Scaling Grants
The team is led by Ghady Haidar, Medicine. Additional team members are from the departments of Biological Sciences and Physical Therapy.
Antibiotic resistance is one of the most pressing public health threats of our time. We are working to develop new antibiotics to treat antibiotic-resistant bacterial infections like those caused by Pseudomonas aeruginosa. This project leverages bacteriophages (phages)—naturally-occurring viruses that kill bacteria—to tackle the challenge of antibiotic-resistant Pseudomonas aeruginosa infections. Since 2020, the Pittsburgh Phage Program has treated over 30 patients with phage therapy through the FDA’s single patient investigational new drug pathway. To move phage therapy forward, here we propose a phase 1 clinical trial to evaluate the safety, pharmacokinetics, pharmacodynamics, and microbiological efficacy of PseudoPhage, a cocktail identified at Pitt containing multiple phages targeting Pseudomonas aeruginosa for the treatment of ventilator-associated pneumonia caused by this pathogen. Our multidisciplinary team builds on a track record of industry-sponsored and investigator-initiated phage trials.
The team is led by Paul Ohodnicki, Mechanical Engineering & Materials Science. Additional team members are from the Departments of Civil & Environmental Engineering, Computer Science, Geology & Environmental Science, and Mechanical Engineering & Materials Science.
Abstract: The project involves application of distributed sensing combined with advanced analytics using machine learning and artificial intelligence as well as physics based modeling and digital twins to the real-time monitoring of critical infrastructure. Distributed sensing and related technologies will leverage synergies in needs and attributes of infrastructure across application domains spanning sectors (e.g. transportation and energy) and importance of considering national and regional objectives in deployment and implementation. An interdisciplinary team comprised of engineers, physical scientists, computer scientists, and geologists will develop, adapt, and apply these burgeoning concepts to exemplary infrastructure monitoring applications in transportation and energy systems. The team will build upon existing collaborations established through University of Pittsburgh Infrastructure Sensing Collaboration (UPISC) to pursue major initiatives centralized at University of Pittsburgh with regional and national partners and to formalize a new university-industry-government consortium entitled the INfrastructure Sensing for Intelligent Transportation and Energy Systems (INSITES).
Teaming Grants
The team is led by Jennifer Iriti, Learning Research and Development Center. Additional team members are from Computer Science, the Center for Teaching & Learning, English, Informatics & Networked Systems, and Teaching, Learning & Leading.
Abstract: How can emergent technologies and teaching and learning practices be co-created while centering responsible use, equity, and learning sciences principles? This initiative will establish a regional network of partners to expand the team of researchers and practitioners in the Learning Sciences Emergent Technologies (LSET) Hub. The LSET Hub, a collaborative effort of Pitt’s research and practice communities, focuses on co-creation of emergent technologies (e.g., generative AI, extended reality) and teaching and learning practices through the application of learning sciences and design-based implementation research. This effort will create a comprehensive database of Pitt emergent technology research and establish the relationships and teams necessary to pursue specific research-practice projects spanning regional PK-12 and Pitt higher education.
The team is led by Jill Demirci, Health Promotion & Development. Additional team members are from the Departments of Pediatrics, Industrial Engineering, and Human Genetics.
Abstract: Human milk is crucial for infant health, yet approximately 25% of mothers experience insufficient milk production. We aim to create an algorithmic system connecting mothers with excess frozen breast milk to those in need but ineligible for costly milk bank donor milk. The algorithm will screen out donors with breastfeeding contraindications and prioritize geographic proximity, infant sex and age, and other factors to ensure matches meet safety, nutritional, and immunological needs while enhancing access. To initiate this project, we will: 1) conduct interviews and surveys to gather insights from mothers, healthcare providers, milk banks, and insurers, addressing logistical, ethical, safety, and financial aspects, 2) integrate these data into development of a proof-of-concept algorithm match system using mathematical modeling with a dataset and test in a pilot study to evaluate small-scale feasibility and efficiency. Project outcomes will position the program for scalable implementation in health systems, contributing to the overarching goal of equitable infant nutrition.
