phd computer science tandon

PhD in Computer Science

The Tandon School of Engineering offers a PhD in Computer Science. Cybersecurity is a particular research strength of the program. Learn more and apply to the PhD in Computer Science  through the Tandon School of Engineering.

CAS AND TANDON CS PROGRAMS

NYU has two excellent computer science departments, one in the the College of Arts and Science and one in the Tandon School of Engineering. Both offer degree programs at the undergraduate, masters and PhD level and have vibrant research programs. Graduates of both programs have excellent job prospects and are well prepared for graduate study. However there are some distinctions between the programs offered by the two departments. We hope this comparison of the two undergraduate computer science majors will help you decide which is right for you. For detailed descriptions of the programs see NYU Courant's Computer Science page and NYU Tandon's BS in Computer Science curriculum page .

For more information about the BA in Computer Science in the College of Arts and Science, contact [email protected] .

For more information about the BS in Computer Science in the Tandon School of Engineering, contact [email protected] .

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Computer Science PhD Program

Supervising faculty.

• Program Structure

Current Students

• Application

NYU Shanghai invites applications from exceptional students for PhD study and research in Computer Science. Two programs are available: one offered in partnership with the NYU Graduate School of Arts and Science and the NYU Courant Institute of Mathematical Sciences; and the second offered in partnership with the NYU Tandon School of Engineering and the NYU Department of Computer Science and Engineering.   Participating students are enrolled in either the NYU GSAS Computer Science PhD program or the NYU Tandon Computer Science PhD program, complete their coursework in New York, and then transition to full-time residence at NYU Shanghai where they undertake their doctoral research under the supervision of NYU Shanghai faculty.

Highlights of the Program

• NYU degree upon graduation
• Graduate coursework at NYU New York, either at the Courant Institute or Tandon Department of Computer Science and Engineering
• Research opportunities with and close mentorship by NYU Shanghai faculty
• Access to the vast intellectual resources of the NYU Computer Science community
• Cutting-edge research environment at NYU Shanghai, including the Center for Data Science and Artificial Intelligence, activities such as a regular program of seminars and visiting academics, a thriving community of PhD students, post-doctoral fellows, and research associates, and links with other universities within and outside China
• Financial aid through the NYU Shanghai Doctoral Fellowship , including tuition, fees, and an annual stipend
• Additional benefits exclusive to the NYU Shanghai program, including international health insurance, housing assistance in New York, and travel funds

Theoretical Computer Science, Cryptography, Computational Complexity

Trustworthy Networks, Software Defined Networking, Network Function Virtualization, Cloud & Edge Computing, The Internet of Things

Nasir Memon

Media Forensics, Biometrics, Authentication, Network Security, Data Compression, Cybersecurity​

Machine Learning and Data Mining, Graph Learning, Foundation Model, Multimodal Learning

Shengjie Wang

Machine Learning, Deep Learning, AI for Science, Optimization

Recommender Systems, Data Mining, Human-centered AI

Computational Geometry, Algorithms, Data Structures, Graph Theory, Parameterized Complexity

Natural Language Processing, Human-Computer Interaction, Machine Learning

Recent Publications by NYU Shanghai Faculty

Nick Gravin, Siyao Guo, Tsz Chiu Kwok and Pinyan Lu:  Concentration Bounds for Almost K-wise Independence with Applications to Non-Uniform Security. In SODA 2021.

Siyao Guo, Qian Li, Qipeng Liu and Jiapeng Zhang:  Unifying Presampling via Concentration Bounds. In TCC 2021.

Yevgeniy Dodis, Siyao Guo, Noah Stephens-Davidowitz and Zhiye Xie:  No Time to Hash: Provable Super-Efficient Entropy Accumulation. In CRYPTO 2021.

Yevgeniy Dodis, Siyao Guo, Noah Stephens-Davidowitz and Zhiye Xie:  On Linear Extractors for Independent Sources. In ITC 2021.

Alexander Golovnev, Siyao Guo, Thibaut Horel, Sunoo Park and Vinod Vaikuntanathan: Data Structures Meet Cryptography:  3 SUM with Preprocessing.  In STOC 2020.

Divesh Aggarwal, Siyao Guo, Maciej Obremski, Joao Ribeiro and Noah Stephens-Davidowitz:  Extractor Lower Bounds, Revisited.  In RANDOM 2020.

Kai-Min Chung, Siyao Guo, Qipeng Liu and Luowen Qian:  Tight Quantum Time-Space Tradeoffs for Function Inversion. In FOCS 2020.

Siyao Guo, Pritish Kamath, Alon Rosen, Katerina Sotiraki:  Limits on the Efficiency of (Ring) LWE based Non-Interactive Key Exchange. In PKC 2020 (and Invited to Journal of Cryptology). 

Marshall Ball, Siyao Guo and Daniel Wichs:  Non-Malleable Codes for Decision Trees.  In CRYPTO 2019.

