Support for Affiliated Research Teams
StART Awards
2022 – 2023 Awardees
Colleen Crouch
Mechanical, Aerospace and Biomedical Engineering Department
Brain Spatial Multi-omic Imaging & Analysis Protocol: Alzheimer’s Disease Proof of Concept
Mapping phenotypic signatures of the brain can be used to identify changes due to aging or disease through development of multimodal research protocol. Better understanding of the spatial heterogeneity of phenotypic changes occurring during disease progression will allow more targeted diagnosis and treatment. Using computational algorithms to integrate data, generated from mass spectrometry imaging, microscopy-guided single cell MS profiling, and spatial single-cell RNA sequencing from tissue sections of targeted brain regions to create a 3D atlas/map of a portion of the brain. Previously, these 3 techniques have not been combined previously.
Doowon Kim
Min H. Kao Department of Electrical Engineering and Computer Science
Enhancing the Security of Connected and Automated Vehicles Ecosystem
Connected and automated vehicles (CAVs) are considered a future, intriguing technology that can change our daily lives on future roadways in terms of drivers’ safety and fuel efficiency through the communications between vehicles and infrastructures. Despite these advantages, security has not been the first priority in the CAVs ecosystem; instead, the efficiency of algorithms has always been the higher priority. Therefore, this can open up unprecedently various attack surfaces for adversaries.
Joon Sue Lee
Department of Physics and Astronomy
Topological Quantum Materials Prepared by Epitaxy
Topology, a way of thinking about objects based on their broad properties that are preserved under continuous deformations, can be applied to condensed matter physics. In the context of quantum materials, topology can explain and predict why novel electronic states emerge at surfaces/edges and interfaces with unusual properties such as spin-charge coupling and novel superconducting features, which make the topologically nontrivial materials (topological materials) exciting candidates for future device applications.
Rachel Patton McCord
Biochemistry & Cellular and Molecular Biology
Predicting and modifying cellular radiosensitivity with 3D genome folding
Radiation that damages DNA can be both dangerous to healthy tissues and used as a cancer therapeutic. To develop therapeutics that destroy cancer while minimizing damage to surrounding tissues, it is critical to understand, and even manipulate, how different cell types respond to radiation. A cell’s sensitivity to radiation is in part affected by how the DNA is arranged in 3D inside the nucleus. Iterative prediction and experiment, will reveal the relationship between 3D genome structure and cell radiosensitivity and identify structure manipulations that increase cancer susceptibility to radiation therapies.
Gila Stein
Chemical and Biomolecular Engineering
Design Rules to Elevate Ionic Conductivity in Block Copolymer Electrolytes
Block copolymers comprised of a polymeric ionic liquid linked to a nonionic polymer are a class of “safe” electrolytes with tunable mechanical properties. Recent work has showed that ionic conductivity in these materials is significantly depressed relative to predicted values, posing a challenge for applications in energy storage. It’s believed his depression is a result of mesoscale defects that inhibit ion transport, local effects associated with molecular design, or a combination of these factors. Systematic structure function studies will provide a foundation to improve ionic conductivity through materials processing and/or molecular design.
Himanshu Thapliyal
Min H. Kao Department of Electrical Engineering and Computer Science
A Cross-Layer Application of Approximate Computing to Increase Noise Resilience of NISQ Quantum Circuits
Fully fault-tolerant quantum computation will take a significant amount of resources. Therefore, one of the current focuses in quantum computation is to establish the utility of small scale, error-prone, or “noisy intermediate-scale quantum” (NISQ), machines. In NISQ machines, the application of quantum gates as well as the measurement operations can introduce errors
2021 – 2022 Awardees
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.
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.
Second Year Funding Awards
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.
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.
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.
2020 – 2021 Awardees
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.
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.
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 Funding Awards
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.
Sindhu Jagadamma
Department of Biosystems Engineering and Soil Science
Is Soil Manganese a Major Driver for Organic Carbon Cycling in Croplands?
Manganese (Mn), an essential plant micronutrient, is believed to play a critical yet poorly understood role in terrestrial ecosystem carbon (C) cycling, particularly in human manipulated agoecosystems. Her team proposes a series of field and laboratory experiments that simulate excess Mn mobilization in agricultural soils to quantitatively assess the role of Mn in ecosystem C cycling. The findings of this proposed study will inform whether acidity-induced elevated Mn availability is a critical driver in soil organic C storage/loss in highly managed agroecosystems.
Hugh 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
Department of Mechanical, Aerospace and Biomedical Engineering
Real-Time Control for Connected and Automated Vehicles using Traffic Signal Information
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.
Xiaopeng Zhao
Department of Mechanical, Aerospace and Biomedical Engineering
A Multisensory Brain-Computer Interface for Intelligent Driving
Smart cars and intelligent driving have moved into the forefront of vehicle technology, opening up a number of areas of research. Zhao’s team proposes to investigate intelligent driving through the development of an interface designed to communicate information about a vehicle’s driving conditions to the driver.
2019 – 2020 Awardees
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.
Sindhu Jagadamma
Department of Biosystems Engineering and Soil Science
Is Soil Manganese a Major Driver for Organic Carbon Cycling in Croplands?
Manganese (Mn), an essential plant micronutrient, is believed to play a critical yet poorly understood role in terrestrial ecosystem carbon (C) cycling, particularly in human manipulated agoecosystems. Her team proposes a series of field and laboratory experiments that simulate excess Mn mobilization in agricultural soils to quantitatively assess the role of Mn in ecosystem C cycling. The findings of this proposed study will inform whether acidity-induced elevated Mn availability is a critical driver in soil organic C storage/loss in highly managed agroecosystems.
Zhenbo Wang
Department of Mechanical, Aerospace and Biomedical Engineering
Real-Time Control for Connected and Automated Vehicles using Traffic Signal Information
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.
Constance Bailey
Department of Chemistry
Biochemical and Computational Probing of Mutant-Biocatalyst Relationships in Polyketide Synthase Ketoreductases
Carbonyl reduction is a commonly used biocatalytic transformation in the manufacture of chiral pharmaceutical intermediates and other high value chemicals. Bailey’s lab has focused on developing ketoreductase (KR) domains from bacterial biosynthetic enzymes as a model for developing stereoselective biocatalysts. Her team plans to use computational and experimental methods to probe mutant-biocatalyst relationships in these enzymes; work that has applications in pharmaceutical and chemical manufacturing.
Francisco Barrera
Department of Biochemistry & Cellular and Molecular Biology
Stimuli-responsive Neuromorphic Computing
Neurons in the human brain achieve a breadth of computing capabilities that are superior to man-made computers. Neuromorphic computing seeks to create computing systems inspired by the firing of ion channels as the synapse that connects neurons. However, current neurotrophic computing systems are underwhelming in their efficiency, versatility, and energy consumption. Barrera’s team hopes to address this by developing improved membrane networks to encourage more versatile and responsive neurotrophic computing systems.
Hugh 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.
Xiaopeng Zhao
Department of Mechanical, Aerospace and Biomedical Engineering
A Multisensory Brain-Computer Interface for Intelligent Driving
Smart cars and intelligent driving have moved into the forefront of vehicle technology, opening up a number of areas of research. Zhao’s team proposes to investigate intelligent driving through the development of an interface designed to communicate information about a vehicle’s driving conditions to the driver.