Applicants must:

• Be an undergraduate student not set to graduate prior to Fall 2025
• Must provide proof of U.S. Citizenship or U.S. Permenant Resident status
• Recommended GPA of 3.0 or above
• Indicate three project areas interest, choosing from the options in the Research Areas tabs below
• Provide two letters of recommendation
  • If recommenders prefer direct contact, letters must be emailed to physics-reu@utrgv.edu no later than 10 pm Sunday, March 16. The email subject line should include the phrase "REU Summer 2025" along with the applicant's name.
• Provide a curriculum vitea (CV)
  • Should include relevant research experience, academic achievements, and memberships in professionl organziations and honor societies
• Provide current transcripts

Physics, astronomy, chemistry, computer science, engineering and other STEM majors are highly encouraged to apply.

Fine arts majors should apply for the Arts & Science Award Program (ASAP).

 Applicants must:

• Be high-school level educator
• Be from the local region
• Provide a resume
  • Should include education, teaching experience, memberships in professional organizations, honors and awards
• Provide two references

 

Faculty Mentor: Ivan Davila

The Art and Science Award Project (ASAP) is designed with artists in mind. Following the portfolio review and review, accepted applicants will discuss with the advisor how best develop new skills and explore new media in accordance with the participants research interest.

Participants are strongly recommended to work outside of their comfort zone. The program consists of:

• Art history pertaining to the project
• Literature review
• Artistic interpretation of a scientific concept or concepts that will lead to a research poster
• A finished art pieces

Time Domain Astronomy and Optical Followup of Gravitational Wave Triggers

Faculty Mentor: Dr. Mario Diaz

The research interest is in the optical detection of electromagnetic counterparts of gravitational wave events, mainly neutron star- neutron star or neutron star-black hole collisions. Another area is the search for and identification of high-energy optical transients, and optical follow up of Gamma ray bursts. There are several short-term observational projects including debris observation, photometric observations of eclipsing binary stars and variable stars (i.e. chromospherically variable stars). The group members participate in all the processes from image reduction to applications of Machine Learning techniques to identify and classify transients.

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Machine Learning Classification of Variable Stars
Faculty Mentor: Dr. Ryan Oelkers

The Transiting Exoplanet Survey Satellite (TESS) has observed nearly every luminous object in the sky. We are seeking to classify and characterize the TESS lights curves of millions of classically variable objects (like eclipsing binaries, RR-lyrae, Cepheids, etc.) using machine learning methods.

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Stellar Remnants Harbored by Binary Stars
Faculty Mentor: Dr. Liliana Rivera-Sandoval

This research project focuses on the study by observational astrophysicists of binary stars that harbor stellar remnants, mainly of the white dwarf type. The goal is to work toward an understanding of how external factors affect their formation and evolution, as well as how these binaries affect their environment, for example in globular clusters.

Machine Learning Applied to Bioaccoustics
Faculty Mentor: Dr. Andrea Contina

The North American Flyway provides habitat for hundreds of resident and migratory bird species. However, evaluating regional and continental species richness is challenging due to the extensive effort needed for direct field observations. To address this we will analyze acoustic data (bird calls) using Neural Network algorithms, which will allow us to efficiently assess species diversity. This approach relies on machine learning models trained on territorial vocalizations and nocturnal flight calls to identify birds on the ground and migrants in flight, respectively. For more info about this research program visit Dr. Contina's lab page.

Characterization of Background Noise in LIGO

Faculty Mentor: Dr. Soma Mukherjee

One of the major areas of research in LIGO is called Detector Characterization. This involves looking at and characterizing the real data coming out of the LIGO detectors, both in real time, and off-line. Noise analysis provides feedback to the experimentalists so that the instrument can be diagnosed for spurious behavior. This provides a great platform for under-graduate students to learn about both the detector physics and the data analysis techniques. Students will work on developing and testing methods to detect correlation between time series from different detector channels. Specifically, students will learn about LIGO data, methods of storage and extraction of LIGO data, MATLAB as a data analysis tool, methods of statistical data analysis and methods of looking at glitches seen in the data to understand their possible origin in the detector sub-systems.

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Detector Characterization for the LIGO Project
Faculty Mentor: Dr. Volker Quetschke

LIGO, the  (Laser Interferometer Gravitational-Wave Observatory) is a facility that detects and measures gravitational waves from space. LIGO has two L-shaped detectors, each with two 4 km long vacuum chambers. The detectors are located 3,000 kilometers apart. LIGO is part of a global network of gravitational wave observatories. LIGO's discovery of gravitational waves in 2015–2017 earned the three principal characters a 2017 Nobel Prize in Physics. The REU project will work on determining sensitivity limitations through environmental disturbance on the detector system. This is also known as Detector Characterization.

Particle Swarm Optimization for Maximum Likelihood Estimation
Faculty Mentor: Dr. Soumya Mohanty

Particle Swarm Optimization (PSO) is a method for finding the global maximum of high dimensional functions that have multiple local optima. It is one among a diverse set of methods inspired by examples of optimization in biology. For example, Genetic Algorithm (GA) is inspired by Darwinian evolution, while Ant Colony Optimization (ACO) is modeled on the foraging behavior of ants. Since challenging optimization problems are ubiquitous in every field of science and engineering, methods like PSO, GA and ACO have made a significant and broad impact across a wide range of application areas. The same is true for gravitational wave (GW) data analysis, where the basic optimization problem is that of statistical regression— finding the signal model that best fits some given noisy data.

Space Simulator
Faculty Mentor: Dr. Teviet Creighton

The project will involve a student in testing equipment or small experiments in a cryogenic, vacuum environment that can simulate the conditions at various points in the solar system or its planets.

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High Density Nanoenergetic Micropropulsion System
Faculty Mentor: Dr. Karen Martirosyan

The goal of this task is to advance the multi-physics knowledge and nanotechnology bases of micropropulsion systems by devising and synthesizing novel high-energy density nanoenergetic materials which will be integrated with microelectromechanical systems (MEMS). These transformative developments will significantly contribute to micro- and nano- satellite missions as well as national security and U.S. technology dominance. We will develop nanoenergetic composites that will surpass the existing energetic materials in terms of high energy density, energy release, shock waves, gas pressure discharge, and stability.

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Lasers for the LILA Project
Faculty Mentor: Dr. Volker Quetschke

LILA, the Laser Interferometer Lunar Antenna is a proposed interferometric gravitational wave detector located on the moon. Lasers for the future LILA project will be based on a fiber based laser system that consists of a fiber coupled laser that will be amplified using a fiber amplifier. The project for the REU participant will evaluate the splicing of various gain fibers to non-active fibers and evaluate the coupling and losses to inform future LILA laser development.

 

Harvesting Wave energy
Faculty Mentor: Dr. Yingchen Yang

Blue energy research focuses on energy harvesting from ocean waves. Compared to wind, ocean waves offer more power density (about 25 times greater) and more consistent flux (that is, they produce power at all times and in all weather). Due to the complex nature of water flow in waves, however, energy extraction from waves is much more challenging than from winds. While various wave energy converter (WEC) technologies have been proposed or even tested, economically competitive means are yet to be demonstrated. The ongoing blue energy research at UTRGV aims to realize a new WEC technology that is expected to produce electricity at a low cost. The current efforts combine fundamental research with prototype development. The blue energy lab is very close to the Gulf coast, and has a large indoor wave tank.