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 Ecology & Evolutionary Biology
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.
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.
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.