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.

Fan Bu

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I am broadly interested in Bayesian and computational statistics for analyzing large-scale and complex data. I am particularly interested in spatio-temporal statistics, network inference, infectious disease models, and distributed learning. My methodological research has been motivated by applications in public health, observational healthcare studies, computational social science, and sports sciences.

I came from a math background but studied statistics in order to become a sports analyst (yes, Moneyball!). Throughout my PhD and postdoc training, I grew a strong appreciation for social sciences (how people behave and interact) and health sciences (how to provide high-quality healthcare for everyone). I see data science as the field to help us make sense of complex data that arise from our daily life and scientific endeavors, by building reliable and reproducible frameworks that transform data to evidence and then to scientific findings and decisions.

Dani Jones

Dani Jones

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Dani Jones’ research program drives CIGLR’s portfolio of research in data science, machine learning, and artificial intelligence, as applied to physical limnology, weather forecasting, water cycle predictions, ecology, and observing system design. This research program aims is to advance societal adaptations to the effects of climate change, including flooding of coasts, rivers, and cities. Dani’s background is in physical oceanography, with specific expertise in adjoint modeling for comprehensive sensitivity analysis and unsupervised classification for data analysis, mostly applied to the North Atlantic and Southern Ocean. In Dani’s current role, they are establishing CIGLR’s new Artificial Intelligence Laboratory, leveraging the institute’s extensive observing assets, datasets, modeling capacity, interdisciplinary expertise, and numerous regional and international partnerships.

Maggie Makar

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My research focuses on developing reliable and efficient machine learning methods for causal inference as well as predictive models that leverage causal reasoning. My work typically involves applications to healthcare.

Jacob Allgeier

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My goal as an ecologist is to apply ecological theory to help solve real-world conservation issues. Specifically, I seek to identify the mechanisms by which behavioral, population, and community dynamics mediate nutrient and energy pathways. The objective is to improve our ability to predict ecological outcomes, and enhance conservation efficacy such as the sustainability of ecosystem services (e.g., fisheries). Much of this research takes place in tropical coastal ecosystems (mangroves, seagrass beds, and coral reefs) where I study gradients created by anthropogenic impacts to test theory directly within the context of environmental change and biodiversity loss. My research is broad and multifaceted, and includes a combination of extensive field-based research and computational analyses.The type of data we collect in the field has endless potential to be better understood through collaborations with MIDAS. I rely on (and very much enjoy) integrative collaborations across a variety of fields.

What are some of your most interesting projects?

We have recently generated one of the most extensive high-resolution dataset of fish movement in any system that we are aware of. We are using these data to understand the role of consumers in moving nutrient and energy through these ecosystems, and also to better understand the relative ecological importance of individual-level vs species-level variation.

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

I would like to improve our ability to predict fish production in tropical coastal ecosystems to improve food security. I would also like to help improve our ability to manage seagrass ecosystems to maximize carbon sequestration and storage.

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

A central goal of my lab is to collect extremely high-end and extensive empirical data such that it can inform models that help us forecast ecological processes at the scales of entire ecosystems. We are currently using a suite of techniques to do so, including the use of individual-based modeling in particular. The type of data we are generating is absolutely ripe for being used with high-power data science and AI research. I honestly believe the applications are endless and would be extremely excited to team up with folks to build on these exciting possibilities.

What are some interesting facts about yourself?

Backpacking and woodworking are how I unwind. I love being outside, and I love doing field work.

Jacob Underwater

Studying our artificial reefs in Haiti

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.

Tian An Wong

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Analysis of policing technology and police data, including impact assessment of surveillance technology, media sentiment analysis, and fatal police violence. Methods include topological data analysis, natural language processing, multivariate time series analysis, difference-in-differences, and complex networks.

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.

Changxiao Cai

Changxiao Cai

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Changxiao Cai’s research interests lie broadly in the intersection of statistics, optimization, and machine learning. He is interested in developing provably scalable methods for information extraction from high-dimensional data, with an aim to achieve the optimal interplay between statistical accuracy and computational efficiency.

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.