Requirements

Academic Requirements by Department


The Doctor of Philosophy is a degree designed to prepare students to discover, assimilate, and apply knowledge as well as to communicate and disseminate it.

phd rules cover

Degree Requirements

The Ph.D. degree in Biomedical Engineering is awarded to a student upon successful completion of 75 credits and the defense of a comprehensive dissertation research project. The credits are broken down as minimums of 27 course credits and 27 doctoral dissertation research credits, with flexible choices in coursework and/or research for the remaining credits. A maximum of 30 course credits may be transferred from previous graduate coursework. Doctoral dissertation credits can only be taken upon passing a comprehensive doctoral qualifying exam (DQE), which is given once a year. (Additional details on the qualifying examination are described in the Ph.D. Guideline).Candidates for the degree Doctor of Philosophy in Biomedical Engineering should plan their programs in accordance with the following requirements:

Students must take at least one course in advanced mathematics and/or statistics for biomedical engineering, one course in biomedical/biophysical science, and one course in biomechanics or bioinstrumentation. One course in responsible conduct of research is required, in accord with rules of the National Institutes of Health for student training programs. Students may choose to enroll in guided studies involving optional laboratory rotations during the first year, with approval of the BME program advisor.

In addition, students must participate in noncredit student seminars for professional development for at least four semesters, and attend the Departmental Colloquium lecture series with distinguished invited speakers each semester. All students will be required to take university training in laboratory safety and other regulatory policies as appropriate.

Passing a doctoral qualifying examination is required in order to begin taking dissertation research credits (BE-GY 999x). The qualifying exam will be based on assigned thematically focused publications. This exam may be taken as early as the end of the first year, and not later than the middle of the second year. In the case of failure, the right to a second examination within six months is at the discretion of the examination committee in consultation with the BME program committee. The qualifying examination must be passed by the end of the second year. The results of each student’s examination will be delivered to the Registrar of NYU Tandon in writing, no later than one week following the exam.

Dissertation Advisor and Committee:
Students must choose a dissertation research advisor by the end of the first year, with approval of the departmental BME program committee. A dissertation guidance committee, comprised of the research advisor and three other faculty members (preferably two BME faculty members and one external faculty member) will be named with approval of the BME program committee. The function of the dissertation guidance committee will be to monitor the student’s progress throughout the program.

Research Proposal Examination:
A Research Proposal examination, overseen by the dissertation guidance committee and based on a dissertation research proposal and preliminary data, must be passed by the end of the third year. The objective of this exam is to ensure the student has chosen an appropriate PhD research topic and that the research plan is rigorous and has high likelihood of success. The results of each student’s proposal examination will be delivered to the Registrar of NYU Tandon in writing, no later than one week following the exam.

Annual Progress Assessment:
The dissertation guidance committee will continue to meet once per year with the student for a review of progress, and will provide detailed feedback advice to the student. A report following each annual meeting must be filed with the BME program committee.

Dissertation Defense:
With the dissertation research advisor’s and the dissertation guidance committee approval, the student will submit a written dissertation meeting all requirements of NYU Tandon. The dissertation must be provided to the dissertation guidance committee members at least one week, but preferably two weeks, prior to the defense. The defense includes a formal, public presentation by the student, with questions from the audience. Following the public presentation, the student meets privately with the committee members for questions. The committee makes a decision that is then transmitted, in writing, to the Registrar.

Please peruse the guidelines for NYU-Tandon Biomedical Engineering Ph.D. students. All students are required to read this document and are responsible for abiding by the herein stated deadlines and rules.
Download the Committee Form for completion of a proposal defense, annual progress meeting, or data defense.

Degree Requirements

Each doctoral candidate must complete a minimum of 75 credits of academic work past the bachelor’s degree, including a minimum of 36 credits of dissertation research, to complete the Ph.D. in Chemical Engineering program. A minimum of 30 graduate credits beyond the bachelor’s degree (not including Ph.D. dissertation and non-dissertation research credits) are required in chemical engineering or related subjects. Of the 30 credits, 12 are to be taken as part of the required graduate core courses in Chemical Engineering and 18 are taken as electives. For electives: at least 3 electives (9 credits) are to be chosen from approved CBE courses, 6000-level and above. The remaining electives need to be selected in consultation with and with the explicit approval from the chemical engineering graduate adviser. In addition to the required coursework, attendance is required at departmental colloquia. Students must also pass a comprehensive qualifying examination in chemical engineering and present a doctoral dissertation. The qualifying exam is given once a year. Additional details on the qualifying examination will be provided by the graduate adviser. To meet graduation requirements, students must have an overall GPA of 3.0 or higher, excluding dissertation credits, and must not obtain a grade of C or lower in more than two required core courses. A student who has earned graduate level credits and/or been awarded an MS degree should consult with the graduate adviser for course registration and possible credit transfer. Candidates for the degree Doctor of Philosophy in Chemical Engineering should plan their programs in accordance with the following requirements:

Applied Mathematics in Engineering CBE-GY6153 (3 Credits)

This course covers mathematical formulation of chemical engineering problems in terms of ordinary, partial differential and differential equations. Topics include solutions of boundary and initial value problems using Green's functions and other techniques; characterization of second-order partial differential equations and properties of their solutions; asymptotic methods and numerical techniques.
Prerequisite: MA-UY 2122 and MA-UY 2132 or adviser's approval.

Transport Phenomena CBE-GY6333 (3 Credits)

The topics in this course include vector analysis review; diffusive fluxes; conservation equations for chemical species and thermal energy; boundary conditions; scaling and approximation techniques; solution methods for conduction and diffusion problems; transient unidirectional diffusion and conduction; momentum diffusion and viscous stress; conservation equation for momentum and the Navier-Stokes equations; unidirectional and lubrication flows; and low- and high-Reynolds number flows.
Prerequisite: CBE-UY 3313 or adviser's approval.

Chemical Reactor Analysis and Design CBE-GY6813 (3 Credits)

The topics in this course include trends and issues in modern reactor design; kinetics of complex homogenous and heterogeneous reactions: determination of nonlinear kinetic parameters, effects of transport processes, and catalyst deactivation; analysis and design of reactors; laminar flow reactors; dispersion model; split boundary condition problems; effects of non-ideal flow on conversion; and fixed-bed, fluidized-bed and multiphase reactors.
Prerequisite: CBE-UY 3223 or adviser's approval.

Seminar in Chemical & Biology Engineering CBE-GY9910

Recent developments in chemical and biomolecular sciences and engineering are presented by engineers and scientists from industry and academia. Four semesters are required for PhD candidates.

Seminar in Chemical & Biological Engineering CBE-GY9920
Chemical Laboratory Safety CM-GY5040

This course discusses problems of health and safety in chemical laboratories, including how to work safely with dangerous chemicals. This course must be completed by graduate and undergraduate gn students before they begin laboratory research.

Chemical Engineering Thermodynamics CBE-GY6733 (3 Credits)

This course is an organized exposition of fundamental concepts of classical thermodynamics and traditional tools that will help chemical engineers understand and analyze systems they are likely to encounter in practice and/or original research. This course is for students who seek a much deeper understanding of classical thermodynamics than a typical undergraduate course provides. Topics include phase, chemical, and reaction equilibria, ideal and non-ideal solutions, stability of thermodynamic systems and thermodynamics of surfaces.
Prerequisite: CBE-UY 3153 or adviser's approval.


*Note: Two Seminar courses (CBE-GY 9910/CBE-GY 9920) must be taken each semester.

