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2021-2022 StART Awards

First Year Projects

Brett Compton

Department of Mechanical, Aerospace, and Biomedical Engineering

High temperature, damage-tolerant hybrid materials through precision additive
manufacturing of multi-phase architectures

Compton’s project will investigate the processing and properties of high-precision brick-and-mortar architectures composed of high temperature ceramic and carbon materials for applications in nuclear power, air- and land-based turbines, high power electronics, and more.

Subhadeep Chakraborty

Department of Mechanical, Aerospace and Biomedical Engineering

Artificial Intelligence based impairment detection system for vehicle operators through combined analysis of physiological and traffic sensor data

Impaired driving is a key contributing factor leading to more than 10,000 fatalities in 2016. By integrating and fusing multiple data sources such as driver biometrics, vehicle kinematics, and roadway and environmental conditions in real-time, this project aims to generate an intelligent Advanced Driver Assist System (iADAS) which will provide useful feedback to drivers and potentially mitigate accidents.

Kristina Kintziger

Department of Public Health

Prospective and Longitudinal Multivariate Study of Post-Acute SARS-COV-2 Infection
Syndrome (PaLM-COVID)

Kintziger plans to conduct a prospective longitudinal study of a large group of individuals recovering from COVID-19. This study should increase scientific understanding of the COVID-19 respiratory illness and how if affects people over time.

Eric Lass

Department of Materials Science and Electrical Engineering

Al-Ce Deformation Processing

Al-Ce-based eutectic alloys exhibit remarkable strength and better microstructural stability than many other alloys, but the ultrafine eutectic microstructure credited with this behavior only forms at high solidification-rates. This project will investigate the use of thermo-mechanical processing to create similar microstructures, broadening potential applications of the alloys.

Jian Liu

Min H. Kao Department of Electrical Engineering and Computer Science

Towards Robust and Trustworthy Federated Learning for Ubiquitous Cyber-Physical Systems: Security, Privacy, and Scalability

Different from traditional centralized training, federated learning distributes the training process to the edge, enabling edge-computing devices to collaboratively learn/update a shared model using the data that is kept locally on the device. Liu’s project hopes to build a foundation for understanding how to push AI gains in performance, robustness, and scalability to CPS in mobile edge computing.

Second Year Projects

Mahshid Ahmadi Mahshid Ahmadi

Department of Materials Science and Engineering

Machine Learning Driven Experimental Approach for the Discovery of New Organic-Inorganic Halide Perovskites for Optoelectronic Applications

Organic-inorganic halide perovskites have emerged as materials of choice for low-cost photovoltaics and optoelectronics due to relatively easy solution synthesis and unique spectrum of functional properties. The objective of this project is to establish a machine learning based experimental approach towards discovery and prediction of hybrid perovskites properties via combinatorial synthesis and evolutionary experiment optimization.

Johnathan Brantley

Department of Chemistry

Cyclic Cumulenes as Enabling Motifs in Functional Materials

Polymers that contain cyclic repeating units are important synthetic targets, given they often exhibit unique physical properties. Brantley’s project will explore vinyl-addition polymerizations of cyclic allenes to access materials with new properties and potential applications.

Hugh MedalHugh Medal

Department of Industrial and Systems Engineering

Machine-Learning-Enabled Modeling for High-Dimensional Dynamics of Materials Processing

Understanding the movement of ions through material is crucial to understanding important properties such as radiation, stress cracking, and ion conductivity. Because of this, there is an urgent need to map high-dimensional energy landscapes (HDEL). Medal’s team plans to address this need via machine-learning-enabled modeling.

Zhenbo Wang Zhenbo Wang

Department of Mechanical, Aerospace and Biomedical Engineering

Real-Time Control for Connected and Automated Vehicles using Traffic Signal

Future roadways will rely on connected and automated vehicles (CAVs) to reduce traffic congestion, maximize fuel economy, and increase safety. Wang’s team plans to develop a novel method to produce the best speed profile using traffic signal phase and timing data. Successful completion of this project would result in a new method for real-time optimal control of CAVs. This work is essential to mainstream the use of CAVs for future transportation systems.