Cheng Li

By |

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.

Alauddin Ahmed

Alauddin Ahmed

By |

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.

Maggie Makar

By |

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.

Tian An Wong

By |

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.

Mohammed Ombadi

Mohammed Ombadi

By |

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

By |

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.

Yan Chen

Yan Chen

By |

Yan Chen’s research interests are in behavioral and experimental economics, market and mechanism design. She conducts large-scale randomized field experiments on gig economy platforms to test the efficacy of team formation algorithms on gig worker productivity and retention. She also conducts experiments in online communities to evaluate what increases pro-social behavior. Her experiments are informed by economic theory and causal inference techniques.

Runzi Wang

Runzi Wang

By |

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.

Lise Wei

Lise Wei

By |

My research focuses on developing machine learning and statistical methods to analyze multi-modality data for patient outcome modeling. These models can be used to personalize cancer patients’ treatment and improve their prognosis. We emphasize on interpretable AI in health care to understand the underlying biological mechanisms that contribute to the specific outcomes for different individuals to provide robust treatment assist for sequential decision making in the practice.

Justine Zhang

By |

I develop computational methods to study conversations. I am interested in study how conversationalists use language to do things with and to each other, and how they navigate often-challenging interactions. I’m particularly interested in settings where people have conversations on behalf of institutions, and in analyzing conversations as a window into how these institutions work in practice. Drawing on techniques from natural language processing, computational social science, and causal inference, I examine large datasets containing conversation transcripts. Past and present work has considered settings such as political discourse, mental health counseling, and law enforcement.

Additional Information

What are some of your most interesting projects?

How did you end up where you are today?

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

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

What are 1-3 interesting facts about yourself?