At least three electives (9 credits) must be chosen from approved CBE courses, 6000-level and above. The remaining courses may be chosen from other graduate programs with the approval of the graduate adviser in chemical engineering.
PhD Dissertation in Chemical and Biological Engineering CBE-GY999X (36 Credits total, each 3 Credits)

Theses for the PhD degree must give results of independent investigations of problems in chemical engineering and may involve experimental or theoretical work. Theses must show ability to do creative work and must show that original contributions, worthy of publication in recognized journals, are made to chemical engineering. Candidates are required to take oral examinations on thesis subjects and related topics. Doctoral-degree candidates must submit five unbound thesis copies to advisers before or on the seventh Wednesday before commencement.
Prerequisite: Adviser's approval and students must have passed the doctoral qualifying examination.

Research in Chemical & Biomolecular Engineering CBE-GY998X

*Up to 9 credits of CBE-GY 998X Research in Chemical & Biomolecular Engineering can be included here.

Dissertation research for PhD students who have not completed their qualifying examination. No more than a maximum of 9 credits can be taken or counted toward the PhD dissertation. Minimum registration is 3 credits. Prerequisites: Admission into the CBE PhD degree program & consent of PhD academic and thesis advisors.

Please peruse the guidelines for NYU-Tandon Chemical Engineering PhD students (pdf). All students are required to read this document and are responsible for abiding by the herein stated deadlines and rules.
Please peruse the guidelines for NYU-Tandon Chemical Engineering PhD students (pdf). All students are required to read this document and are responsible for abiding by the herein stated deadlines and rules.
After completion of your program, sign up for our PhD Exit Survey (pdf), to provide confidential feedback on your experience in the Chemical Engineering Ph.D. program.

Degree Requirements

To earn a doctoral degree in Civil Engineering, you must meet the following requirements:

1. 54 credits of graduate coursework (not including the Ph.D. dissertation) in relevant major and minor areas of study beyond the bachelor’s degree, with an average grade of B or better (cumulative average of 3.0 or better on a 0-4 scale). Up to 6 credits of the 54 credits may be satisfied by individual guided studies, readings, projects, and theses.

2. Completion and successful defense of a 21-credit dissertation related to the major area of study. Dissertations must consist of original research that advances meaningfully the state of the art in the research subject area and should result in the publication of at least one paper in a strictly peer-reviewed technical journal related to the subject. A grade of B or better must be achieved for the dissertation. There are 2 types of dissertation credits:

  • CE-GY 998X: Independent original investigation demonstrating creativity and scholarship worthy of publication in a recognized engineering journal. Registration for a maximum of 6 credits is allowed before registering for CE-GY 999X.
  • CE-GY 999X: Independent original investigation demonstrating creativity and scholarship worthy of publication in a recognized engineering journal. Candidates must successfully defend dissertations orally. Registration for 3 to 6 credits per semester is permitted after successfully completing the doctoral qualifying examination, but a minimum of 12 credits must be completed before the defense. Registration must be continuous (excluding summer semesters), unless a formal leave of absence is requested and approved. Registration for 3-12 credits per semester is permitted. In the final semester of work, registration for credit is permitted with the approval of the department head. Prerequisites: Degree status, successful completion of doctoral qualifying examinations, and approval of the dissertation adviser.

3. Completion of 1 minor area of study, as follows:

  • In or Out of Department Minor: Completion of 9 credits of graduate coursework in 1 technical areas of study.

Please refer to Tandon’s Bulletin, Academic Department, Degree and Program Information, for a detailed description of Transfer Credits Requirements.

4. Residency requirements for the Ph.D. (Civil Engineering) include the 21-credit dissertation, plus a minimum of 15 credits of applicable graduate course work taken at the School of Engineering.

5. In satisfying the 54-credit course requirement (Item 1), you must satisfy all requirements for the major and minor areas selected or their equivalent.

6. In satisfying these basic Ph.D. requirements, you also must satisfy 1 of the 2 following conditions:

  • 48 credits of relevant graduate coursework, not including individual guided studies (readings, projects, theses, etc.) beyond the bachelor’s degree, with an average grade of B or better (cumulative average of 3.0 or better on a 0-4 scale).
    OR
  • 24 credits of approved graduate coursework, not including individual guided studies (readings, projects and theses) beyond the master’s degree, with an average grade of B or better (cumulative average of 3.0 or better on a 0-4 scale). Satisfying condition 2 requires that the department accept the student’s MS degree without regard to its specific content. This acceptance requires a recommendation from the department’s Graduate Committee and department head approval.

7. Although publication is not required as a condition for graduation at this time, journal publication is strongly encouraged. Every Ph.D. candidate is expected to generate knowledge worthy of publication in 2 or more reputable journals.

Before becoming a candidate for the Ph.D., you must pass a qualifying examination. Immediately after you pass your qualifying exam, a Dissertation Committee will be formed. This panel of experts will guide your course of study and research work. You are required to submit and present a dissertation proposal. The culmination of your Ph.D. work will be the defense of the final draft of your dissertation. There are important requirements involved in the qualifying examination and dissertation processes.

Degree Requirements

*Note: for pre-fall 2015 Ph.D. students, please see the pre-fall 2015 Ph.D. Curriculum.

To receive a Ph.D. in Computer Science at the NYU Tandon School of Engineering, a student must:

  • satisfy a breadth course requirement, intended to ensure broad knowledge of computer science,
  • satisfy a depth requirement, consisting of an oral qualifying exam presentation with a written report, to ensure the student's ability to do research,
  • submit a written thesis proposal and make an oral presentation about the proposal,
  • write a Ph.D. thesis that must be approved by a dissertation guidance committee and present an oral thesis defense, and
  • satisfy all School of Engineering requirements for the Ph.D. degree, as described in the NYU Tandon School of Engineering bulletin, including graduate study duration, credit points, GPA, and time-to-degree requirements.

Upon entering the program, each student will be assigned an advisor who will guide them in formulating an individual study plan directing their course choice for the first two years. The department will hold an annual Ph.D. Student Assessment Meeting, in which all Ph.D. students will be formally reviewed.

In order to obtain a Ph.D. degree, a student must complete a minimum of 75 credits of graduate work beyond the BS degree, including at least 21 credits of dissertation. A Master of Science in Computer Science may be transferred as 30 credits without taking individual courses into consideration. Other graduate coursework in Computer Science may be transferred on a course-by-course basis. Graduate coursework in areas other than Computer Science can be transferred on a course-by-course basis with approval of the Ph.D. Committee (PHDC). The School of Engineering places some limits on the number and types of transfer credits that are available. Applications for transfer credits must be submitted for consideration before the end of the first semester of matriculation. Further details can be found in the School of Engineering bulletin.

Each incoming Ph.D. student will be assigned to a research advisor, or to an interim advisor, who will provide academic advising until the student has a research advisor. The advisor will meet with the student when the student enters the program to guide the student in formulating an Individual Study Plan. The purpose of the plan is to guide the student’s course choice for the first two years in the program and to ensure that the student meets the breadth requirements. The plan may also specify additional courses to be taken by the student in order to acquire necessary background and expertise. Subsequent changes to the plan must be approved by the advisor.

Each Ph.D. student must complete a breadth requirement consisting of 6 courses. To remain in good academic standing, students must fulfill the breadth requirement within 24 months of entering the Ph.D. program.

Students who do not fulfill the breadth requirement within 24 months will be dismissed from the program unless an exception is granted by the PHDC. The PHDC will consult with the student’s research advisor to decide whether an exception is granted and to determine the conditions the student needs to meet.

Details of Breadth Requirement

The courses used to fulfill the breadth requirement must satisfy the following:

(a) Approved list courses: At least 4 of the courses must be taken from the approved list of courses; see below "CS Ph.D. Breadth Requirement: Approved List of Courses." The 4 courses must satisfy the following two requirements:

i) Theory requirement: At least one of the 4 courses must be taken in the Theory area.

ii) Systems & Applications Requirement: At least two of the 4 courses must be taken in Systems & Applications.

