User Modeling and Adaptive Support for MOOCSUser Modeling and Adaptive Support for MOOCS
Massive open on-Line courses (MOOCS) have great potential to innovate education, but suffer from one key limitation typical of many on-line learning environments: lack of personalization. Intelligent Tutoring Systems (ITS) is a field that leverages Artificial Intelligence and Machine Learning to devise educational tools that can provide instruction tailored to the needs of individual learners, as good teachers do. In this project, Drs. Conati and Roll aim to apply some of the concepts and technique from ITS research to MOOCS.
Using text analysis for chronic disease management
The diagnosis, management, and treatment of chronic diseases (e.g., diabetes, chronic obstructive pulmonary diseases, and heart failure) have traditionally been focused on longitudinal histories and physical examinations as primary tools of assessment, and augmented by laboratory testing and imaging. Equally important to history taking and physical examinations is the objective assessments and understanding of the contribution of the patients' states of mind to their disease states. This is historically only documented qualitatively but highly challenging to measure quantitatively.
Application of deep learning approaches in modelling cheminformatics data and discovery of novel therapeutic agents for prostate cancer
The recent explosion of chemical and biological information calls for fundamentally novel ways of dealing with big data in the life sciences. This problem can potentially be addressed by the latest technological breakthroughs on both software and hardware frontiers. In particular, the latest advances in artificial intelligence (AI) enable cognitive data processing at very large-scale by means of deep learning (DL).
A platform for interactive, collaborative, and repeatable genomic analysis
Computer systems – both hardware and software – currently represent an active barrier to the scientific investigation of genomic data. Answering even relatively simple questions requires assembling disparate software tools (for alignment, variant calling, and filtering) into an analytics pipeline, and then solving practical IT problems in order to get that pipeline to function stably and at scale. This project will employ a whole system approach for providing a framework for genomic analysis.
From heuristics to guarantees: the mathematical foundations of algorithms for data science
Many of the most successful approaches commonly used in data-science applications (e.g., machine learning) come with little or no guarantees. Notable examples include convolutional neural networks (CNNs) and data-fitting formulations based on non-convex loss functions. In both cases, the training procedures are based on optimizing over intractable problems.
Modeling multiple types of "omics" data to understand the biology of human exposure to pollution and allergens
Inhaled environmental and occupational exposures such as air pollution and allergens are known to have a profound effect on our respiratory and immunological health. This collaborative project seeks to better understand how the human body responds adversely to these perturbants by developing and applying new computational models for analyses of integrated molecular data sets, collectively known as 'omics profiling (e.g., genomics, proteomics, metabolomics, epigenomics, transcriptomics, and polymorphisms).
Data science over graphs, streams, and sequences: From the analysis of fake news to prediction and intervention
Fake news and misinformation have been a serious problem for a long time and the advent of social media has made it more acute, particularly in the context of the 2016 U.S. Presidential election. This illustrates how social networks and media have started playing a fundamental role in the lives of most people--they influence the way we receive, assimilate, and share information with others.