​Liu, Guyue, et al. "Don't Yank My Chain: Auditable {NF} Service Chaining." 18th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 21). 2021.

Ren, Y., Liu, G., Nitu, V., Shao, W., Kennedy, R., Parmer, G., ... & Tchana, A. (2020). Fine-grained isolation for scalable, dynamic, multi-tenant edge clouds. In 2020 {USENIX} Annual Technical Conference ({USENIX}{ATC} 20) (pp. 927-942).

Qiaoyu Tan​

Junnan Dong, Qinggang Zhang, Xiao Huang, Keyu Duan, Qiaoyu Tan and Zhimeng Jiang. Hierarchy-Aware Multi-Hop Question Answering over Knowledge Graphs, The Web Conference (WWW), 2023.

Qiaoyu Tan, Ninghao Liu, Xiao Huang, Soo-Hyun Choi, Li Li, Rui Chen, and Xia Hu. S2GAE: Self-supervised graph autoencoders are generalizable learners with graph masking. In Proceedings of ACM International Conference on Web Search and Data Mining (WSDM), 2023.

Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, Soo-Hyun Choi and Xia Hu. Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection, ACM International Conference on Web Search and Data Mining (WSDM), 2023.

Qiaoyu Tan, Xin Zhang, Xiao Huang, Hao Chen, Jundong Li, and Xia Hu. Collaborative graph neural Networks for attributed network embedding. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023. 

Sirui Ding, Qiaoyu Tan, Chia-yuan Chang, Na Zou, Kai Zhang, Nathan R. Hoot, Xiaoqian Jiang, and Xia Hu. Multi-task learning for post-transplant cause of death analysis. In Proceedings of AMIA Annual Symposium (AMIA), 2023.

Yucheng Shi, Yushun Dong, Qiaoyu Tan, Jundong Li and Ninghao Liu. GiGaMA: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction. ACM International Conference on Information and Knowledge Management (CIKM), 2023.

Daochen Zha, Louis Feng, Qiaoyu Tan, Zirui Liu, Kwei-Herng, Bhargav, Bhushanam, Yuandong Tian, Arun Kejariwal and Xia Hu. DreamShard: Generalizable Embedding Table Placement for Recommender Systems. Neural Information Processing Systems (NeurIPS), 2022.

Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, and Xia Hu. Dynamic memory based attention network for sequential recommendation. In Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2021.

Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, and Xia Hu. Learning to hash with graph neural networks for recommender systems. In Proceedings of The Web Conference (WWW), 2020.

Machine Learning Force Fields with Data Cost Aware Training. A. Bukharin, T. Liu, S. Wang, S. Zuo, W. Gao, W. Yan, T. Zhao. ICML23

Constrained Robust Submodular Partitioning. S Wang*, T Zhou*, C Lavania, J Bilmes. NeurIPS21

Robust Curriculum Learning: from clean label detection to noisy label self-correction.T Zhou*, S Wang*, J Bilmes. ICLR21

Bias also matters: Bias attribution for deep neural network explanation. S Wang*, T Zhou*, J Bilmes. ICML19

Analysis of deep neural networks with extended data Jacobian matrix. S Wang, A Mohamed, R Caruana, J Bilmes, M Plilipose, M Richardson, K Geras, G Urban, O Aslan. ICML16

Yuanhe Guo, Haoming Liu, and Hongyi Wen. "Towards Personalized Prompt-Model Retrieval for Generative Recommendation." arXiv preprint arXiv:2308.02205 (2023).

Hongyi Wen, Xinyang Yi, Tiansheng Yao, Jiaxi Tang, Lichan Hong, Ed H. Chi. 2022. Distributionally-robust Recommendations for Improving Worst-case User Experience. In Proceedings of the ACM Web Conference 2022 (WWW ’22).

Hongyi Wen, Michael Sobolev, Rachel Vitale, James Kizer, JP Pollak, Frederick Muench, Deborah Estrin. “mPulse Mobile Sensing Model for Passive Detection of Impulsive Behavior: Exploratory Prediction Study”. JMIR Mental Health, 2021.

Hongyi Wen, Longqi Yang, Deborah Estrin. “Leveraging post-click feedback for content recommendations”. Proceedings of the 13th ACM Conference on Recommender Systems (RecSys), 2019.

Hongyi Wen, Julian Ramos Rojas, and Anind K. Dey. "Serendipity: Finger gesture recognition using an off-the-shelf smartwatch." Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI), 2016.

Haitao Wang*, Jie Xue*, "Near-optimal algorithms for shortest paths in weighted unit-disk graphs". In the 35th International Symposium on Computational Geometry (SoCG), 2019. Also in Discrete & Computational Geometry, 2020.

Pankaj K. Agarwal*, Hsien-Chih Chang*, Subhash Suri*, Allen Xiao*, Jie Xue*, "Dynamic geometric set cover and hitting set". In the 36th International Symposium on Computational Geometry (SoCG), 2020.

