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Bernardo Modenesi

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The labor market is a setting increasingly disrupted by AI (both in allocation of wages and of jobs) and yet understudied in the ethical AI research space. My research agenda has been focused on the combination of unsupervised learning methods, from network theory, and discrete choice tools, in order to improve the understanding of labor market dynamics and consequently promote evidence for oversight and regulation towards labor market fairness.

AI also shapes the lives of households through mortgages. I have been also interested in exploring interpretability and fairness questions related to AI automated decisions in the mortgage industry, in partnership with the Rocket Companies. In addition to topics related to the nature of the mortgage decision algorithms, I also plan to explore the impact of mortgage decisions in opportunities in life.

Elyse Thulin

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My research focuses on applications of computational methods to better understand human behaviors. One of my main projects applies natural language processing methods to examine interactions in an online substance use recovery group to better understand substance use recovery pathways, mental health, and social relationships.

Maryam Bagherian

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My background is in applied mathematics and my primary research interest centers on developing algorithms and methodologies for data science using machine learning. More specifically, I am focused on developing algorithms for multidimensional multimodal big data which find primary application in medicine yet it is generalizable to other branches where bid data emerge. 

My current research focuses on online tensor recovery methods (i.e. complication and decomposition) using simultaneous auxiliary information. They are mainly designed for multi-omic data (e.g. spatial transcriptomic data, genomic data and etc.).

The proposed methods will have collateral benefits for the scientific community and for the diagnosticians. The former may investigate new approaches and the latter may utilize these methods with the purpose of developing online/user-friendly platforms for the end-users. 

Stephen Ajwang

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Stephen Ajwang is a Tutorial Fellow in the Department of Informatics and Information Science at Rongo University, Kenya, where he also received his MSc in Information Technology. He is also pursuing a PhD in Information Technology. His research interest lies in the realm of Big data analytics for climate smart agriculture.

The increasing volume of data and the availability of advanced technologies such as machine learning and big data analytics have revolutionized the way data is captured, processed, stored and mined. Accordingly, almost all facets of everyday life have applied these technologies to enhance efficiency and increase productivity through creation of seamless systems that are intuitive and capable of providing real-time, high-quality, and accessible data to facilitate decision making. It is on this basis that I intend to work on a project on Leveraging Big Data Analytics for Climate Smart Agriculture. The goal of this project is to develop a data lake and an information system which integrates analytics and machine learning to provide appropriate and accessible climate-based information.

efrén cruz cortés

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efrén studies the way algorithms reproduce bias and discrimination. Automated procedures are often designed to mimic the historical data humans have generated. Therefore, unintendedly, they have learned to discriminate based on class, race, gender, and other vulnerable groups. Such a phenomenon has serious consequences, as it may lead to furthering economic inequality, depriving the poor of resources, over-incarceration of people of color, etc. efrén’s goal is to understand the dynamics of the system the algorithm belongs to and assess which structural interventions are the best actions to both avoid discrimination and accomplish the desired goal for the population of interest.

Dan Li

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Intelligent systems behaviors, usually concealed, are almost everywhere nowadays: human-like virtual interactions, responsive financial systems, flexible traffic allocation, and more. Beneath the surface, intelligent systems encompass the developments of learning, abstraction and inference, of which large amounts of data are the core. My research focuses on developing online-data-driven approaches to support theoretical and algorithmic foundations of real-time intelligent behaviors of systems. Directions include safe planning, robust operation and as well as reliable anomaly detection.

Blair Winograd

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I will be working with the University of Michigan M-Write initiative that incorporates writing-to-learn pedagogies into university wide gateway STEM courses.  Writing-to-learn engages students by asking them to write about science topics, to interact with one another through peer review, and to learn through a revision process. While research widely supports the benefits of writing and peer review in the learning process, writing in large gateway courses amasses a corpus of essays for which educators cannot quickly provide feedback.  The M-Write initiative strives to build a digital toolkit supporting the application of writing-to-learn pedagogies at scale. I am working with M-Write to combine conceptual writing prompts, automated peer review, natural language processing, and automated personalized feedback to create an infrastructure for writing at scale. This will expand the use of content-focused writing activities to engage students in course material and thereby increase their comprehension of key concepts.

Edgar Franco Vivanco

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I am a political scientist with a focus on Latin America. I am a collaborator with the Poverty, Governance, and Violence Lab at Stanford University, and with the Digging Early Colonial Mexico project at the University of Lancaster.
In my research agenda, I explore how colonial-era institutions and contemporary criminal violence shape economic under-performance. My book project Strategies of Indigenous Resistance and Assimilation to Colonial Rule examines the role that Indigenous groups have played in the state-building process of the region since colonial times. As a MIDAS postdoctoral fellow, I plan to advance this project by implementing machine learning techniques to analyze thousands of colonial court records and maps, some of them dating from the 16th century.
My examination of contemporary development challenges focuses on criminal violence and policing. In this collaborative project (with Prof. Beatriz Magaloni), I draw on extensive fieldwork in Rio de Janeiro, Brazil, to explore the problems of crime, social order and policing. I study the differentiated effects of state interventions against organized criminal groups. During my tenure as a MIDAS fellow I plan to implement text analysis algorithms on a dataset of anonymous calls reporting criminal activity.

Maria Han Veiga

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The field of computational fluid dynamics (and more broadly, computer simulations) gives us tools to help understand and study scientific/engineering problems without the need to run many costly, real life experiments. However, because the mathematical models underlying these simulations are (often) simplifications of the real processes of interest, their modelling/predictive power depends on the simulation’s resolution, and time or spatial scales of the problem of interest. During my MIDAS Postdoc, I will work towards developing techniques for multi-scale modelling through assimilation of experimental data (to account for the simplifications of the models), and towards developing machine learning techniques compliant with a set of constraints (for example, physics laws).

Elyas Sabeti

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My primary focus is on developing foundational theory and methodology for data science using information theory, machine learning and signal processing. More specifically, my data science research aims to develop theory and algorithms for analysis of medical Big Data using Data In Motion with applications in Digital Health. In particular, my planned research objective is to design robust and validated clinical, physiologic, cellular, and genomic predictive of infection, as defined by viral shedding. In particular, we will identify parameters that predict a person’s contagiousness at the earliest possible time following exposure using genomic data and real-time physiological signals recorded by wearables (Empatica, Fitbit, UnitedHealth). As such, we design novel approaches tailored to complex data (high-dimensional, missing values, time-series, multi-modality) and identify low dimensional biological signatures characterizing the host response of individuals to virus inoculation. At the completion of this study we will have developed a model for contagion that will have significant military and public health impact, since soldiers as well as the general public pose infectious risks to those around them. The data generated will have collateral benefit for the scientific community investigating host-pathogen interactions and for the diagnostics and pharmaceutical communities for development of platforms to diagnose and treat pre-symptomatic infectious disease.