Apr 30
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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.

Apr 15
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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.

Apr 10
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Angela Violi

Angela Violi

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The Violi Lab carries out cross-disciplinary research at the intersection of nanoscience and data science. By integrating machine learning techniques with molecular simulations, the team strives to unravel fundamental scientific principles while tackling practical problems in material science, healthcare, and environmental sustainability. Their methodological toolkit encompasses various cutting-edge approaches: active learning and Bayesian experimental design to improve sample efficiency; advanced gradient boosting techniques for predictive modeling; specialized neural networks to decode protein-nanoparticle interactions; and Lasso-like algorithms for feature selection and regularization. Through this integrated approach, the lab aims to make significant contributions to both scientific understanding and technological innovation.

Apr 03
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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

Apr 03
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Johanna Mathieu

Johanna Mathieu

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My research focuses on ways to reduce the environmental impact, cost, and inefficiency of electric power systems via new operational and control strategies. I am particularly interested in developing new methods to actively engage distributed flexible resources such as energy storage, electric loads, and distributed renewable resources in power system operation. This is especially important in power systems with high penetrations of intermittent renewable energy resources such as wind and solar. In my work, I use methods from a variety of fields including control systems and optimization. I also use engineering methods to inform energy policy.

Feb 16
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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.

Jan 10
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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.

Jan 03
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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.

 

Accomplishments and Awards

Nov 27
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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.

Accomplishments and Awards

Oct 30
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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