odu scheduling

OnlineScheduling@odu.edu, David Sorey dsorey@odu.edu

scheduling@odu.edu

ODURF GRA support request

 Here’s a streamlined protocol you can follow next time you submit GRA support in the Research Foundation portal.

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Protocol: Creating a GRA EPASS / Assignment in the Research Foundation Portal

0. Before you start

Have these items ready:

• GRA’s full name and ODU email

• Their home academic department/program (critical for routing)

• Employee type: GR (Graduate Research Assistant)

• Pay basis: semester or annual (you used semester basis)

• Stipend for the semester (e.g., $11,000)

• Hours per week: usually 20 hours

• Funding project (RF project number)

• Whether there is a tuition exemption, and if so:

  • Source (e.g., ODU Research Foundation)

  • Level (Master’s or Doctoral)

Important: You must know the student’s home department/program. The EPASS routes to that chair/dean for approval and cannot be changed later. If it’s wrong, the assignment must be deleted and recreated.

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1. Start a new assignment

1. Log in to the Research Foundation portal. https://hera.odurf.odu.edu/RFPortal 

2. Go to “Research Assignments”.

3. In the blue bar, click “Add Assignment”.

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2. Add or select the GRA as an employee

1. Next to Employee ID, click “Select”.

2. Try typing the student’s name:

   • If found: select them.

   • If not found:

     • Click “Start a new employee” at the bottom.

     • Enter first name, last name, and email.

     • Save.

     • Then click “Select” again and choose the new employee.

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3. Set employee type, department, and term

1. Set Employee Type to GR.

2. Choose Pay Basis:

   • For GRA by term, select Semester basis.

3. Select Employee Department from the dropdown: (eg 6093 Computer Science)

   • This must be the student’s home department/program (not your department if they’re different).

   • Do not proceed until you are sure this is correct; it controls the routing path.

4. Select the semester (e.g., Fall).

5. Click “Save and Next”.

If the wrong department is chosen at this step, it cannot be edited later. The EPASS must be deleted and recreated.

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4. Enter salary and hours

1. In Annual/Term Salary, enter the semester stipend amount (since you selected semester basis).

2. Enter Hours per Week = 20.

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5. Set tuition exemption (if applicable)

1. Locate the Tuition Exemption section.

2. Select the appropriate option (e.g., ODU RF Tuition Exemption).

3. Choose the degree level: Master’s or Doctoral (for your case: Doctoral).

4. Indicate if you are covering 100% of tuition or another percentage, as required.

(Note: this tuition entry is separate from salary and fringe.)

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6. Add the payline

1. Scroll down to Payline and click “Add Payline”.

2. Select the correct project from the list.

3. For the payline details:

   • You can enter hours/week (e.g., 20) for the project.

   • Do not manually type the budget amount.

4. To calculate salary for that payline:

   • Click “Calc” next to Budget on the right.

   • The system will calculate the salary based on the previously entered stipend and hours.

5. Adjust any rounding (e.g., remove a $0.01 extra) if needed.

6. Click “Create” to finalize the payline.

(This covers salary only – no tuition, no fringe.)

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7. Review, edit, and submit

1. Click “Save” to save the assignment.

2. To review or change details:

   • Go to the top and click “Edit Assignment” (green button).

   • Confirm:

     • Employee type = GR

     • Correct home department

     • Semester basis and semester

     • Stipend amount and 20 hours/week

     • Tuition exemption details

     • Correct project and calculated payline

3. When everything looks correct, click “Submit”.

4. The status will show pending chair approval, routed through the student’s home department.

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8. If the department is wrong

• The department cannot be edited in an existing assignment.

• The RF staff must delete the assignment, and you must create a new one with the correct department.

• If you are unsure of the student’s department:

  • Check the offer letter, the program catalog, or contact the Graduate School / program.

  • You can also coordinate with RF staff to help verify if needed.

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9. New hire paperwork

• RF will request new hire paperwork from the student if needed.

• Remind the student to complete all HR documents promptly so the GRA appointment can be processed on time.

BMS program

 

BMS meeting. 

