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.

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.

Alauddin Ahmed

Alauddin Ahmed

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My core research expertise involves developing and employing a wide array of computational methods to discover, design, and characterize materials and systems that address critical challenges in energy and the environment. These methods span from stochastic techniques to molecular dynamics, density functional theory, quantum chemistry, and data science. Beyond contributing fundamental design principles for high-performing materials, my research has led to the discovery of record-breaking materials for hydrogen storage, natural gas storage, and thermal energy storage, alongside creating open-access databases, machine learning models, and Python APIs.

In data science, I have uniquely contributed to feature engineering, compressed sensing, classical machine learning algorithms, symbolic regression, and interpretable ML. My approach to feature engineering involves crafting or identifying a concise set of meaningful features for developing interpretable machine learning models, diverging from traditional data reduction techniques that often disregard the underlying physics. Moreover, I have enabled the use of compressed sensing-based algorithms for developing symbolic regressions for large datasets, utilizing statistical sampling and high-throughput computing. I’ve also integrated symbolic regression and constrained optimization methods for the inverse design of materials/systems to meet specific performance metrics, and I continue to merge machine learning with fundamental physical laws to demystify material stability and instability under industrial conditions.

Looking forward, my ongoing and future projects include employing machine learning for causal inference in healthcare to understand and predict outcomes and integrating AI to conduct comprehensive environmental and social impact analyses of materials/systems via life cycle analysis. Furthermore, I am exploring quantum computing and machine learning to drive innovation and transform vehicle energy systems and manufacturing processes.

Anne McNeil

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Our research is aimed at addressing some of the world’s biggest challenges through chemical recycling or upcycling of waste plastics, developing methods to capture microplastics, measuring microplastics in the environment, and designing redox active molecules for energy storage applications.

What is your most interesting project?

Synthetic polymers have an enormous impact on our lives, yet the manner in which they are produced, used, and disposed of is unsustainable. Globally, we produce over 300 million tons of plastics per year, and a stunning >90% of plastics are made from petroleum feedstocks and only a scant 9% of plastics are recycled. Products that are recycled through current mechanical processes are frequently downgraded into lower-quality materials. We are currently exploring methods for “chemical recycling”. This approach includes developing depolymerization procedures and synthetic methods for repurposing degraded polymers into equal-quality or value-added materials.

Microplastics are everywhere due to the world’s prolific use of plastics in everyday items. Microplastics have been found in indoor and outdoor environments, in urban and rural areas, and even in the most remote locations on the planet. While most research has focused on microplastics in water and land environments, far fewer studies have examined microplastics in the atmosphere. Yet inhaling airborne microplastics is likely more harmful to human health than ingesting them through food and water. How many microplastics are found in our air? Where are the biggest emission sources? How are microplastics transported through the air? How does your race, income, and/or geographic location correlate with your exposure levels? Shouldn’t we know? This project is a collaboration with Profs. Andy Ault and Paul Zimmerman (in Chemistry), Ambuj Tewari (in Statistics), and Allison Steiner (in CLASP).

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

I am a newbie to data science and AI, and I am excited to learn more alongside my collaborators (Ambuj Tewari and Paul Zimmerman). In particular, I am excited by the opportunity to leverage the power of existing data to identify and create new pathways for chemical recycling of waste plastics.

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

Mohammed Ombadi

Mohammed Ombadi

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My research focuses on understanding and quantifying climate change impacts on hydroclimatic extremes. From heavy storms and floods to extreme heatwaves and droughts, I study the changing characteristics of these events and their impacts on our daily lives. I use a wide range of data-driven methods such as causal inference, information theory, nonlinear dynamics and machine learning in the analysis of environmental systems. I am primarily interested in using causal inference to obtain new mechanistic insights on the impact of global warming on extreme weather events. My recent work has explored global warming impact on extreme events such as rainfall extremes, using a combination of observations and model simulations. Additionally, recent work has focused on developing new techniques to measure resilience of environmental systems to extreme events, with the ultimate goal of informing mitigation and adaptation strategies to climate change.Map of the Northern Hemisphere showing the projected increase in extreme daily rainfall by 2100 relative to 1950–1979 (the risk ratio). Darker areas are predicted to be more prone to increased rainfall extremes with global warming.

Map of the Northern Hemisphere showing the projected increase in extreme daily rainfall by 2100 relative to 1950–1979 (the risk ratio). Darker areas are predicted to be more prone to increased rainfall extremes with global warming.

What are some of your most interesting projects?

Resilience of Watersheds to Extreme Weather and Climate Events:

Hydrologic watersheds are the fundamental units of the land surface used in the analysis and management of water resources systems. The response of watersheds to extreme events is highly complex and determined by a multitude of factors, including the presence of dams and reservoirs, snowpack, groundwater-surface water interaction, and vegetation cover, among others. One significant knowledge gap in this field of research is how to objectively and unambiguously quantify the resilience and resistance of watersheds to extreme events, such as droughts and floods. Developing metrics to quantify resilience is of utmost importance, particularly in light of the changing characteristics of extreme events due to global warming.

In this project, I employ a wide range of statistical methods to quantify resilience. I then apply methods of machine learning, causal inference, and graph-based techniques to explore patterns of resilience across watersheds worldwide.