Jie Xue, Yuan Li, Rahul Saladi, Ravi Janardan, "Searching for the closest-pair in a query translate". In the 35th International Symposium on Computational Geometry (SoCG), 2019.

Zhao, C., Su, Y., Pauls, A., & Platanios, E. A.  Bridging the generalization gap in text-to-SQL parsing with schema expansion. ACL 2022.

Zhao, C., Xiong, C., Boyd-Graber, J., & Daumé III, H. (2021). Distantly-supervised evidence retrieval enables question answering without evidence annotation. EMNLP 2021.

Zhao, C., Xiong, C., Qian, X., & Boyd-Graber, J. . Complex factoid question answering with a free-text knowledge graph. WWW 2020.

Zhao, C., Xiong, C., Rosset, C., Song, X., Bennett, P., & Tiwary, S. (2020). Transformer-xh: Multi-evidence reasoning with extra hop attention. ICLR 2020.

Selected Faculty and Student Features

" When the Going Gets Tough, the Tough Get Going " (Yanqiu Wu)

" NYU Shanghai Awards First-ever PhD " (Sean Welleck)

" Faculty Spotlight: Guo Siyao " (Siyao Guo)

" Professor Zhang Zheng to Head Amazon's New AI Lab in Shanghai " (Zheng Zhang)

Structure of Program

Participating students complete the PhD degree requirements set by their respective department (either Courant or Tandon CSE) and in accordance with the academic policies of their respective school (either NYU GSAS or NYU Tandon). Each student develops an individualized course plan in consultation with the Director of Graduate Study at the student’s department and the student’s NYU Shanghai faculty advisor. A typical sequence follows:

Begin program with funded research rotation, up to 3 months preceding first Fall semester, to familiarize with NYU Shanghai and faculty as well as lay a foundation for future doctoral study.

Complete PhD coursework in New York alongside other NYU PhD students.

Return to Shanghai for second funded research rotation to solidify relationships with NYU Shanghai faculty and make further progress in research.

Under supervision of NYU Shanghai faculty advisor, pursue dissertation research and continue coursework. Depending on each student’s individualized course of study, return visits to New York may also occur. Complete all required examinations and progress evaluations, both oral and written, leading up to submission and defense of doctoral thesis.

To learn more about the NYU GSAS PhD program degree requirements, please visit this page .

To learn more about the NYU Tandon PhD program degree requirements, please visit this page .

Application Process and Dates

The choice between the NYU GSAS or the NYU Tandon Computer Science program is for each student to decide. Students may apply to either or both.

Applications are to be submitted either through the NYU GSAS Application portal or the NYU Tandon Application portal . Within each portal, students should select the Computer Science PhD as their program of interest, and then indicate their preference for NYU Shanghai by marking the appropriate checkbox when prompted. Applicants will be evaluated by a joint admissions committee of New York and Shanghai faculty. Application requirements are set by each department (either Courant or Tandon CSE ) and are the same as those for all NYU PhD applicants; however, candidates are recommended to elaborate in their application and personal statements about their specific interests in the NYU Shanghai program and faculty.

For admission in Fall 2024, the application deadline is December 1, 2023 with NYU Tandon and December 12, 2023 with NYU GSAS.

Interested students are welcome to contact Vivien Du , program coordinator of the NYU Shanghai Computer Science PhD, via email at [email protected] with any inquiries or to request more information.

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Interim Chair : Lisa Hellerstein

Mission Statement

The Department of Computer Science and Engineering is committed to preparing undergraduate and graduate students for leadership roles in professional and research activities in the information-technology sector. The department fosters an environment that encourages lifelong learning in the Information Age. Graduates lead and grow in diverse working environments and apply the theories and skills of computer science to real-world problems. Toward this end, the department conducts state-of-the-art research in theoretical and applied computer science and maintains strong educational programs that emphasize breadth and depth in technical knowledge and proficiency in spoken and written communication skills. The environment encourages Invention, Innovation and Entrepreneurship (i 2 e).

The Department

Computers are now used in practically every area of human endeavor and are radically changing both the way people live and how they view the limits of human capabilities. Job opportunities in computer science and engineering are challenging and diverse. According to the U.S. Bureau of Labor Statistics, current job growth in computer science is among the highest of any technical profession.

NYU Tandon’s Department of Computer Science and Engineering offers programs leading to a B.S., M.S. and Ph.D. in Computer Science   , and an M.S. in Cybersecurity   . The department offers joint programs with the Department of Electrical and Computer Engineering   , leading to a Computer Engineering, B.S.   , and the NYU School of Law, leading to a  Cybersecurity Risk and Strategy, M.S. (Offered jointly by the NYU School of Law and NYU Tandon School of Engineering)    The department also offers an advanced certificate in software engineering and cybersecurity and minors in Computer Science    and Game Engineering   .

The department is active in research in several key areas of computer science. Its particular strengths are in security and privacy; big data analysis and visualization; computer vision, game engineering; and algorithms and theoretical computer science.

