Spring 2023

Global Sustainability

  • Biowall for WL Public Library
    William Hutzel, Dhanurja DeSilva

  • Investigation of processability and mechanical properties of compression molding recycled fiber reinforced thermoplastic composite
    Garam Kim, Byron Pipes

  • Thermal Conduction in Advanced Composites for Electronics Packaging
    Amy Marconnet, Nik Chawla

  • Renewable Energy and Water Technologies
    David Warsinger, Sultan Alnajdi

Global Security

  • Applications of Quantum Computing in High Energy Physics
    Andy Jung

  • Control My News Feed: Exploring How End Users Interact with and Manipulate Social Media Recommendation Systems
    Tianyi Li

  • Towards Prediction of A User's Identity from Missing Biometric Data from IoT Devices and Understanding Associated Risks
    Sudip Vhaduri

  • Semiconductors Supply Chain: Challenges and Lessons Learned
    Tho Le, Son Young-Jun

Global Health

  • Investigating Explainability of ML Model Decisions for Injury Surveillance
    Romila Pradhan, Gaurav Nanda

  • SHH Study 2.0
    A.J. Schwichtenberg

  • Transforming Patient Health Information Exchange with Blockchain Technology
    Nan Kong, Baijian Yang

  • 4-dimensional ultrasound assessment of cardiac remodeling during pregnancy and postpartum lactation
    Craig Goergen, Natalia Rodriguez

 

Fall 2022

Global Sustainability

  • Nanostructured Membrane Heat Exchanger for Efficient Air Conditioning
    Buildings consume over 41% of the energy in the US, and space cooling is a large portion of this energy usage. Thus, there is a great need for energy efficient cooling technologies to help improve the efficiency of buildings. The Membrane Heat Exchanger, developed by our lab, is the most efficient...
  • Recycling of fiber reinforced polymer composite for construction applications
    The usage of fiber-reinforced polymer composites in various fields has increased significantly due to their advantageous physical and mechanical properties. However, the sustainability of composite parts has not been fully solved for a long time. Most end-of-life (EOL) composite parts have been...
  • On the Use of Machine Learning for Causal Inference in Extreme Weather Events
    In atmospheric and climate science, significant progress has been made to use machine learning approaches to predict extreme weather events. However, these machine learning-based forecasts struggle with causal inference - the process of identifying the physical reasoning for an effective...
  • Design, Planning, and Fabrication of Greenery on Buildings for Urban Environmental Sustainability
    Greenery on buildings, known as green façades, offer many benefits to building occupants and the building itself, such as providing healthier spaces for the occupants, reducing energy consumption, moderating temperatures, and decorating the building exterior for aesthetics. However, planting...
  • Data Collection and Analysis of Smart Building
    With a growing population of building users, it is increasingly important to improve the holistic performance of building systems. Lack of real-time information about the building can cause structural damage, energy waste, and human discomfort. In the project, we will collect data related to...

Global Security

  • Thermal Conduction in Advanced Composites for Electronics Packaging
    Heat dissipation in electronics and other devices is crucial to performance and improvements to heat dissipation can reduce energy consumption in the now ubiquitous devices, thereby improving energy efficiency. To enhance the thermal conductivity of polymers used in electronics packaging...
  • Life cycle assessment for infrastructure vulnerable to flood hazard in the Great Lake region
    Ensuring the coastal infrastructure lifeline security and users’ safety in the Great Lake region is crucial to maintain the sustainability and resilience against more frequently happened inclement climates. Flooding in the Great Lake area have recently caused infrastructure damage over large areas...
  • Towards a Burden-free Implicit Authentication for Wearable Device Users
    With the emergence of the internet of things (IoT) and the recent advancement of smart sensing technology, smart wearables, such as Fitbits or Apple watches, are packed with a range of sensors helping us with a range of services from unlocking cars and homes to validating financial...

