Current Graduate Students

This section explains what each role does and who to contact when you have questions about advising, policies, funding or paperwork.

QPA is calculated as follows:

• For each course, the weighted grade is multiplied by the credits attempted
• The adjusted grades, multiplied by credit hours, are added
• The sum of the adjusted grades is divided by the total number of credits attempted
• If a course is repeated, both grades count in the calculation of the QPA

Satisfactory and Unsatisfactory (S/U)
S indicates credit earned. U indicates no credit earned. These grades do not count in the QPA.

Resign (R)
A student may resign from a course through the 11th week of the semester. This course will not be included in QPA calculations. An R will appear on the transcript.

It is the policy of the UB Graduate School that students must attain a QPA of at least 3.0 to earn a graduate-level degree.

Grades will be determined on a standard scoring system. The grade will be the average of performance on each section of the course. Cheating, plagiarism and other attempts at academic dishonesty will result in a failing grade for the section and a failing grade for the course.

The Director of Graduate Studies, the Director of the MA program, and the advisory committees jointly monitor the progress of graduate students in Biological Sciences.

For PhD students, the PhD Advisory Committee and faculty advisor also assess the student’s progress.
For MS and MA students, the faculty advisor monitors progress.

Graduate students will be in good standing provided that:

• The requirements outlined in this handbook are met in a timely fashion and
• The research project is judged to be progressing satisfactorily

Unsatisfactory performance in any phase of the degree program may lead to dismissal from the graduate program.

The following policy governs the use of generative artificial intelligence (AI) tools by graduate students in the UB Department of Biological Sciences. This policy applies to all aspects of graduate study, including coursework, teaching responsibilities, and graduate research projects. Graduate students are expected to familiarize themselves with and adhere to all sections of this policy. Additional guidance may be provided by individual instructors, principal investigators (PIs), and relevant journals or conferences.

Graduate students bear full responsibility for all work they submit, regardless of whether generative AI tools were used in its preparation. The use of generative AI does not diminish a student’s scholarly obligation to ensure the accuracy, integrity, and originality of their work. Students will be held accountable for any content that is inaccurate, falsified, plagiarized, or derived from AI-generated hallucinations, as well as for any material that infringes on copyright. Students are expected to carefully proofread and critically evaluate all work prior to submission, including any content generated or modified with the assistance of AI tools

Graduate students must comply with AI-use policies established by the instructors of any courses in which they are enrolled. This obligation extends equally to graduate students serving as teaching assistants (TAs): TAs are expected to follow both the course instructor’s policies for enrolled students and any TA-specific guidance regarding the use of AI in grading, preparation of lecture or discussion materials, provision of written feedback to students, and other instructional responsibilities.

Instructors are strongly encouraged to explicitly outline their expectations regarding AI use at the beginning of the course, addressing both student-facing and TA-specific contexts.

Graduate students are required to discuss their use of generative AI with their PI for any work related to their graduate research project, and to follow their PI’s guidance. This requirement applies broadly across all phases and activities of the research enterprise, including but not limited to:

• Ideation, hypothesis generation, and scientific springboarding
• Data analysis, interpretation, and visualization
• Writing, editing, and revision of manuscripts, grants, or reports
  
• Literature searching, summarization, and synthesis
• Coding, programming, machine learning

PIs are encouraged to establish clear, written lab-level policies regarding acceptable AI use that reflect the norms of their discipline and the expectations of relevant funding agencies and journals. In the absence of a formal lab policy, students should seek explicit approval from their PI before using generative AI in any research context.

Graduate students are required to include a generative AI disclosure statement in any work for which AI use is not already governed by course policies, PI or lab policies, or applicable journal and conference submission guidelines. This requirement applies to candidacy exam documents, dissertation proposals, master’s and doctoral dissertations/project summaries, and any other scholarly work prepared in the absence of superseding guidance.

The disclosure statement must include:

• A description of how generative AI tools were used in the preparation of the document
• The name(s) of the AI model(s) used
• The disclosure statement does not need to include the specific prompts used.

An example of a complete AI attestation statement is provided below for reference:

"I attest that any use of generative artificial intelligence (AI) tools in the preparation of this document has been fully disclosed. All substantive ideas, analyses, interpretations, and conclusions are my own unless otherwise cited. Generative AI tools were used in an editorial capacity during the preparation of this dissertation. Specifically, Claude (Anthropic) was used to suggest alternative wording, refine sentence structure, and assist with grammar and clarity during revision of Chapter 3. All scientific interpretations, experimental designs, data analyses, and conclusions are solely my own. No AI tools were used to generate data, figures, or substantive scientific content."

Graduate students are strongly encouraged to familiarize themselves with and adhere to the AI use policies of major scientific journals and conferences relevant to their field, particularly when preparing manuscripts for submission. Many journals have adopted specific requirements regarding the disclosure or prohibition of AI-generated content, and non-compliance may result in rejection or retraction of submitted work. Students are encouraged to review the Committee on Publication Ethics (COPE) for guidance on AI in publishing. https://publicationethics.org/

Students should also be mindful that information entered into publicly available large language models (LLMs) may be incorporated into those models’ training data or otherwise enter the public domain. Accordingly, students must exercise caution when sharing the following with external AI systems:

• Unpublished research data, results, or findings
• Proprietary methods, protocols, or materials
  
• Confidential patient, participant, or clinical data
• Manuscript or grant content that has not yet been submitted or published

When in doubt, students should consult with their PI before entering any research-related content into an AI system. Use of institutional or otherwise privacy-preserving AI platforms, where available, is encouraged for sensitive research activities.

