Graduate Student Handbook

Institute for Artificial Intelligence and Data Science

Graduate Student

Handbook

AY 22-23

Version 1.0 – January 2023

Page 1 of 32

CONTENTS

1 General Information ............................................................................................................................. 4

1.1 Introduction .................................................................................................................................. 4

1.2 Revisions ....................................................................................................................................... 4

1.3 Petitions ........................................................................................................................................ 4

1.4 Orientation, Initial Advisement, and Course Registration ............................................................ 4

1.5 Program Contacts.......................................................................................................................... 4

1.6 Other Documents .......................................................................................................................... 5

2 Admissions ............................................................................................................................................ 6

2.1 Overview ....................................................................................................................................... 6

2.2 How to apply ................................................................................................................................. 6

2.3 Application Components............................................................................................................... 7

2.4 Exceptions to Admissions Requirements ...................................................................................... 7

2.5 Admissions Classifications ............................................................................................................. 7

3 Master’s Program Degree Requirements ............................................................................................. 8

3.1 The MS Degrees ............................................................................................................................ 8

3.2 Master of Science (MS) in Engineering Science with a focus in Artificial Intelligence.................. 8

3.2.1 Core Courses ......................................................................................................................... 8

3.2.2 Electives ................................................................................................................................ 8

3.2.3 Culminating Experience ........................................................................................................ 9

3.3 Master of Science (MS) in Engineering Science with a focus in Data Science ............................ 10

3.3.1 Core Courses ....................................................................................................................... 10

3.3.2 Electives .............................................................................................................................. 11

3.3.3 Culminating Experience ...................................................................................................... 11

3.4 Master of Professional Students (MPS) in Data Science and Applications ................................. 12

3.4.1 Core courses ........................................................................................................................ 12

3.4.2 Electives .............................................................................................................................. 12

3.4.3 Culminating Experience ...................................................................................................... 14

4 Doctor of Philosophy (Ph.D.) Degree Requirements .......................................................................... 15

4.1 Overview ..................................................................................................................................... 15

4.2 Coursework and Credit Hour Requirements ............................................................................... 15

4.3 CDSE Courses .............................................................................................................................. 16

4.3.1 Data Science ........................................................................................................................ 16

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4.3.2 Applied Numerical Mathematics ........................................................................................ 16

4.3.3 High Performance and Data Intensive Computing ............................................................. 16

4.4 Ph.D. Research Requirements and Milestones ........................................................................... 16

4.5 CDSE Milestones Detailed Description ....................................................................................... 17

4.5.1 Form CDSE Ph.D. Committee .............................................................................................. 17

4.5.2 Prospectus and Project Plan Approval ................................................................................ 18

4.5.3 Oral Examination ................................................................................................................. 18

4.5.4 CDSE Ph.D. Proposal ............................................................................................................ 19

4.5.5 CDSE Dissertation Completion and Defense ....................................................................... 19

5 General Student Requirements .......................................................................................................... 21

5.1 Registration Requirements ......................................................................................................... 21

5.2 Transferring Credits .................................................................................................................... 21

5.3 “Double Dipping” Course Credit ................................................................................................. 22

5.4 Inapplicable Credits ..................................................................................................................... 22

5.5 Resigning from a course .............................................................................................................. 23

5.6 Repeating Course ........................................................................................................................ 23

5.7 Student Status ............................................................................................................................. 23

5.8 Certification of Full-time Status .................................................................................................. 23

5.9 Reduced Course load .................................................................................................................. 24

6 Graduation Requirements .................................................................................................................. 25

6.1 Requirements for Master’s Students .......................................................................................... 25

6.1.1 Application to Graduate ...................................................................................................... 25

6.1.2 Petition to Change Graduation Conferral Date ................................................................... 25

6.1.3 Degree Time Limits ............................................................................................................. 25

6.2 Requirements for doctoral candidates ....................................................................................... 26

6.2.1 Checklist for Ph.D. graduation ............................................................................................ 26

6.2.2 Required Training ................................................................................................................ 26

6.2.3 Application to Candidacy .................................................................................................... 27

6.2.4 Time Limits .......................................................................................................................... 28

6.2.5 Required Forms for Graduation .......................................................................................... 28

6.2.6 Dissertation Requirements: ................................................................................................ 28

7 Academic Standards ............................................................................................................................ 30

7.1 Grading Policy ............................................................................................................................. 30

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7.1.1 Incomplete Grades .............................................................................................................. 30

7.1.2 S/U Grades .......................................................................................................................... 30

7.2 Scholastic Standing ..................................................................................................................... 30

7.3 Review of Academic Progress ..................................................................................................... 30

7.4 Probation .................................................................................................................................... 30

7.5 Academic Dismissal and Transcripts ........................................................................................... 31

7.6 Academic Integrity ...................................................................................................................... 31

7.6.1 Examples of Academic Dishonesty...................................................................................... 31

7.6.2 Academic Integrity Contract ............................................................................................... 32

Page 4 of 32

1 GENERAL INFORMATION

1.1 Introduction

This manual is designed to be a general reference for affiliated faculty and/or students pursuing a

graduate degree in one of the following programs: Master of Engineering Science degree with a course

focus in Artificial Intelligence; Master of Engineering Science degree with a course focus in Data Science;

Master of Professional Studies in Data Sciences and Applications; or Ph.D. in Computational and Data-

enabled Science and Engineering. Included are policies and procedures set forth by the School of

Engineering and Applied Sciences and the Graduate School of the University at Buffalo.

1.2 Revisions

This document may be revised annually. When this happens, a new edition will be issued. The edition for

the academic year in which you were admitted is the one that governs your entire graduate career.

1.3 Petitions

Should a student need special consideration regarding any of the policies or procedures outlined in this

handbook, they may submit a petition in writing to their program director for review.

1.4 Orientation, Initial Advisement, and Course Registration

Students should be familiar with the Graduate coordinator's office, located at 415 Bonner Hall. The

Graduate coordinator is the central resource for all administrative issues related to graduate studies. The

graduate coordinator can assist with initial course advisement and the registration process. The

registration procedure may vary by program.

International students, particularly those registering for the first time, should be familiar with the

International Student Services (ISS) office in Talbert Hall, room 210. This office advises on issues related

to immigration and visa status. ISS hosts a mandatory orientation for international students.

The School of Engineering and Applied Sciences (SEAS) also hosts an orientation for all incoming graduate

students the week before the fall semester. Program-specific orientations will also be offered. Each

orientation is mandatory for all students to attend.

1.5 Program Contacts

The program director and graduate coordinator are the primary contacts for all students within that

degree program. The program director will advise on all issues related to the academic program and

professional development. The graduate coordinator will assist with student-related services such as

course scheduling and registration and serve as a liaison between the graduate school, the Registrar's

office, etc. If uncertain, it is acceptable to contact both in a joint email.

The program directors for each program are:

Engineering Science MS Artificial Intelligence: Dr. Sreyasee Das Bhattacharjee

Engineering Science MS Data Science: Dr. Johannes Hachmann

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MPS Data Science and Applications: Dr. Rachael Hageman Blair

Computational and Data-enabled Science and Engineering Ph.D.: Dr. Margarete Jadamec

1.6 Other Documents

Information on University policies and procedures is available on the website and updated regularly. It is

the student’s responsibility to become familiar with these policies, including the following:

• The Graduate School Policy Library (for graduate students and advisors)

https://grad.buffalo.edu/succeed/current-students/policy-library.html

• Uniform Policies for SEAS Graduate Students:

http://engineering.buffalo.edu/home/academics/grad/policies.html

In addition, students will find the following websites useful throughout their time of study:

Title

Publisher URL Address

UB Rules &

Regulations /

Student Code of

Conduct

Student Conduct and

Advocacy

http://www.buffalo.edu/studentlife/who-we-

are/departments/conduct.html

Forms for

Graduate

Students

The Graduate School http://grad.buffalo.edu/succeed/current-

students/forms.html

Estimated Cost of

Attendance

The Graduate School http://grad.buffalo.edu/explore/funding/cost.html

Financial Aid Financial Aid https://financialaid.buffalo.edu/costs/

SEAS Website SEAS http://engineering.buffalo.edu/

Institute Website IAD https://www.buffalo.edu/ai-data-science.html

Registrar

Website

Registrar http://registrar.buffalo.edu/

UB Directory The University at

Buffalo

https://www.buffalo.edu/search/search.html

Student Life

Gateway

Student Life http://www.buffalo.edu/studentlife/who-we-are/about-

student-life-gateway.html

Accessibility

Resources

Student Guide https://www.buffalo.edu/studentlife/who-we-

are/departments/accessibility.html

Counseling

Services

Student Guide https://www.buffalo.edu/studentlife/who-we-

are/departments/counseling.html

ISS Office of International

Student Services

https://www.buffalo.edu/international-student-and-

scholar-services.html

Page 6 of 32

2 ADMISSIONS

2.1 Overview

Applicants must have a bachelor's degree from an accredited college or university. Applicants to the Ph.D.

program must also have a master's degree from an accredited college or university.

