Click any of the Digital Twin proposal summaries below for more information and to apply as a Rising Researcher.
Your deadline to apply is June 16.
Return to the complete list of available research opportunities.
Adaptive Therapy Optimization via Surrogate Tumor Digital Twins and Deep Reinforcement Learning (Soundar Kumara)
This proposed project aims to address computational bottleneck by developing physically and biologically informed surrogate Tumor Digital Twins (TDTs) integrated with Deep Reinforcement Learning (DRL) for adaptive therapy optimization. (Learn more and apply – Penn State login required)
Simulate and forecast magma propagation through fractures using extended finite elements (Christelle Wauthier)
We will develop and apply computationally intensive simulations methods to natural hazards processes and forecasting of eruption location. (Learn more and apply)
Using Artificial Intelligence (AI) to Understand Neural and Behavioral Variability (Xiao Liu)
We will develop and apply state-of-art AI models to understand brain functions. The project is also to understand the ANN from the perspective of the brain science. (Learn more and apply)
TwinSight: A Data-Driven Digital Twin Framework for Human-Centric Health Monitoring (Dhananjay Singh)
This project aligns with ICDS’s mission by integrating data science, AI, and IoT technologies to address the critical societal challenge of aging-in-place through a privacy-conscious digital twin framework. It supports interdisciplinary research, real-world impact, and the development of AI-enabled solutions to improve health and quality of life. (Learn more and apply)
AgriTwin: Real-Time Digital Twin Framework for Climate-Smart Farming (Dhananjay Singh)
AgriTwin is a scalable, AI-enabled Digital Twin platform designed to support emission sustainability in agriculture. (Learn more and apply)
Developing Functionally Equivalent Proxy Systems for AI: A Framework for Code Similarity Analysis, Asynchronous Digital Twin Proxies, and Proxy Repository Implementation (Joanna F. DeFranco)
The research team will investigate targeted techniques for analyzing AI systems to develop functionally equivalent proxy systems. (Learn more and apply)
Probabilistic Digital Twins for Predicting Chaotic Bifurcations in High-Speed Aeroelasticity (Ashwin Renganathan)
We will develop probabilistic AI/ML methods to reduce, interpret, and learn data. This project will include both large-scale data generation by running finite-volume based multiphysics codes on Roar Collab, as well as developing AI/ML methods on that data with GPU acceleration. (Learn more and apply)
Development of Digital Twin for Liquid Cooled Data Centers (Wangda Zuo)
This project develops a digital twin of a liquid-cooled data center system to address energy efficiency, resilience, and modernization of HPC cooling infrastructure, leveraging digital twins for real-time monitoring and optimization. (Learn more and apply)
Game-Style Outdoor Airflow Simulation for Fast Design of Cooling Systems (Wangda Zuo)
This joint project with Center for Immersive Experience develops a game style digital twin of outdoor airflow simulation for engineering design. (Learn more and apply)
Spinal Fatigue Prediction in High-G Environments Using Human Digital Twins (Reuben Kraft)
This project develops a digital twin framework to evaluate spinal fatigue in pilots subjected to high G acceleration. (Learn more and apply)
Digital Thebes: A Comprehensive Database for Egyptian Antiquities Education and Research (Ziting Wang)
The “Digital Thebes” project aims to create a comprehensive, interactive website and database of digital educational resources focused on ancient Egyptian archaeological sites, particularly nonroyal tombs in the Theban cemetery in the New Kingdom period. (Learn more and apply)
LLM-Augmented Digital Twin Framework for Building Material Reuse and Recycling Assessment (Yuqing Hu)
This project proposes to develop a digital twin framework powered by large language models (LLMs) and large vision models (LVMs) to support component-level material reuse and recycling assessment. (Learn more and apply)
Developing Workforce-Informed Digital Twins for Smart Redevelopment Site Classification (Yuqing Hu)
This project addresses that gap by developing a graph-based digital twin framework to classify and prioritize redevelopment sites based on workforce and infrastructure readiness. (Learn more and apply)
Building Digital Twins of Personalized Models for Alzheimer’s Disease Prevention and Treatment (Zi-Kui Liu)
The proposed project aims to develop a Zentropy-Enhanced Neural Network (ZENN) that learns the configurations, total energy, and entropy of brain states using data related to Alzheimer’s disease (AD). (Learn more and apply)
Learning on the Edge With Hyperdimensional Computing (Vasant Honavar)
This project aims to develop and evaluate lightweight, HD computing based machine learning framework for learning on the edge, that ls, learning predictive models from data being acquired by edge devices. The resulting methods will also help significantly reduce the carbon footprint of machine learning. (Learn more and apply)
Federated estimation of causal effects from observational data (Vasant Honavar)
A long term goal of this project is to develop robust federated algorithms for causal effect estimation for a broad range of applications in healthcare, education, public policy, etc. where it is generally neither feasible nor desirable to aggregate data collected by independent entities into a centralized repository. (Learn more and apply)
Efficient Adaptation of Trained Models When Utilities of Model Predictions Change (Vasant Honavar)
The long-term goal of this project is to develop methods for efficient adaptation of predictive models trained using machine learning when the utilities of model predictions change, with practical applications across a broad range of real-world applications. (Learn more and apply)
Machine Learning for Health Risk Prediction from Longitudinal Health Data (Vasant Honavar)
The long term goal of this project is to establish a unified, modular framework for temporal clinical modeling that is generalizable across datasets, interpretable for clinicians, and adaptable to other domains of risk prediction. (Learn more and apply)
Developing Workforce-Informed Digital Twins for Smart Redevelopment Site Classification (Yuqing Hu)
This project addresses that gap by developing a graph-based digital twin framework to classify and prioritize redevelopment sites based on workforce and infrastructure readiness. (Learn more and apply)
ML-Enhanced Multiphysics Modeling for Packed-Bed Thermal Energy Storage Optimization (Olumide Ogunmodimu)
This research introduces a comprehensive multiphysics modeling framework that integrates machine learning and digital learning tools for enhanced simulation and analysis. (Learn more and apply)
Development of a Digital Twin Model for Stirred Milling Process by Integrating Machine Learning Models and Discrete Element Method Simulations (Olumide Ogunmodimu)
This project aims to contribute to the evolution of digital twin applications by integrating a combined approach of machine learning models, including Support Vector Machines (SVM), Convolutional and Graph Neural Networks (CCNN, GNN), Physics-Informed Neural Networks (PINN), and Discrete Element Method (DEM) simulations. (Learn more and apply)