Advance CS Theory, Lead Original Research and Shape the Future of AI, Communications, Cybersecurity and Beyond
Earn your Ph.D. in Computer Science at a U.S. News Top 100 University in the heart of New York City.
Ph.D. in Computer Science
Making the World Smarter, Safer and Healthier
Ph.D. in Computer Science
66-credit doctorate (36 credits above master’s)
On-Campus in New York City I Full-Time or Part-Time
The Ph.D. in Computer Science educates the next generation of researchers who will advance CS theory and applications across academia and industry — helping address a nationwide shortage of doctoral-qualified computer scientists. Students join an intellectually rigorous, interdisciplinary research community tackling critical problems in healthcare, telecommunications, finance, security, climate, and energy.
The program provides deep training in theoretical and applied computer science, research methods, and emerging technologies, along with hands-on experience in AI, machine learning, IoT, cybersecurity, data science, mobile and cloud computing, human-computer interaction, and augmented and virtual reality. Students graduate prepared for faculty positions and industry R&D roles including AI and CS researcher, ML engineer, algorithm developer, security specialist, robotics researcher, and systems engineer. In addition to coursework, students complete a qualifying exam and an original doctoral thesis.
Program Highlights
Research-driven faculty with grants from NSF, NIH, DoT, and industry, and deep ties to New York's tech and health ecosystems — serving as Ph.D. supervisors across AI, machine learning, cybersecurity, networking, smart health, autonomous systems, finance, and sustainability.
Publication and conference opportunities in peer-reviewed journals and national and international venues.
External funding through NSF, NIH, DoT, NASA, the Simons Foundation, and other agencies.
Scholarships and assistantships available, including competitive teaching and research assistantships with tuition waivers and stipends.
Top-ranked university in the heart of NYC: #1 Best Value and Top 100 University by U.S. News; #63 in the U.S. by QS World.
Full Program Breakdown
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Ph.D. in Computer Science
66-credit doctorate (36 credits above master’s)
On-Campus in New York City I Full-Time or Part-Time
The Ph.D. in Computer Science educates the next generation of researchers who will advance CS theory and applications across academia and industry — helping address a nationwide shortage of doctoral-qualified computer scientists. Students join an intellectually rigorous, interdisciplinary research community tackling critical problems in healthcare, telecommunications, finance, security, climate, and energy.
The program provides deep training in theoretical and applied computer science, research methods, and emerging technologies, along with hands-on experience in AI, machine learning, IoT, cybersecurity, data science, mobile and cloud computing, human-computer interaction, and augmented and virtual reality. Students graduate prepared for faculty positions and industry R&D roles including AI and CS researcher, ML engineer, algorithm developer, security specialist, robotics researcher, and systems engineer. In addition to coursework, students complete a qualifying exam and an original doctoral thesis.
Research-driven faculty with grants from NSF, NIH, DoT, and industry, and deep ties to New York's tech and health ecosystems — serving as Ph.D. supervisors across AI, machine learning, cybersecurity, networking, smart health, autonomous systems, finance, and sustainability.
Publication and conference opportunities in peer-reviewed journals and national and international venues.
External funding through NSF, NIH, DoT, NASA, the Simons Foundation, and other agencies.
Scholarships and assistantships available, including competitive teaching and research assistantships with tuition waivers and stipends.
Top-ranked university in the heart of NYC: #1 Best Value and Top 100 University by U.S. News; #63 in the U.S. by QS World.
Program Links
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Admissions & Financial Aid
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Admissions Requirements
Successful incoming students to the Ph.D. program have a master’s degree in computer science or a closely related field (e.g., computer engineering, data science, applied math) from an accredited institution. Students without a related master's are normally expected to start in a Katz CS (or related) MS first.
Application Information
Visit Graduate Admissions for up-to-date application requirements and deadlines.
Questions?  if you have questions about your qualifications, financial aid opportunities and financing your graduate degree. We can do a preliminary transcript review and discuss your admissions and financing options with the Katz School. 
Tuition, Financial Aid and Scholarships
The Office of Student Finance maintains current tuition and fees for all graduate programs.
All applicants are automatically considered for the program. You do not need to submit any additional information.
Admissions & Financial Aid
Admissions Requirements
Successful incoming students to the Ph.D. program have a master’s degree in computer science or a closely related field (e.g., computer engineering, data science, applied math) from an accredited institution. Students without a related master's are normally expected to start in a Katz CS (or related) MS first.
Application Information
Visit Graduate Admissions for up-to-date application requirements and deadlines.
Questions?  if you have questions about your qualifications, financial aid opportunities and financing your graduate degree. We can do a preliminary transcript review and discuss your admissions and financing options with the Katz School. 
Tuition, Financial Aid and Scholarships
The Office of Student Finance maintains current tuition and fees for all graduate programs.
