Projects_updated

  • Fairness-aware Recommendation
    • Fairness-aware recommendation refers to recommendation contexts in which the system has objectives related to social good: such as equity or diversity. Critics have noted that users may prefer highly-personalized “filter bubbles" in which only safe and familiar items and viewpoints appear. However, it is well understood that civil society benefits from broad discourse and diverse voices about problems and issues, and such broad awareness is much less likely in an atomized filter bubble society.
    • In educational settings, a tutorial system may have pedagogical goals aimed at providing students with a breadth of experiences, which are not compatible with a strict focus on personalizing for the student. Even in the commercial realm, cycles of self-reinforcing consumption can form that create bias towards early market entrants and established brands, and against new products, thus creating stagnation and market inefficiencies.
    • All of these problems are difficult to confront when personalization is posited as the single desirable recommendation goal. A multistakeholder approach offers the opportunity to design recommender systems with a range of objectives including such notions as equity and balance.
    • Now looking for students in: Undergraduate, Master and PhD.
    • Master students can work on this project for Capstone.

  • Functional Neural Mapping for Behavior Modeling Using Big Data Computing
    • A major goal in neuroscience research is to understand behavior at the level of neural networks. While many studies have attempted to tackle this goal, their resolution is not at the single neuron level or their scope is not extensive enough to make a concrete connection between behavior and neural networks. Caenorhabditis elegans provides clear advantages to overcome both of these challenges due to its simple nervous system and completely deciphered anatomical neural map. Moreover, C. elegans exhibits behaviors found in higher organisms, including food search behavior. In this interdisciplinary collaborative project between DePaul University and Rosalind Franklin University Medical School, we will use C. elegans to build functional networks of interneurons for food search behavior.
    • We propose to perform in-depth research and develop new, powerful, and scalable image processing, indexing and data mining methods for efficient and effective analysis-based mapping of neural networks to locomotory search behaviors. Our proposed study will work on neuron-ablated C. elegans image datasets, and focus on (1) extracting representations of movement characteristics, (2) discovering and indexing behavior patterns in large sequential image data, (3) modeling search behavior similarity based on the discovered patterns, and (4) learning functional neural networks from combinations of behavioral models. The amount of data that will be generated from this research study will be in the petabytes range, making it crucial to employ cutting edge big data computing techniques on advanced large-scale distributed systems to make this study tractable.
    • Now looking for students in: Undergraduate, Master and PhD.
    • Master students can work on this project for Capstone.

  • I3RIS: Interactive, Iterative, Integrated Radiology Image Search
    • The advancements in medical imaging technologies have generated billions of images that are digitally stored and indexed in different data repositories worldwide. Current search mechanisms and query tools used to access these images in clinical practice are text-based only and are not sophisticated enough to fulfill the types of queries that clinicians need. Leveraging the richness of the medical data, the long-term objective of this interdisciplinary effort between DePaul University and University of Chicago is to provide the most useful information, the best images, and the most relevant data sources to clinicians at the point of care.
    • Our specific goals are to design, develop, and evaluate a hybrid search engine that unlocks valuable information from onsite and online radiology data sources (in-house proprietary teaching files and publically available online peer-reviewed teaching files, radiology journals, and imaging related textbooks) to provide radiologists the most relevant information needed at the time of patient care. Our central hypothesis is that having a search mechanism that maps naturally from the user’s limited internal memory of observed cases to a wealth of examples available onsite and online would allow clinicians to make faster, more confident and accurate diagnoses by removing the innate error caused by the limits of human memory. To test the central hypothesis, we propose to 1) create a hybrid text and image distributed database by integrating radiology teaching files, textbooks, and journals, 2) extract knowledge from integrated data sources to augment medical decision making, and 3) develop a domain-specific interactive user interface with iterative query refinement.
    • Now looking for students in: Undergraduate, Master and PhD.
    • Master students can work on this project for Capstone.

  • Medical Health Informatics
    • Suicide is an alarming public health problem accounting for a considerable number of deaths each year worldwide. Many more individuals contemplate suicide. Understanding the attributes, characteristics, and exposures correlated with suicide remains an urgent and significant problem.
    • As social networking sites have become more common, users have adopted these sites to talk about intensely personal topics, among them their thoughts about suicide. Such data has previously been evaluated by analyzing the language features of social media posts and using factors derived by domain experts to identify at-risk users.
    • In this project, we automatically extract informal latent recurring topics of suicidal ideation found in social media posts. Our evaluation has demonstrated that we are able to automatically reproduce many of the expertly determined risk factors for suicide. Moreover, we have identified many informal latent topics related to suicide ideation such as concerns over health, work, self-image, and financial issues.
    • Current projects include 1) expanding this work to other mental health issues, 2) testing additional feature extracting techniques, 3) Designing procedures to acquire a robust and reliable ground truth.
    • Now looking for students in: Undergraduate, Master and PhD.
    • Master students can work on this project for Capstone.

