Catherine Kaczorowski

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The Kaczorowski laboratory, led by Dr. Catherine Kaczorowski, pioneers techniques to identify and validate genetic and cellular mechanisms that promote resilience to cognitive aging, Alzheimer’s disease, and other age-related dementias. By combining mouse and human systems; genomic, anatomic, and behavioral approaches; and integrative analyses across multiple scales, data types, environmental factors, and species, we are accelerating the discovery of the precise genetic mechanisms of cognitive resilience that could yield the next generation of targets and therapeutic strategies for promoting brain health. We are now uniquely poised to propel the field of personalized medicine forward using our genetically diverse, yet reproducible models of human neurodegenerative dementias, having already contributed conceptual and technical advances that revolutionized our ability to study complex diseases, specifically human AD dementia.

Peter Reich

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Reich conducts global change research on plants, soils, ecosystems and people across a range of scales. His work links fundamental physiology with community dynamics and ecosystem structure and function, from the patch to the globe, within the context of the myriad of global environmental challenges that face us. This includes studying the effects on natural and human ecosystems of rising CO2 and associated climate change, biodiversity loss, and wildfire. This research involves a variety of tools and approaches (long-term experiments, observations, global data compilations, statistical and simulation models), a diverse set of ecosystems (boreal forest, temperate grassland, and more), and a range of scales (local, regional, global). The overarching goal is to understand what we humans are doing to nature in order to help orchestrate a shift towards a nature-forward prioritization that will in turn support and sustain human society.

I studied physics and creative writing and became interested in the fate of our environment; over time I began using tools from each focal area to advance ecological science in a changing world

Terra Sztain

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The Sztain research group is broadly focused on computer-aided molecular design, intersecting fields of chemistry, physics, biology, and computer science. Ongoing projects involve integrating experimental data and enhanced sampling molecular dynamics simulations to improve computational models for allosteric inhibitor design and protein engineering.

Alexander Rodríguez

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Alex’s research interests include machine learning, time series, multi-agent systems, uncertainty quantification, and scientific modeling. His recent focus is on developing trustworthy AI systems that can offer insightful guidance for critical decisions, especially in applications involving complex spatiotemporal dynamics. His work is primarily motivated by real-world problems in public health, environmental health and community resilience.

Irina Gaynanova

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Dr. Gaynanova’s research focuses on the development of statistical methods for analysis of modern high-dimensional biomedical data. Her methodological interests are in data integration, machine learning and high-dimensional statistics, motivated by challenges arising in analyses of multi-omics data (e.g., RNASeq, metabolomics, micribiome) and data from wearable devices (continuous glucose monitors, ambulatory blood pressure monitors, activity trackers).Dr. Gaynanova’s research has been funded by the National Science Foundation, and recognized with a David P. Byar Young Investigator Award and an NSF CAREER Award. She currently serves as an Associate Editor for Journal of the American Statistical Association, Biometrika and Data Science in Science.

Liu Liu

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My primary career interest is making new discoveries through creative thinking and innovative investigations. My long term research interests in the molecular mechanisms of heart regeneration to effectively prolong and improve the lives of heart patients, particularly in the development of a comprehensive understanding of post-translational/epigenetics regulation for cardiac reprogramming based heart therapy. I am developing a novel concept for a post-translational modification (PTM) code that is applicable across different proteins. I am utilizing computational methods to gain insights into the functional implications of PTMs that transcend protein boundaries.

Qiong Yang

Qiong Yang

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My research program at the University of Michigan (UM) integrates the fields of biophysics, quantitative systems biology, and bottom-up synthetic biology to understand complex stochastic cellular and developmental processes in early embryos.
We have developed innovative computational and experimental techniques in microfluidics and imaging to allow high-throughput quantitative manipulation and single-cell lineage tracking of cellular spatiotemporal dynamical processes in various powerful in vitro and in vivo systems we established in my lab. These systems range from cell-free extracts, synthetic cells reconstituted in microemulsion droplets, presomitic mesoderm (PSM) and progenitor zone (PZ) cells dissociated from the zebrafish tail buds, their re-aggregated 2D and 3D cell-cell communications, ex vivo live tissue explants, and live embryos.
Our current research questions center around the understanding of the design-function relation of robust biological timing, growth, and patterning, how individual molecules and cells communicate to generate collective patterns, and how biochemical, biophysical, and biomechanical signals work together to shape morphogenesis during early embryo development.

John Prensner

John Prensner

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My research group uses molecular techniques and computational methods to dissect the biology of pediatric cancers. We are invested in fundamental genomic discovery of non-canonical open reading frames that are dysregulated in cancer. We use functional genomics techniques to facilitate biological analysis. We employ data science methodologies to model and predict the molecular biology of cancers. Our particular focus is on RNA translation and its regulation. We also focus on therapeutic interventions that may represent novel treatment strategies for cancer.

What are some of your most interesting projects?

I am fascinated by the ways in which cancer cells pattern RNA translation. We are currently performing large-scale -omic analyses to identify patterns in RNA translational control across pediatric brain cancers.

How did you end up where you are today?

I came to science late in life. I studied English Literature as an undergraduate at Tufts University before deciding to study biochemistry as a senior in college. This led me to join a cancer biology lab after college, which motivated me to go to medical school. I enrolled in the University of Michigan Medical School in 2006 but quickly became interested in the biology of cancer. I then joined the Medical Scientist Training Program and graduated with an MD/PhD dual degree in 2014. I pursued Pediatrics clinical training at Boston Children’s Hospital and pediatric hematology/oncology at Dana-Farber Cancer Institute. I then completed post-doctoral research at the Broad Institute of MIT and Harvard prior to joining the faculty of the University of Michigan.

What is the most significant scientific contribution you would like to make?

My goal is to cure childhood brain cancers. That is what motivates me every day!

Chuan Zhou

Chuan Zhou

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With a passion for developing decision support systems that integrate cutting edge techniques from artificial intelligence, quantitative image analysis, computer vision, and multimodal biomedical data fusion. Research interests have been focusing on characterizing diseases abnormalities and predicting their likelihood of being significant, with the goal to enable early diagnosis and risk stratification, as well as aiding treatment decision making and monitoring.

Kamran Diba

Kamran Diba

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My lab is primarily interested in how the brain represents, coordinates, and stores memories. Hippocampal neuronal networks generate an assortment of firing patterns that vary depending on the behavior and state of an animal, from active exploration to resting and different stages of sleep. In our lab’s extracellular recordings from large populations of spiking neurons in rodents, we observe state-dependent temporal relationships between activities at multiple timescales. Recent work in my lab is aimed at understanding what role these unique spike patterns play and what they tell us about the function and limitations of different brain states for memory in healthy and compromised animals. To answer these and related questions, we combine behavioral studies of freely moving, learning and exploring rats, multi-channel recordings of the simultaneous electrical (spiking) activity from hundreds of neurons during behavior, sleep and sleep-deprivation, statistical and machine learning tools to uncover deep structure within high-dimensional spike trains, and chemogenetics and optogenetics to manipulate protein signaling and action potentials in specific neural populations in precise time windows.

Spike times recorded from a population of hippocampal neurons during running on a maze.

Spike times recorded from a population of hippocampal neurons during running on a maze.

What are some of your most interesting projects?

Evaluating the impact of sleep loss on hippocampal replay.
Using unsupervised machine learning to evaluate the temporal structure of hippocampal firing patterns during sleep.

What is the most significant scientific contribution you would like to make?

Understand how the hippocampus serves memory and what role sleep plays in this process.

What makes you excited about your data science and AI research?

The potential for AI models to help explain how the brain works.