The security and privacy concentration-also including cybersecurity, one of the largest growing fields in computer science-has research strengths in peer-to-peer security, digital forensics, biometrics, wireless security, and usable security. Big data analysis is strong in data management, computing, analyzing, and visualizing urban, scientific, and Web data. Computer vision puts a primary focus on medical image analysis. Game engineering focuses on computer graphics and perceptual science as well as artificial intelligence in gaming and player modeling. Finally, theoretical computer science is based in computational and discrete geometry, data structures, and machine learning.

The CSE department is at the center of a high-tech start-up culture where student and faculty innovation and entrepreneurship activities are supported and nurtured both in New York City, Brooklyn and across the NYU Global Network University. The faculty works closely with NYU Tandon’s Center for Advanced Technology in Telecommunications (CATT) and has relationships with industries that support research and activity in their special interests.

NYU Tandon School of Engineering has been designated as a Center of Excellence for Information Assurance Education for research by the National Security Agency (NSA) and operates the Scholarship for Service Program (SFS) in Information Assurance.

The department provides students with a wide variety of advanced computer and software systems. These support PC and UNIX technology along with highly distributed networks. The department has four dedicated computer-science laboratories (virtual lab) for upper-level undergraduate students. They are the Software Engineering Laboratory, Parallel and Distributed Systems Laboratory, Visualization and Graphics Laboratory and Computer System and Security Integration Laboratory. Multimedia and Web-based laboratories are also available.

NYU Tandon School of Engineering Computer Science and Engineering Department 370 Jay Street, 8th floor, rm 851 Brooklyn, NY 11201

Tel: (646) 997-3440 Web: http://engineering.nyu.edu/academics/departments/computer-science-engineering

Degrees Offered

Bachelor of Science

• Computer Engineering, B.S.    offered by the Computer Engineering Program    
• Computer Science, B.S.     
• Computer Science Minor    
• Game Engineering Minor    

Master of Science

• Computer Science Tandon, M.S.    
• Cybersecurity, M.S.    
• Cybersecurity Risk and Strategy, M.S. (Offered jointly by the NYU School of Law and NYU Tandon School of Engineering)    

Doctor of Philosophy

• Computer Science, Ph.D.    

Undergraduate Programs

For undergraduates, the department offers two degrees: a Bachelors of Science in Computer Science (B.S. CS) and a Bachelors of Science in Computer Engineering (B.S. CompE). The Bachelor of Science in Computer Science is a rigorous program that not only covers fundamental computer science subjects, such as object-oriented programming, computer architecture and operating systems, but also provides a number of exciting avenues for specialization including computer and online game development, cyber security, Internet/web systems and applications, bioinformatics, graphics and vision, digital media and management and entrepreneurship. Strong students can also apply to the B.S./M.S. Program where it’s possible to earn the B.S. and M.S. in computer science within approximately 5 years.

The department jointly administers the Bachelors of Science in Computer Engineering with the Department of Electrical and Computer Engineering. It draws on the two departments’ strengths to provide a focus on computer system design with integrated understanding of computer hardware and software.

Master’s Programs

The M.S. in Computer Science permits students to take courses either on a full-time or part-time basis. The curriculum has been designed for maximum flexibility. It includes fundamental courses in computer science as well as electives in specialized advanced courses on topics including computer and network security, distributed systems and networking, computer graphics, computer vision, databases and web search technology. By electing the masters-thesis option, students may also pursue research with faculty members who are internationally recognized in their fields.

The M.S. in Cybersecurity is a highly innovative program that provides students with the critical knowledge and skills to become experts in cybersecurity, the science of protecting vital computer networks and electronic infrastructures from attacks. The program responds to the growing demand for security specialists in industry as well as government organizations.

Ph.D. Program

The Ph.D. program develops graduate skills in a broad range of areas as well as expertise in one or more specific areas and the ability to think critically and conduct independent research. Outstanding Ph.D. students are advised to apply for financial aid in the form of teaching assistantships, research assistantships or partial-tuition remission.

Boris Aronov Ph.D., Courant Institute of Mathematical Sciences, New York University Algorithms, computational and combinatorial geometry

Juan Pablo Bello Ph.D., Queen Mary, University of London Digital signal processing, machine listening and music information retrieval, sound and music informatics

Juliana Freire Ph.D., State University of New York at Stony Brook Data analysis and visualization, Big Data, provenance management and analytics, scientific data management, large scale information, web information retrieval and analysis, web crawling, hidden web

Guido Gerig Ph.D., Swiss Federal Institute of Technology Zurich (ETH-Z) Image processing & analysis, medical image processing, 3D computer vision, shape analysis, spatiotemporal modeling

Lisa Hellerstein Interim Department Chair Ph.D., University of California at Berkeley Computational learning theory, machine learning, algorithms, complexity theory, discrete mathematics

