surgery: specifically thyroid surgery for benign and malignant disease, minimally invasive thyroid and parathyroid surgery, and adrenal surgery, as well as advanced Melanoma Surgery including developing and running the hyperthermic isolated limb perfusion program for in transit metastatic melanoma (the only one in the state of Michigan) which is now one of the largest in the nation.
Dr. Fernandez is a clinical psychologist with extensive training in both addiction and behavioral medicine. She is the Clinical Program Director at the University of Michigan Addiction Treatment Service. Her research focuses on the intersection of addiction and health across two main themes: 1) Expanding access to substance use disorder treatment and prevention services particularly in healthcare settings and; 2) applying precision health approaches to addiction-related healthcare questions. Her current grant-funded research includes an NIH-funded randomized controlled pilot trial of a preoperative alcohol intervention, an NIH-funded precision health study to leverage electronic health records to identify high-risk alcohol use at the time of surgery using natural language processing and other machine-learning based approaches, a University of Michigan funded precision health award to understand and prevent new persistent opioid use after surgery using prediction modeling, and a federally-funded evaluation of the state of Michigan’s substance use disorder treatment expansion.
Andrew uses mathematical and statistical modeling to address public health problems. As a mathematical epidemiologist, he works on a wide range of topics (mostly related to infectious diseases and cancer prevention and survival) using an array of computational and statistical tools, including mechanistic differential equations and multistate stochastic processes. Rigorous consideration of parameter identifiability, parameter estimation, and uncertainty quantification are underlying themes in Andrew’s work.
My research primarily focuses on the following main themes: 1) development of methods for risk prediction and analyzing treatment effect heterogeneity, 2) Bayesian nonparametrics and Bayesian machine learning methods with a particular emphasis on the use of these methods in the context of survival analysis, 3) statistical methods for analyzing heterogeneity in risk-benefit profiles and for supporting individualized treatment decisions, and 4) development of empirical Bayes and shrinkage methods for high-dimensional statistical applications. I am also broadly interested in collaborative work in biomedical research with a focus on the application of statistics in cancer research.