Exemptions from approved list courses: With the approval of the Ph.D. Committee, students who have previously received a grade of A or A- in a course that is on the approved list, while enrolled in another NYU graduate program, can use that course towards the breadth requirement in lieu of taking it while in the Ph.D. program.  Also, students who have previously received a grade of A or A- in a graduate course similar to one on the approved list, while enrolled in a graduate program at another university with standards comparable to those at NYU, can use that course in lieu of taking the course on the approved list. The determination of whether a course previously taken at another university can be used in this way will be made by the PHDC.

Approved Course List: The list of approved courses will be reviewed regularly by the PHDC and is subject to change. Any changes must be approved by the CSE Department. In order for a course to be considered for inclusion in the list, the course must be rigorous and the students in it must be evaluated individually. Examples of inappropriate courses include those in which students are traditionally not differentially evaluated (e.g., all students receive A's or "pass") and courses in which grades are based on attendance or making a presentation of someone else's work, rather than on tests and assignments. Students, under their advisors’ guidance, should select their courses from the approved list so that they are exposed to a broad set of topics in computer science.

(b) Free choice courses: Students must take 2 free choice courses in addition to the 4 required courses from the approved list. Students can use any graduate course at NYU as free choice courses but must obtain advisor approval to use a course not on the approved list. Students cannot use independent study courses (such as Advanced Project CS-GY 9963 or Readings in Computer Science, CS-GY 9413 and CS-GY 9423) or dissertation. Both free choice courses must be taken while in the CS Ph.D. program. No exemptions are available for free choice courses.

(c) GPA requirement: Students must receive a grade of at least B in each of the six courses used to fulfill the breadth requirement. The average in the 4 approved list courses used to fulfill the breadth requirement must be at least 3.5. (For students who receive exemptions allowing them to take fewer than 4 approved list courses while in the CS PhD program, the average will be calculated over the approved list courses that were taken while in the CS Ph.D. program.) The average in the 2 free choice courses must also be at least 3.5.

(d) Requirement for Students who have never taken an Algorithms Course: Any student who has not taken a course in Algorithms prior to entering the Ph.D. program, at either the undergraduate or the graduate level, must take a graduate course in algorithms while in the Ph.D. program. Students may take CS-GY 6033 (Design and Analysis of Algorithms I), CS-GY 6043 (Design and Analysis of Algorithms II), or CSCI-GA.3520 (Honors Analysis of Algorithms) to fulfill this requirement. The department may revise this list in the future depending on course offerings. Alternatively, students may petition the PHDC to use another course. The grade received in the course must be at least B.

By the end of a student’s third semester (throughout this document, the word “semester” is used to refer to fall or spring semester) in the program, at the latest, the student must be involved in a research project under the guidance of a faculty research advisor. It is the responsibility of each student to find a faculty advisor and a research project, and to inform the PHDC Chair about his/her choice of advisor. Students must inform the PHDC chair if they change their research advisor.

To satisfy the depth requirement, students must take a qualifying exam (QE) based on their research. The QE must be taken before the start of the student’s fifth semester in the program. Students are required to form a QE committee, select an exam topic, and a tentative date approved by the advisor and committee, by the end of their third semester.

The QE committee must consist of the advisor and at least two other members. The committee must be approved by the advisor and the PHDC. The advisor is the designated chair of the committee. All members of the QE committee must be CSE faculty, faculty from other departments at NYU, or individuals of like standing from outside the university. At least two of the QE committee members must be tenured or tenure-track members of the CSE department unless permission is obtained from the PHDC to include only one such member.

For the QE, the student must give an oral presentation of her/his research accomplishments to the QE committee and write a detailed document describing those accomplishments. The document must be submitted to the QE committee and the PHDC no later than one week before the oral presentation. A student is expected to have conducted original research by the time of the exam. This research may have been carried out independently or in collaboration with faculty, research staff, or other students. Students are encouraged, but not required, to have publication-worthy results by the time of the exam. It is not sufficient for a student to present a survey of previous work in an area or a reimplementation of algorithms, techniques, or systems developed by others.

The committee, by majority vote, gives a grade for the exam as either "Pass" or "Fail." The chair of the QE committee will send this grade in writing to the student and to the PHDC chair, together with a written evaluation of the student's performance, approved by the QE committee members. A student who does not receive a “Ph.D. pass” may request permission from the PHDC to retake the exam. The PHDC will consult with the QE committee, review the case and make the final decision as to whether a retake is allowed or not. A student may petition the PHDC to change one or more members of the QE committee, but approval of the request will be at the PHDC’s discretion.

If the request for a retake is approved, the QE committee will determine the date for the second attempt. If the student is not allowed to retake the exam, the student will not be allowed to continue in the Ph.D. program in the following semester. If the student does not pass the qualifying exam on the second attempt, or otherwise does not satisfy the conditions given to her/him upon failing the exam the first time, the student will not be allowed to continue in the Ph.D. program in the following semester.

If a student has passed the QE and then changes his/her area of research, the student need not retake the QE.

Part-time students can petition the PHDC for extensions to the deadlines associated with the qualifying exam. Extensions should be for at most 2 semesters, except in extraordinary cases. Approval of extensions is at the discretion of the PHDC.

Within 6 months of passing the QE, each student is required to form a dissertation guidance committee. This committee must be approved by the student’s research advisor and by the PHDC. The committee must include at least four members. The committee members can be CSE faculty, faculty from other departments at NYU, or individuals of like standing from outside the university. At least one member of the dissertation guidance committee must be a tenured or tenure-track CSE faculty member, and at least one member of the committee must be from outside the CSE department.

By the end of the student’s fifth semester in the program, the student and committee must set a tentative date for the thesis proposal presentation. The presentation must be done prior to the start of the student’s seventh semester in the program.

Before finalizing the date of the presentation, the student must submit a written thesis proposal to the dissertation guidance committee which should include:

  • a description of the research topic
  • an explanation of how the research will advance the state of the art, and
  • a tentative research plan

After the dissertation guidance committee has approved the thesis proposal, the student should schedule the thesis proposal presentation and notify the PHDC chair once this has been finalized. The presentation should be announced to the faculty by the PHDC chair at least one week before it occurs. The presentation is open to all faculty. It may also be open to others at the discretion of the research advisor.

Substantial subsequent changes to the thesis topic must be approved by the dissertation guidance committee.

The last and most substantial aspect of the Ph.D. program is the dissertation. 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.

Other requirements for the Ph.D. dissertation and defense can be obtained from the Office of the Associate Dean for Graduate Academics in the NYU School of Engineering.

All Ph.D. students will be formally reviewed each year in a Ph.D. Student Assessment Meeting. The review is conducted by the entire CSE faculty and includes at least the following items (in no particular order):

  • All courses taken, grades received, and GPAs.
  • Research productivity: publications, talks, software, systems, etc.
  • Faculty input, especially from advisors and committee members.
  • Student’s own input.
  • Cumulative history of the student's progress.

As a result of the review, each student will be placed in one of the following two categories, by vote of the faculty:

  • In Good Standing: The student has performed well in the previous semester and may continue in the Ph.D. program for one more year, assuming satisfactory academic progress is maintained.
  • Not in Good Standing: The student has not performed sufficiently well in the previous year. The consequences of not being in good standing will vary, and may include being placed on probation, losing RA/GA/TA funding, or not being allowed to continue in the Ph.D. program.

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. A copy of each student’s letter will be placed in the student’s file.

Other School of Engineering requirements can be found in the School of Engineering Bulletin. Students must meet all applicable requirements, including graduate study duration, credit points, GPA, and time-to-degree requirements.

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.

Any person attending an exam remotely must have a two-way video and audio connection.

1. 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 the permission of the PHDC.

If a Ph.D. 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.

Any other arrangements must be approved by the PHDC.

2.  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.

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.

