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.

Patrick Park

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Patrick’s research interest centers on the structure and evolution of large-scale human social networks. His recent work aims to address how social networks adapt in the long term to heightened uncertainty caused by sudden and often unforeseen societal shocks, such as economic busts, corporate M&As and scandals, geopolitical conflicts, and natural disasters. When an area experiences a devastating storm, for example, do people turn to friends and family or acquaintances for information and support? How does the structure of people’s networks influence their communication patterns in response to such societal shocks? How do social networks respond differently depending on the nature of the shock (e.g., political coups vs. hurricanes)? These questions hold increasing significance as societies face mounting uncertainties due to climate change and computation-driven transformations in labor markets and industries. Understanding how social networks change amid heightened uncertainty, then is key to understanding and predicting important social, political, and economic processes that are shaped by social networks, from information diffusion, political polarization, to technological innovation. Patrick investigates these questions around uncertainty and network change, first, by empirically exploring the change and recovery of interpersonal communication networks after societal shocks using social media data (e.g., Twitter) and, second, by devising computational models to theorize the macro-structural implications of the changes in individual-level communication behaviors induced by the shocks. 

Qianying Lin

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With the explosion of the volume and variety of epidemic related data, we propose an efficient, theoretically sound, and easily-implemented framework, which employs both the regularly reported infections and the asynchronous genetic samples to make inference on the epidemic parameters (eg. transmission rate) and forecast the epidemic trends and genealogical patterns.


COVID-19 Publications

S Zhao, et al. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak.  Int J Infect Dis. 2020 Mar;92:214-217. DOI: 10.1016/j.ijid.2020.01.050. Epub 2020 Jan 30.

S Zhao, et al. Estimating the Unreported Number of Novel Coronavirus (2019-nCoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak. J Clin Med. 2020 Feb 1;9(2). pii: E388. DOI: 10.3390/jcm9020388.

Q Lin, et al. A conceptual model for the outbreak of Coronavirus disease 2019 (COVID-19) in Wuhan, China with individual reaction and governmental action. Int J Infect Dis. 2020. DOI: 10.1016/j.ijid.2020.02.058.