Applications for admission are evaluated based on criteria reflecting the academic quality and probable

success in advanced study. These criteria are:

• Undergraduate grades:

o A minimum cumulative undergraduate grade point average (GPA) of 3.0 on a United

States 4.0 scale

• Prior knowledge/coursework

o This may vary by each degree program and may include but is not limited to the following:

 Math (calculus, multivariate calculus, linear algebra)

 Statistics (basic statistics and probability)

 Computer Science (programming- at least one language):

• Two letters of recommendation

o Letters should come from professional sources, including, but not limited to supervisors,

former advisors, or instructors.

• Personal Statement

o A statement outlining past accomplishments, professional objectives, special interests,

and educational plans

The GRE is not required by the master’s level programs. However, it is highly recommended. Potential

students can submit their GRE scores, even if not required by the program for admissions. The GRE is

required for the CDSE PhD program.

The Graduate School requires that students who are not native English speakers must demonstrate

English proficiency. Applicants must take the Test of English as a Foreign Language (TOEFL), Pearson Test

of English (PTE), Duolingo English Test (DET), or the International English Language Testing System (IELTS)

within two years prior to the proposed admission date to UB. The State University of New York at Buffalo

has a minimum TOEFL score requirement of 550 (paper-based) or 79 (internet-based). On IELTS, UB

requires an overall score of 6.5 with no band score below 6.0. On PTE Academic, the university minimum

score is 55, with no subsection score below 50. The DET minimum score requirement is 120.

Applicants can review this requirement under the English Language Proficiency Requirement web page

2.2 How to apply

Applications will be completed online. Students can access the application management system by going

to https://ubseasconnect.buffalo.edu/apply/. The student must complete all fields and upload the

required documentation. Applications will not be reviewed until the application has been marked

submitted by the student and all materials have been uploaded.

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2.3 Application Components

Using the online application, every applicant must submit the following:

• A completed application on the website, filling out all required fields

• Email address for two (2) individuals who will provide letters of recommendation

• Resume/Curricula Vitae

• Personal Statement

• GRE scores (if applicable)

• English language proficiency scores (if applicable)

• Scanned copies of academic transcripts from undergraduate and graduate (if applicable) - English

translation is required.

• Pay the $85 application fee.

Applicants may start and return to the application at any time, and you do not need to submit all

components at once. However, an application will not be reviewed until you have formally submitted it,

paid the application fee, and all supplemental materials are received.

2.4 Exceptions to Admissions Requirements

The program director will be responsible for evaluating the applicant's admissions materials to determine

if an exception can be granted and the grounds upon which such an exception can be made. If a student

wishes to enter an alternative program, they must fulfill the normal application process for that program.

Exceptions and re-routing are subject to approval by program directors.

2.5 Admissions Classifications

• Degree Student: students whom the department has admitted with an undergraduate grade point

average of 3.0/4.0 or better.

• Non-degree student: students with suitable academic qualifications but do not wish to take a

degree program.

• Non-Matriculated student: a student who has been admitted but has not enrolled in the academic

program.

• Provisional admission: students are accepted on a provisional basis but must fulfill specific

requirements before enrolling in the program.

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3 MASTER’S PROGRAM DEGREE REQUIREMENTS

3.1 The MS Degrees

The Institute for Artificial Intelligence and Data Science offers three masters-level graduate degrees:

• Master of Science (MS) in Engineering Science with a course focus in Artificial Intelligence

• Master of Science (MS) in Engineering Science with a course focus in Data Science

• Master of Professional Students (MPS) in Data Science and Applications

Each degree program has its own admission and degree requirements and separate program directors.

Students admitted into their respective degree programs must adhere to their curriculum requirements

and the policies and procedures outlined in this handbook.

3.2 Master of Science (MS) in Engineering Science with a focus in Artificial

Intelligence

The Engineering Sciences MS, with a course focus on Artificial Intelligence, is a 1.5-year (30 credit hour)

multidisciplinary program. The program is designed to train students in the areas of machine learning,

programming languages that are needed to design intelligent agents, deep learning algorithms, and

advanced artificial neural networks that use predictive analytics to solve real-world problems. Students in

this program are offered a set of foundational core courses in AI and the flexibility to choose from elective

concentration areas that include data analytics, computational linguistics, information retrieval, machine

learning and computer vision, knowledge representation, and robotics.

Students must complete nine courses (27 credits) and a capstone project (3 credits). The course

requirements are shown below.

3.2.1 Core Courses

• EAS595 Fundamentals of Artificial Intelligence

• CSE 555 Introduction to Pattern Recognition

• CSE 574 Intro to Machine Learning

• EAS 501 Intro to Numerical Mathematics for Computing and Data Science

• CSE 568 Robotics Algorithms

3.2.2 Electives

Students can mix and match between categories; they do not have to pick one category and complete all

electives from the list. Students will take 4-5, 3-credit electives. The number of electives needed to

graduate is based on the culminating experience option.

DATA ANALYTICS GROUP

• CSE 601 Data Mining and Bioinformatics

• EE 539 Principles of Information Theory and Coding

• EE 559 Big Data Analytics

• MAE 509 Probability and Stochastic Process

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COMPUTATIONAL LINGUISTICS AND INFORMATION RETRIEVAL GROUP

• CSE 567 Computational Linguistics

• CSE 535 Information Retrieval

• CSE 635 Natural Language Processing and Text Mining

MACHINE LEARNING AND COMPUTER VISION GROUP

• CSE 674 Advanced Machine Learning

• CSE 676 Deep Learning

• MAE 600 Deep Learning for Mechanical Engineering

• CSE 573 Introduction to Computer Vision and Image Processing

KNOWLEDGE REPRESENTATION GROUP

• CSE 563 Knowledge Representation

• EAS 524 Ontological Engineering (Cross-Listed with Philosophy)

HUMAN/MACHINE INTERACTION GROUP

• MAE 527 Intelligent Machine Interfaces

• MAE 502 Human-Robot Interaction

• IE 535 Human Computer Interaction

OTHER

• MAE 593 Robotics 1

• CE 551 Computer-Aided Research in the Chemical and Materials Sciences

If a student is interested in an elective not mentioned on the above list for degree credit, they must get

approval from the program director. Students can get approval by emailing [email protected] the

course name, number, description, and how the class relates to their AI interests and future career

aspirations in AI. Failure to get approval before registering for the class could result in the student needing

additional credits to graduate.

3.2.3 Culminating Experience

Students can satisfy their culminating experience through one of two options:

1. All courses: complete 15 credits of electives (5, 3-credit electives)

2. Project: complete 12 credits of electives and a 3-credit project. The project can be an approved

internship in the industry or a faculty-based research project on campus. Students must complete

this via three credits of EAS 563 AI Capstone; no other course can be used for this project

requirement.