Global Health

  • Blocking entry SARS-CoV2 into cells with cytokines
    The objective of this project is to learn differential gene expression analysis for RNA-seq and
    microarray platforms in order to analyze the differences in gene expression for macrophages infected
    with SARS-CoV-2 in comparison to IL-27 treated macrophages.
  • Data-driven Prediction and Resource Optimization for Fighting Opioid Epidemic
    The recent trend in opioid drug abuse and overdose deaths has reached an epidemic level. According to CDC, opioids were involved in 47,600 overdose deaths in the United States in 2017 – more than 130 deaths per day; the same year, 1.7 million people suffered from substance use disorders...
  • Investigating Explainability of ML Model Decisions for Injury Surveillance
    One of the key applications of machine learning (ML) in the area of public health and safety is to assign diagnostic codes such as International Classification of Diseases (ICD) codes to safety incidents based on the narratives recorded at hospital emergency rooms or reported by employers...

 

Summer 2022

Global Sustainability

  • Low-cost user-friendly biosensors for animal health

    Infectious diseases are a leading cause of economic burden on food production from animals. For example, bovine respiratory disease leads to a loss of ~$1 billion annually. Current methods for tackling these diseases includes the administration of antibiotics by trial-and-error. This approach leads to failure of treatment in up to one-third of the cases. In addition, it also leads to a proliferation of antibiotic resistance in pathogens.
    Our research project focuses on developing a low-cost user-friendly biosensor based on paper that can detect which pathogen is causing the disease and whether it exhibits antibiotic resistance. Such a biosensor would provide a readout to the farmer or the veterinary physician and suggest which antibiotics are likely to be successful.

  • Heat waves and their role in the food energy water security nexus

    The water-food nexus is of particular concern for global food security as both climate change and population growth will increase demand for food and water. We propose to add to this food-energy-water nexus framework interactions with human and livestock heat stress, whether due to natural variability or due to climate change. Heat stress associated climate extremes may negatively affect the productivity of livestock and threaten human labor capacity to perform many types of work important. Huber''''s group has developed a software suite that enables advanced calculations of heat stress. These heat stress variables will be adapted to the SIMPLE economic modeling framework as a perturbation to livestock and human labor productivity as a consequence of climate variability and climate change. Opportunities also exist to study human health impacts of heart stress on humans an animals.
  • Climate change and emerging infectious diseases of wildlife and livestock

    You will use GIS software and/or R statistical software and/or Python to explore the relationships between climate factors, distributions of wildlife species and/or disease outbreaks. The project will start with downloading, exploring, cleaning and merging the data. After this step, you will produce maps to show areas of high risk for disease outbreaks and/or statistical analyses to test your hypotheses. You will be jointly supervised by Dr Beauvais (veterinary epidemiology) and Dr Wang (climate science), with support from a graduate student mentor and/or postdoctoral associate and a team of three-to-four undergraduates in total working on parallel projects funded by the Morris Animal Foundation and the Purdue Center for Climate Research.

  • Sustainable Energy Household System Modeling, Design, and Implementation for Rural Electrification

    The main goal of this proposal is to address the need for cheap, affordable, and clean electricity for rural areas in Indiana and in the world to help in ending poverty. It aims to satisfy various levels of electricity demand in rural areas by designing a new intelligent electrification ladder. So, to address these challenges, the research team is proposing the following: model, design, and implement small-scale optimum photovoltaic (PV) system as a smart nano-grid equipped with energy storage, rural electrification ladder, and required converters. The project targets to maximize the penetration of clean solar power utilizing innovative strategies and technologies. The proposed system can help in clean electricity, agriculture, and pure water filtering purposes customized for low-income households. The proposed system will take into consideration size and cost decreases to supply low electric loads (< 100W). This system will overcome various technical and socio-economic challenges. Additionally, “a single-input-multi-output (SIMO) and multi-input-multi-output (MIMO)” power converters will be designed and implemented to supply both dc and ac loads from solar panels. Moreover, new maximum power point tracking (MPPT) and modern control strategies schemes will be designed and implemented with the aid of artificial intelligence (AI) such as Genetic, and Particle-Swarm techniques for storage devices charging and load management.
    The project aims to cultivate the development of specific communities, gaining a grasp on sustainable energy economics.

Global Security

  • Multifunctional photonic metasurfaces for national security (with CINT)

    Maintain global security and peace requires the ability to travel at speeds well above the speed of sound. One of the key challenges in realizing this goal is in the surface of materials - as they heat up from friction, they start to emit heat. Being able to control which surfaces emit how much heat, even at high temperatures, is thus a highly desirable capability that our interdisciplinary project, involving physics, mechanical engineering, materials science, and electrical engineering aims to achieve.