Nasir Memon Ph.D., University of Nebraska Data compression, image and video processing, computer security, multimedia computation and communication

Keith W. Ross, Leonard J. Shustek Distinguished Professor Ph.D., University of Michigan Computer networking, Internet research, multimedia networking, scholastic modeling

Claudio T. Silva Ph.D., State University of New York at Stony Brook Big Data and Urban Systems, Visualization and Data Analysis, Geometry Processing

Torsten Suel Ph.D., University of Texas at Austin Design and analysis of algorithms, database systems, parallel computation, experimental algorithmics

Paul Torrens Ph.D., University College London Development and application of modeling and simulation tools for exploring and explaining complex urban systems

Associate Professors

Justin Cappos Ph.D., University of Arizona Practical security, virtualization, cloud computing, software update systems, testbeds

Yi-Jen Chiang Ph.D., Harvard University Computer graphics: out-of-core scientific visualization, isosurface extraction, surface simplification, virtual reality, air traffic control. Computer algorithms: I/O algorithms, computational geometry, graph algorithms, approximation algorithms, data structures

Rumi Chunara Ph.D., Harvard University Information retrieval, spatio-temporal analyses, data mining, machine learning and epidemiological methods for new data sources

Rachel Greenstadt Ph.D., Harvard University Designing more trustworthy intelligent systems via highly interdisciplinary approach by incorporating ideas from artificial intelligence, psychology, economics, data privacy, and system security

Damon McCoy Ph.D., University of Colorado, Boulder Security and privacy of large-scale systems

Julian Togelius Ph.D., University of Essex AI, player modeling, procedural content generation, automatic game design, believable bot behavior, coevolution, neuroevolution, genetic programming and monte carlo tree search

Edward Wong Ph.D., Purdue University Computer vision, image analysis, pattern recognition, computer graphics

Assistant Professors

Brendan Dolan-Gavitt Ph.D., Georgia Institute of Technology Program analysis, virtualization security, memory forensics, and embedded and cyber-physical systems

Chinmay Hedge Ph.D., Rice University Machine Learning, Algorithms, Big Data, Signal and Image Processing

Christopher Musco Ph.D., MIT Scalable machine learning, foundations of data science, numerical linear algebra, theory of algorithms, randomized algorithms, sketching and streaming

Julia Stoyanovich Ph.D., Columbia University Responsible data management and analysis practices

Qi Sun Ph.D., Stony Brook University Virtual/Augmented Reality, Computer Graphics, Computer Vision, Computational Perception

Industry Faculty

Greg Aloupis Ph.D., McGill University Algorithms

Peter DePasquale Ph.D., Virginia Polytechnic Computer Science Education, Cloud Computing, Web Development and Security

Ratan Dey Ph.D., New York University Privacy & Security, Online Social Networks, AI & Machine Learning, Big Data & Databases, Internet Measurement 

Jeffrey Epstein Cambridge University Computer Science Education, Cloud Computing, Web Development and Security

Daniel Katz-Braunschweig M.S., Iona College

Thomas Reddington M.S. Physics, University of Pittsburgh, PA Networking and networking security

Darryl Reeves Ph.D., Cornell University Computational biology, machine learning

Gustavo Sandoval M.S., California State University at Sacramento Machine Learning, Distributed Systems, Operating Systems, Mobile applications, and Project Management

Linda Sellie Machine Learning

John B. Sterling M.S., New York University Game programming, software development

Fred J. Strauss, Director of CSE programs in Melville Campus-Long Island M.S., Polytechnic Institute of New York Software engineering, project management, distributed systems

Itay Tal M.S., Tel-Aviv University

Global Network Faculty

Nizar Habash Ph.D., University of Maryland College Park Natural language processing and computational linguistic

Affiliated Faculty

Enrico Bertini Ph.D., Sapienza University of Rome in Italy Data visualization methods

Cameron Craddock Research professor

Semiha Ergan Ph.D., Carnegie Mellon University IT to support design, construction and operations of civil infrastructure systems

Chen Feng Ph.D., University of Michigan Robot vision and machine learning

Siddharth Garg Ph.D., Carnegie Mellon University Machine learning, cybersecurity and computer hardware design

Danny Y. Huang Ph.D., University of California, San Diego Cybersecurity, privacy, and Internet of things (IoT)

Ramesh Karri Ph.D., University of California, San Diego Trustworthy hardware, nanoscale architectures, and cybersecurity

Oded Nov Ph.D., Cambridge University Human computer interaction

Brandon Reagan Ph.D., Harvard University Computer architecture, hardware acceleration, and VLSI

Faculty Emeriti

Phyllis G. Frankl Ph.D., New York University Software analysis and testing

Haldun Hadimioglu Ph.D., Polytechnic University Computer architecture, parallel processing, reconfigurable systems and application specific processors

Kok-Ming Leung Ph.D., University of Wisconsin Scientific computing, computer simulation, neural networks

Henry Ruston

​ Martin Shooman Ph.D., Polytechnic Institute of New York

Stuart Steele

Computer Science (BS)

Program description.