The following courses at NYU Tandon School of Engineering can be used to satisfy the breadth requirements:

Theory

This course covers techniques in advanced design and analysis. 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.

Knowledge of algorithms and data structures equivalent to CS-GY 6033.
Prerequisite: Graduate standing.

This course gives a behind-the-scenes look into the algorithms and computational methods that make machine learning and data science work at large scale. How does a service like Shazam match a sound clip to a library of 10 million songs in under a second? How do scientists find patterns in terabytes of genetic data? How can we efficiently train neural networks with millions of parameters on millions of labeled images? We will address these questions and others by studying advanced algorithmic techniques like randomization, approximation, sketching, continuous optimization, spectral methods, and Fourier methods. Students will learn how to theoretically analyze and apply these techniques to problems in machine learning and data science. They will also have the opportunity to explore recent research in algorithms for data through a final project and optional reading group. This course is mathematically rigorous and is intended for graduate students or strong, advanced undergraduates.

Knowledge of machine learning (equivalent to CS-UY 4563, CS-GY 6923, or ECE-GY 6143), algorithms (equivalent to CS-UY 2413, CS-GY 6033, or CS-GY 6043), and linear algebra (equivalent to MA-UY 2034, 3044, or 3054).
Prerequisite: Graduate standing.

This course introduces the theory of computation. 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. Time complexity, P and NP, Cook-Levin theorem, NP completeness.

Knowledge of discrete math (equivalent to CS-GY 6003).
Prerequisites: Graduate standing and CS-GY 6003 (or instructor's permission).

This course introduces data structures and algorithms for geometric data. 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.

Knowledge of algorithms and data structures equivalent to CS-GY 6033.
Prerequisites: Graduate standing.


Systems and Applications

An overview of state-of-the-art single-core systems, including advanced pipelining, super-scalar, vector processors, VLIW and vector processing. High-performance computing systems: Computer systems that improve performance and capacity by exploiting parallelism. Selected topics in parallel computing are introduced, such as interconnection networks, parallel algorithms, GPUs, PRAMs, MIMD and SIMD machines. Alternatives to traditional computing are discussed, including GPUs, TPUs, systolic arrays, neural networks and experimental systems.

Prerequisites: Graduate standing and CS-GY 6133.

This course surveys recent important commercial and research trends in operating 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.

Prerequisites: Graduate standing and CS-GY 6233.

This course introduces distributed-networked computer systems. Topics: Distributed control and consensus. Notions of time in distributed systems. Client/Server communications protocols. Middleware. Distributed File Systems and Services. Fault tolerance, replication and transparency. Peer-to-peer systems. Case studies of modern commercial systems and research efforts.

Big Data requires the storage, organization, and processing of data at a scale and efficiency that go well beyond the capabilities of conventional information technologies. 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. Students should have experience in programming: Java, C, C++, Python, or similar languages, equivalent to two introductory courses in programming, such as ?Introduction to Programming? and ?Data Structures and Algorithms.

Knowledge of Python.
Prerequisites: Graduate standing.

This course takes a top-down approach to computer networking. After an overview of computer networks and the Internet, the course covers the application layer, transport layer, network layer and link layers. Topics at the application layer include client-server architectures, P2P architectures, DNS and HTTP and Web applications. 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. Topics at the network layer include forwarding, router architecture, the IP protocol and routing protocols including OSPF and BGP. Topics at the link layer include multiple-access protocols, ALOHA, CSMA/CD, Ethernet, CSMA/CA, wireless 802.11 networks and linklayer switches. The course includes simple quantitative delay and throughput modeling, socket programming and network application development and Ethereal labs.

Knowledge of Python and/or C.
Prerequisites: Graduate standing.

This course begins by covering attacks and threats in computer networks, including network mapping, port scanning, sniffing, DoS, DDoS, reflection attacks, attacks on DNS and leveraging P2P deployments for attacks. 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. 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.

Prerequisites: Graduate standing. * Online version available.

This course broadly introduces database systems, including the relational data model, query languages, database design, index and file structures, query processing and optimization, concurrency and recovery, transaction management and database design. Students acquire hands-on experience in working with database systems and in building web-accessible database applications.

Knowledge of basic data structures and algorithms (search trees, hash tables, sorting and searching). Knowledge of principles of operating systems and of the client-server architecture. Basic familiarity with the UNIX operating systems. Programming proficiency.
Prerequisites: Graduate standing.

This course covers compiler organization. Topics: Lexical analysis, syntax analysis, abstract syntax trees, symbol table organization, code generation. Introduction to code optimization techniques.

Knowledge of discrete math equivalent to CS-GY 6003, and knowledge of fundamental data structures.
Prerequisites: Graduate standing.

This course introduces the fundamentals of computer graphics with hands-on graphics programming experiences. 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.

Knowledge of Data Structures and Algorithms, and be comfortable with C/C++ programming.
Prerequisites: Graduate standing.

Artificial Intelligence (AI) is an important topic in computer science and offers many diversified applications. 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. This course introduces students to the many AI concepts and techniques.

Knowledge of Data Structures and Algorithms.
Prerequisites: Graduate standing.

This course addresses the design and implementation of secure applications. 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.

Knowledge of Information, Security and Privacy equivalent to CS-GY 6813.
Prerequisites: Graduate standing.

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.

Knowledge of Database Systems equivalent to CS-GY 6083 and experience with a relational database system.
Prerequisites: Graduate standing.

An important goal of artificial intelligence (AI) is to equip computers with the capability of interpreting visual inputs. 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.

Knowledge of Data Structures and Algorithms, proficiency in programming, and familiarity with matrix arithmetic.
Prerequisites: Graduate standing.

This course covers the basic technology underlying Web search engines and related tools. 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. 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.

Prerequisites: Graduate standing.

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.g. text, audio, images). Students will learn to train and validate machine learning models and analyze their performance.

Knowledge of undergraduate level probability and statistics, linear algebra, and multi-variable calculus.
Prerequisites: Graduate standing.

An introductory course on Information Visualization based on a modern and cohesive view of the area. Topics include visualization design, data principles, visual encoding principles, interaction principles, single/multiple view methods, item/attribute, attribute reduction methods, toolkits, and evaluation. Overviews and examples from state-of-the-art research will be provided. The course is designed as a first course in information visualization for students both intending to specialize in visualization as well as students who are interested in understanding and applying visualization principles and existing techniques.

Prerequisites: Graduate standing.

Designing a successful interactive experience or software system takes more than technical savvy and vision--it also requires a deep understanding of how to serve people's needs and desires through the experience of the system, and knowledge about how to weave this understanding into the development process. This course introduces key topics and methods for creating and evaluating human-computer interfaces/digital user experiences. Students apply these practices to a system of their choosing (I encourage application to prototype systems that students are currently working on in other contexts, at any stage of development). The course builds toward a final write-up and presentation in which students detail how they tackled HCI/user experience design and evaluation of their system, and results from their investigations. Some experience creating/participating in the production of interactive experiences/software is recommended.

Knowledge of the design of user experiences and interfaces is desirable but not required.
Prerequisites: Graduate standing.

This course is about experimental game design. Design in this context pertains to every aspect of the game, and these can be broadly characterized as the game system, control, visuals, audio, and resulting theme. We will explore these aspects through the creation of a few very focused game prototypes using a variety of contemporary game engines and frameworks, high-level programming languages, and physical materials. This will allow us to obtain a better understanding of what makes games appealing, and how game mechanics, systems, and a variety of player experiences can be designed and iteratively improved by means of rapid prototyping and play-testing. The course combines the technology, design, and philosophy in support of game creation, as well as the real-world implementation and design challenges faced by practicing game designers. Students will learn design guidelines and principles by which games can be conceived, prototyped, and fully developed within a one-semester course, and will create a game from start to finish. The course is a lot of (team)work, but it's also a lot of fun. Programming skills are helpful, but not a hard requirement. Artistic skills, or a willingness to learn them are a plus.