Global Health

  • Effect of Motion in the Metastatic Microenvironment

    Metastasis is the single greatest driver of cancer related mortalities regardless of the tumor’s tissue of origin. This is particularly true for breast cancer, where the five-year survival rate is exceptional if the disease remains local. However, once breast cancer has metastasized, patient survival drops almost 75%. A defining hallmark of metastasis is the ability for tumor cells to modulate the microenvironment to facilitate invasion and colonization. After tumor cells have invaded into a new tissue, the unique features of the metastatic site play a key role in determining if the cell enters a growth cycle or dormancy. Therefore, there is a critical need to determine how the microenvironment at the metastatic location dictates the tumor cell fate. Given these advances, we seek to address the central hypothesis that the cyclical strains that occur during breathing function to suppress tumor growth, and that changes in the biochemical composition of the niche lead to increased stiffness of the tissue that functions to shield tumor cells from cyclic strains and allow for tumor outgrowth.
  • Studying service dog efficacy for military veterans with posttraumatic stress disorder

    The proposed project will use an interdisciplinary data science approach to compile, process, and analyze data from service dog efficacy studies addressing global health outcomes. Students will work on two projects evaluating the efficacy of service dogs for military veterans with posttraumatic stress disorder (PTSD) and their spouses. The first project is a national clinical trial examining the effects of psychiatric service dogs for military veterans with PTSD and their families (NIH R21). The second project is a follow up to the first, an upcoming randomized clinical trial (NIH R01). Our lab has extensive, multi-modal data streams spanning human and canine psychology and physiology. Both projects are interdisciplinary in their evaluation of human and animal data as well as its use of qualitative and quantitative data methods. Both projects represent a collaboration across three Purdue Colleges, including the College of Veterinary Medicine (Associate Professor Marguerite O’Haire), College of Science (Associate Professor Arman Sabbaghi), College of Health and Human Sciences (Professor Shelley MacDermid Wadsworth). The goal is to assess the impact of service dogs for psychosocial outcomes using data science approaches. The projects aim to address the global health challenge of mental health and wellbeing among military veterans and their families. We approach the studies through standardized surveys as well as biomarkers across multiple data streams, presenting unique and innovative data opportunities.
  • Text mining and classification of injury narratives

    In injury surveillance, different aspects of an injury event are captured using injury codes such as the External-cause-of-injury (E-code), Major Injury Factor (MIF), and Intent. These are usually assigned by human coders based on accident narratives and the codes are then used for statistical analysis of data to identify the leading causes of injuries and identifying updates to safety policies for preventing these injuries. This project is aimed towards identifying various elements of injury surveillance (cause of injury, product involved, nature of injury, etc.) from the textual narrative of the injury using machine learning and natural language processing approaches.
  • Photocatalysis for Indoor Air Quality and COVID prevention

    COVID-19 pandemic has underscored the importance of indoor air quality (IAQ). There are many technologies like filters, air ionizers, UV disinfection, and photocatalysis to improve IAQ. This project in project focuses on using photocatalysis (a light-activated catalyst) to improve IAQ. Photocatalysis degrades harmful pollutants such as gaseous chemical contaminants and bioaerosols (aerosols carrying viruses, bacteria, etc.) into harmless products such as water and carbon-dioxide through photochemical reactions. Furthermore, this project specifically focuses on the use of photocatalysis in HVAC(Heating Ventilation and Air Conditioning) systems to improve indoor air quality by inactivating the bioaerosols. This work aims to create the first framework to make photocatalytic systems effective for remediating airborne pathogens like COVID. Project mentors include Prof's David Warsinger, Travis Horton, and Chip Blatchley.

 

Spring 2022

Global Sustainability

  • Climate Justice in Engineering Education
    Donna Riley | School of Engineering Education
    Ensuring equitable climate change mitigation and adaptation will be an essential element of future engineering practice. At present, the First-Year Engineering Curriculum at Purdue does not systematically include material related to climate justice, but offers elective courses, learning communities, and other supplemental experiences to address climate change and/or environmental justice topics. This project will draw on prior engineering education and social science research, as well as research conducted as part of the ASPIRE Engineering Research Center, the Purdue Climate Change Research Center, and student experience in existing supplemental courses to design learning experiences that can be scaled for implementation with a wider First-Year Engineering audience.

