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.

Benjamin Goldstein

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Benjamin Goldstein is Assistant Professor of Environment and Sustainability and head of the Sustainable Urban-Rural Futures (SURF) lab. The SURF Lab (www.surf-lab.ca) studies and emphasizes urban sustainability at multiple scales. Through his work at the SURF Lab, Benjamin helps understand how urban processes and urban form drive the consumption of materials and energy in cities and produce environmental change inside and outside cities. He develops methods and tools to quantify the scale of these changes and the locations where they occur using life cycle assessment, input-output analysis, geospatial data, and approaches from data science. Benjamin is particularly interested in combining quantitative methods with theory rooted in social science to explore multiple dimensions of sustainability and address issues of distributive justice. His topical foci include urban food systems (esp. urban agriculture), agri-commodities, residual resource engineering, global supply chains, sustainable production and consumption, and energy systems.

Mosharaf Chowdhury

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I am a computer scientist and an associate professor at CSE Michigan, where I lead the SymbioticLab (https://symbioticlab.org/). My research improves application performance and system efficiency of AI/ML and Big Data workloads with a recent focus on optimizing energy consumption and data privacy. I lead the ML Energy initiative (https://ml.energy/), a consortium of researchers focusing on understanding, controlling, and reducing AI/ML energy consumption. Over the course of my career, I have worked on a variety of networked and distributed systems. Recent major projects include Infiniswap, the first scalable memory disaggregation solution; Salus, the first software-only GPU sharing system for deep learning; FedScale, a scalable federated learning and analytics platform; and Zeus, the first GPU energy optimizer for AI. In the past, I invented the coflow abstraction for efficient distributed communication, and I am one of the original creators of Apache Spark. Thanks to my excellent collaborators, I have received many individual awards, fellowships, and paper awards from top venues like NSDI, OSDI, ATC, and MICRO.

Carol Menassa

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My group’s research focuses on understanding and modeling the interconnections between human experience and the built environment. We design autonomous systems that support wellbeing, safety and productivity of office and construction workers, and provides them opportunities for lifelong learning and upskilling. In all research projects, we work hard to ensure that the results are inclusive and benefit people of different abilities in their daily activities and empower them for nontraditional careers.

Zach Landis-Lewis

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My research focuses on the use and effectiveness of coaching and appreciation feedback in healthcare. I lead a team that develops a software-based precision feedback system to generate messages about performance to healthcare professionals and teams. My work involves the processing of performance data to detect signals of motivating information that can be delivered with algorithmically prioritized messages, to support performance improvement and sustainment. I lead the DISPLAY-Lab, which collaborates with researchers in a range of clinical and health-related domains, including biomedical informatics, implementation science, and human-centered design.

Cheng Li

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My research focuses on developing advanced numerical models and computational tools to enhance our understanding and prediction capabilities for both terrestrial and extraterrestrial climate systems. By leveraging the power of data science, I aim to unravel the complexities of atmospheric dynamics and climate processes on Earth, as well as on other planets such as Mars, Venus, and Jupiter.

My approach involves the integration of large-scale datasets, including satellite observations and ground-based measurements, with statistical methods and sophisticated machine learning algorithms including vision-based large models. This enables me to extract meaningful insights and improve the accuracy of climate models, which are crucial for weather forecasting, climate change projections, and planetary exploration.

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.

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.

Bing Ye

Bing Ye

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The focus of our research is to address (1) how neuronal development contributes to the assembly and function of the nervous system, and (2) how defects in this process lead to brain disorders. We take a multidisciplinary approach that include genetics, cell biology, developmental biology, biochemistry, advanced imaging (for neuronal structures and activity), electrophysiology, computation (including machine learning and computer vision) and behavioral studies.

We are currently studying the neural basis for decision accuracy. We established imaging and computational methods for analyzing neural activities in the entire central nervous system (CNS) of the Drosophila larva. Moreover, we are exploring the possibility of applying the biological neural algorithms to robotics for testing these algorithms and for improving robot performance.

A major goal of neuroscience is to understand the neural basis for behavior, which requires accurate and efficient quantifications of behavior. To this end, we recently developed a software tool—named LabGym—for automatic identification and quantification of user-defined behavior through artificial intelligence. This tool is not restricted to a specific species or a set of behaviors. The updated version (LabGym2) can analyze social behavior and behavior in dynamic backgrounds. We are further developing LabGym and other computational tools for behavioral analyses in wild animals and in medicine.

The behavior that this chipmunk performed was identified and quantified by LabGym, an AI-based software tool that the Ye lab developed for quantifying user-defined behaviors.

The behavior that this chipmunk performed was identified and quantified by LabGym, an AI-based software tool that the Ye lab developed for quantifying user-defined behaviors.

What are some of your most interesting projects?

1) Develop AI-based software tools for analyzing the behavior of wild animals and human.
2) Use biology-inspired robotics to test biological neural algorithms.

How did you end up where you are today?

Since my teenage years, I have been curious about how brains (human’s and animals’) work, enjoyed playing with electronics, and learning about computational sciences. My curiosity and opportunities led me to become a neuroscientist. When I had my own research team and the resources to explore my other interests, I started to build simple electronic devices for my neuroscience research and to collaborate with computational scientists who are experts in machine learning and computer vision. My lab now combines these approaches in our neuroscience research.

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

I am very excited about the interactions between neuroscience and data science/AI research. This is a new area and has great potential of changing the society.

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.