Computer science is the study of computation, information processing, and the design of computer systems, encompassing both theoretical foundations and practical applications. It involves developing algorithms, software, and systems to solve complex problems, analyze data, and create technologies that power everything from smartphones and websites to artificial intelligence and scientific discovery.

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 and TOEFL scores are optional and not required to complete an application, but we encourage you to include them if available. If you are able to include these, score reports 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 January and continues until open slots are filled. Upon application, 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 office director/manager at cs-grad@tulane.edu.

Artificial Intelligence and Data Science

Students with an interest in multi-agent systems, artificial intelligence, and/or data science with applications to group decision-making, recommender systems, and issues in AI, ethics, and society, are invited to contact Professor Nicholas Mattei. Possible areas for interdisciplinary collaboration include mathematics, economics, psychology, and law.

Accessible Computing and Human-Computer Interaction

Students with an interest in human-computer interaction, accessible computing, and computational social science are invited to contact Professor Saad Hassan. Professor Hassan conducts interdisciplinary research on technologies to facilitate inclusion, learning, and creative expression for individuals with disabilities, with a focus on Deaf and Hard of Hearing (DHH) users. Possible areas for collaboration include linguistics, psychology, neuroscience, sociology, art, and design. Students with disabilities are welcome and encouraged to apply.

Computational Biology and Bioinformatics

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, and computational immunology. This research is performed in collaboration with faculty from the Tulane Medical School.

Computational Geometry, Shape Matching, or Trajectory Analysis

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.

Machine Learning

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 advanced AI learning algorithms to generate accurate, reliable, and transparent decisions including topics such as deep learning, transfer learning, multi-view learning, and applies to AI-assisted interdisciplinary applications such as smart transportation, medical data analysis, WiFi-based robot localization, and material fracture detection.

Students interested in reinforcement learning and multi-agent learning are invited to contact Professor Zizhan Zheng. Professor Zheng's current research topics include (deep) reinforcement learning, federated learning, learning in games, machine learning security and safety, and their applications in robotics, healthcare, environmental science, and social sciences.

Networking and Security

Students interested in the theory and applications of networking and security are invited to contact Professor Zizhan Zheng. Professor Zheng develops optimization and learning-based solutions to improve the efficiency and security of wireless networks, cyber-physical systems, and cloud and edge computing systems. Possible areas for interdisciplinary collaboration include smart transportation, energy systems, and environmental science.

Scientific Visualization and Computer Graphics

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.

Computer Systems and Architecture

Students with interest in computer systems and architecture are invited to contact Professor Lu Peng. Currently, his research topics include GPU/CPU performance, power, reliability, and security design; deep learning neural network accelerators and applications; blockchain acceleration and applications; and quantum processor architecture and compilers. Possible areas for an interdisciplinary collaboration include physics, healthcare, and biological sciences.