Quant Eng Phys Lab I

Last Updated: Fri, 02/20/2026
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Course prefix:
BMED
Course number:
3110
Semester:
Spring
Academic year:
2026
Course description:

This is a hands-on lab that is taught, in part, away from the UAW lab space. We believe in hands-on learning and want you to have the best learning opportunity possible. Especially in our current environment, it is critical that you prepare and participate. 

  • We expect you to engage with provided content.
  • Be on time to meetings (Lab, team meetings, troubleshooting sessions)
  • Act with integrity and not cheat. If you cheat individually, you will get a zero. If one person cheats in a team, we will send the case to OSI to adjudicate.
  • Help us be better instructors. If we do a bad job explaining something, let us know.
  • Treat all of your classmates with kindness. There will be several peer-review assignments. You can provide constructive positive or negative feedback. Make sure it’s constructive.
  • If you show symptoms of any illness, stay home. You or your team, for team-based assignments, can request an extension up to 24 hours before the due date. Requests for extensions beyond 72 hours will only be considered under special circumstances.
  • During the semester, it is possible that you have a major disruption in your life. We do not need to know the details but know that we are willing to work with you. We do, however, expect you to communicate with us by email or in person as soon as possible so that we can put a good working plan in place. 
Academic honesty/integrity statement:

Many deliverables in this course are team-based, and you must work together with your team members to complete this work. You may also talk with anyone else enrolled in the course about specific questions; however, when composing, you may not work with students outside of your team or use other tools like generative AI. Plagiarism of any form will not be tolerated as it is a violation of the GT Academic Honor Code.   

We want you to learn to write technical communication; we do not want to read AI generated text, data, or figures, which is boring and will be considered plagiarism. Unauthorized use of any previous semester coursework is prohibited in this course — this includes GT ‘word’ and generated text that has been previously used because of past AI use. 

Having said that, we recognize that AI and in particular generative AI is transforming all aspects of biomedical engineering and want to encourage teams to use AI responsibly.  All assignments will require an attestation where each individual will need to clearly state how they leveraged AI in the preparation of the deliverable. In the attestation, if you used generative AI, include a note that you are giving away copyright of your created work for all users to use, modify and distribute the work without the need to give it credit.   

All written assignments must be generated in a digital document stored in your MS Teams folder that allows the review of every change made by each team member. There also has to be traceability in all forms, not just written reports.  This includes raw data, processed data, and tools used to create the work. The attestation should include accessible links to the locations where these items are located.  Deliverables without this completed attestation will not be graded and considered to be a late assignment.  

All written documents will also be analyzed by an AI/plagiarism checker. You will have immediate access to the report when you submit your assignment. Please carefully review this before submission any suspected instance of dishonesty will be reported to the office of student integrity. If you have any questions about the appropriate use of AI please ask your TA and/or Instructor prior to submission. 

Using materials without attestation will be considered a direct violation of academic policy and will be dealt with according to the GT Academic Honor Code. Each violation of the honor code will be immediately and without question reported to the Office of Student Integrity and will result in a minimum of a lower letter grade or a zero on the assignment, whichever is higher. For team-based assignments, your team will be referred to the Office of Integrity. 

 

Instructor first name:
Essy
Instructor last name:
Behravesh
Section:
A
CRN
27252
Department (you may add up to three):

Physiology of Cellular and Molecular Systems

Last Updated: Thu, 01/15/2026
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Course prefix:
BMED
Course number:
3600
Semester:
Spring
Academic year:
2026
Course description:

The goal of this course is to prepare you to understand cell and molecular biological technologies and apply them to real-world problems in a respectful and welcoming classroom environment. To do this, you will need to understand the basics of cell biology for both single cells and groups of cells. More specifically, we will discuss the building blocks of cells, gene expression and genetic engineering, the organization and function of organelles, cell signaling, the cytoskeleton, the cell cycle, and the extracellular matrix.

Academic honesty/integrity statement:

Students are expected to maintain the highest standards of academic integrity. All work submitted must be original and properly cited. Plagiarism, cheating, or any form of academic dishonesty will result in immediate consequences as outlined in the university's academic integrity policy.

Instructor first name:
Marian
Instructor last name:
Ackun-Farmmer
Section:
C
CRN
27650
Department (you may add up to three):

Biotransport

Last Updated: Thu, 12/18/2025
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Course prefix:
BMED
Course number:
3310
Semester:
Spring
Academic year:
2026
Course description:

The course introduces students to the fundamentals of momentum, heat, and mass transport and their application to biomedical engineering problems. Students will build upon and apply a breadth of knowledge in all three domains of Biotransport.

Topics include, but are not limited to: hydrostatics, Reynolds transport theorem, Bernoulli's equation, the Navier-Stokes equation, conduction and diffusion, heat and mass convection, heat and mass differential balances.

Academic honesty/integrity statement:

Students are expected to maintain the highest standards of academic integrity. All work submitted must be original and properly cited. Plagiarism, cheating, or any form of academic dishonesty will result in immediate consequences as outlined in the university's academic integrity policy.

Honor Code: Students are expected to abide by the GT Honor Code (https://policylibrary.gatech.edu/student-life/academic-honor-code) at all times. The objective of the honor code is “to prevent any student from gaining an unfair advantage over other students through academic misconduct”. Starting with the first offense, any potential violations of the honor code will be immediately reported to the Office of Student Integrity to be reviewed. To preserve the integrity of the classroom and the instructor-student relationship, we cannot use personal discretion in instances of potential honor code violations – consider this the first and only warning. For any questions involving these or any other Academic Honor Code issues, please consult your instructor or the student code of conduct. Included in this policy is the use of ANY resources not allowed on an assignment. Specific examples of this are the use of sites like Chegg or Course Hero for help on quizzes or exams. We do monitor this type of activity. We consider the use of ANY resources not allowed on an assignment as a violation of the Honor Code and will be treated as such. The instructional team may collect photo/video evidence to document instances of suspected academic misconduct.

Artificial Intelligence (AI) Use Policy: AI programs (e.g. ChatGPT) may be used as a learning tool but should not be a substitute for your own independent and critical thinking.  Additionally, it is important to note that the material generated by these programs may be inaccurate or incomplete. Be aware that an over-reliance on AI programs can stifle your learning and impact your performance on AI-prohibited assessments.

AI use is strictly prohibited on in-class assessments (quizzes and exams). For assignments completed outside of class (e.g. homework), you may not submit any work generated by an AI program as your own. Violations of this policy will be considered academic misconduct.

Core IMPACTS statement(s) (if applicable):

This course is not eligible to satisfy any Core IMPACTS area attributes. 

Instructor first name:
James
Instructor last name:
Blumling
Section:
A
CRN
27249
27320
27321
27322
Department (you may add up to three):