 Robert Bruno, 3D bioprint and cancer

 Lifang Yang

 Larry Sanford

 Frank Lattanzio

 Patrick Sachs

 Siqi Guo

 Ebony Clark

 Lisa Shollengerger

 Peter Mollica

• Lifang Yang: Focuses on fundamental and translational cancer research, specifically cancer pathogenesis, biomarker development, and therapeutic approaches. Her lab studies tumor cells, tumor microenvironment, extracellular vesicles, proteomics, and cancer disparities to advance precision oncology through multi-omics and bioinformatics approaches.
• Frank Lattanzio: Connected to bioelectrics research, including applications of nanosecond pulsed electric fields (nsPEFs) in cancer treatment and cellular electropermeabilization mechanisms.
• Patrick Sachs: Associated with biomedical and translational sciences, likely focusing on biomedical engineering and tumor microenvironment studies combined with 3D bioprinting and cancer research models.
• Siqi Guo: Involved in bioelectric research, including DNA vaccination delivery, electrotransfer, and electroporation-mediated gene transfer techniques; associated with cellular and molecular response to electric fields.

• Siqi Guo is a Research Associate Professor affiliated with the Frank Reidy Research Center for Bioelectrics. His grants include a commercial contract worth about $102,900 (2016-2017) for studying nanosecond electric pulses (NSEPS) as an ablation-immunotherapy for advanced pancreatic cancer. His work focuses on cancer, biotechnology, and bioelectric therapies like nano-pulse stimulation and gene electrotransfer for cancer treatment. He leads projects advancing novel immunotherapy techniques based on electric pulse technology.
• Lifang Yang, listed as an instructor at Eastern Virginia Medical School collaborating with ODU, shares in recent multidisciplinary seed funding ($42,000) supporting research on synergistic effects of nano-pulse treatment combined with cold plasma reactive species for cancer treatment. Her expertise centers on cancer pathogenesis, tumor microenvironment, extracellular vesicles, and biomarker discovery aiming to improve precision oncology through multi-omics and bioinformatics.

ODU faculty profile link

 ODU faculty profile link, where profile image and some information can be updated

https://monarchprofile.odu.edu/

ODU CS and DSC courses taught by Hong Qin

 

https://catalog.odu.edu/courses/cs/#graduatecoursestext

https://catalog.odu.edu/courses/dasc/

CS 781  AI for Health Sciences  (3 Credit Hours)  

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

Prerequisites: Prior programming experience  

CS 782  Generative AI  (3 Credit Hours)  

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Prerequisites: Prior programming experience  

CS 881  AI for Health Sciences  (3 Credit Hours)  

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

Prerequisites: Prior programming experience  

CS 882  Generative AI  (3 Credit Hours)  

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Prerequisites: Prior programming experience  

DASC 781  AI for Health Sciences  (3 Credit Hours)  

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

Prerequisites: Prior programming experience  

DASC 782  Generative AI  (3 Credit Hours)  

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Prerequisites: Prior programming experience  

DASC 881  AI for Health Sciences  (3 Credit Hours)  

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

Prerequisites: Prior programming experience  

DASC 882  Generative AI  (3 Credit Hours)  

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Prerequisites: Prior programming experience  

ODU PhD admssion requirement

 

https://catalog.odu.edu/graduate/sciences/#:~:text=GRE%20scores:%20310%20combined%20verbal,if%20the%20student%20is%20accepted.

Minimum criteria for eligibility are as follows:

1. GRE scores: 310 combined verbal and quantitative, and at least a 4.0 on the analytical writing section.
2. GRE scores (older version): 1200 combined verbal and quantitative, or 1300 in any two of verbal, quantitative, or analytical.
3. Undergraduate GPA of 3.20 overall and 3.50 in the major, out of 4.00 maximum.
4. Evidence of research aptitude by undergraduate thesis/research, publications, M.S. thesis and/or letters of reference.
5. Information concerning the Dominion Graduate Scholar Program may be obtained from the graduate program director for the program of interest.
6. Written acknowledgment from a faculty member agreeing to serve as the student’s major advisor, if the student is accepted.

Spring 2025 course schedule

 CS 795/895 DASC, AI for health and life sciences. 

