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.
The primary tools currently in use are variations of linear models (regression, MLM, SEM, and so on) as we pursue the initial aims of the NICHD funded work. We are expanding into new areas that require new tools. Our adolescent sample is diverse, selected through quota sampling of high schools close enough to UM to afford the use of neuroimaging tools, but it is not population representative. To overcome this, we have begun work to calibrate our sample with the nationally representative Monitoring the Future study, implementing pseudo-weighting and multilevel regression and post-stratification. To enable much more powerful analyses, we are aiming toward the harmonization of multiple, high quality longitudinal databases from adolescence through early adulthood. This would benefit traditional analyses by allowing cross-validation with high power, but also provide opportunities for newer data science tools such as computational modeling and machine learning approaches.
I have been involved in the building of data infrastructure in the study of elections, political systems, violence, geospatial units, demographics, and topography. This infrastructure will eventually lead to the integration of data across many domains in the social, health, population, and behavioral sciences. My core research interests are in elections and political organizations.
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.
Kevin’s research is focused on to identifying and interpreting the mechanisms responsible for the complex dynamics we observe in ecological and epidemiological systems using data science and modeling approaches. He is primarily interested in emerging and endemic pathogens, such as SARS-CoV-2, influenza, vampire bat rabies, and a host of childhood infectious diseases such as chickenpox. He uses statistical and mechanistic models to fit, forecast, and occasionally back-cast expected disease dynamics under a host of conditions, such as vaccination or other control mechanisms.