How did you end up where you are today?

I originally hail from Sudan, nestled at the heart of Africa. Growing up along the banks of the Nile River, I developed an early fascination with water and its profound connection to humanity. On one hand, it provides people with their needs for drinking, agriculture, transportation, and recreation. On the other hand, an excess or shortage of water often results in devastating natural disasters, such as floods, droughts, and famines. Throughout antiquity, humans have endeavored to regulate rivers by building dams, canals, and various other structures. This deep-seated interest in water sciences and related engineering disciplines led me to pursue a degree in Civil Engineering.

Following my college graduation, I chose to embark on graduate studies to delve deeper into the intricate relationship between climate change and the water cycle. Embarking on an arduous 18-hour flight, I traversed the vast Atlantic Ocean and the continental expanse of the United States to arrive in California, the Golden State. There, I successfully completed my MSc and PhD degrees at the University of California, Irvine, in just under five years. Subsequently, I relocated northward to Berkeley, where I conducted my postdoctoral research, focusing on the impact of global warming on climate extreme events. I come to University of Michigan with a deep research interest in exploring the impact of global warming on extreme events and the resilience of ecosystems to such events. My research group harness recent advances in data science (machine learning, causal inference and information theory) to obtain new mechanistic and predictive insights on these questions with the ultimate goal of informing climate change adaptation strategies.

Saif Benjaafar

Saif Benjaafar

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I used the tools of operations research (optimization, stochastic modeling, and game theory), machine learning, and statistics to study problems in operations management broadly defined, including supply chains, service systems, transportation and mobility, and markets. My current research focus is on sustainable operations and innovative business models, including sharing economy, on-demand services, and online marketplaces.

Stella Yu

Stella Yu

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My research lies at the intersection of computer vision, human vision, and machine learning. Visual perception presents not just a fascinating computational problem, but more importantly an intelligent solution for large-scale data mining and pattern recognition applications.
My research has thus three themes.
1. Actionable Representation Learning Driven by Natural Data. I attribute our fast effortless vision to actionable representation learning driven by natural data, where mid-level visual pieces can be reassembled and adapted for seeing the new.
2. Efficient Structure-Aware Machine Learning Models. I view a computational model as dual to the data it takes in; since visual data are full of structures, models reflective of such structures can achieve maximum efficiency.
3. Application to Science, Medicine, and Engineering. I am interested in applying computer vision and machine learning to capture and exceed human expertise, enabling automatic data-driven discoveries in science, medicine, and engineering.

Runzi Wang

Runzi Wang

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Runzi Wang is a transdisciplinary researcher who studies change in natural and urban environments across space and over time, with the objective of driving positive change with ecological planning and design strategies. Combining technologies such as big data, machine learning, remote sensing, and spatial statistics, her primary research explores how land cover change and urban development pattern influence stream water quality and stormwater quality at the watershed basis, together with various environmental, climatic, and sociocultural factors. By enhancing the interpretability of machine learning in its application to landscape architecture, the most innovative part of her research is to uncover the nonlinear, interacted relationships between environmental, technological, and sociocultural dimensions of landscape systems.

What are some of your most interesting projects?

  1. I conducted the first continental-scale urban stream water quality study funded by MIDAS. We applied geospatial analysis to investigate the characteristics of the built environment (e.g., building footprint, street length, land use spatial pattern) associated with urban stream water quality, the social inequities regarding exposure to stream water contamination, as well as the spatial variations in the above processes. We developed data integration protocols for data from remote sensing products, in-situ observations, and the US Census Bureau. Using Bayesian hierarchical models, we concluded that watersheds with a higher percentage of minorities are associated with higher nutrient pollution, with the relationship being more significant in the American Northwest.
  2. I investigated how land use planning and best management practices mitigated climate change effects on Lake Erie’s water quality. With the integration of longitudinal watershed land cover, agricultural, and climatic data from 1985-2017, we found that no-tillage and reduced tillage management were effective mitigation strategies that could decrease water quality sensitivity to climate change. We plan to advance this work by fusing remote sensing-based bloom detection and process-based simulation to investigate how climate change, land cover change, and anthropogenic activities will impact the eutrophication of Lake Erie.

How did you end up where you are today?

I have a highly interdisciplinary background, receiving training in architecture, landscape architecture, urban planning, statistics, hydrology and water quality, and broader social science topics. This forms my research topic to study the relationships between people, land, and water. Specifically, I study the interconnectedness between people living in the watershed, the land use and urban form of the watershed’s built form, the resulting water quality conditions, and the ecosystem services urban streams provide for people. This background also leverages many different methodologies in my work, including data science, hydrological models, social science methods, and so on. In addition, the most important thing about my research journey is that I have a few excellent friends/researchers who help me a lot on my way and make my research life inspiring and delightful most of the time.

Peter Song

Peter Song

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My research interests lie in two major fields: In the field of statistical methodology, my interests include data integration, distributed inference, federated learning and meta learning, high-dimensional statistics, mixed integer optimization, statistical machine learning, and spatiotemporal modeling. In the field of empirical study, my interests include bioinformatics, biological aging, epigenetics, environmental health sciences, nephrology, nutritional sciences, obesity, and statistical genetics.