The Computer Science Department offers M.S. and PhD degrees. Below are outlines of the requirements for these programs, the areas of faculty expertise, as well as instructions on how to apply. Interested students also can direct inquiries to the graduate coordinator, Professor Ramgopal Mettu, at email@example.com.
The Master's Program in Computer Science is offered in coursework and thesis tracks. The coursework option requires both breadth and depth requirements. The breadth requirement ensures students obtain a solid foundation in core computer science areas, while the depth requirement allows students to design a sequence of courses to target a particular area of interest. The thesis track further allows students to conduct research in a chosen area of interest. The Master’s degree can also be pursued in a 4+1 format in conjunction with the Coordinate major in Computer Science.
The M.S. program requires 30 credit hours of graduate coursework. Coursework requirements vary slightly depending on the chosen track, but consist of 12 credits of breadth coursework and 12-18 credits of depth coursework. A more detailed listing of requirements and example curricula can be found in the resources section below.
The objective of the PhD program is to train students in a chosen area of depth so that they can ultimately conduct original research. The program requires both breadth and depth in coursework. The breadth requirement ensures students obtain a solid foundation in core computer science areas, while the depth requirement allows students to gain an in-depth and up-to-date understanding of a particular area of concentration. In parallel with coursework, students are also expected to engage in research as early as their incoming semester. This will be accomplished by the research courses and research seminars that will expose students to ongoing research in the department.
The program requires 48 credit hours of graduate course work, including core computer science courses, research courses starting in the first year, as well as an interdisciplinary research project. After an oral qualifying examination at the end of the fifth semester, the prospectus presentation is scheduled at the beginning of the seventh semester, and the final milestone is to complete and defend a dissertation. A more detailed listing of requirements and example curricula can be found in the resources section below as well as in the program catalog listing.
Up to 24 credit hours of graduate work at Tulane or another university may be transferable for credit, if the work is in Computer Science or in a related area. In particular, students who have completed a Master's degree may be able to have some of their Master's coursework count for the PhD degree. The suitability of a course transfer is approved on a course-by-course basis and is not automatically guaranteed.
Students with interest in artificial intelligence and/or data science, including issues in AI, ethics, and society, are invited to contact Professor Nicholas Mattei. Possible areas for interdisciplinary collaboration include mathematics, economics, psychology, and law.
Students interested in research in computational biology and bioinformatics are invited to contact Professor Ramgopal Mettu. Professor Mettu currently conducts research in protein structure prediction, protein-protein interactions, compound screening, computational immunology, and in the application of computational methods to model vector-borne infectious disease. His research is performed in collaboration with faculty from the Tulane Medical School.
Students with interest in computational geometry, shape matching, or trajectory analysis, possibly in combination with topology or statistics or with biomedical image analysis, are invited to contact Professor Carola Wenk. Possible areas for an interdisciplinary collaboration include mathematics, biomedical engineering, and biology.
Students interested in working in the theory, algorithms and applications of machine learning are invited to contact Professor Jihun Hamm. Professor Hamm's current research topics include deep learning, adversarial machine learning, non-convex optimization, and machine learning applications in biomedical sciences.
Students interested in machine learning algorithms for natural language processing are invited to contact Professor Aron Culotta. Professor Culotta conducts interdisciplinary research on social media analysis with applications to public health, emergency management, and political science. His recent methodological focus includes domain adaptation, semi-supervised learning, and causal inference.
Students interested in working on the algorithms and applications of computer vision and machine learning are invited to contact Professor Zhengming (Allan) Ding. Professor Ding's current research focuses on developing scalable algorithms to learn robust representations from large-scale data, including topics such as deep generative learning, transfer learning, multi-view learning, few-shot learning, zero-shot learning.
Students interested in working on the theory and applications of reinforcement learning and multi-agent learning are invited to contact Professor Zizhan Zheng. Professor Zheng's current research topics include reinforcement learning, federated learning, trustworthy learning, learning in games, and distributed optimization.
Students interested in working in the mathematical and logical foundations of computer science, or in quantum information and quantum computation, are invited to contact Professor Michael Mislove. Professor Mislove conducts research on computational models, which are mathematical and logical systems used to analyze computational processes. His recent work has focused on probabilistic models and includes exploring the development of computational approaches to probability, and to quantum information and computation.
Students with interest in networking, cloud computing, and/or cybersecurity are invited to contact Professor Zizhan Zheng. Professor Zheng currently conducts research in optimization and mechanism design for networked systems, and in active/cognitive defense against advanced cyber threats using game theory and reinforcement learning. Possible areas for an interdisciplinary collaboration include economics, psychology, and environmental sciences.
Students with interest in data visualization, computer graphics, and/or image processing are invited to contact Professor Brian Summa. Possible areas for interdisciplinary collaboration include mathematics, biology, neuroscience, physics, earth and environmental sciences, and chemical and biochemical engineering.
Students interested in applying should use the School of Science and Engineering Graduate Online Application Forms. When completing the "Application Information" section of the application forms, candidates should select the following options (in bold below) in the "Department/Program & Area of Specialization" subsection:
Department/Program: Computer Science
Area of Specialization: N/A
GRE scores are not required to complete an application, but we encourage you to include them if available. TOEFL examination scores are required for non-US citizens who are not native English speakers. GRE score reports and TOEFL score reports (when applicable) should be directed to Institutional Code 6173 when requested from the testing agency.
Applications to our programs begin in September each year and review of applications begins in February and continues until open slots are filled.
Interested students also are encouraged to contact the graduate faculty member whose research interests most closely resemble their own. Please see https://sse.tulane.edu/cs for more information about the department faculty. For general questions about graduate programs, please contact the graduate coordinator, Dr. Ramgopal Mettu, at firstname.lastname@example.org.