Ocean and Coastal Research Experiences for Undergraduates

REU: OCEANUS is an interdisciplinary research program that advances scientific understandings of coastal system sustainability. Funded by the National Science Foundation (Award Number: 1950910), OCEANUS invites talented students from diverse backgrounds to participate in a 10-week online research experience.

Program Description

May 31st - August 6th, 2021

Program Description:

OCEANUS is a fully remote summer program, with no residency requirement or face-to-face meetings. OCEANUS advances coastal system sustainability, while also training the next generation of scientists and engineers who are essential for making the scientific discoveries and technologies of the future.

OCEANUS features:

  • Independent student research, supervised by TAMUG faculty and staff
  • Individualized student mentorship by TAMUG faculty and staff
  • Research and professional development workshops on research design and analysis, scientific communication, ethics in research, and more
  • Student research presentations at the annual OCEANUS research symposium
  • Post-graduation preparation for graduate school and careers in STEM


  • Enhance scientific research and communication skills through high-impact learning and hands-on training
  • Gain tools to navigate the academic pipeline
  • Build social capital through the Aggie Network

Program Requirements


Open to all STEM majors.



Previous research experience not necessary.


Applicants must be U.S. citizens, U.S. nationals, or permanent residents. Students currently enrolled in an undergraduate program and expected to graduate in December of 2021 or later are eligible to apply. Students from underrepresented groups, affiliated with the Louis Stokes Alliance for Minority Participation (LSAMP) program, enrolled in minority-serving institutions, or attending colleges or universities with limited STEM research are especially encouraged to apply.

How to apply

  1. Complete Application via  https://tamu.qualtrics.com/jfe/form/SV_aVnvmNiGTvOod6u
  2. Submit:
    Unofficial academic transcripts
    Personal statement
    Diversity statement
    Contact information for two references
  3. Applications will be accepted until February 15th, 2021. Late or incomplete applications will not be reviewed.
  4. Applicants will be notified by March 15th, 2021.

Research Areas

Marine Biology

Animal Behavior

Lene H. Petersen

Understanding an animal’s behavior is important not only to increase our understanding of animals but this knowledge is pertinent to ensure successful breeding programs of endangered species and to enhance success of animal rehabilitation and conservation efforts. This project will train the student to observe behaviors of animals and teach the student how to construct ethograms, analyze and interpret data collected. The student will receive thorough training in observation techniques, data collection, analysis and interpretation of data. The student is to write a small research paper over their project to learn how to disseminate their results to the scientific community. The student can choose to study one animal or a group of animals of their choice. The behavior under investigation can be foraging, social, parental care, play behavior etc. The behavior under study will be selected in collaboration and mentorship of the P.I. The student can choose to conduct their research at their local zoo or aquarium (please be mindful of COVID-19 restrictions) or select to study an animal from national or international zoos or aquaria that allow observations via the organizations webcams. The proposed study will train the student on various aspects of animal behavior studies which are useful for careers in science, conservation, animal rehabilitation and rescue.

The student will perform preliminary observations, on the animal of their choice, to construct an initial ethogram. Behaviors will be studied using observational charts to list duration and/or frequency of displayed behaviors. Following preliminary observations (1-2 weeks), the student will construct a final ethogram and will, together with the mentor, determine a time of day for actual observations (this will be based on preliminary observations). Behaviors listed in the ethogram will then be recorded (using either time or continuous sampling methods) 3-4 times a week for at least 3-4 weeks. The student will then tabulate, analyze and present data in figures and tables. Data will be entered in Excel, analyzed and presented via Excel software unless the student has access to other graphic/statistical programs. The student will need access to search engines, such as Google Scholar, Pubmed or Web of Science to search published papers in the field of study which will used to interpret results in a small (5 pages) research paper (~ 2 weeks). Besides a computer, and e.g. cell phone for time taking, no other equipment is needed.