The team is led by Juan José Mendoza Arenas, Mechanical Engineering and Materials Science. Additional team members are from the departments of Computer Science, Physics and Astronomy, and Chemical and Petroleum Engineering.
Abstract: Quantum computing is rapidly becoming a crucial technology for the next generation, with the potential for quantum algorithms to outperform classical ones in science and engineering applications. However, identifying significant real-world applications for quantum computing remains challenging. In this work, the extent to which hybrid quantum-classical algorithms optimize solutions to critical problems will be assessed. The areas of fluid dynamics and power systems will be the focus of assessment. The work will rely on variational quantum algorithms for solving differential equations and will leverage matrix product states for information encoding. The aim is to demonstrate that hybrid quantum-classical approaches are powerful tools for characterizing fluid dynamics in engineering contexts and optimizing smart grids in power systems. By bridging quantum and classical computing, this research will bring effective solutions to complex science and engineering challenges.
Priming Grants
The team is led by Andrew Dierkes, Acute & Tertiary Care. An additional team member is from the Department of Industrial Engineering.
Abstract: How can hospitals optimize nurse-patient assignments (NPAs) to improve clinical outcomes and resource utilization? This project seeks to address this critical question by exploring the impact of NPA design on patient care. By leveraging machine learning techniques to analyze a novel dataset that integrates shift-level NPA data with electronic health records and human resources profiles, we aim to develop predictive models that assess clinical outcomes such as mortality as well as resource usage metrics like hospital length of stay and readmissions. Ultimately, we will create algorithms that provide optimal nurse allocations based on both nurse and patient characteristics. This research not only aims to enhance nursing efficiency within staffing constraints but also promises to advance the field of health services research and contribute an operational strategy for hospitals faced with nursing shortages, leading to improved patient care and outcomes across hospitals.
The team is led by Haitao Liu, Chemistry. An additional team member is from the Department of Chemical & Petroleum Engineering.
Abstract: Thermal transport across liquid-solid interfaces is typically limited by energy mismatch and poor coupling between the vibrational modes of liquid molecules and phonons in solids. Recent research has shown that single-layer graphene, due to its atomic thinness, allows liquid molecules on either side of it to "feel" each other. This result suggests the possibility of achieving direct vibrational coupling across graphene and enhanced thermal transport. This project aims to test the hypothesis that graphene is "thermally transparent" in cross-membrane heat transport. We will fabricate single-layer graphene membranes sandwiched between water and toluene and measure the thermal resistance at the liquid-liquid junctions with and without graphene. If successful, this new thermal transport mechanism could significantly enhance heat exchange efficiency at the nano and microscale, with broad applications in energy and microelectronics.
The team is led by Heeyoung Lee, Health & Community Systems. Additional team members are from the Departments of Medicine and Psychiatry.
Abstract: Individuals with schizophrenia who take antipsychotic drugs (APDs) experience a 15-20 year reduction in life expectancy, partly due to APD-induced metabolic dysfunction (e.g., diabetes), which increases the risk of cardiovascular disease and mortality. Bromocriptine, an inexpensive, FDA-approved treatment for diabetes, has limited research in schizophrenia. This study aims to evaluate the safety and tolerability of bromocriptine and to explore its potential to improve these metabolic dysfunctions.
The team is led by Vikas Khanna, Civil & Environmental Engineering. Additional team members are from the Department of Civil & Environmental Engineering.
Abstract: Allegheny County’s combined sewer system is inadequate for handling wet weather events, leading to approximately 9.5 billion gallons of combined sewer overflow (CSO) and sanitary sewer overflow annually. This poses significant risks to public and environmental health, exacerbated further by climate change and increasing extreme weather events. While green-grey infrastructure addresses stormwater capture, there are significant knowledge gaps regarding the economic and environmental feasibility of treating stormwater for potable and non-potable uses. This project will use a system-level approach to assess the economic and environmental sustainability of reclaiming stormwater for beneficial uses in Pittsburgh, bringing together experts in water and wastewater technologies, environmental microbiology and public health, contaminant transport, and water treatment processes. The results will inform broader strategies for managing stormwater in Northeastern U.S. cities with CSO issues and contribute to the Civil and Environmental Engineering Department's “One Water” initiative for sustainable water systems.