Phd thesis proposals computer science

The research for the dissertation should be conducted in close consultation with the research advisor. When the adviser determines that the student is ready to defend the thesis, a dissertation defense will be scheduled. For the defense, the student will give an oral presentation describing the thesis research, which is open to the public. Following the oral presentation and an initial question and answer session, the dissertation committee and CSE faculty may ask the student further questions in closed session.

All Ph. The review is conducted by the entire CSE faculty and includes at least the following items in no particular order :. As a result of the review, each student will be placed in one of the following two categories, by vote of the faculty:. Following the review, students will receive formal letters which will inform them of their standing. The letters may also make specific recommendations to the student as to what will be expected of them in the following year. Students must meet all applicable requirements, including graduate study duration, credit points, GPA, and time-to-degree requirements.

PHD RESEARCH TOPICS IN COMPUTER SCIENCE

The following is the department policy concerning remote attendance at qualifying exams, dissertation proposal exams, and dissertation defenses, along with rules regarding the location and scheduling of these exams. Qualifying exams and proposal exams should be held at the Tandon campus in Brooklyn, except as indicated below. It is preferable that all committee members be present in person. However, in cases where attendance in person would be difficult, committee members other than the advisor are allowed to attend remotely.

The advisor may attend remotely only with permission of the PHDC. If a PhD student is working with a research advisor at an NYU campus outside of the United States, and both student and advisor are at that campus at the time of the qualifying exam, the student may take the exam on that campus with the advisor present. The remaining members of the committee may attend remotely. All dissertation defenses must take place on the Tandon campus. Defenses should be held on a day in which the Tandon School of Engineering is open for business. It is not a requirement that classes be in session.

Permission must be obtained from the PHDC to hold a dissertation defense on a weekend, or on a holiday or vacation day when the school is not open for business.

Thesis Proposal for PhD in Computer Science

The student, research advisor, and any members of the committee who are on the CSE department faculty, should be present in person at the defense. If a member of the CSE department faculty who is on the committee is unable to attend in person, permission must be obtained from the PHDC for that person to attend remotely. It is highly desirable for all other members of the committee to be present in person. However, if it is difficult for other committee members to attend in person, they may attend remotely. Topics: Amortized analysis of algorithms.

Advanced data structures: binomial heaps, Fibonacci heaps, data structures for disjoint sets, analysis of union by rank with path compression. Graph algorithms: elementary graph algorithms, maximum flow, matching algorithms. Randomized algorithms. Theory of NPcompleteness and approach to finding approximate solutions to NPcomplete problems.

Selected additional topics that may vary.


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Topics: Formal languages and automata theory. Deterministic and non-deterministic finite automata, regular expressions, regular languages, context-free languages. Pumping theorems for regular and context-free languages. Turing machines, recognizable and decidable languages. Limits of computability: the Halting Problem, undecidable and unrecognizable languages, reductions to prove undecidability. Topics include intersection, polygon triangulation, linear programming, orthogonal range searching, point location, Voronoi diagrams, Delaunay triangulations, arrangements and duality, geometric data structures, convex hulls, binary space partitions, robot motion planning, quadtrees, visibility graphs, simplex range searching.

High-performance computing systems: Computer systems that improve performance and capacity by exploiting parallelism. Alternatives to traditional computing are discussed, including GPUs, TPUs, systolic arrays, neural networks and experimental systems. Topics may include virtualization, network server design and characterization, scheduling and resource optimization, file systems, memory management, advanced debugging techniques, data-center design and energy utilization. Topics: Distributed control and consensus.


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  • Notions of time in distributed systems. Distributed File Systems and Services. Fault tolerance, replication and transparency. Peer-to-peer systems.

    PhD Thesis Proposal Defence Guidelines - Computer Science - Dalhousie University

    Case studies of modern commercial systems and research efforts. In this course, we will study the state of art in big data management: we will learn about algorithms, techniques and tools needed to support big data processing. In addition, we will examine real applications that require massive data analysis and how they can be implemented on Big Data platforms. The course will consist of lectures based both on textbook material and scientific papers. It will include programming assignments that will provide students with hands-on experience on building data-intensive applications using existing Big Data platforms, including Amazon AWS.

