Aditi Verma

Aditi Verma

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I am interested in how nuclear technologies specifically and complex technologies broadly—and their institutional infrastructures—can be designed in more creative, and participatory ways that are epistemically inclusive of both lay and expert perspectives. To this end, my work focuses on developing a more fundamental understanding of the early stages of the design process to improve design practice and pedagogy, and also improve the tools with which designers of complex systems work. I am particularly interested in developing novel approaches for gathering both qualitative and quantitative data generated throughout the design processes of complex systems and using this data to improve design outcomes. In my work, I focus on three main research questions:

  1. How can a fundamental understanding of design be used to improve design practice, design tools, and engineering pedagogy?
  2. How can design processes be made more open and participatory such that epistemic plurality and inclusivity are achieved as part of the design process?
  3. How can insights from design research be applied to the designs of policies and institutions for the governance — both innovation and regulation — of nuclear technologies?

The picture depicts the Sociotechnical Readiness Level which is a framework which critiques and reimagines the traditional Technology Readiness Level Framework. As part of the new SRL framework, we are developing new metrics and indicators to measure both social and technical aspects of a technology's readiness throughout its design and development processImage: The Sociotechnical Readiness Level, which is a framework which critiques and reimagines the traditional Technology Readiness Level Framework. As part of the new SRL framework, we are developing new metrics and indicators to measure both social and technical aspects of a technology’s readiness throughout its design and development process.

What are some of your most interesting projects?

Two new projects I am most excited about are (1) developing a sociotechnical readiness level framework for the assessment of novel technologies and (2) diagnosing the causes of cost overruns in the early stages of technology design.
In the first project, we are critiquing and reimagining the traditional Technology Readiness Level framework to capture both societal and engineering aspects of a technology’s readiness and integrate them into a single framework. As part of this work, we will develop metrics and indicators for assessing a technology’s readiness across the nine levels of sociotechnical readiness.
In the second project, we are studying how decisions made in the early stages of design impact overall system cost. This work is especially crucial in a nuclear energy context because recent nuclear plant construction projects have been severely impacted by cost overruns as well as project delays. It is vitally important to diagnose the causes of these cost overruns and address them in the early stages of the design if nuclear reactors are to play a significant role in our low-carbon energy systems.

How did you end up where you are today?

I was drawn to the field of nuclear engineering because of the enormous potential nuclear technologies have for solving one of the grandest challenges of our time: access to reliable, clean, and abundant energy.

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

I hope to develop a deeper and more fundamental understanding of the design processes of complex systems that leads to more creative, robust, societally, and environmentally beneficial design outcomes.

What makes you excited about your data science and AI research?

There is an enormous opportunity to use data science and AI to study the early stages of the design processes of complex systems and develop new tools to stimulate a broader exploration of the design space and to ensure better design outcomes.

What are 1-3 interesting facts about yourself?

I love learning new languages. Depending on how you count, I speak (with varying levels of ability) six (or seven).


Research Highlights

Ken Resnicow

Ken Resnicow

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Dr. Resnicow studies tailored behavior change interventions, natural language processing of clinical encounters, analyses of large datasets related to obesity and other chronic diseases, novel designs such as SMART, MOST, and reinforcement learning, and applied complex systems and chaos theory in behavior change.

 


Accomplishments and Awards

Justine Zhang

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I develop computational methods to study conversations. I am interested in study how conversationalists use language to do things with and to each other, and how they navigate often-challenging interactions. I’m particularly interested in settings where people have conversations on behalf of institutions, and in analyzing conversations as a window into how these institutions work in practice. Drawing on techniques from natural language processing, computational social science, and causal inference, I examine large datasets containing conversation transcripts. Past and present work has considered settings such as political discourse, mental health counseling, and law enforcement.

Accomplishments and Awards

Additional Information

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?


Eric Swanson

Eric Swanson

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Most of my current work is in social and political philosophy of language and metaethics. It pays special attention to relationships between language, context, and ideology, to the particularities of speech situations, and to the norms governing language use. I’m especially interested in how the force of language can go beyond the information that is apparent to or shared amongst discourse participants.

Recently I’ve been thinking about how the above makes trouble (a.k.a. interesting but sometimes scary new things to think about) for work on artificial intelligence, artificial general intelligence, and automated content moderation and promotion.

I have continuing interests in the interfaces between language and epistemology (epistemic modals and conditionals), language and metaphysics (causal talk and the logic of causation), language and ethics (deontic modals), and on two frameworks for linguistic theorizing that bear on the above—‘constraint semantics’ and ‘ordering super­valua­tionism.’

Additional Information

What are some of your most interesting projects?

One of my recent papers argues that not saying a particular thing can generate a conversational implicature — a means of conveying a message without explicitly committing oneself to that message. Debates about such ‘omissive implicatures’ are especially common in online language use.

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?

Contemporary work in AI interacts with countless interesting and pressing philosophical questions.

What are 1-3 interesting facts about yourself?

Anne Draelos

Anne Draelos

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My lab’s research focuses on understanding computation in large-scale neural circuits through adaptive perturbations and real-time inference. We develop statistical machine learning algorithms to adaptively build models of neural and behavioral data online, and use them for understanding the mapping between multidimensional neural stimulations and complex behavioral outcomes. We emphasize data-driven Bayesian approaches suited for real-time prediction of latent neural and behavioral dynamics.

Additional Information

How did you end up where you are today?