Prerequisites: (Graduate Standing AND CS-GY 6533) for SoE students OR (OART-UT 1600 and OART-UT 1605) for Game Center MFA students OR instructor permission.

This course covers artificial intelligence techniques used with games. The course is an advanced course that presupposes a good understanding of standard AI techniques, and much of the course material will consists of recent research papers. While the course will cover recent methods for playing games, in particular for general game playing, it will also go beyond that application domain to cover methods for generating games and game content and for modeling players. Many of these methods are based on evolutionary computation, others on stochastic tree search, cellular automata or grammar expansion. Approximately the first half of the course will consist of lectures, and the second half of the group projects.

Prerequisites: Graduate Standing and CS-GY 6613 or similar introductory Artificial Intelligence courses.

Special Topic


The following courses, offered the Computer Science Department at the Courant Institute of Mathematical Sciences at NYU, can also be used to satisfy the breadth requirements:

Theory

  • Honors Analysis of Algorithms CSCI-GA.3520

Systems and Applications

  • High Performance Computer Architecture CSCI-GA.2243
  • Networks and Distributed Systems CSCI-GA.2620
  • Honors Programming Languages CSCI-GA.3110
  • Honors Compilers CSCI-GA.3130
  • Honors Operating Systems CSCI-GA.3250
  • Computer Graphics CSCI-GA.2270
  • Computer Vision CSCI-GA.2271
  • Advanced Database Systems CSCI-GA.2434
  • Artificial Intelligence CSCI-GA.2560
  • Machine Learning CSCI-GA.2565
  • Foundations of Machine Learning CSCI-GA.2566
  • Natural Language Processing CSCI-GA.2590

Degree Requirements

Graduate students who have exhibited a high degree of scholastic proficiency and have given evidence of ability for conducting independent research may consider extending their goals toward the doctorate. The Ph.D. degree is awarded after completing the program of study and research described below, and upon preparation and defense of a dissertation representing an original and significant contribution deemed worthy of publication in a recognized scientific or engineering journal.

Many factors enter into a student’s choice of an advisor for his/her research. In addition to the scientific, intellectual and personality factors which influence the pairing of student and professor, financial aspects must also be considered. For most full-time students, the ideal situation is to find an advisor who has a research topic of mutual interest, as well as funds available from research grants and contracts which can support the student as a Research Assistant (RA). A prospective student is encouraged to contact faculty members in his/her research area regarding the possibility of advising before applying to the Ph.D. program. A student who joins the Ph.D. program without securing a thesis advisor will be assigned an academic advisor, who will guide the student in terms of course selection and research activities before the qualifying exam. A Ph.D. student candidate must obtain the commitment of a faculty member in the student’s chosen area of major research interest to be the student’s thesis advisor before taking the qualifying exam.

Usually, the thesis advisor is a full-time faculty member in the Electrical and Computer Engineering Department and as such is considered chair of the student’s Guidance Committee. If a student wishes to have someone outside the ECE department to serve as his/her advisor, the student should submit the CV of the person and a letter of commitment from the person to serve as the advisor to the Ph.D. EE Program Director for approval. The thesis advisor must have a Ph.D. degree in the student’s proposed area of research.

A. Requirements to be satisfied before taking the oral exam

1) The student must have registered at NYU-Tandon for at least one semester and taken at least 3 graduate-level courses and the student’s cumulative GPA from formal courses (not including MS thesis, independent projects and readings) should be 3.5 or above.
2) The student must have completed at least 2 core courses (See Section on Course Requirement), with GPA over the core courses being 3.5 or above, and each core course earning a grade of B or above.
3) The student must have completed a research project under the supervision of a project advisor. The advisor can be any faculty member associated with ECE department. Notice that an external researcher may serve in this role, subject to approval by the chair of the ECE Graduate Curriculum and Standards Committee (to be referred to as the Graduate Committee subsequently). Examples of the project include, but are not limited to, an in-depth literature review of a certain topic, demonstrating solid understanding of a certain set of papers, or implementation and validation of some algorithms in past literature, or a study based on ideas initiated by the advisor or the student. Publication is not a requirement, but is encouraged if the student and the advisor find the contributions by the student worthy of publication. The project advisor should ensure that the project topic is appropriate for evaluating the student’s potential for Ph.D. research. It is the student’s responsibility to identify and secure a project advisor.
4) The student should have secured an ECE faculty member (or an external member approved by the Chair of the Graduate Committee) prior to taking the qualify exam, who will serve as the student’s Ph.D. advisor if the student passes the oral exam. The project advisor does not have to be the Ph.D. advisor. The prospective Ph.D. advisor is not obligated to provide financial support for the candidate. The advisor’s letter of support must state a commitment of advising should the student pass the exam. It may also contain a narrative summarizing student’s progress in the program.

B. Oral exam

1) The oral exam committee should include the prospective Ph.D. advisor, and three other faculty members chosen by the student in consultation with the Ph.D. advisor. The committee should have at least three Tandon ECE tenure or tenure track (T/TT) faculty (including advisor), the fourth one can be a faculty member or an industry/research professor (with Ph.D. in ECE. or a related area) from NYUAD, NYUSH, or any other NYU department. At most one member can attend the exam remotely if the member is at NYUAD or NYUSH. The student is responsible to secure the committee members to attend the oral exam and identify a time at which all committee members can attend. The exam should be scheduled for 1.5 hours to allow sufficient time for questions and answers and final discussion among the committee members. Once the schedule is fixed, the advisor should announce the exam to all ECE faculty and invite them to attend the exam.
2) A student must send in an official application, along with other required material, for taking the oral exam to the Ph.D. EE qualifying exam coordinator, at least two weeks before the target date of the oral exam. The application form can be downloaded from: http://engineering.nyu.edu/academics/departments/electrical/students/student-resources. The student must be registered for RE-GY 9990 at the time of the application. This zero-credit course is used for recording the exam results and follows the standard add/drop deadlines. A permission code for RE-GY should be requested from Prof. XK Chen with a copy to the student’s advisor.
3) The student must submit a written project report to the exam committee at least one week before the exam date. The written report should be self-contained, and follows the standard format of a conference paper. It is recommended that the report size is between 4 - 6 pages in double column, font size 11.
4) During the exam, the student should give a 30-minute project presentation, followed by questions from the committee members, which should cover both the topic areas of the project and the foundational knowledge in the student’s chosen research area. Each committee member (excluding the advisor) is expected to engage in about 15 minutes of questions and answers with the student, with a total of 45 minutes for questions and answers. The student may ask each committee member about from which area will the faculty member ask fundamental questions, although the faculty member is not obliged to provide a detailed answer.
5) The committee will provide a written evaluation of the student’s potential for Ph.D. research to the department. The committee members can seek input from the prospective Ph.D. advisor when making such evaluation, but the advisor is excluded from participating in voting and writing the evaluation report. The evaluation criteria can be found from the evaluation form posted here: http://engineering.nyu.edu/academics/departments/electrical/student-resources
6) The ECE department will make the final decision of pass or fail based on the exam committee’s recommendation. If the student and advisor intent is to take the dissertation credits ECE-GY 999X during the same term as the RE-GY 9990 qualifying exam, the exam committee’s recommendation must reach the PhD qualifying exam coordinator at least a week in advance of the add/drop deadline for that term.
7) Result (Pass or fail) of the qualifying exam (RE9990) will be recorded in the student’s transcript.
8) The student should prepare the report and the presentation independently, without the help from his/her advisor.
9) If a student wants to present a work described in a published, accepted or submitted paper of which the student is not the sole author, the student should submit a short report (2 pages) that is an extensive summary of the work, or a literature survey of the area, and his/her future work, written by the student only, to be submitted along with the paper.
10) The student can present a work that has been presented at a conference, but the presentation should be modified as necessary to fit the qualifying exam oral presentation time limit and provide sufficient background material. The modification should be done by the student independently, without the help of the advisor.

