Joyce Chai

Joyce Chai

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My research interests are in the area of natural language processing, situated dialogue agents, and artificial intelligence. I’m particularly interested in language processing that is sensorimotor-grounded, pragmatically-rich, and cognitively-motivated. My current work explores the intersection of language, vision, and robotics to facilitate situated communication with embodied agents and applies different types of data (e.g., capturing human behaviors in communication, perception, and, action) to advance core intelligence of AI.

Vineet Kamat

Vineet Kamat

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My group conducts research in automation and robotics to improve work processes in the construction, operation, and maintenance of civil infrastructure and the built environment. Our research has developed several licensable technologies that include visualization, perception, and modeling techniques to help on-site construction robots with autonomous decision making. We are particularly interested in exploring new methods for enabling collaborative work strategies for human-robot teams jointly performing field construction work. In addition, we are also interested in exploring methods to integrate data to support semi-autonomous mobility for people with physical disabilities in the urban built environment.

Data-Driven Co-Robotic Field Construction Work

Dimitra Panagou

Dimitra Panagou

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Dimitra Panagou’s research lies in the areas of multi-agent systems and control, with applications in multi-robot/vehicle systems. She is particularly interested in establishing safety and resilience against adversity and uncertainty for multi-robot/vehicle systems using techniques from (networked) control theory, estimation theory, and machine learning.

Mariel Lavieri

Mariel Lavieri

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Dr. Lavieri’s group is focused on creating novel modeling frameworks that utilize the rich datasets available in healthcare to personalize screening, monitoring, and treatment decisions of chronic disease patients. Her group has also created models for health workforce and capacity planning.

Anthony Bloch

Anthony Bloch

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My research interests include : Hamiltonian and Lagrangian mechanics, gradient flows on manifolds, integrable systems stability, the motion of mechanical systems with constraints, the relationship between continuous and discrete flows, nonlinear and optimal control and the control of quantum systems. I also interested in data-guided control and in particular the dynamics and control
of networks and systems arising from large sets, particularly in biological applications.

Majdi Radaideh

Majdi Radaideh

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Prof. Majdi Radaideh leads the Artificial Intelligence and Multiphysics Simulations lab (AIMS), which focuses on the intersection between nuclear reactor design, nuclear multiphysics modeling and simulation, advanced computational methods, and machine learning algorithms to drive advanced reactor research and improve the sustainability of the current reactor fleet. AIMS extensively employs data science and machine learning methods for various goals including but not limited to:
1- Development of surrogate models for expensive nuclear reactor simulations in steady-state and time-dependent modes using convolutional and recurrent neural networks.
2- Large-scale combinatorial optimization to improve the performance of the nuclear fuel inside nuclear power plants using physics-informed reinforcement learning and neuroevolution algorithms.
3- Long-short term memory and ensemble methods for anomaly detection and fault prognosis to monitor the health of the nuclear power plant components.
4- Uncertainty quantification of data-driven models with Bayesian inference and Gaussian processes.
5- Natural language processing methods to process nuclear plant maintenance and burnup records.

AIMS lab aims on bridging the gap between nuclear reactor design, nuclear multiphysics modeling and simulation, advanced computational methods, and machine learning algorithms to drive advanced nuclear reactor research and improve the sustainability of the current reactor fleet to promote nuclear power as a carbon-free energy source in order to achieve net-zero carbon emission.

Sabine Loos

Sabine Loos

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My research focuses on natural hazards and disaster information, everything from understanding where disaster data comes from, how it’s used, and its implications to design improved disaster information systems that prioritize the human experience and lead to more effective and equitable outcomes.

My lab takes a user-centered and data-driven approach. We aim to understand user needs and the effect of data on users’ decisions through qualitative research, such as focus groups or workshops. We then design new information systems through geospatial/GIS analysis, risk analysis, and statistical modeling techniques. We often work with earth observation, sensor, and survey data. We consider various aspects of disaster information, whether it be the hazard, its physical impacts, its social impacts, or a combination of the three.

I also focus on the communication of information, through data visualization techniques, and host a Risk and Resilience DAT/Artathon to build data visualization capacity for early career professionals.

Geospatial model for predicting inequities in recovery from the 2015 Nepal earthquake

Krishna Garikipati

Krishna Garikipati

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My research is in computational science and scientific artificial intelligence, including machine learning and data-driven modelling. I have applied these approaches to physics discovery by model inference, scale bridging, partial differential equation solvers, representation of complexity and constructing reduced-order models of high-dimensional systems. My research is motivated by and applied to phenomena in bioengineering, biophysics, mathematical biology and materials physics. Of specific interest to me are patterning and morphogenesis in developmental biology, cellular biophysics, soft matter and mechano-chemical phase transformations in materials. More fundamentally, the foundations of my research lie in applied mathematics, numerical methods and scientific computing.

A schematic illustrating the range of ML methods comprising the mechanoChemML code framework for data-driven computational material physics.

Davon Norris

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I try to understand how our tools for determining what is valuable, worthwhile, or good are implicated in patterns of inequality with an acute concern for racial inequality. Often, this means my work investigates the functioning and consequences of a range of scores or ratings, from the less complex government credit ratings to the extremely complex algorithmic scores like consumer credit scores.

In related work, as a part of a multi-university team of researchers, I am using administrative credit report data from one of the largest credit reporting agencies to study credit and debt outcomes for millions of consumers in the United States.