In addition to the coursework requirement for either culminating experience option, students must

complete a capstone experience before graduation to meet their culminating experience requirement to

graduate. This includes completing an oral presentation and a final written paper. Details will be sent out

to students in their final semester once they have applied for graduation. The due date for the oral

presentation and paper typically aligns with the final examinations.

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3.3 Master of Science (MS) in Engineering Science with a focus in Data Science

The Engineering Science MS with a course focus in Data Science is a 30-credit hour program that trains

students in the emerging and high-demand area of data and computing sciences. Students will be trained

in sound basic theory, emphasizing practical aspects of data, computing, and analysis. Graduates will be

able to serve the analytics needs of employers and will be exposed to several application areas. The degree

can be specialized using electives and a project. The curriculum for the MS in Data Science and

Applications program includes courses primarily from the School of Engineering and Applied Sciences

(SEAS).

The program is taught in a cohort-based model offering both Fall and Spring semester admission. Students

will take a combination of core courses (24 credits), electives (3 credits), and a culminating experience

requirement (3 credits).

3.3.1 Core Courses

The cohort design of the program ensures that students in the entering class (Fall, Spring) will be in the

same classes together through the program's required courses. The graduate coordinator sets schedules

to ensure students are in a suitable class with their cohort. Students are not permitted to create their

schedules or adjust their existing schedules. All requests must go through the graduate coordinator.

Therefore, students cannot choose when they take which courses. The exception for this is in the final

semester(s), where students have the flexibility to choose when they complete the project/survey course,

as exemplified in the course flowsheets below.

Flowsheet: Fall Intake

Fall Spring Summer Fall

EAS 501 EAS 509 EAS 560 EAS 504 (optional)

EAS 502 CSE 560 EAS 504 (optional) EAS 560 or Elective

EAS 503 CSE 574

EAS 508 Elective

In the above example, the student could take EAS 560 in the summer to graduate in 1 year OR take their

project or elective in the final fall semester to extend their graduation to 1.5 years.

Flowsheet: Spring intake

Spring Fall Spring

EAS 501 EAS 509 EAS 560 or Elective

EAS 502 CSE 560 EAS 504

EAS 503 CSE 574

EAS 508 Elective

In the above example, the student can choose EAS 560 or the second elective in the final semester.

Finishing in 1 year is not an option due to course sequencing issues.

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3.3.2 Electives

Students are required to take one elective in their second semester of the program. An optional second

elective may be taken for students pursuing the all-course option.

The following courses are pre-approved electives students can take to fulfill their elective requirements.

• CSE 535 Information Retrieval

• CSE 562 Database Systems

• CSE 573 Introduction to Computer Vision and Image Processing

• CSE 586 Distributed Systems

• CSE 587 Data Intensive Computing

• CSE 601 Data Mining for Bioinformatics

• CSE 633 Parallel Algorithms

• CSE 635 Natural Language Processing and Text Mining

• CSE 636 Data Integration

• CSE 674 Advanced Machine Learning *

• CSE 676 Deep Learning *

• STA 517 Categorical Data Analysis

• STA 567 Bayesian Statistics

• CDA 609 High-Performance Computing

• IE 575 Stochastic Methods

• IE 535 Human-Computer Interaction

• EE 634 Principles of Information Theory and Coding

• MTH 558/559 Mathematical Finance

*Courses are available to students who wish to enroll as a second elective/pursue an all-course

option. Require completion of CSE 574 before entering. It is up to the instructor if students can co-

enroll in one of the courses

Elective courses are not guaranteed to be offered every semester.

Only lecture courses can be used to fulfill the elective requirement; seminars courses are not eligible.

In the event a student is interested in taking a course that is not on the abovementioned list, they must

send a course description and syllabus to the program director for approval to enroll to fulfill elective

requirements in the program. If the program director approves, the student must get departmental

permission to be force enrolled in the course.

Example: Student A wants to enroll in CSE 510 / 610 (special topics). The student forwards the course

information to the graduate coordinator at [email protected]

. The graduate coordinator will consult

with the program director. The program director approves. The student will then submit a request in the

SEAS force registration portal to be enrolled in the course. If the instructor approves, the request will be

processed, and the student will get an acceptance notification.

3.3.3 Culminating Experience

Students can complete their culminating experience requirement in one of two ways:

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1. Master's project: Students may elect to complete a project-based internship or a research project

with a faculty member. Students will register for three EAS 560 Master's Project course credits.

2. All-course option: Students may elect to complete a second elective. At the end of their final

semester, students will complete either a 4–5-page writeup or a 20-minute oral presentation on

the topic of their choice and/or the type of job the student is interested in pursuing and how it

relates to what the student learned in the program; must integrate key concepts learned from

core coursework (semesters 1 and 2). The program director will grade this as a pass/fail.

3.4 Master of Professional Students (MPS) in Data Science and Applications

This Master of Professional Studies degree is skills-oriented and provides training in the practice of data,

computing, and analysis. Students will need some prior knowledge of mathematics, statistics, and

computing, and bridge classes are available to prepare students for success in the program. However, the

program is designed to support students from various academic backgrounds, which are not necessarily

STEM. The program is well-suited for STEM and non-STEM and often attracts professionals working in the

industry that seeks to bring data science into a particular field of the workplace. The MPS program in Data

Science and Applications emphasizes applications of methods to various datasets and problems. The

curriculum for the MPS in Data Science and Applications program spans five different schools at the

university.

The program comprises ten 3-credit courses for 30 credits total and can be completed in one calendar

year of study in an intensive program or 1.5 years for a standard time to completion.

3.4.1 Core courses

• CDA 501/EAS 503: Introduction to Data-Driven Analysis

• CDA 502/MGS 613: Database Management Systems

• CDA 511/MTH537: Introduction to Numerical Analysis

• CDA 531/MTH 511 Probability and Data Analysis

• CDA 532/STA 545: Statistical Data Mining 1

• CDA 546/STA 546: Statistical Data Mining 2

• CSE 574: Intro to Machine Learning

• CDA 551/MGS 639: Cybersecurity, Privacy, & Ethics

• EAS 504: Applications of Data Science Industry Overview

• CDA 571: Project Guidance OR an elective from the approved list

3.4.2 Electives

Effective Fall 2022, students have the option to finish the program by taking an elective in place of CDA

571 Project Guidance. Students can only take classes from the approved list below.

• BMI 503 Biomedical Informatics

• CDA500 Special Topics

• CSE 531 Algorithms Analysis and Design

• CSE 535 Information Retrieval

• CSE 526 Blockchain Application Development

• CSE 529 Algorithms for Modern Computer Systems

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• CSE 546 Reinforcement Learning

• CSE 555 Pattern Recognition

• CSE 560 Data Models Query Language

• CSE 562 Database Systems

• CSE 573 Introduction to Computer Vision and Image Processing

• CSE 676 Deep Learning

• ECO 525 Economics of Financial Institutions

• GGB 502 Essentials of Genetics and Genomics

• GEO 511 Spatial Data Science

• GEO 514 GIS and Machine Learning

• IE 511 Social Network Behavior Models

• IE 535 Human-Computer Interaction

• IE 572 Linear Programming

• IE 575 Stochastic Methods

• MGS 614 Systems analysis and design

• MGS 616 Predictive analytic

• MGS 628 Data visualization for business insights

• MGS 653 Social network analysis

• MGS 655 Distributed computing and big data technologies

• MGS 657 Online analytical processing: data warehousing

• MGS 659 Web Analytics and Optimization Techniques for eCommerce

• MGS 660 Big Data Information Management: Architecture and Tools

• MGS 662 Optimization Methods for Machine Learning

• MGS 670 Health Care Analytics

• MTH 543 Fundamentals of Applied Math

• MTH 550 Network Theory

• MTH 558/559 Mathematical Finance

• PHY 501 Introduction to Math Physics

• PSC 508 Basic Statistics for the Social Sciences

• PSC 534 Text as Data

• STA 503 Introduction to Applied Statistics 1

• STA 504 Introduction to Applied Statistics 2

• STA 505 Introduction to Biostatistics

• STA 509 Statistical Genetics

• STA 517 Categorical Data Analysis

• STA 527 Statistical Analysis 1

• STA 528 Statistical Analysis 2

• STA 529 Statistical Analysis 3

• STL 502/IE 550 Introduction to Operations Research

• STL 505 Transportation Modeling Fundamentals

Alternative courses will be considered by the Director of the program. Students must email the director

the course of interest and a rationale that relates the proposed course to the program curriculum.

