The Rabosky lab seeks to understand how and why life on Earth became so diverse. We focus primarily on large-scale patterns of species diversification (speciation and extinction) and on the tempo and mode of phenotypic evolution, to better understand what regulates the “amount” of biodiversity through Deep Time. To this end, we develop theoretical frameworks and computational tools for studying evolutionary dynamics using DNA-sequence-based evolutionary trees (phylogenies), the fossil record, as well as phenotypic data from present-day species (morphology, ecology). We develop and apply a range of methods involving supervised and unsupervised learning, including Markov chain Monte Carlo, hierarchical mixture models, hidden Markov models, latent feature models, and more. We are increasingly interested in complex morphological and ecological traits, which – due to a rapidly expanding data universe – represent a tremendous opportunity for the field to answer long-standing questions about how organisms evolve. At these same time, we are embracing the analytical challenges of these data, because fully realizing their potential requires the development of new analytical paradigms that go beyond the limitations of traditional parametric models for low-dimensional data.
My lab studies how information from one sensory system influences processing in other sensory systems, as well as how this information is integrated in the brain. Specifically, we investigate the mechanisms underlying basic auditory, visual, and tactile interactions, synesthesia, multisensory body image perception, and visual facilitation of speech perception. Our current research examines multisensory processes using a variety of techniques including psychophysical testing and illusions, fMRI and DTI, electrophysiological measures of neural activity (both EEG and iEEG), and lesion mapping in patients with brain tumors. Our intracranial electroencephalography (iEEG/ECoG/sEEG) recordings are a unique resource that allow us to record neural activity directly from the human brain from clinically implanted electrodes in patients. These recordings are collected while patients perform the same auditory, visual, and tactile tasks that we use in our other behavioral and neuroimaging studies, but iEEG measures have millisecond temporal resolution as well as millimeter spatial precision, providing unparalleled information about the flow of neural activity in the brain. We use signal processing techniques and machine learning methods to identify how information is encoded in the brain and how it is disrupted in clinical contexts (e.g., in patients with a brain tumor).
My research focus the application and development of new algorithms for solving complex business analytics problems. Applications vary from revenue management, dynamic pricing, marketing analytics, to retail logistics. In terms of methodology, I use a combination of operations research and machine learning/online optimization techniques.
My research focuses on developing and using methods in machine learning and natural language processing to learn about society from text, promoting better and more reproducible data science, and studying the societal impacts of these technologies. I collaborate with colleagues in statistics, linguistics, political science, and other areas of computational social science to investigate how people communicate, the effects of this communication, and to better understand the potential consequences and limitations of data science and artificial intelligence.
I am an Assistant Professor in the School for Environment and Sustainability at the University of Michigan and am part of the Sustainable Food Systems Initiative. My research examines the impacts of environmental change on agricultural production, and how farmers may adapt to reduce negative impacts. I also examine ways that we can sustainably enhance agricultural production. To do this work, I combine remote sensing and geospatial analyses with household-level and census datasets to examine farmer decision-making and agricultural production across large spatial and temporal scales.
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.