Center for Undergraduate Research in Viterbi Engineering (CURVE) Fellowship

Project Title: Oxidation of trace organic organic contaminants in recycled wastewater with heterogeneous catalysts

Faculty / PI: Daniel McCurry

Lab Name: McCurry Lab

Department: CEE (Astani)

Research Website: mccurrylab.com

# of Freshmen Positions: 1

# of Continuing Student Positions: 1

Project Description: The McCurry research group currently consists of three PhD students and three undergraduate researchers who all do research in the areas of water treatment and environmental chemistry. Our major research activities include identifying the precursors and formation pathways of disinfection byproducts formed during water treatment and wastewater reuse, developing new chemical technologies for oxidation of trace organic contaminants, and developing new analytical techniques for identification and quantification of chemical pollutants using mass spectrometry. Our research primarily takes place in the BHE Water Lab, and relies heavily on recently purchased analytical instrumentation, including an ion mobility QTOF mass spectrometer, a gas chromatograph/triple quadrupole mass spectrometer, and an inductively-coupled plasma mass spectrometer. These tools allow us to quantify chemicals at extremely low concentrations (e.g., parts per trillion) and to identify previously unknown compounds in water samples.

Student Responsibilities: Assisting graduate students with experiments on water treatment, water reuse, and quantification of trace contaminants in water. Specific responsibilities will include preparing for and setting up batch water treatment experiments, sample preparation for mass spectrometry-based identification and quantification of pollutants, and analyzing data produced by analytical instruments. Especially dedicated students may eventually have the opportunity to advance to a fully-independent project advised directly by the PI.

Interview Required? No

Skills and Competencies (Preferred)

• General chemistry lab skills
• Some exposure to analytical chemistry is helpful but not required
• General chemistry coursework (e.g., CHEM105 here or AP equivalent)

Project Title: Quantification of parabens as contaminants in decentralized greywater reuse systems, and identifying their formation pathway to halogenated byproducts

Faculty / PI: Daniel McCurry

Lab Name: McCurry Lab

Department: CEE (Astani)

Research Website: mccurrylab.com

# of Freshmen Positions: 1

# of Continuing Student Positions: 2

Project Description: The McCurry research group currently consists of three PhD students and three undergraduate researchers who all do research in the areas of water treatment and environmental chemistry. Our major research activities include identifying the precursors and formation pathways of disinfection byproducts formed during water treatment and wastewater reuse, developing new chemical technologies for oxidation of trace organic contaminants, and developing new analytical techniques for identification and quantification of chemical pollutants using mass spectrometry. Our research primarily takes place in the BHE Water Lab, and relies heavily on recently purchased analytical instrumentation, including an ion mobility QTOF mass spectrometer, a gas chromatograph/triple quadrupole mass spectrometer, and an inductively-coupled plasma mass spectrometer. These tools allow us to quantify chemicals at extremely low concentrations (e.g., parts per trillion) and to identify previously unknown compounds in water samples.

Student Responsibilities: Assisting graduate students with experiments on water treatment, water reuse, and quantification of trace contaminants in water. Specific responsibilities will include preparing for and setting up batch water treatment experiments, sample preparation for mass spectrometry-based identification and quantification of pollutants, and analyzing data produced by analytical instruments. Especially dedicated students may eventually have the opportunity to advance to a fully-independent project advised directly by the PI.

Interview Required? No

Skills and Competencies (Preferred)

• General chemistry lab skills
• Some exposure to analytical chemistry is helpful but not required
• General chemistry coursework (e.g., CHEM105 here or AP equivalent)

Project Title: Printed strain sensor arrays for large-area structural health monitoring

Faculty / PI: Sifat Muin

Lab Name: Muin Research Group

Department: Civil and Environmental Engineering

Research Website: sifatmuin.com

# of Freshmen Positions: 1

# of Continuing Student Positions: 1

Project Description: Large-area structural health monitoring (SHM) is becoming essential for assessing damage in the vulnerable large-scale structures such as bridges and dams. Reliable SHM of large infrastructures requires a vast number of sensors making it extremely expensive from a maintenance perspective. Printed and flexible strain sensors provide an opportunity to monitor large-scale structures at low cost due to their large-volume manufacturing capabilities and ease of installation. This project aims to develop printed and flexible strain sensors with wireless communication capabilities to improve post-earthquake large-area SHM and test them in an experimental test setting. The project will be carried out through four tasks: development of the sensor array, development of the communication system, construction of structural test samples, and the experimental tests.

Student Responsibilities: The student will assist other members of the project team in the tasks of the project. They will do lab work on developing and testing the sensor, and if interested, they can get involved in app development. Other responsibilities will include attending meetings and documenting the work.

Interview Required? No

Skills and Competencies (Preferred)

• Attention to detail
• Dedication
• Teamwork
• Good communication
• Electrical Engineering courses

Project Title: Experimental modeling of dynamic choice in uncertain and resource-constrained environment

Faculty / PI: Ketan Savla

Lab Name: Savla Research Group

Department: Civil and Environmental Engineering

Research Website:

# of Freshmen Positions: 1

# of Continuing Student Positions: 1

Project Description: We are interested in econometric modeling of human decision making in repeated instances of uncertain and resource-constrained environments. Examples include route choice or time of departure choice for daily commute in a city like Los Angeles with dynamic traffic situation. A salient feature of such systems that an individual’s experience, e.g., commute time, depends also on similar decision of others. A recommender system could play an important role in both offloading decision-making at an individual level but also to achieve balanced resource allocation at the population level. Behavioral considerations naturally will determine feasible recommendation strategies. Existing decision-making models either assume perfect rationality, perfect recall of past experience, or extreme patience. We aim to investigate the validity of these hypotheses, as well as formulate new ones, through laboratory experiments involving human subjects. The application context will primarily be civil infrastructure systems with particular focus on transportation.

Student Responsibilities:

• Development of interactive user interface to simulate repeated decision-making scenarios in civil infrastructure systems. This involves coding.
• Development of database to collect and archive data collected during laboratory experiments, to facilitate quick query. This involves coding.
• Collaborating with faculty, postdoc and PhD students in conducting experiments (subject to IRB approval).

Prior relevant experience is desired, but an interest and commitment to the project will be given priority.

Interview Required? No

Skills and Competencies (Preferred)

• Commitment
• Coding
• Outreach for recruiting participants

Project Title: Deep Learning to Estimate the Reliability of Structural Dynamical Systems

Faculty / PI: Erik Johnson

Lab Name: Structural Monitoring, Control and Modeling

Department: Civil & Environmental Engineering

# of Freshmen Positions: 0

# of Continuing Student Positions: 1

Project Description: Surrogate assisted reliability analysis using generative models

Student Responsibilities: Students are expected to perform research that will involve the application of generative deep learning models to reliability estimation tasks accelerated by multi-fidelity surrogate modeling techniques. From time to time, students will be asked to present reports/presentations on literature review, assist in the development of necessary software tools, and perform data analysis.

Interview Required? Yes

Skills and Competencies (Preferred)

• Python programming
• ideally: Some experience with deep learning frameworks e.g. PyTorch
• Interest in solving physics-based mechanics problems
• General quantitative skills, knowledge of basic statistical methods
• Bonus: experience with structural analysis softwares/packages

Additional Details: Selected student(s) will be building on a an existing framework for reliability estimation utilizing generative deep learning models. The end-goal of the project is the development of an efficient black-box framework for reliability estimation.