C. Time Limit and Timelines of the First and Repeat Oral Exams

1) Qualifying Exam Limit: It is important to note that students must pass the qualifying exam within 2 years of starting the PhD program or they can be dismissed from the PhD program. The 2 years is “academic years,” i.e., fall/spring, fall/spring. In other words, the summer after the 2nd year is not included.
2) First Exam: For students (both full-time and part-time) who started the Ph.D. program with prior MS degree in electrical engineering or a related area, the first oral exam should be taken no later than one year after starting in the Ph.D. program. For students (both full-time and part-time) who started the Ph.D. program without a prior MS degree, the first oral exam can be taken either in the first year or the second year but the max of 2 years to pass the qualify still applies. If a student does not meet the requirement for taking the exam by this deadline, the student might be disqualified from the program.
3) Repeat Oral Exam and Disqualification: Students who failed the first oral exam but otherwise successfully meet the requirement for taking the oral exam can repeat the exam at most once, which should be completed within one year after the first exam. Students who fail to pass the repeat exam will be disqualified from the program.
4) Scheduling of First Exam and Repeat Exam: The first or repeat oral exam should be scheduled before a semester starts so that the student will be informed of the exam result on time for his or her course planning. A student who needs to repeat the qualify exam cannot repeat the exam in the same semester and must wait at least three months from the time when the first exam was taken.
5) More on the Repeat Exam: When a student is found to be deficient only in one part of the exam (e.g. written report, presentation of the project, answering fundamental questions), the student may be asked to repeat just that part of the exam. The repeat of a portion of the exam is treated the same as the repeat of the qualifying exam and is subject to the same deadline

1) Core Courses: A student, in consultation with and upon approval by the Ph.D. advisor, should choose at least 4 ECE-GY courses (12 credits) among courses with numbers ECE-GY6xxx, ECE-GY7xxx, ECE-GY8xxx, as their core courses. Transferred courses cannot be used to satisfy the core course requirement. To graduate, each course must have a grade of B or above and the average grade of the four courses must be 3.5 or above. The student must have completed at least 2 such courses with the average grade of taken courses being 3.5 or above, before taking the oral qualifying exam. The remaining core courses must be completed before graduation. The list of core courses a student (with a prior MS degree) will register for must be approved by his or her Ph.D. advisor.
2) ECE-GY courses: A student must choose at least 24 credits of ECE-GY courses, including the core courses. The robotics courses (ROB-GY) listed below may count as ECE-GY courses. This requirement can be satisfied by the 30 credits transferred from a prior MS degree in electrical engineering or computer engineering.

The following robotics courses count as ECE-GY prefixed courses:

  • ROB-GY 6003 Foundations of Robotics
  • ROB-GY 6213 Robot Localization and Navigation
  • ROB-GY 6323 Reinforcement Learning and Optimal Control for Robotics
  • ROB-GY 6333 Swarm Robotics
  • ROB-GY 6423 Interactive Medical Robotics

3) Non-ECE Courses: A student must choose at least 2 non-ECE graduate-level courses (6 credits or more) that are in either Science or Engineering discipline. These courses should be chosen from areas that are distinct and yet consonant with the student’s research area. Please note the courses in management cannot be counted towards this requirement. Courses taken at other schools of NYU will be counted towards this requirement provided that the PhD advisor approves them. Transferred courses taken at other accredited graduate programs are subject to approval by the Ph.D. EE program director.
4) Other courses: The degree requires a total of 75 credits with at least 21 Ph.D. dissertation credits taken at Tandon. A student must take a minimum of 42 credits in formal courses (as distinct from “independent study” credits such as reading, project or thesis), with a minimum of 24 course credits in ECE-GY courses. The student has freedom in choosing courses, provided that he or she satisfies the requirements specified in 1), 2) and 3). The student should consult with his/her Ph.D. advisor or academic advisor in devising a course plan as early as possible so that the course work covers sufficient depth for the student’s chosen area of research and related field, as well as sufficient breadth. Note that credits from CS5000-level courses cannot be counted towards Ph.D. EE degree.
5) GPA requirement: As with all the graduate programs at NYU-Tandon, a student must maintain a GPA of 3.0 or above among all courses taken at NYU. A student with GPA below 3.0 has up to two semesters on probation. If at the end of the second semester on probation, the GPA is still below 3.0, the student will be disqualified from the program. The Ph.D. EE program further requires that a student must have a GPA of 3.5 or above among all formal courses (not including dissertation or other independent studies) taken at NYU to graduate, in addition to the GPA requirement for the core courses as specified in Item 1).
6) Internships: International students must register for an internship course to do an internship. Up to 6 credits of approved internships for Ph.D. (CP-GY 9941CP-GY 9951CP-GY 9961CP-GY 9971, 1.5 credits each) can be applied towards the 75 credits Ph.D. degree requirement, and in particular, the ECE-GY course requirement as specified in Item 2) above. These credits can be part of the 45 credits beyond the 30 credits of a prior MS degree, which may include up to 3 credits of approved internships for MS (CP-GY 9911CP-GY 9921). For an internship to be approved for credits, the internship must provide training relevant to the student’s research area. All internship must be approved and supervised by the student’s Ph.D. advisor. The internship supervisor should submit a midterm and a final term evaluation report to the Ph.D. advisor. The student must submit a project report to the advisor upon completion of the internship for the evaluation and grading of the internship course.

For Ph.D. students with a prior MS degree, they are allowed to transfer up to 36 credits, of which 30 credits must be from their prior MS degree in ECE or a closely related field. For Ph.D. students admitted without a prior MS degree, they can transfer at most 6 credits. For the blanket transfer of 30 credits from a prior MS degree in ECE or a closely related field toward the PhD degree in EE, the student must provide a copy of his or her prior MS degree and the official academic transcripts. For individual course transfer, the student must provide an official transcript in a sealed envelope as well as catalog descriptions of the courses to be transferred, for evaluation and approval by the department graduate advisor. The official transcript and/or diploma submitted during the student’s admission process can be used in place of new submission. Graduate courses taken at other schools of NYU or taken as an undergraduate student at NYU Tandon School of Engineering are exempt from this policy, but are subject to the general polity of the Tandon School of Engineering regarding such courses. This policy is effective for students entering in Spring 2018 and later.

On passing the qualifying examination, the student should consult with his or her thesis advisor to identify additional members and form a guidance committee. The committee should be composed of at least three members with the thesis advisor usually acting as Chairperson. If the dissertation advisor is not a tenured or tenure track (T/TT) Tandon faculty member of the Department, then a T/TT Tandon faculty member of the Department in the student’s research area must be invited to serve as the Committee Chair. The committee should include at least two ECE T/TT faculty (including the advisor, and the NYUAD and NYUSH T/TT faculty), and may include at most two external members from outside the Department who are in the student’s area of major research interest. The student must submit the names of the members of his or her Guidance Committee to the Office of Graduate Studies with a copy to the ECE Graduate Office within 6 months of passing the qualifying exam. The Guidance Committee conducts the area examination and thesis defense, and approves the final thesis. The Guidance Committee appointment form can be obtained from the Office of Graduate Studies.

In the area exam, the student reviews the prior research in the student’s chosen dissertation topic and presents preliminary research results and additional research plan. The area exam is conducted by the Guidance Committee, but may be open to other interested faculty and students. The Guidance Committee attends and evaluates the student’s performance and determines whether the student demonstrates the depth of knowledge and understanding necessary to carry out research in the chosen area. Results of the exam will be recorded in the student’s transcript as ECE-GY 9980.