  • Efficient and sustainable water technology
    David Warsinger | Mechanical Engineering
    Water and energy are tightly linked resources that must both become renewable for a successful future. However, today, water and energy resources are often in conflict with one another, especially related to impacts on electric grids. Further, advances in nanotechnology, material science and artificial intelligence allow for new avenues to improve the widespread implementation of desalination and water purification technology. This project aims to explore nanofabricated membranes, light-driven reactions, artificial intelligence control algorithms, and thermodynamic optimization of systems. The student will be responsible for fabricating membranes, building hydraulic systems, modeling thermal fluid phenomenon, analyzing data, or implementing control strategies in novel system configurations. 
  • Low-cost user-friendly biosensors for animal health
    Mohit Verma | Agricultural and Biological Engineering
    Infectious diseases are a leading cause of economic burden on food production from animals. For example, bovine respiratory diseases lead to a loss of ~$1 billion annually. Current methods for tackling these diseases includes the administration of antibiotics by trial-and-error. This approach leads to failure of treatment in up to one-third of the cases. In addition, it also leads to a proliferation of antibiotic resistance in pathogens. Our research project focuses on developing a low-cost user-friendly biosensor based on paper that can detect which pathogen is causing the disease and whether it exhibits antibiotic resistance. Such a biosensor would provide a readout to the farmer or the veterinary physician and suggest which antibiotics are likely to be successful.Lab members working in the team have three objectives: i) design, test, and optimize primers for detecting pathogens associated with bovine respiratory diseases, ii) build a paper-based device for conducting loop-mediated isothermal amplification, and iii) build a heating/imaging device for conducting the paper-based assay in the field.

  • Understanding of Public Discuss on Equity Issues in Transportation Electrification Using Social Media Crowdsourcing Data
    Rosalee Clawson | Political Science
    As the Biden administration decides to raise the sales of electric vehicles (EVs) to 50% of all new car purchases by 2030, this action will accelerate the deployment of electric vehicles and make driving an electric vehicle convenient in every part of the country. However, we are also facing equity issues when we are promoting this new sustainable transportation mode (e.g., electric vehicle rebate allocation, charging facility distribution, and fuel tax). Currently, there is limited information about the public discussion on equity issues during the transportation electrification process. To provide a big picture of equity issues from public view and offer information support for policy design, this research is aimed to use crowdsourcing data (i.e., Twitter data) to analyze public discussion on equity issues in transportation electrification. To be more specific, this project will answer the following questions: 1) what does the social media discussion of EVs and equity look like compared to the equity discussion of other transport modes (e.g., shared micro-mobility)?; 2) how does the discussion of equity in EVs change over time, (e.g., change in discussion topics; 3) how are the discussions in EV equity spatially different in the U.S., (e.g., difference in discussions between California and Indiana).

Global Security

  • Wargaming
    Sorin Matei | College of Liberal Arts
    We are developing the next online strategy game! We have developed a wargaming engine and now are building the body, the instrument panel, and the drive train. A group of interdisciplinary professors and students at Purdue are building a new type of strategy game online using advanced fluid dynamics to model troop movements and decision-making theory to scaffold game playing. We are at the stage of designing the interfaces, front and back end. We need YOU! 

  • Smart secure wearable human-drone interfaces
    Shaoshuai Mou | School of Aeronautics and Astronautics
    The development of wearable sensors that can capture and interpret human intent/inputs with the secure and trusted access and management of the sensor data could usher in unprecedented opportunities in enabling reliable and resilient human-machine interface, robotics, and human-integrated sensors network. In this project, we will explore and develop wearable sensors for smart and secure human-drone interfaces. In this challenging scenario, both ends – human and machine – are vulnerable as sensitive biometric information would be extensively exploited to control the drone and ultimately affect the safety of our physical world. Therefore, how to protect the private data while responsibly harnessing the power of human-drone interface in an authenticated way will be an important aspect of this project.