  • Mental Health Informatics Using Unsupervised Learning Techniques
    • Suicide is a major cause of mortality in the United State and leading billion dollars loss per year. Understanding the causes and impact of this major mental health issue is a priority to public health, which will save lives and properties. With the development of social media and internet, a growing number of people willing to talk about their thoughts, situations and personal lives on social media. Therefore, posts on social media become an ideal source for studying suicide related topics.
    • The goals of this research are to 1) extract latent topics from social media posts that could be indicative of language patterns used by people experiencing certain mental health problems such as depression, and suicide ideation, plan and attempt, 2) build, test, and evaluate machine learning models for predicting users at high risk of committing suicide, and 3) create human computer interfaces and technology that can be easily used by domain experts to provide ground truth as well as validate computer-based outputs for mental health applications.
    • Now looking for students in: Undergraduate, Master and PhD.
    • Master students can work on this project for Capstone.

  • Multistakeholder Recommendation
    • Multistakeholder recommendation refers to settings in which the user is not the only party with an interest in recommendation outcomes. This shift in perspective recognizes the ways in which recommender systems can become contested territory. In such settings, the system must consider the impact that recommendations may have on different parties, and negotiate between interests that are not always aligned, a line of work that has obvious connections to game theory and microeconomics. To be most effective, a multistakeholder recommender system must incorporate multiple perspectives explicitly in its design and evaluation.
    • Now looking for students in: Undergraduate, Master and PhD.
    • Master students can work on this project for Capstone.

  • Reading Chicago Reading
    • Each year the Chicago Public Library produces such a recommendation by choosing a book around which to organize discussions and events: the well-known “One Book, One Chicago" program.
    • Together with an interdisciplinary team with expertise in literature, library science, and sociology, I have been turning the tools of predictive analytics to study the historical record of the “One Book" program. Our NEH-supported Reading Chicago Reading program is using data-intensive techniques to support librarians in choosing books for such civic events, working to predict the reception of texts from patrons across the city, and understanding the impact of promotional events on the program's success.
    • The project is unique in its integration of library circulation data, social media data, historical event data, and full-volume text mining from the HathiTrust digital library.
    • Now looking for students in: Undergraduate and Master.
    • Master students can work on this project for Capstone.

  • Recommender Systems
    • Recommender systems assist users in navigating complex information spaces and focus their attention on the content most relevant to their needs. Often these systems rely on user activity or descriptions of the content. Social annotation systems, in which users collaboratively assign tags to items, provide another means to capture information about users and items. Each of these data sources provides unique benefits, capturing different relationships. We propose leveraging multiple sources of data: ratings data as users report their affinity toward an item, tagging data as users assign annotations to items, and item data collected from an online database. Taken together, these datasets provide the opportunity to learn rich distributed representations by exploiting recent advances in neural network architectures.
    • Now looking for students in: Master and PhD.
    • Master students can work on this project for Capstone.

  • Revolutionizing Medicine with Machine Learning
    • Machine Learning is on the cliff of revolutionizing medical diagnosis. Diagnostic applications of machine learning are rapidly transitioning from the theoretical to the real-world. The transformational potential of diagnostic applications cannot be overstated from an at-home tool for early detection to an instant “second opinion” for a complex diagnostic case. Machine learning as a diagnostic tool will generate incredible efficiencies and cost savings for patients, doctors, and hospitals, and most importantly of all, it will save lives.
    • In a quest to build more trustable Computer-Aided Diagnosis (CAD) systems for lung cancer, the CDM Medical Informatics Lab and the Imaging Institute at University of Chicago have been collaborating for over a decade to build the next generation CAD system with advanced imaging analytics and reasoning capabilities that can assist in the clinical decision making process. The collaboration involves three stages of research: 1) predictive modeling for high-level diagnostic interpretation derived from low-level image data, 2) learning the human visual perception of similarity using low-level image features and expert-in-the-loop feedback, and 3) evaluating the effects of smart capabilities on traditional CAD systems and medical experts' performance.
    • Now looking for students in: Undergraduate, Master and PhD.
    • Master students can work on this project for Capstone.

  • Sciunits: Tools for conducting Reproducible Science
    • Sciunits are efficient, lightweight, self-contained packages of computational experiments that can be guaranteed to repeat or reproduce regardless of deployment issues. Sciunit answers the call for a reusable research object that containerizes and stores applications simply and efficiently, facilitates sharing and collaboration, and eases the task of executing, understanding, and building on shared work. Explore Sciunits at: http://sciunit.run
    • Now looking for students in: Undergraduate, Master and PhD.
    • Master students can work on this project for Capstone.

Interested?

If you are interested to work on any of these projects as part of your capstone project or independent study, please contact Dr. Daniela Raicu or Dr. Raffaella Settimi.