Scheduled Meeting Times

Type	Time	Days	Where	Date Range	Schedule Type	Instructors
Scheduled In-Class Meetings	4:30 pm - 7:10 pm	F	ENGINEERING & COMP SCI BLDG 2120	Jan 11, 2025 - Apr 28, 2025	LECTURE	HONG QIN (P)

DASC new courses

  

1. DASC 728/828, “Deep Learning Fundamentals and Applications” (Deep Learning Fund & App) (frank)
   “This course covers key components of deep learning framework, including loss functions, regularization, training and batch normalization. The course also covers several fundamental deep learning architectures such as multilayer perceptrons, convolutional neural network, recurrent neural network and transformers, as well as some advanced topics such as graph neural network and deep reinforcement learning. The class activities include traditional lectures, paper reading and presentation, and projects.”
   Prerequisites: be: CS 422 or CS 522 or CS 480 or CS 580 or CS 722 or CS 822 or CS 733 or CS 833 or CS 620, or other equivalent courses at the discretion of the instructor. 
2. DASC 605, “Statistical Inference and Experimental Design for Data Science” (Stat Inf & Exp Design for Data Sci) (Trent)
   “description”
   Prerequisites: STAT 603 and instructor approval
3. DASC 715/815 Generative AI (3 credits)
4. ·         Course Description: This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.
5. ·         Grading: Normal/Letter, Pass/Fail, Audit allowed.
6. ·         Prerequisite courses: Prior programming experience are expected.
7.  
8. DASC 717/817 AI for Health Sciences (3 credits)
9. ·         Course Description: This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.
10. ·         Grading: Normal/Letter, Pass/Fail, Audit allowed.
11. ·         Prerequisite courses: Prior programming experience are expected.
12. DASC 7xx/8xx, “Data-Driven Computational Imaging” (Dushan)
    “please update course number, title and description after coordination with CS”
13. DASC 600 (Sampath)
    “please update title and description”
14. DASC 699  Thesis Research  (3 Credit Hours) 
    Prerequisites: Departmental permission required
15. DASC 697 Independent Study in Data Science  (1-3 Credit Hours)
    Independent study under the direction of an instructor.
    Prerequisites: permission of the instructor 
16. DASC 668 Internship (1-3 credits) (P/F only)
    Requirements will be established by the School of Data Science and Career Development Services and will vary with the amount of credit desired. Allows students an opportunity to gain a short duration career-related experience.

Actually submitted 

CS 781 AI for Health Science, 

Cross-listed and/orEquivalent Courses

CS 881, DASC 781, DASC 881

CS 782 Generative AI , cross listed with CS 882, DASC 782, DASC 882

ODU international travel documents

 

 

 ALL internation travel requires pre-approval – in advance.  

 

 Please make sure you include appropriate documentation of the following: 

• Purpose of your trip – conference registration, presentation information, research or other meeting.  If you do not have something official, please write a paragraph explaining the purpose.  
• If you are making multiple stops, you must document the purpose of each leg of your trip.
• If you are requesting airfare (which is usually the largest amount), please attach documentation regarding how the dollar amount was determined.  
• Please attach any and all documentation which may be helpful.  I would prefer more than less.  

 

DASC 715/815 Generative AI; DASC 717/817 AI for Health Sciences

 

• Title: DASC 715/815 Generative AI (3 credits)
• Course Description: This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.
• Grading: Normal/Letter, Pass/Fail, Audit allowed.
• Prerequisite courses: Quantitative reasoning and prior programming experience are expected.

 

• Title: DASC 717/817 AI for Health Sciences (3 credits)
• Course Description: This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.
• Grading: Normal/Letter, Pass/Fail, Audit allowed.
• Prerequisite courses: Quantitative reasoning and prior programming experience are expected.

ODU CS795/895 spring 2025

 

 scheduled to teach a CS 795/895 course in Spring 2025.  We assign each of these courses to a Research Area so that PhD students can use them to count towards their breadth requirement.  Please let me know where you would like your course classified.

 

Here are the courses:

TPCS: AI SECURITY & PRIVACY	NING,RUI
TPCS: ADV ML & DEEP LEARNING	LIU,FRANK
TPCS:FOUND MODELS FOR DATA SCI	WU,JIAN
TPCS: PRAT MACHN LEARN & APPLI	LI,YAOHANG
TPCS: UTIL SCALE QUANTUM COMP	CHRISOCHOID,NICOLAOS
TPCS: AI FOR HEALTH SCIENCE	QIN,HONG
TPCS: GRAPH NEURAL NETWORKS	RANA,PRATIP
TPCS: COMPUTATIONAL IMAGING	WADDUWAGE, DUSHAN

 

The PhD research areas are:

• Bioinformatics
• Systems: Networks, Mobile Computing, Security
• Machine Intelligence and Data Analytics (MIDA)
• Web Science and Digital Libraries
• Medical and Scientific Computing

https://www.odu.edu/computer-science/academics/graduate/research-area-committees