The student must submit a written report that summarizes prior research and the future plan at least one week before the scheduled exam time. The report should follow the Ph.D. dissertation template and be at least 25 pages long. The student must take and pass the area exam within 2 years after passing the Ph.D. qualifying exam. Students who fail to pass the exam by the deadline will be disqualified from the program.

After passing the qualifying exams, and with the agreement of the Thesis Advisor, the Ph.D. candidate may begin registration for dissertation credits ECE-GY 999x. (The student’s failure to abide by this rule may result in loss of credit for the dissertation registration.) A student must register at least 3 credits for ECE-GY999x each semester. A minimum of 21 credits is required for the Ph.D. degree. The student must register for thesis continuously, every Fall and Spring semester, unless a Leave of Absence has been granted by the Office of Graduate Studies.

Upon completion of the doctoral dissertation, the candidate undergoes an oral thesis defense. The defense is conducted by the Guidance Committee, but is open to all members of the ECE faculty and other invited people. The student must submit a complete draft of the dissertation to the Guidance Committee members at least one week before the scheduled defense. The student should consult the Office of Graduate Studies regarding how to submit, reproduce and bind the final manuscript.

Ph.D. students are required to register for a 0-credit Research Seminar course (ECE-GY 9900) for at least 4 semesters. Satisfactory grade is given only if the student attends more than 2/3 of the seminars offered in a semester. Part-time students who have difficulty attending the seminar because of work conflict may be exempted from this requirement upon approval of the Ph.D. EE program director. The student should submit the approval note when applying for graduation.

To be granted the Ph.D. degree, a Ph.D. candidate must either have a peer-reviewed journal paper (accepted or published), or have at least one paper under review by a peer-reviewed journal on the thesis research subject.

For the journal paper(s), a letter of acceptance by a journal, or a letter of submission to a peer-reviewed journal along with acknowledgment of its receipt by the journal, will constitute the required evidence. If there is no accepted/published journal paper, the student should have at least one accepted conference paper that appeared in the proceedings of a peer-reviewed conference.

Students who entered before Fall 2014 can either follow the requirements described above, or the requirement effective at the time of matriculation. The requirements posted in the NYU-Tandon catalog as of Sept. 2013 differ from the new requirements in the following aspects. For a complete description, please consult the ECE Graduate Student Manual published in Spring 2013.

Course and Thesis Requirements: A minimum of 75 credits of academic work beyond the bachelor’s degree, including a minimum of 21 credits of NYU-Tandon dissertation research, is required. A minimum of 42 credits in formal courses (as distinct from independent study credits such as reading, project or thesis) are required. A student entering with a MS from a reputable graduate program may transfer 30 credits. PhD students are required to take a minimum of 9 credits of courses in a minor area outside of electrical engineering. The minor must be taken in an area that is both distinct from and yet consonant with the student’s major study area. Students work with thesis advisers to develop their major study program. The major program should constitute a coherent, in-depth study of the most advanced knowledge in the student’s area of concentration.

Publication Requirement: To be granted the PhD degree, a PhD candidate must have at least one accepted or submitted journal paper on the thesis-research subject.

Transfer credits: For Ph.D. students entered before Spring 2015, the following policy as stated in the NYU-Tandon catalog as of Sept. 2013 are applicable: Doctoral candidates may transfer a maximum of 48 credits, including a 30-credit blanket transfer from a prior MS degree in Electrical Engineering or a closely related field, and additional courses in Science and Engineering not included in the prior MS that are individually transferred. For the blanket 30-credit transfer, the prior MS need not be a 30-credit MS, so long as an MS degree (or equivalent) was granted, and a copy of the degree and detailed transcripts are presented. Additional courses individually transferred cannot include project, thesis, dissertation, guided studies or readings, or special topics credits. Applications for transfer credits must be submitted for consideration before the end of the first semester of matriculation. The student’s major academic department evaluates graduate transfer credits, but no courses with grades less than B will be considered.

PhD Time Limits: The PhD time clock begins at the time of enrollment in the PhD program. Full-time PhD students who have completed an MS degree or who transfer 24 or more graduate credits towards their PhD degree must complete their PhD degree requirements within six years from the beginning of their PhD studies. Full-time PhD students who transfer in or have completed fewer than 24 credits when they begin their PhD studies have a maximum of seven years to complete their PhD. Part-time PhD students must complete their PhD degree requirements within nine years from the beginning of their PhD studies. Approved leave of absence will stop the time clock.


Graduate Manual

For further information, please refer to the graduate manual, which can be found on the student resources page.

Credit Requirements

The general credit requirements for the Doctor of Philosophy in Mechanical Engineering degree at the School of Engineering are:

  • Transfer from MS degree (30 credits)
  • Approved coursework beyond the MS degree (21 credits minimum)
  • Ph.D. dissertation (21 credits minimum)
  • Minimum Total Required: 75 Credits

The credits above include MS degree credits but go beyond those for the BS degree.

Your studies must also be completed 5 years after the MS degree or the date of admission, whichever is later, unless a formal leave of absence is approved before the period for which the studies are interrupted.

In addition, you must take a written and oral departmental qualifying examination within the first 2 times it is offered after the date you join the doctoral program. Upon passing, you must then form a Ph.D. Guidance Committee and begin your dissertation. To do so, you will need to register for at least 3 credits of ME 9999 each fall and spring semester. Actual registration should reflect the pace of the work and your activity.

An exception to the minimum registration requirement may be made in the last semester of registration if that semester is devoted primarily to complete the work and dissertation. A dissertation grade of U for 2 consecutive terms affects whether or not you will be allowed to continue doctoral work. You must present progress on your dissertation to your guidance committee at least once a year. You can find additional details on degree requirements in the departmental pamphlet available at the department's main office.

Degree Requirements and Curriculum

The curriculum for the Ph.D. in Human-Centered Technology, Innovation & Design Program fosters a research-intensive doctoral education relevant to understanding and shaping the impact of new technologies on a complex and rapidly-changing society and its institutions. We focus on how technology shapes and molds society and culture and how, in turn, social and cultural institutions respond to those impacts. This includes the rapidly changing areas of interdisciplinary design and media, human-computer interaction, institutional innovation, and data science and urban studies. 

Core coursework for the Ph.D. in Human-Centered Technology, Innovation & Design exposes students to advanced design and research skills modulated by the development of a critically reflexive understanding of the ways in which society and technology deeply influence design and development. Research methods courses help students develop advanced qualitative and quantitative research skills in the social sciences and humanities as the basis for inquiring into, designing, and evaluating new technologies in the service of society.

Thematic elective courses help students gain in-depth knowledge in a focused thematic area related to designing and making in a number of domains that our faculty specialize in, including human-computer interaction, disability studies and inclusive design, citizen science and urban sustainability, socio-technical transitions and systems design, design for governance based on collaboration and participation, and intersectional politics and ethics related to issues of technology. Students and doctoral advisers work together to curate and develop a rigorous course of study in the program.

Students are required to complete 75 credits, including 51 credits from the course work and 24 credits from the dissertation. For more information on specific faculty interests, please refer to the faculty pages under the relevant programs.

Comprehensive Examinations

Students must successfully pass two comprehensive examinations before starting the dissertation.

  • Part One: This examination includes material covered in the methodology courses. It can be taken after completing 30 graduate credits.
  • Part Two: This examination includes material from the thematic elective and associated thematic research courses, doctoral seminars and research methods courses. It can be taken after completing required course work.

Students can take both examinations together. Results are provided within one month of the examination. Students have only two chances to pass each examination, and we recommend they start during the end of their 2nd year.

Research Training and Interaction with Faculty

Students are expected to work actively with one or more faculty each year, and focus on completing research. Students are strongly encouraged to present research in progress once a year and work towards publishable papers, usually with a faculty as co-author. Students are strongly encouraged to work with their primary advisors to outline a plan of study where they can be involved in institutional research.