Computer science focuses on how to design, build, and effectively use the computers and systems that we interact with every day from the smart phones in our hands to the complex databases in our banks and hospitals. Because computer technology powers the most essential functions of business, industry, government and entertainment, computer scientists have tremendous opportunities for growth and exploration.

In addition to the BS degree in Computer Science, the Computer Science and Engineering department offers minors in Computer Science, Cybersecurity, and Game Engineering. The NYU Tandon School of Engineering also offers a BS/MS Program that enables students to earn both a BS and an MS degree at the same time. For instance, a student can receive a BS in Computer Science and MS in Computer Science, a BS in Computer Engineering and MS in Computer Science, or a BS in Electrical Engineering and MS in Computer Science. Depending on the student’s preparation and objectives, they can complete both degrees within 5 years. More information on the BS/MS program can be found on the “Undergraduate Academic Requirements and Policies” section of the catalog.

The program provides research labs for specialized study in areas such as cybersecurity, game engineering, and big data, areas in which our department has a distinctive strength. In addition, the program’s close ties to our graduate division immerse students in a vibrant, intellectual atmosphere.

New York University's Office of Undergraduate Admissions supports the application process for all undergraduate programs at NYU.  For additional information about undergraduate admissions, including application requirements, see How to Apply . 

Program Requirements

The program requires the completion of 128 credits, comprised of the following:

Note: MA-UY 914 Precalculus for Engineers does not count toward the Math requirement.

Note: MA-UY 2034 Linear Algebra and Differential Equations or another linear algebra course is recommended, but not required. Some CS electives have knowledge of linear algebra as a prerequisite. Students planning to take such electives should plan accordingly.

Students may choose any three natural science courses (each at least 3 credits) offered by the NYU Tandon School of Engineering, provided that they meet pre-requisites and co-requisites.

One must be an Advanced Seminar course.

With approval of the CSE department, certain closely related courses in EE, Math or other related disciplines may be substituted for CS electives. A list of approved substitutions is available in the CSE department.

Note: NYU SPS courses are not accepted as free electives.

Sample Plan of Study

Grade of C- or better is required in CS-UY 1114 INTRO TO PROGRAMMING & PROBLEM SOLVING , CS-UY 1134 Data Structures and Algorithms , and CS-UY 2124 Object Oriented Programming . Students who take CS-UY 1113 PROBLEM SOLVING AND PROGRAMMING I and CS-UY 1123 PROBLEM SOLVING AND PROGRAMMING II may count four credits toward the CS requirements of the major, in lieu of CS-UY 1114 INTRO TO PROGRAMMING & PROBLEM SOLVING . The other two credits will be counted as free electives.

Students who are placed by examination or by an adviser into MA-UY 914 Precalculus for Engineers must defer registration for MA-UY 1024 Calculus I for Engineers .

The Science electives may be chosen from any of the following natural sciences (Physics, Biology, and Chemistry). Many science courses are 4 credits or require co-requisite lab.

At least one Humanities and Social Sciences elective must be a Writing-intensive course. Writing-intensive Humanities and Social Sciences courses are designated by "W." In addition, one Humanities and Social Sciences elective must be a 3XXX or 4XXX level. Approved Humanities and Social Sciences electives span three clusters: CAM, STS and SEG. Students are encouraged to take Humanities and Social Sciences electives across clusters and/or disciplines within a cluster.

Learning Outcomes

Upon successful completion of the program, graduates will:

• Learn fundamentals of computer science theory and practice in order to contribute to industry, academic, and government activities.
• Learn modern design and development techniques.
• Enhance their base of knowledge with appropriate electives.
• Develop laboratory and software skills for advanced project development and research activity.

NYU Policies

Tandon policies.

University-wide policies can be found on the New York University Policy pages .

Additional academic policies can be found on the  Tandon academic policy page . 

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Graduate College

Varadarajan wins graduate college's outstanding mentor award.

Kasturi Varadarajan, professor in the Department of Computer Science, received the Graduate College's Outstanding Faculty Mentor Award in 2024.

Varadarajan, an expert in algorithmic foundations, computational geometry, and combinatorial optimization, has served as a Director of Graduate Studies—leading the orientation of new graduate students and organizing professional development workshops for the department.

During his time at Iowa, Varadarajan has mentored 8 doctoral students to completion with a 9 th now in progress. He has also advised over 70 master’s students on course choices needed for completion of their programs, as well as several non-computer science students transitioning to the program.

His award nominators stressed that all of his students are better for having known him and he is admired for the lasting relationships he creates.

Ph.D. student earns Department of Defense scholarship

Thursday, May 02, 2024 • Brian Lopez : contact

Nolan Gutierrez, a doctoral student in the Department of Computer Science and Engineering at The University of Texas at Arlington, has received a prestigious Science, Mathematics and Research for Transformation (SMART) scholarship from the U.S. Department of Defense (DoD).

