Projects Available for Student Participation
AI Models
Professor Jimeng Sun's research interests focus on developing and evaluating AI-based clinical prediction models, with the ultimate goal of improving patient outcomes and health care delivery. Additionally, he is working on developing and validating generative AI models, such as large language models, for use in clinical decision support, as well as exploring their potential to assist with clinical trial design and optimization. By leveraging the power of AI, he hopes to contribute to the ongoing efforts to improve patient care and promote better health outcomes.
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Survey Design & Resistance Development
Professor Rebecca Smith is designing surveys to inform training courses around ticks and tick-borne diseases, and trying to understand barriers to diagnosis of vector-borne disease. She is also working on understanding antimicrobial decision-making and how it might relate to resistance development.
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A Field Experiment to Evaluate the Efficacy of Convenient Health Kiosks
The goals of Professor Sridhar Seshadri's project are to: 1. Define the requirements of health kiosks from a user perspective. We will employ focus groups and user surveys to define the requirements and functionalities of the kiosk. 2. Second, we will design a prototype kiosk based on the requirements outlined in the step 1. 3. Finally, we will conduct an experiment to validate the efficacy of the kiosk in improving preventative care. Also, the research team will collect feedback to improve the design of the kiosk. The work is intended to lead to large-scale development and deployment of health kiosks in rural and other underserved areas, possibly through a subsequent larger grant.
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Investigating the Impact of Insurance Plans on Variability and Inequity in Patient Care: An Analytical Study Using Transactional Data from a Hospital
Professor Sridhar Seshadri proposes the following aims as part of this project. • Are there significant variations in insurance-guided allowable limits for specific conditions? How do these limits vary across insurance and across disease conditions? Do these variations impact specific patient groups from different socio-economic conditions and specific disease conditions more intensely than others? • What is the impact of such variations on the treatment process and clinical outcomes for different patient groups and disease conditions? We measure impact by the treatment process measures (such as procedures done/testing and detection of disease conditions/admission decision/ discharge decisions), and the resulting clinical outcome measures (such as readmission). We will define the measures more specifically in consultation with healthcare providers to focus on specific conditions. • Finally, what socio-economic and demographic factors are associated with adverse clinical outcomes if any? What is the long-term cost of the inequity in healthcare access on specific patient groups and the overall healthcare system?
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Automated Screening Tools
Health Innovation Professor Mary Pietrowicz has multiple active research projects that aim to provide automated screening tools for a variety of conditions in psychiatry, speech disorders, and upper-GI disorders. As an example, the psychiatry work is end-to-end, and begins with primary data collection and curation of a research corpus focused on audio-video recordings, performative tests, and screening instrument data. This is an original dataset that exceeds the content of the medical record and offers novel opportunities for data science and capstone projects. She is particularly interested in the exploration and creation of machine/deep learning models that can screen for disorders using this dataset. Examples of projects in this space might include design of an automated data collection tool; building one or more models to detect a disorder using speech, language, movement, or gesture data; or building tools that automate the data analysis process. Students could also begin work in exploring creativity as a health mirror.
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Lab Research
Students could participate in sensory studies with clinical populations, in survey studies that look at eating and drinking behavior, or contribute to writing reviews on topics related to research in Professor M. Yanina Pepino's laboratory. For more info on the research done in her laboratory, please see: https://publish.illinois.edu/pepinolab/
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Microbubble Contrast Agents
These agents are gas-filled microbubbles with thin shells and can provide contrast in the vasculature. However, these microbubbles are also being used to produce therapeutic effects in humans. Understanding how these microbubbles react to ultrasound insonification is critical to future developments. Professor Michael Oelze hypothesizes that our novel super-resolution beamforming technique can image the oscillations of microbubbles at a high sampling rate. Professor Oelze would like some students to become involved in the project of characterizing microbubble motion. Students would learn how to operate research ultrasound devices, some simple beamforming techniques, how ultrasound interacts with microbubbles and how to verify these oscillations with a high-speed camera.