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3.4.3 Culminating Experience

Students can satisfy their culminating experience through one of two options:

1. All courses: complete one of three credit electives from the approved list

2. Project: complete a project via internship in industry or research with faculty on campus.

The project is equivalent to 3 credits, and only the CDA 571 Project Guidance Course can

be used to satisfy the project requirements.

All students must submit a final written paper and participate in an exit interview with the program

director. The parameters of these requirements will be sent to students in their final semester upon

registration for CDA 571.

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4 DOCTOR OF PHILOSOPHY (PH.D.) DEGREE REQUIREMENTS

4.1 Overview

The Computational and Data-Enabled Science and Engineering (CDSE) Ph.D. program is an interdisciplinary

Ph.D. program that integrates the core areas of data science, numerical algorithms, and high-performance

computing toward research and discovery building on a graduate student’s domain science/discipline.

Graduate students attending the program are required to have a Master’s degree, which provides the

foundation on which the CDSE Ph.D. program builds. This foundational Master’s work can be in various

disciplines, including but not limited to engineering, mathematics, natural sciences, social sciences,

business, and pharmacy. The program aims for a 3-year timeline to completion.

As part of the program's interdisciplinary nature, Ph.D. students have a home department in their domain

science/discipline with their primary Ph.D. advisor but work interdisciplinarily with their Ph.D. committee

members, which span the domain science/disciplines across UB and the CDSE core Ph.D. areas. In this

way, the CDSE Ph.D. program provides a unique opportunity to train scholars with a combination of

domain science/discipline and technical expertise, preparing the next generation workforce.

The CDSE Ph.D. program has three pillars of core coursework that provide the pedagogical framework for

the degree. These are (1) Data Science, (2) Applied Mathematics and Numerical Methods, and (3) High

Performance and Data Intensive Computing. Graduate students are required to take a suite of courses in

these three core CDSE areas. The coursework is designed to facilitate research that leverages the

fundamentals from these core areas to the graduate student's domain of science/discipline.

4.2 Coursework and Credit Hour Requirements

The CDSE Ph.D. program requires 72 graduate-level credit hours, comprised of a combination of

coursework in the three CDSE core areas, electives in the domain of science/discipline, and dissertation

research and thesis credits. Of the 72 total credit hours required, 30 credit hours are from courses

within the three CDSE core areas. The 30 credit hours of CDSE core courses are, in turn, subdivided, with

nine credits in two of the CDSE core areas and 12 credits in the third CDSE core area (with the total

summing to 30). In collaboration with their Ph.D. committee, the graduate student can determine which

of the three core CDSE areas to take the 12 credits in according to their research plan. The core courses

must be taken for a letter grade, and a minimum GPA of 3.2 cumulative on a 4.0 scale is required for the

CDSE core courses. Up to 36 credits can be research and thesis credits, and at a minimum, students must

have at least 12 credits of dissertation coursework completed via CDA 660 CDSE Dissertation.

Of the total 72 credit hours for the CDSE Ph.D. program, 36 must be taken while enrolled in the CDSE

Ph.D. program. This allows for up to 36 credits to be transferred in from Master’s coursework, with the

approval of the dissertation committee and the Graduate Director. The courses transferred from the

Master’s degree can include coursework in the core of CDSE. However, only 6 hours of research credits

from the Master’s degree can be transferred. A listing of the CDSE core courses that is non-exhaustive is

included below.

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4.3 CDSE Courses

4.3.1 Data Science

• CSE 574: Intro to Machine Learning

• STA 521: Intro to Theoretical Statistics 1

• STA 522: Intro to Theoretical Statistics 2

• STA 534: Design of Experiments

• STA 567: Bayesian Statistics

• MAE 701 Special Topics: Bayesian Methods in Engineering Applications

• CSE 704 Seminar: Big Data

• CSE 740 Seminar: Big Data/Machine Learning

4.3.2 Applied Numerical Mathematics

• MTH 537: Introduction to Numerical Analysis 1

• MTH 538: Introduction to Numerical Analysis 2

• MTH 539: Method of Applied Mathematics 1

• MTH 540: Method of Applied Mathematics 2

• MTH 550: Network Theory

• MTH 555: Introduction to Complex Systems

• MGF 636: Complex Financial Instruments

• MAE 702 Seminar: Applied Functional Analysis

4.3.3 High Performance and Data Intensive Computing

• MTH 548: Data-Oriented Computing for Mathematics

• CSE 570: Introduction to Parallel and Distributed Processing

• CSE 587: Data Intensive Computing

• CDA 609: High-Performance Computing 1

• CDA 610: High-Performance Computing 2

The course selection is made in collaboration with the Ph.D. advisor and committee. However, it is the

graduate student's responsibility to register for classes each semester and be cognizant of registration

deadlines. If a student does not register by the deadline set by the Office of the Registrar, they are

subject to a late fee. Please reference the Registrar's website for information on class registration.

4.4 Ph.D. Research Requirements and Milestones

The CDSE Ph.D. program has a systematic sequence of Ph.D. milestones to help guide the dissertation's

progress. The milestones are incorporated into a timeframe that adheres to the aim of the 3-year time-

frame to completion for the CDSE Ph.D. degree. The milestones are outlined in the list below, and a more

detailed description of each milestone is subsequently provided. As a part of the process, students are

strongly suggested to finish their core coursework within the program's first two years.

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Year 1

First Semester:

Form Ph.D. committee

Submit the Committee Approval Form

Work on prospectus and course plan

Take classes

Second Semester:

Present prospectus to the committee

Submit Project Approval Form

Work on dissertation research

Take classes

Year 2

Third Semester:

Work on dissertation research

Take classes

Take Qualifying Oral Exam (if coursework is completed)

Fourth Semester:

Write Ph.D. proposal

Present Ph.D. proposal to committee (or Year 3)

Work on dissertation research

Year 3

Fifth Semester:

Present Ph.D. proposal to committee (unless done in Year 2)

Work on dissertation research and writing

Sixth Semester:

Complete dissertation

Defend dissertation

Submit M-Form

4.5 CDSE Milestones Detailed Description

CDSE student progress is tracked through milestones that are to be completed during certain semesters

of the program. These milestones require appropriate forms to be completed and signed by the advisor,

dissertation committee, and Director of Graduate Studies. Paperwork should be completed and filed with

the Graduate coordinator via email at cdsedept@buffalo.edu

4.5.1 Form CDSE Ph.D. Committee

The Ph.D. student is to form their Ph.D. committee no later than the end of the first semester in the

program. The Ph.D. committee consists of the Ph.D. advisor and two faculty members. The Ph.D. advisor

and committee members must be affiliated faculty with the Institute for Artificial Intelligence and Data

Science (IAD). The affiliated IAD faculty are identified on the IAD program website. The Ph.D. committee

may include additional members, in addition to the three faculty specified above. All faculty on the

Student's Ph.D. committee must also be on the Graduate Faculty Roster

. Only one faculty member on the

Ph.D. committee may have the same primary academic affiliation as the Ph.D. advisor.

A Committee Approval Form must be signed by every faculty member that has agreed to serve on the

committee, and then be approved by the Director of Graduate Studies. Once all signatures are received

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from the Ph.D. Committee, the student should bring the form to the Graduate coordinator, who will

review and obtain the final signature from the Director of Graduate Studies.