All applicants are automatically considered for the program. You do not need to submit any additional information.
Current PhD Students
Ashikur Nobel
RESEARCH FOCUS: Generative AI and smart health, with an emphasis on building trustworthy models for continuous, unobtrusive monitoring of patient status.
Dengyi Liu
RESEARCH FOCUS: Smart health, wireless networking, and artificial intelligence, with a focus on future communication infrastructures for mHealth and IoT.
Shiman Zhang
RESEARCH FOCUS: Trustworthy machine learning for healthcare and risk analytics, combining supervised, ensemble, and unsupervised methods to improve prediction performance and reliability.
Chengyi Liu
RESEARCH FOCUS: Human activity understanding and motion modeling using large language models and multimodal AI
Xiang Xin
RESEARCH FOCUS: Deep learning, representation learning, and statistical modeling to extract actionable insights from high dimensional, noisy data.
Alexy Skoutnev
RESEARCH FOCUS: Knowledge discovery, automated algorithm design, and applied AI systems.
David Li
RESEARCH FOCUS: Data analytics, machine learning, deep learning, big data, optimization, and algorithmic modeling.
Hanzhi Yan
RESEARCH FOCUS: Security, Advanced machine learning and signal processing techniques.
Program News
Digital Data Processing Focus of IEEE Conference
Read more about the conference
Digital Data Processing Focus of IEEE Conference
A vibrant community of researchers and industry professionals explored cutting-edge developments in digital data processing technologies at the IEEE 4th International Conference on Digital Data Processing, hosted by the Katz School of Science and Health at the Ó£»¨¶¯Âþ Museum in New York City.
NIH Grant Funds Research on Dietary Patterns
Read more about Honggang Wang's research
NIH Grant Funds Research on Dietary Patterns
Dr. Honggang Wang, chair of the Department of Computer Science and Engineering, received a $600,000 grant to create an artificial intelligence platform that would recognize patterns in longitudinal dietary data.
Researchers Develop Algorithms to Assist in Stroke Recovery
Read about the study.
Researchers Develop Algorithms to Assist in Stroke Recovery
Researchers have developed a series of algorithms using Siamese networks, a type of artificial intelligence, to better identify and track the body movements of stroke patients in order to assist in patient treatment and recovery.
Read about the story in the Katz School blog.
AI Diffusion Models Rearrange, Not Reinvent
Read about the study
AI Diffusion Models Rearrange, Not Reinvent
Diffusion models power image generators like DALL·E and Stable Diffusion, producing stunning, lifelike pictures from simple text prompts. But a recent study led by researchers at the Katz School of Science and Health asks a fundamental question: Are these models really creating something new or just rearranging what they’ve already seen?
Hidden Rules Link Geometry and Motion
Read about the study
Hidden Rules Link Geometry and Motion
In a recent study, Marian Gidea, professor of mathematical sciences, explored some of the deepest structures that govern how complex systems evolve and how geometry, topology and dynamics all connect in a special class of systems called conformally symplectic systems.
Digital Data Processing Focus of IEEE Conference
Read more about the conference
Digital Data Processing Focus of IEEE Conference
A vibrant community of researchers and industry professionals explored cutting-edge developments in digital data processing technologies at the IEEE 4th International Conference on Digital Data Processing, hosted by the Katz School of Science and Health at the Ó£»¨¶¯Âþ Museum in New York City.
NIH Grant Funds Research on Dietary Patterns
Read more about Honggang Wang's research
NIH Grant Funds Research on Dietary Patterns
Dr. Honggang Wang, chair of the Department of Computer Science and Engineering, received a $600,000 grant to create an artificial intelligence platform that would recognize patterns in longitudinal dietary data.
Researchers Develop Algorithms to Assist in Stroke Recovery
Read about the study.
Researchers Develop Algorithms to Assist in Stroke Recovery
Researchers have developed a series of algorithms using Siamese networks, a type of artificial intelligence, to better identify and track the body movements of stroke patients in order to assist in patient treatment and recovery.
Read about the story in the Katz School blog.
AI Diffusion Models Rearrange, Not Reinvent
Read about the study
AI Diffusion Models Rearrange, Not Reinvent
Diffusion models power image generators like DALL·E and Stable Diffusion, producing stunning, lifelike pictures from simple text prompts. But a recent study led by researchers at the Katz School of Science and Health asks a fundamental question: Are these models really creating something new or just rearranging what they’ve already seen?
Hidden Rules Link Geometry and Motion
Read about the study
Hidden Rules Link Geometry and Motion
In a recent study, Marian Gidea, professor of mathematical sciences, explored some of the deepest structures that govern how complex systems evolve and how geometry, topology and dynamics all connect in a special class of systems called conformally symplectic systems.
