Alexander Rodríguez

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Alex’s research interests include machine learning, time series, multi-agent systems, uncertainty quantification, and scientific modeling. His recent focus is on developing trustworthy AI systems that can offer insightful guidance for critical decisions, especially in applications involving complex spatiotemporal dynamics. His work is primarily motivated by real-world problems in public health, environmental health and community resilience.

Tian An Wong

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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.

 


Research Highlights

Ying Xu

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Xu’s research is focused on the educational applications of artificial intelligence, in particular, natural language processing and speech technologies. She explores how these conversational technologies play the role of social partners or learning companions for children, and leverages AI to empower teachers to co-create learning resources to support their instructional goals. In addition, Xu’s research also aims to identify and actively challenge biases inherent in AI technologies used for educational purposes, with the goal of making these technologies more responsive and responsible to children, parents, and teachers from diverse backgrounds. To carry out her research, Xu closely collaborates with national media producers, including PBS KIDS and Sesame Workshop, as well as industrial partners and local community organizations. Her work has been supported by funding from the National Science Foundation, Schmidt Futures, and the Corporation for Public Broadcasting.

What are some of your most interesting projects?

Most of my work has been focused on partnering with public media to explore how AI can facilitate more active and educationally beneficial ways for children to engage with digital media. For instance, I collaborated with PBS KIDS to develop interactive television shows that allow children to talk to their favorite characters as they watch STEM-related programs. Think about children spending nearly two hours every day watching television. And considering that public media programs are valuable and also accessible learning resources, especially for children from less privileged backgrounds. If we could transform these hundreds of hours of screentime into active STEM learning experiences, that could have profound implications. We’ve carried out multiple studies to test whether these interactive videos indeed help children learn. One consistent finding is that when having interactions with the media character, children comprehend the science concepts better and are also more motivated to think about science problems than the children who watched the broadcast version that does not have the AI-assisted interactions. I remember testing this interactive program with preschoolers and observing their enthusiastic conversations with Elinor, which is the main character of a show. We also found that, when children used our interactive videos at their homes, their parents were more likely to participate in the discussion with their children. This heightened parent involvement could potentially have lasting impact on children’s STEM learning in the long run. With the support from the National Science Foundation and the Corporation for Public Broadcasting, we are working on making our interactive television shows publicly available on PBS KIDS platforms.

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

I hope that my research can clarify some of our questions regarding how AI might impact child development. Specifically: How do children interact with, perceive, and learn from conversational technologies or non-human entities in general? Can these technologies actually become social partners for children? Ultimately, I hope my research can unpack the complex interplay among children, their social contexts, and technology. Only then will we be able to harness the unique learning experiences conversational agents can provide and ensure that this technology is integrated into children’s existing social contexts and relationships in ways that enhance their development.


Accomplishments and Awards

Merve Hickok

Merve Hickok

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Merve will strengthen MIDAS effort to develop best practices and training for the responsible use of data and AI in academic research, strengthen large U-M grant proposals’ responsible data and AI components, and provide insights on AI policy and regulatory priorities to help bridge research with applied work. 

Merve Hickok is the founder of AIethicist.org. She is a globally renowned expert on AI policy, ethics and governance. Her contributions and perspective have featured in the Guardian, CNN, Forbes, Bloomberg, Wired, Scientific American, Politico, Protocol, Vox, The Economist and S&P. Her research, training and consulting work focuses on the impact of AI systems on individuals, society, public and private organizations – with a particular focus on fundamental rights, democratic values, and social justice. She provides consultancy to C-suite leaders, and training services to public and private organizations on Responsible AI development, due diligence and governance. She also teaches data ethics at University of Michigan, and serves as a Board member in multiple organizations.

Merve is the President and Research Director at Center for AI & Digital Policy, deeply engaged in global AI policy and regulatory work. The Center educates AI policy practitioners and advocates across 60+ countries, advises international organizations (such as European Commission, UNESCO, the Council of Europe, OECD).

Merve has provided testimony to the US Congress, State of California Civil Rights Office, New York City Department of Consumer and Worker Protection, Detroit City Council, and many global organizations interested in AI policy and ethics. 

Merve also works with several non-profit organizations globally to advance both the academic and professional research in this field for underrepresented groups. She has been recognized by a number of organizations – most recently as one of the 100 Brilliant Women in AI Ethics™ – 2021, and as Runner-up for Responsible AI Leader of the Year – 2022 (Women in AI).

Previously, Merve held various senior roles in Fortune 100 companies for more than 15 years.

Cam McLeman

Cam McLeman

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My research interests lie in the application of mathematical tools to machine learning models, e.g., using tools from graph theory and stochastic processes to study graphical neural networks, and conversely, the application of artificial intelligence to mathematical proofs, e.g., automated theorem-proving and theorem-generation. With IDEAS, I also work to use more standard applications of machine learning models to solve problems for groups who traditionally lack access to data science expertise.

My doctoral training was in algebraic number theory, and one of the boasts of number theory is that you are required to use tools from every discipline in mathematics to understand all of its facets. This brought me in contact with graph theory both in the abstract and in the applied setting of Markov chains and stochastic processes, and using these ideas to model evolutions of systems in natural settings. Most recently, the dynamic updating of stochastic processes on graphs is very similar in spirit to the training of many models of neural networks, and exploring the symbiosis between these two sets of ideas has been a driver of my recent research.

In IDEAS, we are excited about taking the reams of student and faculty expertise and research at the University of Michigan and using it “for the people” — finding ways of furthering the goals of small businesses or local community groups that do not have the resources to have a data scientist on staff. On the research front, I personally am very excited to see how the study of mathematics evolves as generative AI models meet formal theorem-proving systems.

Yan Chen

Yan Chen

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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.


Accomplishments and Awards

David Williams

David Williams

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I have several areas of study that touch on the fields of Data Science.

First I am the UM PI of PCORnet a national network of over 80 institutions that support clinical research. PCORnet possesses a common data model allowing for the harmonization of the electronic health record across the network. The common data model is helpful in cohort discovery, development of computable phenotypes, the study of rare diseases, and applications of machine learning for identifying patterns in disease and health care services that can help to form better models of precision care.

My second area of interest is in the use of big data to support behavioral change. PainGuide is a digital pain self-management program developed at UM that offers a variety of evidence-based methods for improving and managing pain. User data can inform AI algorithms to refine content and recommendations for the participants so as to personalize care and improve outcomes.

Nishil Talati

Nishil Talati

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I am a research faculty at the Computer Science and Engineering department at University of Michigan. I work with a group of talented PhD students on computer architecture, compiler techniques, and software engineering. My group focuses on developing novel software and hardware solutions to optimize large-scale data intensive worklods (e.g., graph traversals).

I earned my PhD degree in CSE from University of Michigan, Ann Arbor, USA, master’s degree in EE from Technion – Israel Institute of Technology, Haifa, Israel, and an undergraduate degree in EEE from BITS Pilani, Goa Campus, Goa, India.

Yanna Krupnikov

Yanna Krupnikov

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Yanna Krupnikov uses large survey datasets to consider political attention and political expression.


Accomplishments and Awards


Research Highlights

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

Runzi Wang

Runzi Wang

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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.

 


Accomplishments and Awards