4.5.2 Prospectus and Project Plan Approval

Once the Ph.D. committee is formed, the student next focuses on narrowing down the research topic

and course plan. Specifically, this includes (a) deciding on the research topic, (b) formulating a course

plan to support the research, (c) writing a short prospectus outlining the proposed research, and (d)

presenting a-c to their Ph.D. committee. Steps a-c are worked on iteratively between the graduate

student, Ph.D. advisor, and Ph.D. committee before the presentation.

The prospectus document is approximately 1 page long and describes the research questions and general

directions of the planned research. The Ph.D. Committee will use this information to help the candidate

select appropriate coursework throughout their program. The presentation to the Ph.D. committee is

generally a 10–15-minute presentation on the Prospectus, followed by the course plan. Slides and the

prospectus document for the presentation should be shared with the committee at least three days in

advance.

A Project Plan Approval Form is to be filled out by the student, in collaboration with the Ph.D.

committee, that outlines the courses in each core area the student plans on taking and in what semester

they intend to enroll in the courses.

By signing the form, the Student's Ph.D. committee indicates the following:

1. Their approval that the courses indicated satisfy the CDSE Ph.D. Focus Area course

requirements.

2. They have reviewed the dissertation prospectus and discussed it with the student and

assessed its alignment with the courses listed in the course plan and CDSE Ph.D. program.

The Project Plan Approval form is completed after the presentation of the Prospectus. The Project

Approval form, the dissertation Prospectus, and the student's current unofficial transcript should be

submitted to the CDSE Director of Graduate Studies during the first week of the second semester in the

Ph.D. program.

4.5.3 Oral Examination

Upon completion of the core coursework, typically by the end of the third semester in the CDSE Ph.D.

program, the student must complete an oral examination administered by the Student's Ph.D. committee.

The exam should cover topics from the CDSE core courses and background material relevant to the

student’s research plan. In terms of format, the oral exam typically begins with a 30–40-minute

presentation by the Ph.D. student to the committee on the background and scope of the research that

they plan to propose. The committee then asks questions based on the CDSE Ph.D. student's coursework,

core domain science/discipline area, and how these relate to the Ph.D. research. The exam questioning

committee comprises the graduate student's CDSE Ph.D. committee. The Ph.D. advisor may attend but

may not participate in the examination process or questioning.

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If the graduate student fails the exam, the Ph.D. student may retake the oral exam within one year. Failure

to pass the second exam may result in the student's dismissal from the CDSE Ph.D. program. The

completed form should be returned to the Graduate coordinator in 415 Bonner Hall.

Once the oral examination has been completed, students should submit the oral examination form and

the Application to Candidacy (ATC) form to the graduate coordinator, who will then send it to the CDSE

Director of Graduate Studies for signature and to the Graduate School.

4.5.4 CDSE Ph.D. Proposal

No later than the end of the FIFTH SEMESTER in the CDSE Ph.D. program, the student must complete a

Ph.D. proposal. The proposal should include a substantial written document by the graduate student

formally proposing the dissertation work and a presentation by the student to their Ph.D. committee on

the proposal. The Ph.D. proposal length should be approximately 10-15 pages, not counting references,

following an NSF-style proposal. Note, however, that given the interdisciplinary nature of the CDSE Ph.D.

program, the proposal format may vary by discipline. The Ph.D. proposal must be submitted to the

committee one week prior to the presentation. It is expected that a large portion of the Ph.D. research

has been completed prior to the proposal submission and that a detailed completion plan has been made

and included in the proposal.

After reviewing the proposal document and proposal presentation, the Student's Ph.D. committee must

sign the form, indicating that they approve the student's proposal. If the committee feels any adjustments

need to be made, they may record their feedback on the back of the form under Committee

Comments/Requirements.

The advisor and all committee members must sign the form before being submitted to the Director of

Graduate Studies and the graduate coordinator.

4.5.5 CDSE Dissertation Completion and Defense

Upon completion of the written dissertation, the student must defend their dissertation to their Ph.D.

committee in an oral defense presentation. The dissertation should be submitted to the Ph.D. Committee

at least one week prior to the defense.

The student must email the Graduate coordinator at cdsedept@buffalo.edu

with the dissertation abstract

and the date/time/location of the oral defense. The Graduate coordinator will formally announce an open

invitation to the defense to the IAD community.

Once the student has completed their oral dissertation defense, the advisor and Ph.D. committee must

meet to decide whether the student has successfully defended their dissertation. The advisor and

committee must complete the CDSE Dissertation Approval form with their feedback. This form needs to

be approved by all members before the Director of Graduate Studies will sign off on both this form and

the M-Form that the Graduate School requires for degree conferral.

Each committee member will indicate their approval of the dissertation in one of three ways:

1. No changes are required.

2. Changes are requested, but the committee member acknowledges that the advisor will

ensure the changes are made and do not require seeing the final dissertation.

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3. Changes are requested, and the committee member will review the final dissertation.

In the third case, that committee member must directly contact the Director of Graduate Studies to

approve the changes.

The Director of Graduate Studies will NOT sign the M-Form until this step is complete.

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5 GENERAL STUDENT REQUIREMENTS

5.1 Registration Requirements

Students are required to register every semester; registration options include courses, research, thesis,

or dissertation work. Students should be familiar with the registration deadlines as published on the

Registrar's website and will be liable for financial implications if registration is not c

ompleted by the

required date.

Proper registration is important for the determination of the residence requirements. "Residence" implies

pursu

ing advanced study or research while registered at UB.

All international students need to maintain full-time status during their entire graduate study at the

University at Buﬀalo. As per immigration regulations, international students must maintain full-time

status.

Students are required to register continuously during their period of graduate study until all requirements

for the degree are completed. Students who, for one reason or another, cannot maintain continuous

registration must request a Leave of Absence before the start of the semester for which the leave is being

requested. For this purpose, the student must petition the Dean of the Graduate School and obtain the

approval of the Director of Graduate Studies. A leave of absence will only be granted to students in good

academic standing. If the student is enrolled for less than 12 credits (less than nine credits for TAs, GAs,

or RAs), the Certiﬁcation of Full-Time Status form should also be completed.

Leaves of absence will generally be granted for only one (1) semester at a time. Leaves of more than one

(1) semester may require additional justiﬁcation and documentation from the student and the student's

advisor. Documented cases of ﬁnancial hardship, illness, or compulsory military service constitute valid

justiﬁcation. Students who leave the program after completing some graduate work but have not been

granted an approved leave of absence must reapply and be readmitted as a new student. Continued

leaves of absence beyond two (2) semesters will not be granted.

5.2 Transferring Credits

Students may transfer up to 6 graduate credits into the MS programs and up to 36 credits into the Ph.D.

program, subject to the approval of the major advisor at the doctoral level and the Program Director at

the masters level. This includes any electives that are not on the pre-approved list of electives.

Only graduate courses relevant to the program and completed with letter grades of B or better are eligible

for consideration as transfer credits. If you transfer a course equivalent to a course at UB, you may not

take the equivalent course at UB.

The official graduate school policy states: "The Graduate School will consider for transfer credit graduate-

level coursework from nationally accredited institutions of higher education, as well as graduate-level

coursework from any international institution that UB recognizes as equivalent to a nationally accredited

institution. Only those graduate courses completed at accredited or recognized international institutions

and with grades of full B or better are eligible for transfer credit. Courses with grades of S or P are eligible

for the transfer except when the transfer institution's grading policy equates S or P with lower than a full

B grade."

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To transfer credits outside of UB, students must submit the transfer credit petition found on the

Graduate School Website:

http://grad.buffalo.edu/succeed/current-students/forms.html Waiving

Requirements.

A student may have already taken a graduate course similar to a required core course OR have taken an

undergraduate version of a cross-listed course (Example: CSE 474//574 Intro to Machine Learning). In that

case, the student can request a waiver of that core course from the graduate coordinator.

Students who completed the undergraduate version of a core course and had it count towards their

undergraduate degree requirements cannot double dip. This means that the undergraduate course

cannot be counted towards the graduate requirement, and students cannot take the graduate version of

the course if they receive a grade of "B" or higher. In these situations, the student can work with the

Program Director to either take an independent study in place of the core course or find an advanced level

of the course being offered as a course directive.