“This scholarship will help me pursue a more permanent career within the Department of Defense,” Gutierrez said. “I hope to work with my mentor and their team at the Army Research Lab to delve deeper into my research and propose my own projects that align with their research goals.”

Gutierrez will use the scholarship to build upon research he started last year during an internship with the U.S. Army Combat Capabilities Development Command, or DEVCOM, in the Army Research Lab. Specifically, he will focus on enhancing the adaptability of pan-tilt-zoom (PTZ) cameras through multitask learning.

He will see if training a PTZ control system on a variety of motion profiles—such as square, circular, and straight-line movements, both forward and backward—will improve its adaptability and performance. This approach includes deploying a PTZ-equipped robot in scenarios that mimic real-world operational conditions relevant to the Army.

Gutierrez works with UTA computer science Assistant Professor William Beksi in the Robotic Vision Lab. He is Beksi’s second student in as many years to earn a SMART scholarship, following Minh (Jerry) Tram in 2023.

“Not only does Nolan's SMART scholarship strengthen the ties between the DEVCOM Amy Research Lab and UTA, but it also shows UTA's commitment to train and provide the DoD with STEM Ph.D. graduates,” Beksi said.

SMART scholarship recipients earn full tuition, a significant annual stipend and a book and health allowance. They also complete a summer internship at a DoD facility and are assigned an experienced mentor. The full scholarship and other benefits allow participants to focus on complex research to further the DoD’s mission and create a lasting impact. The program is a one-for-one commitment: For every year of degree funding, the recipient commits to working for a year with the DoD as a civilian employee.

- Written by Jeremy Agor , College of Engineering

Graduation 2024

CDSO Graduation Ceremony for Master’s Degree Candidates

Description.

Join us in celebrating our 2024 Computer Science and Data Science Online master’s students. May 2024 participants include December 2023 graduates and May 2024 and August 2024 potential graduates. Candidates earning their master’s degrees have the option of attending both this ceremony and the University Ceremony where degrees are conferred.

The CDSO ceremony will be followed by a casual reception. The university-wide commencement celebration will take place at 7:30pm at Darrell K. Royal-Texas Memorial Stadium. Please find information about tickets, parking, purchasing regalia and other aspects of UT-wide graduation on this website .

Graduate check in: 8:00-8:30am      

Guest arrivals: 8:00-8:45am      

Ceremony begins: 9:00am               

Reception: 10:15-11:30am     

Suggested Free Parking:

Dobie Garage

Rowling Hall Garage

Guadalupe Garage

AT&T Hotel and Conference Center, Grand Ballroom

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Other Events in This Series

Deadline to apply to graduate or walk.

Natural Sciences undergraduates must apply if they are graduating or walking at a UT College of Natural Sciences ceremony, and they must also RSVP for the ceremony.

Deadline to RSVP to Participate in a Graduation Ceremony

Graduating seniors must first apply by March 26 and then RSVP to attend a UT College of Natural Sciences graduation ceremony.

Deadline to Purchase Regalia

Graduating seniors must have regalia to attend a UT College of Natural Sciences graduation ceremony.

CNS Graduation Ceremony (First Ceremony)

Graduation ceremony for Biochemistry, Biology, Environmental Science, Medical Laboratory Science and Neuroscience majors.

12:00 pm – 2:30 pm • In Person

CNS Graduation Ceremony (Second Ceremony)

Graduation ceremony for Astronomy, Chemistry, Computer Science, Human Development and Family Sciences, Human Ecology, Mathematics, Nutrition, Physics, Public Health and Textiles and Apparel majors.

4:00 pm – 6:30 pm • In Person

University-Wide Graduation

Graduation ceremony for all UT students from the 23-24 academic year.

7:30 pm • In Person

Master’s Degree Requirements

To satisfy the requirements for the master’s degree, the student must complete 30 credits, as described below, with an overall average of B. In addition, a B average is required across the required algorithms course and the four core courses, and a grade of B or better is required for the capstone course, as indicated below. The master’s curriculum has four components: 3 credits of algorithms, 12 credits of core elective courses (one of which may also count as the capstone course), one 3 credit capstone course, and 12 credits of general elective courses.

Required Course in Algorithms

Students are required to take CS-GY 6033 Design and Analysis of Algorithms I    or CS-GY 6043 Design and Analysis of Algorithms II   .  Most students will take the Algorithms I course to satisfy the algorithms course requirement. Advanced students who have taken an equivalent Algorithms I course before with a grade of B or better will have the option of taking the Algorithms II course to satisfy the requirement.

Core Course Requirements

Students must take at least four courses from the list of core courses below. The list will be periodically updated by the CSE Department and certain courses may be substituted with departmental consent.

