One of the benefits of a Trinity education is the opportunity to engage in research with faculty. You don’t have to do research to graduate with a Bachelor of Science in Computer Science from Trinity, but it’s highly recommended for students thinking of going on to graduate school and can be a great learning experience for others as well. It fits well with Trinity’s emphasis on experiential learning, and can result in conference presentations or academic publications.
Faculty active in research generally welcome student involvement on an part-time basis during the school year, often as a one-credit course (CSCI 3190, Directed Study). A directed study is an excellent way to build towards summer research or a thesis project.
Many faculty devote at least part of their summers to research, and they welcome student involvement. Summer research is usually a full-time, ten-week program, and can be supported through summer undergraduate research fellowships, particularly the HEP and Murchison fellowships. Summer projects are arranged by individual faculty members, and fellowship applications are uually due in mid-February.
One of the options for the capstone requirement for the BS in CS is to complete a three-semester thesis project. These projects involve students working closely with faculty to define a question and explore solutions to it. The work culminates in the student writing a thesis, generally 30–50 pages, on what they did and what they discovered.
To find out what your options are, your best bet is to talk to faculty you think you might want to work with and ask. Faculty members listed in the next section have expressed particular interest in supervising student research, but you should feel free to approach any faculty member.
Fogarty’s research interests span automata theory, programming languages, and interdisciplinary projects. He studies the use of automata in formal verification, proving a program correct, including bespoke data structures used in that domain. The dsmodels domain-specific language is a tool built in R for visualizing models studied by mathematicians. He has co-advised research projects in the philosophy of computation and digital anthropology. Summer research projects are ideally preceded by a directed study in the spring semester.
Hibbs’s research is focused on how to best utilize high-throughput data sources to understand biology at multiple levels. This problem has become increasingly challenging over the past decade as new experimental techniques and resources (e.g., gene expression microarrays, deep sequencing, tandem mass spectrometry, etc.) have grown widely available and more affordable. While these data promise to shed light on cellular mechanisms, gene regulation, protein functions, and ultimately human disease, the rate at which these data are translated into knowledge is currently much slower than the rate of data generation. In order to help bridge this gap, his focus is on developing novel algorithms and approaches for the analysis, exploration and visualization of this data. In particular, these methods incorporate biologists into the early phases of analysis in order to utilize their existing, expert knowledge.
Horn's research interests lie in the fields of game design, artificial intelligence, human-computer interaction, and computational creativity. His primary research focuses on leveraging novel AI applications to enhance the efficacy and quality of educational games. In addition, he researches computational creativity methods to design AIs that produce artistic artifacts (e.g. sculpture, music, digital images) in an effort to expand creative expression.
Jiang’s research addresses computational problems arising in game theory, which studies interactions among multiple self-interested parties. He designs efficient algorithms for prediction and decision making in multi-agent scenarios, often utilizing techniques from artificial intelligence and machine learning. He is also interested in applications of game-theoretic computation to real-world domains such as security and electronic commerce. Recent student research topics include: game-theoretic analysis of champion choices in League of Legends; applying deep neural networks to solve game-theory models of infrastructure security; algorithms for dynamic games with applications to computer Poker; using game theory ideas to help improve convergence of training Generative Adversarial Networks, with applications to image style transfer.
Lewis's primary field of research is on the numerical simulations of planetary rings. In addition to the dynamics of rings of material in orbit around astronomical bodies, he also looks at how to optimize and parallelize these simulations, as well as novel techniques for N-body simulation. He often oversees general problems in simulation, optimization, and parallelization. He also does work on visualizing simulation output both as 2D plots and 3D renderings. Recent student research topics: semantic compression, modeling collisions between non-spherical bodies, parallelizing SwiftVis dataflow analysis, using BabelNet for word disambiguation, numerical simulations with Rust, Hamiltonian dynamics with symplectic trees, hard-sphere collision simulations using GPUs.
Myers’s research has included software engineering, theoretical computer science, and the mathematical foundations topic of constructivity (Intuitionism) applied to computer science. More recently he has investigated the historical 1980s-1990s Japanese Fifth Generation Computing Project. He has just begun working in computing/AI/security ethics and a more general notion of socially responsible computing, including the relatively new area of vulnerability theory. This has tied in with some of his past publications regarding women (students) in computer science.
Tan’s research interests span mobile computing and cybersecurity with an emphasis on wireless and mobile sensing. His work utilizes wireless network and mobile devices to sense the human activity at various scales as well as objects in the surrounding environments. He also works on developing biometric-based user authentication protocol to enhance security on smartphones. His current projects include mobile safety system for distracted driver/pedestrian and mobile sensing for human computer interaction applications.
Zhang’s research falls within Agent-based Modeling and Simulation, which is a subfield of Artificial Intelligence. Her previous research concentrates on multi-agent social simulation, which is to understand how the decentralized interactions of agents could generate collective social behaviors. Her current research focuses on deep learning. Her current projects include deep learning neural networks and its applications in bioinformatics.