Every student participates in formal research seminars with departmental faculty and visitors.

Advising and Ph.D. Student Evaluation

The TCS doctoral program faculty director advises all first-year doctoral students. During their first year students have many opportunities to get to know the research interests of all departmental faculty. By the beginning of the second year, students have selected an intermediary adviser who will guide them through the comprehensive exam process and up to the thesis stage. By the middle of the third year students will have selected a thesis adviser. Each year every student submits a report of intellectual progress to their primary adviser.

All faculty meet to review the progress of all students in a day-long meeting each year. At this time, the student’s intellectual progress is reviewed and plans for the following year are considered. The results of this review include a formal letter to the student assessing the previous year’s work and offering guidance and recommendations for the following year’s work.

Prerequisites and Additional Policies

Students who have a master’s degree or who are transferring from other institutions (or other departments within Tandon) are admitted based on the same qualification standards that apply to new students. For each required MS- or PhD-level course, if students have taken a similar course, they may transfer credits for the course. However, students still have to take and pass both qualifying exams. A minimum of 30 credits, including all dissertation credit, must be taken at Tandon. No dissertation credits from other institutions can be transferred.

All students must take the required coursework as assigned and follow the stipulated curriculum. The course work must be finished within the first three years and the dissertation thesis within the next three years, so all students complete the doctorate within six years.

Degree Requirements

  • 51 credits of graduate work (not including the Ph.D. dissertation) in relevant major and minor areas of study beyond the bachelor’s degree, with an average grade of B or better (cumulative average of 3.0 or better on a 0-4 scale).
  • Completion and successful defense of a 24-credit dissertation related to the major area of study. Dissertations must consist of original research that meaningfully advances the state-of-art in the subject area of the research and should result in the publication of at least 1 paper in a strictly peer-reviewed technical journal related to the subject. A grade of B or better must be achieved for the dissertation.
  • Completion of 2 minor areas of study, each consisting of between 9 and 12 credits of graduate work. At least 1 minor area must be outside the transportation area.
  • Residency requirements for the Ph.D. in Transportation Planning and Engineering include the 24-credit dissertation, plus a minimum of 9 credits of applicable graduate course work taken at the School of Engineering.

Conditions

In meeting the 51-credit course requirement, you must satisfy all requirements for the major and minor areas selected or their equivalent. In satisfying these basic Ph.D. requirements, you must also meet 1 of 2 conditions:

  • 39 credits of approved graduate coursework, not including individual guided studies (readings, projects, theses, etc.) beyond the bachelor’s degree, with an average grade of B or better (cumulative average of 3.0 or better on a 0-4 scale).
  • 21 credits of related graduate coursework beyond the master’s degree, with an average grade of B or better (cumulative average of 3.0 or better on a 0-4 scale).

Satisfying condition 2 requires that the department accept your MS degree in toto without regard to its specific content. This requires a recommendation from the department’s Graduate Committee and the approval of the department head.

Details about doctoral committees, qualifying examinations, and dissertation policies and procedures can be found in the School of Engineering Bulletin.

Degree Requirements

The Ph.D. curriculum includes 54 credits of graduate coursework beyond the bachelor’s degree and 21 credits of dissertation research, totaling 75 credits. The required coursework for the Ph.D. in Urban Systems is followed by a qualifying exam, a research proposal, and the dissertation defense.

To earn a doctoral degree in Urban Systems, the candidate must meet the following requirements:

  1. 54 credits of graduate coursework (not including the Ph.D. dissertation) in relevant areas of study beyond the bachelor’s degree, with an average grade of B or better (cumulative average of 3.0 or better on a 0-4 scale). Up to 6 credits of the 54 credits may be satisfied by individual guided studies, readings, projects, and theses.
  2. Completion and successful defense of a 21-credit dissertation related to the area of study. Dissertations must consist of original research that advances the state of the art in the research subject area and should result in the publication of at least two papers in a strictly peer-reviewed technical journal related to the subject.
  3. Successful completion of the qualifying examination. The qualifying examination is a written session and an oral session. Composition of the written exam is based on any of the post-bachelor courses taken by the student, while the oral exam is designed to judge the student's critical thinking.

This exam may be taken as early as the end of the first year, and not later than the middle of the second year. In the case of failure, the right to a second examination within six months is at the discretion of the examination committee and the approval of the program director.


Curriculum

The program includes fifty-four (54) credits of graduate coursework beyond the bachelor’s degree and twenty-one (21) credits of dissertation research. The required graduate coursework is followed by a qualifying exam, a research proposal, participation in the university’s urban seminars, completion of a social service project, and the dissertation defense. 

In satisfying the basic PhD requirements, students must satisfy the two following conditions: 

  • Completion of Core Courses shown below, and
  • Minimum of two (2) courses from the list of Elective Courses.

Ph.D. level coursework is designed to be flexible in order to support the student’s research interests, educational backgrounds, and career goals, offering an integrated education program that blends urban domains with supporting informatics content.

  • Urban Systems; CE-GY 7843 (3 credits)
  • Monitoring Cities; CE-GY 6053 (3 credits)
  • Principles of Urban Informatics; CUSP 5003  (3 credits)
Urban Systems:
  • Building Information Modeling; CE-GY 8383
  • Water, Waste & Urban Environment; GE 2036
  • Urban Ecology; GE 2070
  • Data-driven Mobility Modeling; TR-GY 7353
  • Forecasting Urban Travel Demand; TR-GY 6113
Statistics and Data Science:
  • Introduction to Data Science; DS-GA 1001
  • Regression & Multivariate Anal.; STAT-GB 2301
  • Applied Probability; APSTA-GE 2351
  • Statistics for Data Analysts; MG-GY 6193
  • Probability & Statistics; DS-GA 1002
  • Interactive Web Mapping; URPL-GP 465
Urban Informatics:
  • Machine Learning for Cities; CUSP 5006
  • Urban Big Data Management; CUSP 5008
  • Applied Data Science; CUSP-GX 6001
  • Urban Spatial Analytics; CUSP-GX 7002
  • Large-scale Visual Analytics; CS-GY 6323
Finance, Governance, Society:
  • Financing Urban Government; PADM 4443
  • Project Finance & Investment; FINC 3186
  • Culture: Art, Urban Governance; GG 2840
  • Adapting the Physical City; URPL 2612
  • Planning for Emergencies URPL 2645
  • Doctoral Research Methods; PHD-GP 5902

Dissertation

Students must choose a dissertation research advisor by the end of the first year, with the approval of the program committee. A dissertation guidance committee, composed of the research advisor and three other faculty members and one external faculty member) will be named with the approval of the program committee. The function of the dissertation guidance committee will be to monitor the student’s progress throughout the program.

A Research Proposal examination, overseen by the dissertation guidance committee and based on a dissertation research proposal and preliminary data, must be passed by the end of the third year. The objective of this exam is to ensure the student has chosen an appropriate Ph.D. research topic and that the research plan is rigorous and has high likelihood of success. The results of each student’s proposal examination will be delivered to the Registrar of NYU Tandon in writing, no later than one week following the exam.

The dissertation guidance committee will continue to meet once per year with the student for a review of progress, and will provide detailed feedback advice to the student. A report following each annual meeting must be filed with the program committee.

With the dissertation research advisor’s and the dissertation guidance committee’s approval, the student will submit a written dissertation meeting all requirements of NYU Tandon. The dissertation must be provided to the dissertation guidance committee members at least two weeks prior to the defense. The defense includes a formal, public presentation by the student, with questions from the audience. Following the public presentation, the student meets privately with the committee members for questions. The committee makes a decision that is then transmitted, in writing, to the Registrar.



* For more information, visit Tandon PhD. Programs home page.