• CS-GY 6063 Software Engineering I 3 Credits
• CS-GY 6083 Principles of Database Systems 3 Credits
• CS-GY 6133 Computer Architecture I 3 Credits
• CS-GY 6233 Introduction to Operating Systems 3 Credits
• CS-GY 6313 Information Visualization 3 Credits
• CS-GY 6373 Programming Languages 3 Credits
• CS-GY 6513 Big Data 3 Credits
• CS-GY 6533 Interactive Computer Graphics 3 Credits
• CS-GY 6613 Artificial Intelligence I 3 Credits
• CS-GY 6643 Computer Vision 3 Credits
• CS-GY 6763 Algorithmic Machine Learning and Data Science 3 Credits
• CS-GY 6813 Information, Security and Privacy 3 Credits
• CS-GY 6843 Computer Networking 3 Credits
• CS-GY 6923 Machine Learning 3 Credits

Capstone Course Requirement

Certain courses in our department will be designated as capstone courses. Capstone courses are drawn from key technical areas in the MS program and they involve a substantial amount of programming effort. Students are required to take at least one capstone course with a grade of B or better. The list of capstone courses will be posted by the department and will be updated from time to time. If a course is listed both as a capstone course and as a core course, the course can be used to satisfy both the capstone and core course requirements. An MS thesis can also be used to satisfy the capstone course requirement.

Capstone Courses

Here is the approved list of capstone courses:

• CS-GY 6053 Foundation of Data Science 3 Credits
• CS-GY 6073 Software Engineering II 3 Credits
• CS-GY 6243 Operating Systems II 3 Credits
• CS-GY 6253 Distributed Operating Systems 3 Credits
• CS-GY 6413 Compiler Design and Construction 3 Credits
• CS-GY 6573 Penetration Testing and Vulnerability Analysis 3 Credits
• CS-GY 6823 Network Security 3 Credits
• CS-GY 6943 Artificial Intelligence for Games 3 Credits
• CS-GY 9163 Application Security 3 Credits
• CS-GY 9223 Selected Topics in Computer Science 3 Credits

Distributed Systems

General Elective Requirements

In addition to the core electives, students are required to take four general elective courses with considerable flexibility; the only restriction is that no more than two of the courses may be taken from outside the Department of Computer Science and Engineering. In particular:

• Master’s thesis (6 credits) and/or independent study courses may be part of a student’s elective courses. Note that the master’s thesis (CS-GY 997X) has an important requirement, as described here   .
• Any of the core courses may be chosen as electives.
• Graduate­-level courses from outside of the department (at most two) may be chosen as electives.
• Any CS graduate course not included in the core areas may be chosen as electives.

These courses include (among others):

This list may be updated from time to time based on the current offerings of the department. 

• CS-GY 6003 Foundations of Computer Science 3 Credits
• CS-GY 6033 Design and Analysis of Algorithms I 3 Credits
• CS-GY 6043 Design and Analysis of Algorithms II 3 Credits
• CS-GY 6093 Advanced Database Systems 3 Credits
• CS-GY 6143 Computer Architecture II 3 Credits
• CS-GY 6273 Performance Evaluation of Computer Systems 3 Credits
• CS-GY 6323 Large-Scale Visual Analytics 3 Credits
• CS-GY 6543 Human Computer Interaction 3 Credits
• CS-GY 6553 Game Design 3 Credits
• CS-GY 6703 Computational Geometry 3 Credits
• CS-GY 6753 Theory of Computation 3 Credits
• CS-GY 6803 Information Systems Security Engineering and Management 3 Credits
• CS-GY 6903 Applied Cryptography 3 Credits
• CS-GY 6913 Web Search Engines 3 Credits
• CS-GY 6953 Deep Learning 3 Credits
• CS-GY 6963 Digital Forensics 3 Credits
• CS-GY 9033 Web Services and SOA 3 Credits
• CS-GY 9053 Introduction to Java  3 Credits
• CS-GY 9093 Biometrics 3 Credits
• CS-GY 9103 Object Oriented Design with Java  3 Credits
• CS-GY 9133 Emerging Technology for IP  3 Credits
• CS-GY 9963 Advanced Project in Computer Science 3 Credits
• CS-GY 997X M.S. Thesis in Computer Science Variable Credits

Preparatory Course

The 100% online NYU Tandon Bridge course prepares students without a Computer Science degree or other substantial programming experience to apply for select NYU Tandon master’s degree programs. In the course, students will learn computer science fundamentals and programming with C++. Students’ performance in the Bridge will count toward their master’s degree application decisions. The Bridge is a non-credit certificate course, and those who complete the Bridge with a final grade of C or above will earn a Certificate of Completion, and those who earn a B+ or above will receive a Certificate of Completion with Distinction. Note: regardless of performance, successful completion of the Bridge course does not guarantee admission to any academic program.

The NYU Tandon Bridge course is taught by faculty members of the Computer Science department at the NYU Tandon School of Engineering, aided by NYU Tandon Graduate student teaching assistants. Students will participate in interactive online modules, live webinars, assignments, and tests.

Learn more:  Computer Science Bridge Program