Felipe da Veiga Lerprevost

By |

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.

Omar Jamil Ahmed

By |

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.

Mark Steven Cohen

By |

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 Bakker

By |

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.

Andrew Brouwer

By |

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.

Rajiv Saran

By |

Dr. Saran is an internationally recognized expert in kidney disease research – specifically, in the area of kidney disease surveillance and epidemiology. From 2014 – 2019, he served as Director of the United States Renal Data System (USRDS; www.usrds.org), a ‘gold standard’ for kidney disease data systems, worldwide. Since 2006 he has been Co-Principal Investigator for the Centers for the Disease Control and Prevention’s (CDC’s) National CKD Surveillance System for the US, a one of a kind project that complements the USRDS, while focusing on upstream surveillance of CKD and its risk factors (www.cdc.org/ckd/surveillance). Both projects have influenced policy related to kidney disease in the US and were cited extensively in the July 2019 Advancing American Kidney Health Federal policy document. Dr. Saran led the development of the first National Kidney Disease Information System (VA-REINS), for the Department of Veterans Affairs (VA), funded by the VA’s Center for Innovation, and one that led to the VA recognizing the importance of kidney disease as a health priority for US veterans. Dr. Saran has recently (2018-2021) been funded on a spin off project from VA REINS for investigation of ‘hot-spot’ of kidney disease among US Veterans involving both risk-prediction and geospatial analyses – a modern approach to health system big data being used for prevention and population health improvement, using kidney disease as an example. This approach has broad application for prevention and optimizing management of major chronic diseases.

Bogdan I. Epureanu

By |

• Computational dynamics focused on nonlinear dynamics and finite elements (e.g., a new approach for forecasting bifurcations/tipping points in aeroelastic and ecological systems, new finite element methods for thin walled beams that leads to novel reduced order models).
• Modeling nonlinear phenomena and mechano-chemical processes in molecular motor dynamics, such as motor proteins, toward early detection of neurodegenerative diseases.
• Computational methods for robotics, manufacturing, modeling multi-body dynamics, developed methods for identifying limit cycle oscillations in large-dimensional (fluid) systems.
• Turbomachinery and aeroelasticity providing a better understanding of fundamental complex fluid dynamics and cutting-edge models for predicting, identifying and characterizing the response of blisks and flade systems through integrated experimental & computational approaches.
• Structural health monitoring & sensing providing increased sensibility / capabilities by the discovery, characterization and exploitation of sensitivity vector fields, smart system interrogation through nonlinear feedback excitation, nonlinear minimal rank perturbation and system augmentation, pattern recognition for attractors, damage detection using bifurcation morphing.

Annette Ostling

By |

Biodiversity in nature can be puzzlingly high in the light of competition between species, which arguably should eventually result in a single winner. The coexistence mechanisms that allow for this biodiversity shape the dynamics of communities and ecosystems. My research focuses on understanding the mechanisms of competitive coexistence, how competition influences community structure and diversity, and what insights observed patterns of community structure might provide about competitive coexistence.

I am interested in the use and development of data science approaches to draw insights regarding coexistence mechanisms from the structural patterns of ecological communities with respect to species’ functional traits, relative abundance, spatial distribution, and phylogenetic relatedness, through as community dynamics proceed. I am also interested in the use of Maximum Likelihood and Bayesian approaches for fitting demographic models to forest census data sets, demographic models that can then be used to quantitatively assess the role of different competitive coexistence mechanisms.

Thomas Schmidt

By |

The current goal of our research is to learn enough about the physiology and ecology of microbes and microbial communities in the gut that we are able to engineer the gut microbiome to improve human health. The first target of our engineering is the production of butyrate – a common fermentation product of some gut microbes that is essential for human health. Butyrate is the preferred energy source for mitochondria in the epithelial cells lining the gut and it also regulates their gene expression.

One of the most effective ways to influence the composition and metabolism of the gut microbiota is through diet. In an interventional study, we have tracked responses in the composition and fermentative metabolism of the gut microtiota in >800 healthy individuals. Emerging patterns suggest several configurations of the microbiome that can result in increased production of butyrate acid. We have isolated the microbes that form an anaerobic food web to convert dietary fiber to butyrate and continue to make discoveries about their physiology and interactions. Based on these results, we have initiated a clinical trial in which we are hoping to prevent the development of Graft versus Host Disease following bone marrow transplants by managing butyrate production by the gut microbiota.

We are also beginning to track hundreds of other metabolites from the gut microbiome that may influence human health. We use metagenomes and metabolomes to identify patterns that link the microbiota with their metabolites and then test those models in human organoids and gnotobiotic mice colonized with synthetic communities of microbes. This blend of wet-lab research in basic microbiology, data science and in ecology is moving us closer to engineering the gut microbiome to improve human health.

Carlos Aguilar

By |

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.