Grand Challenges require new approaches. WID sets its sights on the biggest challenges facing humanity. Powerful new tools from data science and biology enable WID scientists, engineers, and mathematicians to seek fundamental discoveries and applications that will improve the quality of human life, fairness in society, and the health of the planet.
Our Grand Challenges, modeled after the White House's 2013 initiatives, are lofty aspirations that inspire us to push the limits of possiblity, unite us in common causes, and guide our approach to science for a better world.
Our Grand Challenges
Based on WID's research strengths, our faculty and fellows have come together to address four Grand Challenges. Our Challenges are ambitious enough to inspire us, but practical enough for us to strive for truly impactful discoveries and applications.
Keep the human brain healthy
Learn more trending_flatMitigate soil loss and climate change
Learn more trending_flatEnsure equity in the impact of data science
Learn more trending_flatBring science to people and people to science
Learn more trending_flatExplore the research groups rising to meet these challenges:
Pimentel-Alarcón Research Group
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Solís-Lemus Research Group
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Claudia Solís-Lemus (PI)
My research involves the development of statistical models to answer biological questions.
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Virtual Environments Group
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Karen Schloss (PI), Kevin Ponto (PI)
The Virtual Environments Group is a space and a place where scholars explore the connections among environment, technology, human action, experience, and visualization, with expertise in virtual and augmented reality.
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Schloss Visual Reasoning Lab
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Karen Schloss (PI)
Investigating how observers make predictions about objects and entities based on their cognitive and emotional responses to perceptual information; focusing on how people’s associations with colors influence cognitive processing in aesthetic response, judgment and decision making, and interpretation of information visualizations.
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Ponto Lab
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Kevin Ponto (PI)
Develop techniques to better the experience of virtual reality through new devices, interfaces, and techniques.
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Wright Research Group
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Stephen Wright (PI)
Numerical optimization, especially problems involving real (as opposed to integer or discrete) variables. Includes theory, algorithms, implementation and application.
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Nowak Research Group
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Robert Nowak (PI)
The NRG focuses on signal processing, machine learning, optimization, and statistics. Areas of focus include sparsity and active learning, learning graphs and networks, and interactive machine learning with humans.
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