Our Scholars

Since its inception, the BSSP has been the flagship recruiting mechanism for attracting junior instructional faculty in key areas of biomedical investigation. Scholars earn many of the most prestigious awards in biomedical sciences.

The program also invigorated a new culture of excellence in the biomedical sciences by establishing world-renowned programs in key scientific fields, taking advantage of U-M’s interdisciplinary culture and mentorship.

Browse scholars by recruitment year, below.

Year Recruited: 2022
( 7 Scholars )

Jailson Brito Querido, PhD

University of Strasbourg
Affiliation(s):
Biological Chemistry, Center for RNA Biomedicine
Research Interests

DEAD-box (DDX) RNA helicases are a highly conserved family of enzymes, characterized by the presence of an Asp-Glu-Ala-Asp (DEAD) motif. They are involved in RNA metabolism ranging from transcription to degradation, and are thereby important for gene expression in eukaryotes. The main aim of my research is to understand the multiple roles of DEAD-box RNA helicases in the regulation of gene expression in health and disease. My lab will use cryo-electron microscopy (cryo-EM) in combination with biochemical and genetic approaches to study the role of DEAD-box RNA helicases in the regulation of mRNA translation in humans. 

Longhua Guo, PhD

University of Utah
Affiliation(s):
Molecular and Integrative Physiology
Research Interests

The Guo lab is broadly interested in the most exciting questions in the areas of aging and regeneration in two non-traditional research organisms, the freshwater planarians, and the leopard geckos. Planarians are champions of whole body regeneration. A single piece of the body can regenerate into a complete individual in a few weeks. Geckos are famous for their colorful skin and are traded as the most popular reptile pets in the world market. They are capable of tail and skin regeneration. The lab aims to utilize state-of-the-art genetics and genomics tools to understand the stem cells, tissue regeneration processes and how things change as the animals age, with the hope that one day such knowledge can be used to help humans heal. 

Thanh Hoang, Ph.D.

Johns Hopkins University, Department of Neuroscience
Affiliation(s):
Ophthalmology
Research Interests

Our lab is interested in studying cell type specification in vertebrate neural development and regeneration of lost neurons via cell fate reprogramming as a potential cell-based therapy for neurodegenerative diseases. The vertebrate central nervous system (CNS) is an amazingly complex structure composed of distinct subtypes of neurons and glia. Neurons in the retina, like elsewhere in the CNS, are highly susceptible to diseases, and their loss often result in blindness. Using a combination of multiomic and genetic tools, we particularly focus on exploring the regenerative capacity and neuroprotective roles of glial cells in injuries and neurodegenerative disorders. Current lab research projects include these related areas: (1) Regeneration of retinal neurons from Muller glial cells; (2) Injury signaling, inflammatory responses and neuroprotective roles of Muller glia and microglia in retinal injury and degeneration;(3) Reprograming astrocytes to neurons as a cell-based therapy for neurodegenerative diseases and brain injuries. 

Changyang Linghu, Ph.D.

Massachusetts Institute of Technology
Affiliation(s):
Cell and Developmental Biology, Neuroscience
Research Interests

The Linghu lab is interested in developing and applying radically new technologies to study how collective dynamics of biological building blocks in cells and in cellular networks process biological information and drive biological outcomes. Unlike electronic computers that use electrical signal alone to process information, cellular machineries use a collection of interacting biological signals to process biological information. These biological signals include messenger molecules, ions, enzymes, and other biophysical dynamics. They form signal transduction networks and collectively convert cellular inputs into cellular outcomes by interacting in complex ways (known as 'cellular symphonies'). Subtle defects in these processes are associated with a wide range of diseases. Technologies that enable observations of many biological signals in parallel across cell populations, as well as the outcomes of these biological computations such as the downstream gene expression dynamics, will help to understand the symphony of cellular activities and provide insights into new treatments for diseases.

Alex Marand, Ph.D.

University of Georgia
Affiliation(s):
Molecular, Cellular and Developmental Biology
Research Interests

TBA

Connie Wu, Ph.D.

Massachusetts Institute of Technology
Affiliation(s):
Biomedical Engineering, Life Sciences Institute
Research Interests

My lab will integrate bioanalytical chemistry, materials engineering, and molecular engineering approaches to develop technologies for diagnostic and therapeutic applications. In particular, we are interested in leveraging highly multiplexed single-molecule analytical methods and the functional versatility of polymeric and biomimetic materials, towards (1) accelerating biomarker signature discovery for cancers and other diseases; and (2) engineering multifunctional RNA therapeutics and rationally designed nanomaterials.

Xinjun Zhang, PHD

University of California, Davis, Genetic Anthropology
Affiliation(s):
Human Genetics
Research Interests

The Zhang lab broadly studies population genetics questions. Our research is driven by a fundamental question of how admixture and natural selection shaped phenotypic and genetic diversity in humans, as the interplay between these two evolutionary processes substantially influence our population history, adaptation to environments, disease dynamics, and health disparity. Combining computational, empirical and biomedical approaches, we develop and apply novel quantitative methods to new and existing genomic datasets to answer questions related to human evolution and health. Current lab projects include: 1) characterizing the genomic landscape of archaic introgression in East Asian populations; 2) developing machine learning-based methods to infer dominance on the human genome; 3) developing non-additive models for complex traits to improve polygenic risk prediction in diverse human populations.