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Medical Devices
Research opportunities with Professor Deana McDonagh: Enhancing patient experience within the hospital; re-imagining a mundane disconnect within the hospital experience that could be improved with design intervention.
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Social Determinants of Health
Professor Catherine Blake's lab is currently focused on how to quantify how social determinants of health impact the endocrine system. For example, foods with estrogen-like properties and chemicals such as Bisphenol A (BPA) can impact the biological mechanisms involved in cancer. By better quantifying risk from these factors, we can put preventative strategies in place and adjust treatment plans accordingly.
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HOrmone and NEurological Study of Texting Youth project, HONESTY project, and Mixed Methods COVID-19 Study
Currently, six papers based on the HONESTY and COVID-19 data are underway. These papers are at various stages of development and provide opportunities for CI MED student involvement and co-authorship. Taken together, Professor Jacinda Dariotis' work on risk-taking determinants and behaviors has shown that impulsivity proclivity, perceived likelihood of consequences (e.g., unintended pregnancies, STIs), level of attraction, sexual networks, and stress reactivity are associated with risk-taking behaviors. Some factors promote risk-taking (e.g., low perceived risk; impulsivity; discounting) while other factors protect against risk (e.g., low attraction; emotional regulation; cognitive control) and these factors are characteristic of some people and not others.
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Development of Machine Learning Approaches to Segment Anatomy from Medical Images
Project lead: Professor Brad Sutton
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Development of Pipelines to Transform Medical Imaging Data into Virtual Reality Objects for Education and Presurgical Planning
Project lead: Professor Brad Sutton
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Application of New Analysis Methods for Functional and Anatomical Imaging Data
Project lead: Professor Brad Sutton
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Examination of Functional Brain Imaging Data to Identify Impact of Interventions
Project lead: Professor Brad Sutton
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Conduct a Literature Review on Surface Treatments Bone Implants for Improved Biocompatibility, Osteointegration, and Antibacterial Properties
Project Lead: Professor Iwona Jasiuk
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Study Bone Quality Using Machine Learning, Microindentation, and Literature Review
Project Lead: Professor Iwona Jasiuk
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Characterize Biological Materials Using Microscopy and Mechanical Testing, Literature Review
Project Lead: Professor Iwona Jasiuk
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Design Impact-Resistant Materials for Scaffolds, Implants, Helmets, and Other Applications
Project Lead: Professor Iwona Jasiuk
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3D Print and Analyze Architectured Materials for Implants and Multi-Material Composites for Medical and Engineering Applications
Project Lead: Professor Iwona Jasiuk
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Single-Cell Analysis of Targeted Transcriptome Predicts Drug Sensitivity of Single Cells within Human Myeloma Tumors
Project Lead: Ujjal Kumar Mukherjee
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A Gene Expression Signature Distinguishes Innate Response and Resistance to Proteasome Inhibitors in Multiple Myeloma
Project Lead: Ujjal Kumar Mukherjee
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Product Recall Decisions in Medical Device Supply Chains: A Big Data Analytic Approach to Evaluating Judgment Bias
Project Lead: Ujjal Kumar Mukherjee
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Evaluation of Reopening Strategies for Educational Institutions during COVID-19 through Agent-Based Simulation
Project Lead: Ujjal Kumar Mukherjee
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Robot-Assisted Surgical Care Delivery at a Hospital: Policies for Maximizing Clinical Outcome Benefits and Minimizing Costs
Project Lead: Ujjal Kumar Mukherjee
