Research Assistant at Louisiana State University under the RISE LabGraduate STEM Student Intern at the U.S. Naval Research Laboratory
I'm currently a first-year PhD student in Computer Science at Louisiana State University (LSU), focused at the intersection of software engineering and robotics.
My primary interests lie in Human-Robot Interaction (HRI) and human-machine teams, particularly exploring how we can enable more intuitive and effective collaboration. My recent work focuses on conversational robot programming, investigating how natural language and dialogue can empower users to program robots with greater ease and flexibility.
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Louisiana State UniversityDepartment of Computer Science and Engineering
Ph.D. StudentAug. 2025 - present -
Southeastern Louisiana UniversityB.S. in Computer Science, Math Minor, Honors DiplomaAug. 2021 - May. 2025
Experience
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Louisiana State UniversityGraduate Assistant at the RISE LabAug. 2025 - present
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Southeastern Louisiana UniversityUndergraduate Research Assistant at The Wright LabJan. 2023 - May. 2025
Honors & Awards
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Senior Tutor Leadership Award2025
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Thirteen Club Honors Recognition2024
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Thomas F. Higginbotham Scholarship Award2024
News
Selected Publications (view all )
EnlightDen: A Software Solution for Supporting Students Without Established Study Habits
Elijah Phifer, Bonnie Achee
Submitted to CCSC SE 2025
With the help of federal student aid, higher education has become more financially attainable, allowing more students than ever the opportunity for economic upward mobility. However, while financial access to higher education may be improving, there is still a systemic void of support for learners of disadvantaged backgrounds, including students who can be described as: nontraditional, low socio-economic status, or having certain learning disabilities. These students often lack study skills, educational support (i.e. tutoring), and expendable time to dedicate to school due to time consuming responsibilities (i.e. maintaining a job to financially support oneself and/or multiple dependents). These observations in tandem with the alarming situation in the United States that involves a decline in reading literacy and math skills raise concerns about having to deal with an emerging national security issue and a situation that may limit the growth of the US economy in the future. EnlightDen uses AI to provide a personalized learning experience that addresses gaps in a student’s academic support system by focusing on individual needs.
EnlightDen: A Software Solution for Supporting Students Without Established Study Habits
Elijah Phifer, Bonnie Achee
Submitted to CCSC SE 2025
With the help of federal student aid, higher education has become more financially attainable, allowing more students than ever the opportunity for economic upward mobility. However, while financial access to higher education may be improving, there is still a systemic void of support for learners of disadvantaged backgrounds, including students who can be described as: nontraditional, low socio-economic status, or having certain learning disabilities. These students often lack study skills, educational support (i.e. tutoring), and expendable time to dedicate to school due to time consuming responsibilities (i.e. maintaining a job to financially support oneself and/or multiple dependents). These observations in tandem with the alarming situation in the United States that involves a decline in reading literacy and math skills raise concerns about having to deal with an emerging national security issue and a situation that may limit the growth of the US economy in the future. EnlightDen uses AI to provide a personalized learning experience that addresses gaps in a student’s academic support system by focusing on individual needs.
Testing Character Evolution Models in Phylogenetic Paleobiology with Reversible Jump Markov-Chain Monte Carlo
Elijah Phifer, David Wright, Peter Wagner, April Wright# (# corresponding author)
Submitted to Paleobiology
Modern phylogenetic paleobiology allows for a meaningful synthesis of quantitative paleobiology and phylogenetics. However, in achieving this synthesis, we have opened new frontiers of questions about appropriate model choice. In this manuscript, we explore the idea of incorporating model uncertainty in phylogenetic estimation with reversible jump Markov-chain Monte Carlo (rjMCMC). Using a previously-published study as a guide, we re-evaluate the strength of evidence for subcomponents of the Fossilized Birth-Death (FBD) process, implemented as a joint model. Using a combination of simulations and an empirical dataset of Cambrian echinoderms, the Cincta, we explore the performance and implications of rjMCMC applied to paleobiological data. Our results highlight the advantages of integrating over multiple sources of uncertainty when making phylogenetic inferences from fossil data, and provide a framework for quantifying the full range of alternative evolutionary scenarios.
Testing Character Evolution Models in Phylogenetic Paleobiology with Reversible Jump Markov-Chain Monte Carlo
Elijah Phifer, David Wright, Peter Wagner, April Wright# (# corresponding author)
Submitted to Paleobiology
Modern phylogenetic paleobiology allows for a meaningful synthesis of quantitative paleobiology and phylogenetics. However, in achieving this synthesis, we have opened new frontiers of questions about appropriate model choice. In this manuscript, we explore the idea of incorporating model uncertainty in phylogenetic estimation with reversible jump Markov-chain Monte Carlo (rjMCMC). Using a previously-published study as a guide, we re-evaluate the strength of evidence for subcomponents of the Fossilized Birth-Death (FBD) process, implemented as a joint model. Using a combination of simulations and an empirical dataset of Cambrian echinoderms, the Cincta, we explore the performance and implications of rjMCMC applied to paleobiological data. Our results highlight the advantages of integrating over multiple sources of uncertainty when making phylogenetic inferences from fossil data, and provide a framework for quantifying the full range of alternative evolutionary scenarios.