Graduate students have the right to petition any decision regarding their standing in the graduate program or to request a change in any of the requirements set forth in this document.

Petitions should be addressed to the Graduate Affairs Committee and must clearly state:

• What requirements are being petitioned
• The justification for the request

The student must formally petition the Graduate Affairs Committee to switch programs.

The petition should include:

• Current transcript
• A letter of petition from the student that includes:
  • The reason the student is requesting the change
  • A justification for the change
• Letter of support from the student’s advisor that includes:
  • Assessment of the student’s ability to complete the degree program under consideration
  • Description of financial support available for the student

Petitions will be considered twice a year and should be submitted within a week of the semester grades being posted.

Students should complete at least two semesters prior to requesting a switch.

Multiple criteria are considered when a student wishes to change programs, including:

• Performance in coursework and lab work
• Acceptance as a student into a research lab
• Appropriate support from the advisor
• Changes in career goals

Students will not be allowed to switch from the MS to PhD program if they wish to work in a lab that does not appear likely to have sufficient resources for the student to finish their thesis research. However, qualified students may join the PhD program if they find a lab with appropriate resources.

A student who has been dismissed due to lack of academic standing may petition the Graduate Affairs Committee to remain in the program.

The petition should include:

• Current transcript
• Letter of petition from the student:
  • Accounts for the academic deficiencies
  • Lays out a plan to remedy these deficiencies within one semester
• Letter of support from the student’s advisor that includes:
  • Assessment of the student’s ability to complete the degree program under consideration
  • Description of financial support available for the student

Petitions should be submitted within a week of the semester's grades being posted.

Support for PhD students is guaranteed for ten semesters, provided they make adequate progress. Support is generally extended beyond this time depending on resources.

Support of PhD students is generally provided through:

• Teaching assistantships
• Research assistantships
• Fellowships

Biological Sciences graduate students in good standing are offered teaching assistantships during their first four semesters in the graduate program. Continuing support during this period is contingent upon the student carrying a full course load.

International students who have been awarded teaching assistantships must pass the Speaking Proficiency English Assessment Kit test.

Students who score just below passing, 45 to 50, will have an interview with a representative of the ESL department to determine whether they have sufficient communication skills to serve as a TA. Based on this interview, the student may be required to take ESL 512 before being allowed to teach.

PhD students who do not pass the test or the interview can only serve as assistant TAs and will have reduced support.

An unsatisfactory grade in the teaching assignment or a complaint by a supervising faculty member can result in immediate loss of support. However, such a loss will not occur without a thorough investigation by the Graduate Affairs Committee.

After the fourth semester and during the first summer of residence, PhD students are usually supported by their advisor’s research grants or their own external fellowships.

Students who do not have a pre-doctoral fellowship and are working with a research advisor who lacks grant support may request departmental funds. These requests will be considered on an individual basis.

If a faculty member loses their research funding, all senior students in good standing, beyond the second year, will be considered for teaching assistantships until the faculty member regains funding or the student graduates.

Departmental support is not available to students in the research track or to adjunct faculty. Research-track and adjunct faculty are expected to support their graduate students immediately upon accepting them into their labs. If funding should fail for a research or adjunct faculty member, they may request support for senior Biological Sciences students.

Because funding policies and resources can change, use current department communications and Graduate School resources for up-to-date information.

For questions about your specific funding situation, start with:

• Your research advisor
• The Graduate Coordinator
• Your program director

The BGSA is a student-run organization affiliated with the university-wide Graduate Student Association.

The BGSA:

• Shares updates on policies, procedures and events
• Helps plan the annual symposium
• Runs journal clubs and social activities

Meetings are usually held monthly. Announcements are shared through the email listserv.

The Graduate Student Employees Union represents Teaching Assistants and Graduate Assistants employed by SUNY and paid by New York State.

If you hold a TA or GA appointment, you are part of the GSEU bargaining unit. The union negotiates contracts related to pay, benefits and working conditions.

To receive full union membership benefits, eligible students must complete a membership form and submit it to a union officer or follow the instructions provided by the union.

For current information about membership, benefits and representation, visit the Graduate Student Association website.

The Department of Biological Sciences hosts events throughout the year that support research, professional development and community connection.

Held each January and organized by the Biology Graduate Student Association, the Annual Research Symposium gives students the opportunity to share their work with the department. Students present posters or short talks and engage in discussion with faculty and peers.

Weekly seminars bring distinguished scientists from UB and other institutions to campus. These talks introduce current research across the life sciences and create space for discussion. Students often have opportunities to meet and speak with invited guests.

The Research Showcase highlights advanced graduate students or postdoctoral scholars who present a developed body of work. These presentations take place during the regular seminar schedule and are selected by the Seminar Committee.

This spring seminar series provides a supportive setting for graduate students to present ongoing research. Short talks encourage feedback, discussion and idea exchange, often in preparation for committee meetings or future presentations.

Alumni working in a range of careers return to share their experiences and connect with current students. These events provide insight into career pathways in academia, industry, education and beyond.