A matrix biology analysis of AHR transcriptional signaling in fish exposed to environmental pollution

David Hala

Transcriptional-regulatory networks (TRNs) comprise a key component in environmental information processing, as they concentrate various receptor-mediated signaling cascades into gene-regulatory responses. A key TRN system involved with sensing and responding to hydrocarbon (or oil) pollution is the aryl hydrocarbon receptor (AHR) TRN network. The AHR network is an ‘ancient’ and conserved system found across various invertebrate and vertebrate taxa. In this project, we will construct a steady-state mathematical matrix model of AHR signaling. We will then simulate the activations of the AHR network under varying (and environmentally relevant) scenarios. It is expected that high-throughput datasets from fish and humans exposed to oil pollution will be used to simulate the various activation states of the AHR system. This project is 100% driven by existing and freely available data and algorithms, and is therefore amenable to a virtual research format. No prior math or computational experience is required, and is a good fit for a biologist seeking experience in mathematical biology. Furthermore, the approach taken in this project is novel and is expected to lend to a publication.

The following methods will be used: 1) Construct a pseudo-stoichiometric matrix model of AHR TRN signaling using already published network maps. 2) Simulate steady-state properties of the AHR TRN network using the freely available extreme pathway analysis (or EXPA) toolbox. 3) Parse freely available datasets on fish and/or human exposures to hydrocarbons to obtain the expression levels of various transcription factors that are required to activate or inactivate the AHR network. 4) Use the parsed data as input to the matrix model to simulate various activation states of the AHR TRN network. Compare and contrast model predictions with experimental results to gain insights into the AHR networks functions. 

Examining the Life Histories of Coastal and Estuarine Predators through Movement Patterns, Habitat Use, and Trophic Ecology

David Wells

Identifying habitat use and movement patterns of top predators in the estuary is critical in order to identify biodiversity hotspots that are used as nursery and/or feeding areas, particularly in the face of increasing environmental stressors. Current research is focusing on examining movement patterns and the feeding ecology of common predators (fishes and sharks) in estuarine and coastal waters. This specific project is using acoustic telemetry techniques to track fine-scale movements of predators in the estuary, while also analyzing the stomachs and tissue for stable isotope analysis in order to better understand how movement and feeding habitats are linked to the environment. Ultimately, information generated from this study will enable us to better understand habitat use and trophic interactions of top predators in the estuary, which can then be used to assist in ecosystem-based fishery management.

Student will analyze current data sets related to movement and feeding.

Marine Sciences

Measuring, Mapping and Managing Flood Risk

Sam Brody and Wesley Highfield

Objectives involve developing novel ways to measure, map, and communicate flood impacts as a guide for local communities seeking to better prepare for and reduce the adverse impacts of future storm events. The project will produce maps and other visuals that paint a more complete picture of flood risk by integrating multiple data sources and models. These data include advanced hydraulic models, insurance- and aid-based flood payouts, crowd sourced data, socioeconomic characteristics, and survey responses. Maps will be shared with local stakeholders to obtain feedback on how to refine our products and make them most effective in helping localities prepare, mitigate, and recover from flood events.

This project integrates advanced risk modeling and community engagement to create new mapping tools that expand stakeholders' capacity to mitigate flood risk. We will integrate two different types of modeling methods to estimate flood risk: physics-based models and machine learning algorithms. Physics-based models are widely used by scientists and engineers to simulate where flood waters pool or move and how long it takes for water to recede. In this study, we will utilize advanced hydraulic models to better capture flooding in areas in and outside of the conventional floodplains. The output from the models will provide estimates of the likelihood that a given location will flood based on the design storms. An emerging approach for flood risk prediction are machine learning (ML) algorithms which quantify the interaction of factors that influence flood risk.

Liberal Studies

Multilevel Climate Governance in Alaska

Elizabeth Nyman and Jenna A. Lamphere

Accelerating impacts from climate change pose numerous risks to coastal landscapes, infrastructure, and ecological and cultural diversity in the state of Alaska. Increased ocean temperatures are melting Artic sea ice and contributing to higher rates of permafrost thaw, coastal erosion, and habitat loss. Climatic change is also destabilizing indigenous communities and threatening Alaska Native ways of life. Although over the last 20 years, increased attention has been paid to climate adaptation in Alaska, many studies have either examined communities with acute impacts or were conducted on behalf of government agencies and reflect western systems of knowledge. Additionally, adaptive efforts have tended to be fragmented and reactionary, as opposed to integrative and embedded in long-term strategic planning. In this study, we examine the complex institutional and policy processes characteristic of multilevel climate governance in Alaska. Specifically, the objectives of this research are to: (1) analyze multilevel governance networks, particularly differences in capacity, power, and preferences among institutional and community actors; (2) identify systemic barriers to adaptability; and, (3) strengthen bottom-up and top-down climate governance by helping align worldviews and adaptive priorities.

