Sophia Brueckner is a futurist artist/designer/engineer. Inseparable from computers since the age of two, she believes she is a cyborg. As an engineer at Google, she designed and built products used by millions. At RISD and the MIT Media Lab, she researched the simultaneously empowering and controlling nature of technology with a focus on haptics and social interfaces. Her work has been featured internationally by Artforum, SIGGRAPH, The Atlantic, Wired, the Peabody Essex Museum, Portugal’s National Museum of Contemporary Art, and more. Brueckner is the founder and creative director of Tomorrownaut, a creative studio focusing on speculative futures and sci-fi-inspired prototypes. She is currently an artist-in-residence at Nokia Bell Labs, was previously an artist-in-residence at Autodesk, and is an assistant professor at the University of Michigan teaching Sci-Fi Prototyping, a course combining sci-fi, prototyping, and ethics. Her ongoing objective is to combine her background in art, design, and engineering to inspire a more positive future.
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.
Dr. Lu brings expertise in machine learning, particularly integrating human knowledge into machine learning and explainable machine learning. He has applied machine learning in a range of domain applications, such as autonomous driving and machine learning for optimized design and control of energy storage systems.
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.
My research interests include health effects of air pollution, temperature extremes and climate change (mortality, asthma, hospital admissions, birth outcomes and cardiovascular endpoints); environmental exposure assessment; and socio-economic influences on health.
Data science tools and methodologies include geographic information systems and spatio-temporal analysis, epidemiologic study design and data management.
As an environmental epidemiologist and in collaboration with government and community partners, I study how social, economic, health, and built environment characteristics and/or air quality affect vulnerability to extreme heat and extreme precipitation. This research will help cities understand how to adapt to heat, heat waves, higher pollen levels, and heavy rainfall in a changing climate.
My primary research interests are understanding the causes and consequences of biased selection and attention to persuasive information, particularly in the context of health promotion. Simply stated, I am interested in what we pay attention to and why, and how this attention (or inattention) influences attitudinal and behavioral outcomes, such as persuasion and healthy behavior. In particular, my work has addressed disparities in attention to information about HIV prevention for African-Americans compared to European-Americans as a predictor of disparities in health outcomes. I am also exploring barriers to attention to health information by African-Americans, including the roles of stigma, shame, fear, and perceptions of irrelevance. At a more basic attitudes and persuasion level, I am currently pursuing work relevant to how we select information for liked versus disliked others, and how the role of choice influences how we process information we agree versus disagree with.
The Ahmed lab studies behavioral neural circuits and attempts to repair them when they go awry in neurological disorders. Working with patients and with transgenic rodent models, we focus on how space, time and speed are encoded by the spatial navigation and memory circuits of the brain. We also focus on how these same circuits go wrong in Alzheimer’s disease, Parkinson’s disease and epilepsy. Our research involves the collection of massive volumes of neural data. Within these terabytes of data, we work to identify and understand irregular activity patterns at the sub-millisecond level. This requires us to leverage high performance computing environments, and to design custom algorithmic and analytical signal processing solutions. As part of our research, we also discover new ways for the brain to encode information (how neurons encode sequences of space and time, for example) – and the algorithms utilized by these natural neural networks can have important implications for the design of more effective artificial neural networks.
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.