    Besides lectures given by the instructor, we will also have guest lectures by experts in some of the topics we will cover. After an overview of computer networks and the Internet, the course covers the application layer, transport layer, network layer and link layers.

    Computer Science and Software Engineering

    Topics at the transport layer include multiplexing, connectionless transport and UDP, principles or reliable data transfer, connection-oriented transport and TCP and TCP congestion control. The course includes simple quantitative delay and throughput modeling, socket programming and network application development and Ethereal labs.

    The course continues with cryptography topics most relevant to secure networking protocols. Topics covered are block ciphers, stream ciphers, public key cryptography, RSA, Diffie Hellman, certification authorities, digital signatures and message integrity.


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    After surveying basic cryptographic techniques, the course examines several secure networking protocols, including PGP, SSL, IPsec and wireless security protocols. The course examines operational security, including firewalls and intrusion-detection systems. Students read recent research papers on network security and participate in an important lab component that includes packet sniffing, network mapping, firewalls, SSL and IPsec. Students acquire hands-on experience in working with database systems and in building web-accessible database applications.

    Prerequisites: Graduate standing, CS-GY or equivalent, familiarity with basic data structures and operating system principles. Topics: Lexical analysis, syntax analysis, abstract syntax trees, symbol table organization, code generation. Introduction to code optimization techniques. Topics include graphics software and hardware, 2D line segment-scan conversion, 2D and 3D transformations, viewing, clipping, polygon-scan conversion, hidden surface removal, illumination and shading, compositing, texture mapping, ray tracing, radiosity and scientific visualization.

    It addresses one of the ultimate puzzles humans are trying to solve: How is it possible for a slow, tiny brain, whether biological or electronic, to perceive, understand, predict and manipulate a world far larger and more complicated than itself? And how do people create a machine or computer with those properties? To that end, AI researchers try to understand how seeing, learning, remembering and reasoning can, or should, be done.

    PhD Thesis Proposal Defence Guidelines

    This course introduces students to the many AI concepts and techniques. Concentration is on writing software programs that make it difficult for intruders to exploit security holes. The course emphasizes writing secure distributed programs in Java. The security ramifications of class, field and method visibility are emphasized. Prerequisite: Gradute standing 3 Credits Advanced Database Systems CS-GY Students in this advanced course on database systems and data management are assumed to have a solid background in databases.

    The course typically covers a selection from the following topics: 1 advanced relational query processing and optimization, 2 OLAP and data warehousing, 3 data mining, 4 stream databases and other emerging database architectures and applications, 5 advanced transaction processing, 6 databases and the Web: text, search and semistructured data, or 7 geographic information systems. Topics are taught based on a reading list of selected research papers. Students work on a course project and may have to present in class.

    Computer vision is an area in AI that deals with the construction of explicit, meaningful descriptions of physical objects from images. It includes as parts many techniques from image processing, pattern recognition, geometric modeling, and cognitive processing.

    This course introduces students to the fundamental concepts and techniques in computer vision. The main focus is on large-scale Web search engines such as Google, Yahoo and MSN Search and their underlying architectures and techniques. Students learn how search engines work and get hands-on experience in how to build search engines from the ground up.

    Polynomial versus non-polynomial time for specific algorithmic problems

    Topics are based on a reading list of recent research papers. Students must work on a course project and may have to present in class. Prerequisite: Graduate standing 3 Credits Machine Learning CS-GY This course is an introduction to the field of machine learning, covering fundamental techniques for classification, regression, dimensionality reduction, clustering, and model selection. A broad range of algorithms will be covered, such as linear and logistic regression, neural networks, deep learning, support vector machines, tree-based methods, expectation maximization, and principal components analysis.

    The course will include hands-on exercises with real data from different application areas e. Students will learn to train and validate machine learning models and analyze their performance.