Students waiving requirements must still meet the 30-credit hour requirement. The waiving of a course

requires the student to replace those 3-credits with another course at the discretion of the Program

Director.

5.3 “Double Dipping” Course Credit

Suppose you received a graduate degree from another department at UB or are in the process of receiving

one. In that case, a limit of 6 credits can be shared from another degree program to satisfy your Master's

degree requirements. This means that 24 credits must be unique to your MS program.

For example, if you took STA 545, STA 546, and STA 517 to earn a degree in Biostatistics at UB, only STA

545/546 would count for the MPS Data Science and Applications program, satisfying the Statistical Data

Mining I and II core course requirements. The third course, STA 517, would not be transferrable as an

elective, and no other courses from that master’s program could be used towards the MPS program. In

this scenario, you do not need to take another course to replace these core course requirements (STA 545

= CDA 541 and STA 546= CDA 532), as they are counted as "shared credits" between both programs.

Similarly, if a student took CSE 474 as an undergraduate at UB, and that course was taken for

undergraduate credit toward their bachelor's degree requirement. In that case, students cannot enroll in

CSE 574 as a graduate if the previous grade earned was a "B" or better. In this scenario, the students

would need to work with the program director to find replacement courses for the machine learning core

requirement, such as CSE 674 Advanced Machine Learning or CSE 676 Deep Learning.

5.4 Inapplicable Credits

The following will not be used to fulfill degree requirements:

a. A graduate course took to fulfill the requirements of an undergraduate degree program.

b. Courses in which a grade of F or U is obtained at the graduate level.

c. English language courses, courses not included in the curriculum outline, and remedial courses

taken to fulfill department admission requirements.

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5.5 Resigning from a course

The current UB Graduate School policy regarding course resignation states:

“Graduate Students have the prerogative to resign any course for which they have registered without GPA

penalty through the end of the 11 weeks of the fall or spring term. All course resignations processed during

the permissible dates (as published in the class schedule available through the Office of the Registrar) will

be indicated as officially resigned courses by the notation R on all grade reports, transcripts, and other

official university documents. Resignation from all courses should be done through the HUB Student

Center, which students may access through the MyUB portal. There are no quality points attached to an

R designation.”

All students must talk to the graduate coordinator before resigning from a course to discuss the

implications and formulate a new course plan to meet degree requirements. International students may

not resign from a course if it brings them below the minimum credit hour requirements to maintain visa

status. International students must have an approved course load reduction to resign from courses if it

drops them below 12-credits (9 credits if TA/RA/GA) and must be approved by ISS.

5.6 Repeating Course

Students are permitted to repeat a course to improve their grade. A core course must be repeated if a

student fails the course. An exception to this is if the grade is a result of an academic integrity violation.

The program director (in consultation with the course instructor) can grant exceptions to this rule if

repeating a course is necessary to fulfill the program requirements.

In the event a course repeat is approved, the graduate school repeat policy will be followed as outlined:

"If a graduate student repeats a course that is not normally "repeatable" ("repeatable" courses include

dissertation, research, thesis, project, or portfolio guidance; independent study; directed readings; etc.),

only the highest grade earned in the course will be counted toward the degree and used to calculate the

grade point average associated with the graduate degree program requirements. However, the student's

official graduate transcript will record all courses attempted (including repeated courses). All resulting

grades earned are calculated in the cumulative GPA reflected on the student's “final official transcript.”

5.7 Student Status

Students are required to be registered for full- or part-time status.

a. Full-Time: A full-time academic status for a graduate student is 12 credits per semester or a

minimum of 9 hours if the student holds a graduate, teaching, or research assistantship position.

b. Part-time: A Student who is registered for less than 12 credits and has not filed a petition certifying

full-time status is considered a part-time student.

5.8 Certification of Full-time Status

Under certain circumstances, graduate students who are under 12 credit hours should submit the full-

time certification status form to the Graduate school. All funded and international Students in this

category must file the petition.

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a. Form: https://www.buffalo.edu/grad/succeed/current-students/forms.html

5.9 Reduced Course load

Immigration regulations require that F-1 and J-1 students maintain an entire course of study (minimum of

12 credits each semester for most students; 9 credits for Graduate Students with an Assistantship; 1 or

more credits for Graduate Students approved by the Graduate School for Full-Time Certification).

However, the regulations permit a "Reduced Course Load" in minimal situations. If a student cannot enroll

full-time, they must be approved by International Student Services for a Reduced Course Load before

dropping below full-time.

a. Academic Reduced Course load

: Permits F-1 and J-1 students to reduce their course load

below full-time (to a minimum of 6 credits) if they experience academic difficulty. It can only

be used one time per degree level, and the student must experience difficulty in one of the

following areas:

a. Initial difficulties with the English language

b. Initial difficulties with reading requirements

c. Unfamiliarity with American teaching methods

b. Medical Reduced Course load

Permits students to reduce course load below full-time (or, if

necessary, not enroll in any course) due to a student's temporary illness or medical condition.

a. A maximum of 12 months of Medical Reduced Course Load is possible per degree

level.

b. It is issued on a per-semester basis.

c. Students must submit a letter from a licensed medical doctor, doctor of osteopathy,

or licensed clinical psychologist practicing in the US.

Final semester Reduced Course load Permits students to enroll below full-time if it is the

final semester of their degree program and they need fewer than 12 credits to graduate.

a. Students must enroll in at least one credit at UB during their final semester and may

not only take distance courses during their final semester.

b. It can only be used one time per degree level.

A student cannot complete an internship if they applied for an academic reduced course load the semester

of, or immediately preceding, the intended internship period.

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6 GRADUATION REQUIREMENTS

6.1 Requirements for Master’s Students

• Maintain continuous registration. Students must register for at least one graduate credit the

semester before degree conferral. Summer registration is required if students plan to confer for

an August 31 conferral date.

• Fulfill the minimum residency requirement of 24 UB credits of registration.

• Complete ten courses, totaling 30 credit hours of graduate coursework – subject to certain

constraints when completing two Master's degrees (as per Graduate School Policies and

Procedures)- distributed based on the specific degree requirements

• Achieve at least a B average in all courses (Cumulative GPA of 3.0 or higher)

6.1.1 Application to Graduate

Masters Students must apply to graduate from HUB before the deadline posted on the Graduate School

Website.

As Per the Graduate School Website :

• Navigate to the HUB Student Center.

• On the left side of the screen, under the "Academics" header, select "Apply for Graduation"

in the dropdown menu.

• Select the "Apply for Graduation” link next to the appropriate program and degree. If the link

is not visible, you may not be eligible for graduation at this time. Please contact the graduate

coordinator for more information.

• In the dropdown menu next to “Expected Graduation Term,” select a valid term to apply for

graduation. Only terms in which you are eligible to apply for graduation will be displayed.

• Review your selection and click the “Continue” button if correct.

• Click the “Submit Application” button to complete your application. You should see a “Submit

Confirmation” message.

6.1.2 Petition to Change Graduation Conferral Date

A student may only apply for graduation in HUB once (per degree program). If you apply for graduation in

HUB, you will not be able to do it again. If you are changing the graduation date from what was noted on

your original application to graduation, you must file a Petition to Amend the ATC Form

that will be

processed through the Registrar's office. Students should sign and have the program director sign as the

director of graduate studies and major advisor.

6.1.3 Degree Time Limits

Master's degrees must be completed within four years from the student's first registration date in that

Master's degree program. Doctoral degrees must be completed within seven years from the student's

initial formal matriculation in that doctoral program. Requests for extensions of time limits must be

petitioned using the Extension of Time Limit to Complete a Degree Program form.

Each divisional or area

committee may establish its own stricter policies within the constraints of these overarching institutional

Page 26 of 32

policies. Due to the COVID-19 pandemic, the spring 2020 term is excluded from UB's time-to-degree

calculations.