In Alaska, tribal governments and regional institutions are nested within borough, state, and federal contexts. By applying a multilevel analytical framework, the REU intern will research how vertical and horizontal networks impact climate adaptation. This is a multi-methods study. The REU intern will collect and analyze documents produced by or about institutional and community actors who are integral to Alaska climate governance. Additionally, the REU intern will receive foundational training in human subjects research and conduct semi-structured interviews with institutional and community actors of interest. The REU intern will also receive training in the qualitative data analysis software QDA Miner, which will be used to analyze interview transcripts, as well as to conduct the document analysis. Lastly, to help meet research object three, the REU intern will gain foundational knowledge in epistemologies for the social and political sciences. No prior experience in human subjects research or qualitative data analysis is necessary. However, preference will be given to applicants with an interest in social policy and passion for climate adaption.

Maritime Business Administration

Marketing Implications in the Maritime Logistics Companies and Sector Associations

Cassia Galvao

Marketing is generally defined as a set of institutions or activities dedicated to create, communicate, deliver, and exchange offerings that have value to customers, partners, or even society at large. Modern marketing scholars and practitioners suggest that today virtually all products depend on some type of service to have the value proposition delivered. Now, if this is true for consumer products (B2C), it is certainly applicable in business markets (B2B), where the development of marketing processes usually involves a co-creation of value through services. In this B2B context, companies tend to develop partnerships or even joint-ventures in an effort to combine their expertise and successfully deliver the value proposition to the customer. That is, like in a chain, the accomplishment of one company became determinant for the success of their partners and final customers. Maritime Logistics segment is notorious for its operations to be delivered by firms that would normally be seeing as competitors. Frequently these companies are also found working together in sector associations, which is typical for ship owners and port operators in various countries. This research investigates the application of marketing principles in maritime logistics aiming at a broader understanding of the value generation and co-creation processes.

In this investigation, we employ a combination of qualitative and quantitative methodologies. We typically work with archival data obtainable through companies’ or sector associations' electronic files, reports, and other searchable databases publicly available. The research employs an in-depth literature review, content analysis, and semi-structured interviews. The project may also explore case studies and comparative analysis of companies and/associations seeking for some patterns or structural similarities in their marketing strategies and value generation. We welcome students of any background, as we will provide the training needed to work on this project, even if the student never studied Marketing or the maritime sector before. All we need is the student’s interest and curiosity for maritime business and marketing.

Marine Engineering Technology

Modular Multilevel UPC For Power Quality Improvement in PV Dominant Microgrids

Irfan Khan

Coastal Communities are prone to hurricanes and natural disasters that cause  power outages. This projects focuses on improving resiliency of the energy distribution system in such communities. The objectives of this research are to: identify the critical Power Quality (PQ) problems in PV generation and investigate the potential application of Modular Multilevel- Unified Power Controller (MM-UPC) to mitigate these issues; model and design the proposed novel MM-UPC for PV dominant microgrid; develop control strategies for MM-UPC in order to address the power quality issues including voltage sag/swell, harmonics and reactive power compensation; and, develop and test scaled down prototype for the proposed MM-UPC converter under emulated grid conditions. The REU intern will have access to resources at the Clean And Resilient Energy Systems (CARES) research lab.

Increasing penetration of PV sources and nonlinear loads brings forth a chain of PQ problems at all levels of usage in the grid for which UPC has been accepted as a universal solution. Traditionally, the application of two-level Voltage Source Inverter (VSI-UPC) or Current Source Inverter (CSI-UPC) faces limitations of low voltage and power ratings, and additional cost due to higher number of switches. To resolve these issues, multilevel configurations including Neutral Point Clamped (NPC), Cascaded H- Bridge (CHB) and multi-cell converters are proposed. The NPC converter suffers from DC capacitor’s voltage-sharing problem, the CHB requires more elements, while the multi-cell converter presents poor reliability against short circuit. This project will focus on developing a novel Modular Multilevel Converter (MMC) for UPC. MMC has numerous advantages including lower harmonic content, modular design with distributed capacitance, desirable dynamic characteristics, filterless grid integration and lower losses. The proposed MM-UPC is novel in a sense that it will require only nine switches of which three switches are common for shunt VSI and series VSI operation. In addition, it offers modular design such that new modules can be added for additional voltage and power requirements.