  • Crowd-Machine Partnership on Road Infrastructure Quality Recognition and Resilience
    Tianyi Li | Computer and Information Technology
    In this research, we aim to combine traditional and crowdsourced road quality monitoring technologies and develop a user-centered information system for road infrastructure quality recognition and resilience. This research will use mobile phones as the main source of data collection and delivery.

  • Obtaining expertise on the Honeywell H1 system
    Andy Jung | Physics and Astronomy
    Classical computers use individual bits which store information as binary 0 or 1 states. However, the power of classical computers is becoming saturated which prevents the most complex problems from being solved. Quantum computing offers an exciting solution as quantum bits (called qubits) can exist as a superposition of both 0 or 1. This greatly increases the potential performance of quantum computers as the computing power increases exponentially with the number of qubits. Because of the difficulty in building quantum computers, they are not readily available to the general public. However, IBM scientists have built the IBM Q experience, a first-of-a-kind quantum computing platform delivered via the IBM Cloud and accessible by desktop or mobile devices. It enables users to run experiments on IBM’s quantum processor, work with individual qubits, and explore tutorials and simulations of the wondrous possibilities of quantum computing. The current proposal seeks undergraduate students to learn how to use and master the IBM Q and apply towards usage of Honeywell H1. The H1 is very similar to IBM-Q in terms of usage but different technology-wise. This is a complex system and it will take significant time to become prolific. The student will apply this knowledge to tackle specific problems in the research areas of chemistry, ECE, or high-energy physics with potential applications towards weather prediction, network security, drug discovery, and materials design, just to name a few. Because learning the basics of the Honeywell H1 will take significant time, the second part could begin at the end of this proposal or in a subsequent DURI.

Global Health

  • Data Science Approach to Studying the Effects of Human-Animal Interactions for Global Health
    Marguerite O'Haire | College of Veterinary Medicine
    The proposed project will use an interdisciplinary data science approach to compile, process, and analyze data from two human-animal interaction studies addressing global health outcomes. In the first, students will work on a project evaluating the effects of full-time hospital therapy dogs (facility dogs) on the healthcare professionals they work alongside. In the second, students will work on a project evaluating the efficacy of service dogs for military Veterans with posttraumatic stress disorder (PTSD) and their spouses. In both projects, our lab has extensive, multi-modal data streams spanning human and canine psychology and physiology. The project is interdisciplinary in its evaluation of human and animal data as well as its use of qualitative and quantitative data methods. It represents a collaboration across two Purdue Colleges, including the College of Veterinary Medicine (Associate Professor Marguerite O’Haire) and the College of Science (Associate Professor Arman Sabbaghi). The goal is to use data science approaches to assess the impacts of service dogs and facility dogs for psychosocial outcomes. The projects aim to address the global health challenge of mental health and wellbeing among military Veterans and their families, as well as healthcare professionals. It is interdisciplinary in its focus on animals (service and facility dogs) and humans (adults with posttraumatic stress disorder and healthcare professionals). We approach the study through standardized surveys, biomarkers (cortisol), human-animal Bluetooth proximity, and ecological momentary assessments (repeated daily check-ins). This incorporation of multi-modal data streams presents unique and innovative data opportunities.
  • Processing and Characterization of Non-Speech Human Sounds -- A Step Towards Automatic Reporting of Respiratory Disease Symptoms
    Sudip Vhaduri | Computer and Information Technology
    Non-speech human sounds, such as coughing, shortness of breathing, and wheezing are common symptoms of many respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, and coronavirus-caused diseases, e.g., COVID-19, SARS, and MARS. Thereby, often physicians and researchers rely on patient-reported surveys, such as the Leicester Cough Questionnaire (LCQ), Cough-Specific Quality-of-Life Questionnaire (CQLQ), and COPD Assessment Test (CAT) for initial screening. Similar to other self-reported responses, these respiratory disease assessment surveys suffer from recall burden, human errors, and biases. However, with the advancement of smartphone sensing and artificial intelligence (AI), we can detect coughing or breathing patterns from smartphone microphone audio signals. Thereby, this smartphone sensing can help us to automate the disease symptom reporting process. This way physicians will get a better understanding of someone’s condition through a remote and instantaneous assessment. Also, this smartphone-based objective reporting reduces the healthcare overheads and increasingly growing expenses.
  • Microbiome in Infectious diseases
    Shankar Thangamani | College of Veterinary Medicine
    Fungal pathogens causes serious infections in immunocompromised individuals. Candida albicans (CA) is the fourth-most-common cause of systemic nosocomial infections, with about 40-60% mortality in infected humans, even with antifungal treatment. Invasive CA infections often arise from initial colonization of the GI tract and subsequent dissemination. We study how microbial metabolites and microbiota interact with host to regulate fungal colonization and dissemination, with the long term aim to develop novel therapeutic approaches to prevent and treat fungal infections.