6.2 Requirements for doctoral candidates

In order for students to be eligible for degree conferral, Ph.D. candidates must meet the following criteria:

• Maintain continuous registration until the Ph.D. degree is conferred.

• Complete a minimum of 72 credit hours of graduate study.

• A minimum of 50 percent of the Ph.D. program must consist of courses completed at UB and

uniquely applied to that degree program.

• Complete UB's Responsible Conduct of Research (RCR) training requirement.

• Complete all CDSE Ph.D. milestones, including committee approval, project plan approval, oral

examination, dissertation prospectus, dissertation proposal, dissertation, and dissertation

defense.

• Apply to Candidacy (ATC) within the proper deadline dates for approval at all levels.

• Successfully complete dissertation proposal.

• Complete and orally defend a doctoral dissertation; electronically submit the dissertation to the

graduate school for final approval and filing in the UB Institutional Repository.

• Meet all CDSE program requirements throughout the course of study.

6.2.1 Checklist for Ph.D. graduation

• Submit a Ph.D. Application to Candidacy (ATC) to the Graduate School by the appropriate

deadlines defined below. The Graduate School must approve your ATC for you to be a candidate

for degree conferral officially. When your ATC is approved, you will receive a letter from the

Graduate School. If you believe your ATC was submitted, but you did not receive a letter, contact

the Graduate School.

• Report any ATC changes to the Graduate School. After your ATC has been submitted to the

Graduate school, changes to your advisor, committee members, expected degree conferral date,

or future registration must be submitted to the Graduate School for approval using the

Change

Expected Conferral Date/Amend ATC form.

• Register for at least one credit during the semester preceding your degree conferral date. Fall

semester registration is required for February conferral, and spring semester registration is

required for June or August conferral.

• Complete the required number of credits. Review your transcript and be sure you have completed

the minimum number of credits required for your degree.

• Maintain the minimum GPA. You must have a minimum 3.0 overall GPA in the courses/credits

applied toward your degree; your program may require a higher GPA.

• Remediate any incomplete grades or missing grades. Be sure there are no incomplete (I/U) grades

or missing grades on your record for courses being applied to your degree program.

6.2.2 Required Training

The following sections outline the required training needed to pass for graduation from the Ph.D.

program. Both must be successfully completed and documented on the ATC forms.

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6.2.2.1 Responsible Conduct of Research (RCR) Training Requirement

The University at Buffalo graduate school policy states that students in any doctoral program are required

to submit successful completion of "Responsible Conduct of Research" (RCR) training when they submit

their Application to Candidacy (ATC) form. Any of the following can fulfill this training requirement:

• Enrolling in and passing with a grade of B (3.00) or better SSI 640 Graduate Research Ethics, LAI

648 Research Ethics or RPN 541 Ethics and Conduct of Research or;

• Complete the Collaborative Institutional Training Initiative (CITI) online Responsible Conduct of

Research Course (RCR) with an average score of 80% or higher

o Students completing the CITI online course must supply documentation of its successful

completion with their Application to Candidacy.

6.2.2.2 CITI Online Program

The University at Buffalo has an institutional membership in the CITI online RCR program so that students

can access the training through the CITI Program website.

Initially, the student needs to register and choose a password, which allows the program to be entered

and re-entered as many times as needed. Also, at the time of initial registration, the student is asked to

enter his/her name, mailing address, phone number, email address, and UB person number. A database

of UB participants is created using that information.

There are four versions of the CITI online RCR course from which the student should choose the version

most appropriate for his/her area of doctoral study: biomedical sciences, social and behavioral sciences,

physical sciences, or humanities. The RCR program comprises a series of modules consisting of readings

and case studies and ends with a quiz covering the material. The program allows the student to enter and

exit at any point and to retake the quiz associated with each section. A minimum score of 80 percent is

required to pass the online course. Assistance is available online at the CITI website if any technical

difficulties are encountered.

Once the student has completed the appropriate version of the CITI RCR program with a passing grade of

80 percent or higher, he/she must print the "Completion Report" from within the CITI program as

documentation of successful completion and submit it with the Ph.D. degree Application to Candidacy.

6.2.3 Application to Candidacy

An ATC is required to officially become a graduate for conferral for any student pursuing a master’s and/or

doctoral degree. Students should file an ATC with the Graduate School (with the help of the Graduate

coordinator) no later than their fifth semester in the CDSE program.

Students applying for ATC should submit their paperwork to the Graduate coordinator for review and

make sure all supplemental materials are included (transcripts, Ph.D. checklist, etc.).

Committees cannot receive ATCs immediately prior to the expected graduation date. If/when the

Graduate School approves your ATC, written notification of the approval will be mailed to you using your

name and address information on file in HUB.

Page 28 of 32

6.2.3.1 Amending the ATC

Minor amendments to the ATC (such as adding or deleting anticipated courses or credits) must be formally

submitted via a Petition to Amend ATC Form to the graduate school. The director of graduate students or

department chair must sign off on this form before being sent to the Graduate School for review.

6.2.4 Time Limits

Asper the graduate school requirements, students enrolled in a doctoral program have seven years from

the first registration date in the program, excluding approved leaves of absence, to complete their degree

requirements.

Requests for extensions of time limits must be petitioned using a Graduate Student Petition Form with

departmental approval through the Director of Graduate Studies. The student must be currently making

active progress toward the degree. The petition will be presented to the divisional committee of the

student's home department for approval before being submitted to the Graduate School. The petition

must delineate reasons for the extension, present a progress schedule, and set a deadline for completion

of the program. The extension of the time limit is typically granted for a maximum period of 1 year.

6.2.5 Required Forms for Graduation

CDSE students must complete the following forms in order to meet degree conferral requirements after

their program:

1. Application to Candidacy: This form should be submitted after four to six semesters of full-time

enrollment. Students should work with the Graduate coordinator to make sure all CDSE

paperwork is included and to submit for review to the Graduate School.

2. M-Form (Multi-Purpose Form): This form is submitted to the Graduate School by the department's

Graduate coordinator, certifying that the defense of the dissertation was satisfactorily completed

and all academic requirements for the degree have been met. This form is signed by the Director

of Graduate Studies, advisor, and Ph.D. Committee. The student must also sign the form. This

form must be submitted prior to Graduate School deadlines in order to meet the established

conferral date.

3. Electronic submission of dissertation: An ETD is an electronic version of a thesis or dissertation.

ETDs are formatted like paper theses or dissertations (title page, table of contents, page

numbering, tables, figures, references, etc.). However, they are submitted to the Graduate School

as a PDF file via our ETD Administrator website.

4. Doctoral degree recipients surveys: The Graduate School requires all doctoral students to

complete two exit surveys before their degree can be conferred. The Doctoral Degree Recipients

Survey (conducted by the University at Buffalo) collects data on a student's experience in his or

her degree program) and the Survey of Earned Doctorates (conducted by various agencies of the

United States government) collects information from all doctoral candidates in the US.

Students should refer to the graduate school website to see the table for when conferral materials are

due each term.

6.2.6 Dissertation Requirements:

As outlined on the Graduate School website

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https://www.buffalo.edu/grad/succeed/graduate/requirements.html

The dissertation should be an original contribution to the field determined by the Ph.D. candidate's

department or program. Unlike those in the romance languages and literature department, doctoral

dissertations are normally written in English.

There are several style manuals available in the UB Libraries, including Strunk and White, Turabian, and

the University of Chicago Press, that answer a host of questions regarding the technical aspects of a

properly prepared dissertation. A bibliography is also available, which provides further examples that are

more specific to various disciplines (e.g., the Publication Manual of the American Psychological

Association). Students should consult the appropriate professional journals and their major professors to

determine the most appropriate style within their area of research.

It is the prerogative and responsibility of the candidate and the sponsoring department to ensure that the

canons of organization, presentation, and documentation usually prescribed for publication in their

discipline are observed. Likewise, the dissertation must be certified as substantially free of errors and

ready for publication before submitting it to the Graduate School.