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Delivering Long-Term Surgical Care in Underserved Communities: The Enabling Role of International NPOs as Partners
Project Lead: Ujjal Kumar Mukherjee
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secDrug: A Pipeline to Discover Novel Drug Combinations to Kill Drug-Resistant Multiple Myeloma Cells Using a Greedy Set Cover Algorithm and Single-Cell Multi-Omics
Project Lead: Ujjal Kumar Mukherjee
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Integrating Pharmacogenomics Data-Driven Computational Drug Prediction with Single-Cell RNAseq to Demonstrate the Efficacy of a NAMPT Inhibitor against Aggressive, Taxane-Resistant, and Stem-like Cells in Lethal Prostate Cancer
Project Lead: Ujjal Kumar Mukherjee
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Epidemic Modeling, Prediction, and Control
Project Lead: Ujjal Kumar Mukherjee
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Major Risk Factors Associated with Severe COVID-19 Outcomes in Patients with Multiple Myeloma: Report from the National COVID-19 Cohort Collaborative (N3C)
Project Lead: Ujjal Kumar Mukherjee
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Sample Average Treatment Effect on the Treated Analysis Using Counterfactual Explanation Identifies BMT and SARS-CoV-2 Vaccination as Protective Risk Factors Associated with COVID-19 Severity and Survival in Patients with Multiple Myeloma
Project Lead: Ujjal Kumar Mukherjee
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Not All COVID-19 Waves Are Similar: Origins, Detection and Mitigation Strategies for Simultaneous Waves
Project Lead: Ujjal Kumar Mukherjee
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Cardiovascular Imaging Using Multimodal Approaches that Combine Various Imaging and Non-Imaging Technologies
Professor Wawrzyniec L. Dobrucki specializes in developing innovative imaging strategies to monitor therapeutic angiogenesis. To accomplish this, he employs novel multimodal probes, such as multimeric RGD-containing peptide constructs. Additionally, researchers evaluate the effectiveness of new therapies, including nanoparticle drug-delivery systems and transplanted cell homing and survival, using reporter gene imaging techniques. This research integrates molecular imaging with imaging physiology, such as perfusion and hypoxia, as well as anatomy.
Hedhli J, Kim M, Knox HJ, Cole JA, Huynh T, Schuelke M, Dobrucki IT, Kalinowski L, Chan J, Sinusas AJ, Insana MF, Dobrucki LW. Imaging the Landmarks of Vascular Recovery. Theranostics. 2020 Jan 1;10(4):1733-1745. doi: 10.7150/thno.36022. PMID: 32042333; PMCID: PMC6993245.
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Cancer Imaging Using Multimodal Approaches that Combine Various Imaging and Non-Imaging Technologies
Professor Wawrzyniec L. Dobrucki's project utilizes quantitative PET-optical imaging to assess the expression of the receptor for advanced glycation end-products (RAGE). Our aim is to identify early markers of the transition from indolent to aggressive cancer. Professor Dobrucki's lab also investigates the impact of diet on cancer initiation and progression and evaluates the effectiveness of anti-RAGE therapies in prostate and breast cancers. To accomplish this, his team employs a combination of imaging techniques that assess molecular events, physiology, and metabolism.
Konopka CJ, Woźniak M, Hedhli J, Siekierzycka A, Skokowski J, Pęksa R, Matuszewski M, Munirathinam G, Kajdacsy-Balla A, Dobrucki IT, Kalinowski L, Dobrucki LW. Quantitative imaging of the receptor for advanced glycation end-products in prostate cancer. Eur J Nucl Med Mol Imaging. 2020 Oct;47(11):2562-2576. doi: 10.1007/s00259-020-04721-1. Epub 2020 Mar 12. PMID: 32166512.
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When Algorithmic Predictions Use Human-Generated Data: A Bias-Aware Classification Algorithm for Breast Cancer Diagnosis
Learn more about Professor Mehmet Eren Ahsen's research: https://doi.org/10.1287/isre.2018.0789
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Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
Learn more about Professor Mehmet Eren Ahsen's research: doi:10.1001/jamanetworkopen.2020.0265
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COSIFER: A Python Package for the Consensus Inference of Molecular Interaction Networks
Learn more about Professor Mehmet Eren Ahsen's research: https://doi.org/10.1093/bioinformatics/btaa942
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