Fall 2021

Global Sustainability

  • Efficient and sustainable water technology

    David Warsinger | Mechanical Engineering

    Water and energy are tightly linked resources that must both become renewable for a successful future. However, today, water and energy resources are often in conflict with one another, especially related to impacts on electric grids. Further, advances in nanotechnology, material science and artificial intelligence allow for new avenues to improve the widespread implementation of desalination and water purification technology. This project aims to explore nanofabricated membranes, light-driven reactions, artificial intelligence control algorithms, and thermodynamic optimization of systems.

  • Use of Microbial Bioleaching to Enhance Metal Recovery from Electronic Waste in Landfills

    Amisha Shah | College of Engineering

    Landfills contain significant levels of electronic waste. Electronic waste has considerable untapped potential for resource recovery since it contains numerous precious, rare, and critical metals that are highly valued in the global marketplace due to their limited supply and wide use in consumer products. Such a valued resource results in billions of dollars of lost revenue annually by not recovering these metals, while recent executive orders highlight the need to secure such critical resources domestically to achieve greater economic and energy security. These factors strongly support the need to recover these

  • Physics-Informed Machine Learning to Improve the Predictability of Extreme Weather Events

    Lei Wang | College of Science

    This project is based on developing and verifying the machine learning algorithm for detecting extreme weather events in an idealized model. We will use Purdue’s supercomputer Bell to conduct the simulations. Undergraduate students will play an active and important role on running the idealized model, and participate in developing the algorithms. As an important component of climate preparedness, the proposed work aims to develop a physics-informed machine learning framework to improve predictability of extreme weather events.

Global Security

  • Wargaming

    Sorin Matei | CLA

    We are developing the next online strategy game! We have developed a wargaming engine and now are building the body, the instrument panel, and the drive train. A group of interdisciplinary professors and students at Purdue are building a new type of strategy game online using advanced fluid dynamics to model troop movements and decision-making theory to scaffold game playing. We are at the stage of designing the interfaces, front and back end. We need YOU! 

  • Security of power systems in satellites

    Ashraf Alam | Electrical and Computer Engineering

    Cyber security capability gaps have been rising exponentially, endangering consumer, business, national infrastructure, space, and military end-users. While the security of internet software attracts a great deal of attention, security and reliability of microelectronic hardware has not received as much focus. However, hardware reliability presents a very serious concern of increasing significance, particularly but not only for the Department of Defense and NASA. One key challenge in the space environment is the reliability of power electronics, including space-based solar cells. In this project, we will investigate the effects of radiation on space solar cell performance and reliability over time. GPA >= 3.2 Semiconductor Device Fundamentals

  • Securing System-on-Chip (SoC) Designs

    Mark Johnson | Electrical and Computer Engineering

    The processors (A.K.A. System-on-Chips) inside your cell-phone, an aircraft, your automobile, television, etc. are some of the most complex and smallest devices created in human history. The security of such devices is critical to your own well-being as well as most other people, organizations, and even nations. You are probably aware of software security issues such as cybercrime, but the security of hardware is equally important even if it is less visible to the end user. Hardware security must be addressed during the design of these devices. On the SoCET team (https://engineering.purdue.edu/SoC-Team) dozens of undergraduates are involved in the design, manufacturing, and test of System-on-Chips (SoCs). On SoCET, we are going to integrate security into our design practices as well as the final product. You are invited to help us identify security vulnerabilities and implement measures to improve security. Education and experience in one more of the following is desirable: (1) Verilog/System Verilog coding skills for logic synthesis and test bench design, (2) Analog and digital integrated circuit design background including circuit simulation and layout, (3) Microcontroller programming in C and assembly language, (4) Computing security practices

Global Health

  • Purdue Canines for Autism Research Study (CARES)