Since 2005, all Master's theses and doctoral dissertations completed by UB students in fulfillment of

graduate program requirements have been archived and accessible through ProQuest's dissertations and

theses database. Beginning with the June 1, 2018, degree conferral, all theses and dissertations will also

be accessible for public access through UB's Institutional Repository. Students will continue to have the

option to request a temporary embargo (delayed release) of their thesis/dissertation containing

patentable material or content being submitted to peer-reviewed journals or for commercial publication.

See the Public Access of Theses and Dissertations and Embargo (Delayed Release) of Thesis and

Dissertation policies.

6.2.6.1 Dissertation Formatting Requirements

The Graduate School will accept any self-consistent format following a recognized discipline's

conventions. However, general formatting standards are also expected, as outlined in the Graduate

School's booklet

entitled Guidelines for Electronic Thesis and Dissertation Preparation and Submission

pdf.

6.2.6.2 Oral Defense of a Doctoral Dissertation

The oral defense is a public event scheduled by the department and must be attended by the candidate's

Ph.D. dissertation committee and, if required, the outside reader. At the department's discretion, the

defense-of-dissertation examination may take the form of a seminar with a more varied selection of

participants. Examination questions will always include questions arising from the dissertation itself. In

many cases, particularly when departments have not required extensive examinations during the

student's tenure, questions will be more general and the examination longer.

Once the student schedules their dissertation defense date, time, and location with their advisor and

committee, CDSE students must send this information to the Graduate coordinator, along with an abstract

of the thesis so that an announcement can be publicly sent out to the CDSE department and the student's

home department.

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7 ACADEMIC STANDARDS

7.1 Grading Policy

Grades in courses applicable to the degree must be letter grades: A, A-, B+, B, B-, C+, C, D, F, and FX (never

attended), carrying quality points of 4.0, 3.67, 3.33, 3.0, 2.67, 2.33, 2.0, 1.0, 0 and 0 respectively. This

requirement applies to informal courses as well.

7.1.1 Incomplete Grades

For all graduate courses, an interim grade of incomplete (I/letter grade earned if incomplete is not

satisfied) may be assigned if the student has not completed all requirements for the course. An interim

grade of Incomplete (IU) shall not be assigned to a student who did not attend the course. The letter grade

assigned to the incomplete will become the default grade of record if the incomplete is not changed

through formal notice by the instructor upon the student's completion of the course within twelve (12)

months after the close of the term for which the incomplete was assigned. The instructor may specify a

shorter time frame for the removal of the IU grade.

7.1.2 S/U Grades

SEAS does not allow S/U grades except for Master's projects, Master's thesis, dissertation, internship, or

courses taken as supervised research or seminar.

7.2 Scholastic Standing

Exclusive of "S" grades, grades earned in courses counted towards the Student's M.S. program must

average a "B" (3.0) grade point average or better to be in good academic standing in the graduate

program.

If a student earns an F in any course, they will be required to retake that course and will be put on

academic probation. Students may not be required to retake courses where they earn a C+, C, C-, or D,

but the cumulative GPA must be at least 3.0. If a student is placed on academic probation due to earning

one of these letter grades. In that case, they must earn the minimum grade in the following semester that

will allow them to boost their cumulative GPA back up to the 3.0 minimum, or the student is at risk of

being dismissed from the program.

7.3 Review of Academic Progress

At the end of each semester, the department will review the progress of all graduate students in the

program. Students who are not making satisfactory progress will be notified by email and should meet

with the graduate coordinator, their faculty advisor, and/or the Program Director to discuss the matter.

7.4 Probation

If a student’s GPA falls below 3.0 at the end of any semester or the student receives a grade of D or F in

any course, they will automatically be put on probation from the start of the next semester. They will be

given a target to be reached to continue in the program. Normally, the target will be that the student

raises their cumulative GPA to 3.0 or higher by the end of the current semester.

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Probation for other causes shall commence from the student being notified in writing by the Program

Director. The student will be given requirements for regaining good academic standing. Being on

probation is grounds for withdrawal of academic and financial support, if applicable.

Graduate students not meeting the written terms of their academic probation may be academically

dismissed from the program by the program director. Such dismissals shall be done in a timely fashion but

no later than three weeks after the completion of the term. The Graduate School will be notified in writing

of all such academic dismissals.

7.5 Academic Dismissal and Transcripts

A student may be dismissed from the program if any of the following conditions apply:

• A grade of "F" is earned in any course that could be applied toward the degree.

• More than two grades are “C," "D," and/or "U" in courses which could be applied to the degree.

• Probationary status has not been removed after one semester or within the timeframe

determined by the Program Director, as noted in the formal letter sent to the student.

• The cumulative GPA for courses that could be counted towards the degree falls below 3.0 at the

end of any semester.

• The student is found guilty of academic dishonesty according to Graduate School regulations.

• More than four resigned “R” grades have been obtained in courses that could be applied to the

degree.

Students who are dismissed will be given a letter from the Program Director. A copy of the letter will be

sent to the Graduate School.

Graduate students who are dismissed for academic reasons from a graduate program will have a "GRD"

(Graduate School) service indicator placed on their academic record to prevent future registration.

7.6 Academic Integrity

Academic integrity is a fundamental university value. Through the honest completion of academic work,

students sustain the integrity of the university while facilitating the university's imperative for

transmitting knowledge and culture based on the generation of new and innovative ideas. When a

student's suspected or alleged academic dishonesty arises, it shall be resolved according to the

procedures set by the Graduate School. These procedures assume that many questions of academic

dishonesty will be resolved through consultation between the student and the instructor (a process

known as consultative resolution, as explained below). It is recommended that the instructor and student

each consult with the department chair, school or college Dean, or the Graduate School if there are any

questions regarding these procedures.

7.6.1 Examples of Academic Dishonesty

a. Previously submitted work. Submitting academically required material that has been previously

submitted – in whole or in substantial part – in another course without prior and expressed

consent of the instructor.

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b. Plagiarism. Copying or receiving material from any source and submitting that material as one’s

own, without acknowledging and citing the particular debts to the source (quotations,

paraphrases, basic ideas), or in any other manner representing the work of another as one’s own.

c. Cheating. soliciting and/or receiving information from, or providing information to, another

student or any other unauthorized source (including electronic sources such as cellular phones

and PDAs) with the intent to deceive while completing an examination or individual assignment.

d. Falsiﬁcation of academic materials. Fabricating laboratory materials, notes, reports, or any forms

of computer data; forging an instructor’s name or initials; resubmitting an examination or

assignment for reevaluation which has been altered without the instructor’s authorization; or

submitting a report, paper, materials, computer data, or examination (or any considerable part

thereof) prepared by any person other than the student responsible for the assignment.

e. Misrepresentation of documents. Forgery, alteration, or misuse of any University or Oﬃcial

document, record, or instrument of identiﬁcation.

f. Conﬁdential academic materials. Procurement, distribution, or acceptance of examinations or

laboratory results without prior and expressed consent of the instructor.

g. Selling academic assignments. No person shall sell or oﬀer for sale to any person enrolled at the

University at Buﬀalo any academic assignment or any inappropriate assistance in the preparation,

research, or writing of any assignment, which the seller knows, or has reason to believe, is

intended for submission in fulﬁllment of any course or academic program requirement.

h. Purchasing academic assignments. No person shall purchase an academic assignment intended

for submission in fulﬁllment of any course or academic program requirement.

Complete policies and procedures regarding academic integrity issues can be found at the following

website: https://grad.buffalo.edu/succeed/current-students/policy-library.html

7.6.2 Academic Integrity Contract

All Master's students will be required to sign and submit an academic integrity contract during the

program's first semester. The contract will be distributed to students after orientation. Students who do

not submit the contract back by the first Friday of classes will have a departmental hold placed on their

HUB student center, preventing future registration until it has been read, signed, and returned to the

Graduate coordinator.