My academic training began in both physics and computer science. My masters work in electrical and computer engineering focused on modeling interconnected resistive systems, and I did my doctoral work in experimental condensed matter physics, focused on the interplay between various quantum phenomena in two-dimensional materials. I am most interested in methods for dissecting complex networked systems, and I ultimately decided to make the switch to neuroscience to study arguably the most complex network around: the brain.v

My postdoctoral work focused on automated neural circuit dissection in larval zebrafish, where I designed both mathematical and software tools for experimental integration of machine learning algorithms. As an Assistant Professor, my lab is focused on leveraging increasingly rich behavioral data (in e.g., mice, monkeys) alongside neural data in streaming contexts to causally relate perturbations of neural function and resultant outcomes in latent behavior spaces. My ultimate goal is to bridge the gap between algorithm and implementation, using adaptive methods as a new design language for neuroscience experiments.

What makes you excited about your data science and AI research?

One of the reasons I decided to move into neuroscience as my application domain was the relatively recent data explosion in the field. We can now record from tens of thousands up to a million neurons simultaneously, at quite high-resolution in time. The field is also increasingly focused on naturalistic and effectively non-repeatable events as behavioral metrics. Both of these items make for incredibly complex and rich problems for neuroscience, which is why I think data science and AI have amazingly interesting roles to play there.


Accomplishments and Awards

Stacy Rosenbaum

Stacy Rosenbaum

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I am a biological anthropologist who studies the ecological causes and evolutionary consequences of social behavior. Humans and many other animals develop myriad social relationships across the course of their lifetimes. My lab members and I study how these relationships develop and are maintained, what their consequences are for participants, and how individual relationships impact evolutionary dynamics. I also study how relationships and physiology impact one another, and how their interactions affect health, longevity, and reproductive success. My primary field and laboratory research focuses on a wild population of endangered mountain gorillas in Rwanda that has been monitored for more than 50 years. In addition to studying gorillas, I also work on complementary research questions about human and non-human primate health, evolution, and sociality using the Cebu Longitudinal Health and Nutrition Survey (a long-term study of humans in Cebu, the Philippines), and data collected for the Amboseli Baboon Research Project, in southern Kenya. My research program integrates behavioral, demographic, genetic, ecological, and physiological data to gain a richer understanding of the evolution of mammals generally, and the hominid lineage specifically.

Preparing samples at a field lab in Rwanda

Additional Information

What are some of your most interesting projects?

The things that happen to us early in our lives often have major impacts on our health and wellness many years later. Interestingly, this phenomenon does not appear to be unique to humans; similar effects are observed in many species. My colleagues and I are working to understand why and how this happens. Using data from long-term primate field sites, we are examining the connections between negative experiences during the nonhuman primate equivalent of childhood, and the animals’ longevity, health, and social relationships as adults. One of the interesting challenges for this project is figuring out how to define and measure ‘health’ in other animals–and in particular, in animals that we can’t touch due to ethical, safety, and conservation concerns. Advances in non-invasive physiological research have improved our ability to determine what is happening inside their bodies without the aid of (e.g.) blood or tissue.

How did you end up where you are today?

As a child, I was endlessly fascinated with other animals and the way they behaved. Everyone, including me, assumed that I would be a veterinarian when I grew up, but I was never excited about the idea of practicing medicine. In college, I accidentally stumbled across a class on primate behavior, and fell completely in love with the subject. I was fortunate to have faculty mentors who encouraged my interest in research, and 20+ years later, now I am working to help others establish their own research careers. Having a job that involves asking and answering questions about what we do (and don’t!) have in common with other species is a dream come true.

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.

Peter Song

Peter Song

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My research interests lie in two major fields: In the field of statistical methodology, my interests include data integration, distributed inference, federated learning and meta learning, high-dimensional statistics, mixed integer optimization, statistical machine learning, and spatiotemporal modeling. In the field of empirical study, my interests include bioinformatics, biological aging, epigenetics, environmental health sciences, nephrology, nutritional sciences, obesity, and statistical genetics.

Thuy Le

Thuy Le

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Dr. Le is an assistant research scientist at the University of Michigan Department of Health Management and Policy. Dr Le is also a member of the UM/Georgetown TCORS Center for the Assessment of Tobacco Regulations (CAsToR). Dr. Le is interested in mathematical modeling for cancer- and tobacco-related problems, and machine-learning applications in tobacco regulatory science. Dr. Le has developed mathematical models to evaluate the benefits and harms of breast cancer mammography and predict the number of white blood cells during acute lymphoblastic maintenance therapy in children. Dr. Le’s recent work focuses on employing mathematical models to quantify the burden of menthol cigarettes on public health and estimate the smoking cessation rate. Dr. Le is working on applying machine learning techniques to predict and understand smoking behaviors.

Photograph of Alison Davis Rabosky

Alison Davis Rabosky

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Our research group studies how and why an organism’s traits (“phenotypes”) evolve in natural populations. Explaining the mechanisms that generate and regulate patterns of phenotypic diversity is a major goal of evolutionary biology: why do we see rapid shifts to strikingly new and distinct character states, and how stable are these evolutionary transitions across space and time? To answer these questions, we generate and analyze high-throughput “big data” on both genomes and phenotypes across the 18,000 species of reptiles and amphibians across the globe. Then, we use the statistical tools of phylogenetic comparative analysis, geometric morphometrics of 3D anatomy generated from CT scans, and genome annotation and comparative transcriptomics to understand the integrated trait correlations that create complex phenotypes. Currently, we are using machine learning and neural networks to study the color patterns of animals vouchered into biodiversity collections and test hypotheses about the ecological causes and evolutionary consequences of phenotypic innovation. We are especially passionate about the effective and accurate visualization of large-scale multidimensional datasets, and we prioritize training in both best practices and new innovations in quantitative data display.