X. Jessie Yang

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Dr. X. Jessie Yang is an Associate Professor in the Department of Industrial and Operations Engineering, with courtesy appointments at the School of Information. She is an expert in human-autonomy/robot interaction, particularly in modeling trust in human-autonomy teams. She and her team use machine learning tools to model human behaviors when interacting with autonomous and robotic agents.

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.

David Kwabi

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We study and develop electrochemical devices containing organic materials for applications in grid energy storage and chemical separations (e.g., CO2 capture and nitrogen recovery). A critical aspect of our work involves discerning the impact of chemical reactions as well as mass and charge transport processes on device-level performance metrics. To accomplish this goal, we often conduct spectroscopic measurements of electrochemical systems while they are in operation. We apply a variety of mathematical modeling techniques to the spectroscopic data, such as multivariate curve resolution and Bayesian inference/model selection, to glean useful information about molecular transformation mechanisms and kinetics. These insights are informing closed-loop discovery of new and better-performing materials.

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

Cyrus Omar

Cyrus Omar

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I lead the Future of Programming Lab (FP Lab), where we design modern user interfaces for modern programming languages. Much of how we program today is rooted in tools designed 40+ years ago, e.g. how we enter code (using simple text editing, which leads to profligate parse errors), how we validate code (using tests or impoverished type systems), how we explore code (in a slow, batched, textual manner), how we communicate change (by throwing away the edits we performed and forcing diff algorithms to guess what we did), and so on. My lab develops new programming language and editor mechanisms, starting from theoretical foundations in mathematics and building up to human interfaces.

Integrating live GUIs into programs with holes

Integrating live GUIs into programs with holes

Allen Flynn

Allen Flynn

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I study medication prescription information and work on teams that create and evaluate applications of natural language processing to medication prescription information. The main thrust of my research in pharmacy informatics focuses on automating subtasks that pertain to medication prescribing by clinicians and medication prescription review by pharmacists. In addition, I work with the Knowledge Systems Lab in the Department of Learning Health Sciences to specify model repository requirements for making AI/ML models findable, accessible. interoperable, and reusable.

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.