    Maggie O'Haire | Veterinary Medicine

    The Purdue Canines for Autism Research Study (CARES) is an interdisciplinary collaboration across the College of Veterinary Medicine (Associate Professor Marguerite O’Haire), College of Health and Human Sciences (Associate Professor Bridgette Kelleher), and College of Education (Professor Mandy Rispoli). The goal is to assess the impact of service dogs for children with autism spectrum disorder (ASD) and their families. The project aims to address the global health challenge of mental health and wellbeing among children with neurodevelopmental disorders and their families. It is interdisciplinary in its focus on animals (service dogs) and humans (children with autism and their caregivers). We approach the study through standardized surveys as well as biomarkers (cortisol).

  • Deep Learning Approach to Improving Image Quality for Medical Diagnostics

    Craig Goergen | Biomedical/ Engineering

    The United Nation’s health-related Sustainable Development Goals are difficult to achieve in low- and middle-income countries due to workforce shortages and inadequate health surveillance systems. However, with the growth of artificial intelligence (AI), it is possible to apply AI to healthcare technologies to improve progress towards these UN standards. This project aims at applying a deep learning algorithm to aid in the extraction of important structural and functional features in various image types (MRI, ultrasound, photoacoustic etc.). The deep learning algorithm, or convolutional neural network, will be applied to preclinical and/or clinical data with the ultimate goal of aiding physicians to diagnose and treat patients. Emphasis will be placed on improving contrast in images, limiting noise, reducing unwanted artifacts, and improving resolution by evaluating the contrast to noise ratio and the signal to noise ratio. We will test the algorithm on simulated data, data acquired on phantoms, and in vivo preclinical and clinical data from which anatomical and physiological information can be extracted.

  • Fair Resource Allocation to Reduce Hospital Readmission and Opioid Relapse

    Pengyi Shi | Krannert

    This research uses *data analytics*, *fair machine learning algorithms*, and *stochastic modeling* to develop a unified data-integrated decision support framework, with the aim of achieving efficient and fair resource allocation to reduce hospital admissions and prevent relapse of SUD. First, we aim to develop a fair prediction model on patient readmission risk/risk of relapse for substance users. The prediction will strike the balance between high accuracy and fairness among different groups. Then we aim to integrate the prediction model into a decision framework that will prescribe fair resource allocation strategies to decide on which set of patients/substance users to provide resource, when to intervene/offer assistance, and which suite of treatment/intervention options to use in prevention given the limited resources.

    From this research, you will get hands-on experience with real data and receive rigorous research training related to modeling and algorithms under close supervision -- particularly beneficial if you intend to apply for graduate programs. You will interact with a team of faculty members and students who are passionate about applying data science, mathematical models, and algorithms to solve real-world problems.

Summer 2021

Global Sustainability

  • Integrating Heat Stress and its Food-Energy-Water Security Impacts in a Global Economic Model
    Matthew Huber | EAPS/Science

  • Cyberinfrastructure for Integrative, Multiscale Resource Sustainability Research
    Carol Song | Research Computing/ITaP

  • Field Characterization of Agricultural Aerosols
    Gouri Prabhakar | EAPS/Science

     

Global Security

  • VR Modeling for Historical Projects
    Sorin Matei | CLA

  • Security of power systems in satellites
    Ashraf Alam | Electrical and Computer Engineering

  • Securing System-on-Chip (SoC) Designs
    Mark Johnson | Electrical and Computer Engineering

     

Global Health

  • Deep-learning Enhanced Hospital Workload Prediction and Resource Allocation for COVID19
    Pengyi Shi | Krannert/RCHE/Industrial Engineering

  • Data Science Approach to Studying Service Dog Efficacy for Global Health
    Maggie O'Haire | Veterinary Medicine

  • Low-energy Advanced Oxidation membranes for water reuse
    David Warsinger | Mechanical Engineering

     

Spring 2021

Global Sustainability

Global Security

Global Health

Fall 2020

Global Sustainability

Global Security

Global Health

Summer 2020

Global Sustainability

Global Security

Global Health

Spring 2020

Global Sustainability

Global Security

Global Health

Fall 2019

Global Sustainability

Global Security

Global Health

Summer 2019

Global Sustainability

Global Security

Global Health