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