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

 

ODU course schedule search

 

https://courses.odu.edu/search?subject=DASC&term=202420&

ODU Smarter Proctoring

 

When we set up the SmarterProctoring options for the exams, we as the faculty get to designate what we consider to be the acceptable proctoring options.  The ones I generally recommend are 

• Institution Testing Centers [this includes our own College of science Testing Center in the Perry Library]
• NCTA Testing Centers
• Professional Testing Centers
• Military Base Testing Center
• High School Testing Center
• Testing Administrator (College, University or Private Testing Service)
• Librarian (Public Library)
• Military Personnel
• Professional Education Center
• Live Online Proctoring    [a SmarterProctoring staff member watching them through their webcam and screen capture software]

 

The Smarter Proctoring staff at ODU can assist students with finding a tutor convenient to their geographic area.  

 

ODU graduate school policy

 

https://catalog.odu.edu/graduate/policies/graduatepolicies/#text

ODU lions server

 

ssh <username>@lin-login.lions.odu.edu

ODU canna request form

 

https://form.asana.com/?k=a7EYq2D-NajaTLGsjDu49A&d=964208595678688

ODU LEO pre-requisite override

 

1. Log into Leo Online:  https://www.leoonline.odu.edu/homepage_securenews.htm  Click Enter Secure Area. Log in with your Midas ID.
   (You use Leo for a lot of course admin tasks, including finding your rosters and entering your final grades.)
2. Click Faculty & Advisors
3. Click Student Menu
4. Click ID selection
   Selecft the fall 2024 semester. Then enter the student’s Univ ID number or name and Submit.
   Confirm that you have selected the correct student (Submit)
5. Click Registration Overrides
6. For the Override, select “PreRequisite Course Required”.  For the course, select your section of CS361.   Submit. 

Approve the summary and submit again.

7. Email the student to say they can register for the course.

 

ODU School of Data Science

* Philippe Giabbanelli is a new member of OERI/VMASC and comes from Miami of Ohio:

https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscholar.google.ca%2Fcitations%3Fuser%3D7YilOHoAAAAJ%26hl%3Den&data=05%7C02%7Chqin%40odu.edu%7Ca29cad16afb04e2e44e708dcb70d4c32%7C48bf86e811a24b8a8cb368d8be2227f3%7C0%7C0%7C638586514951429970%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=OSCh1bhPybIMHddYmdhL2QtiXz0O5DQM%2BWiMfw7HYV4%3D&reserved=0
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As a research professor, his focus is on funding and scholarship and has no teaching responsibilities.  

* Hong Qin is a member of the School of Data Science and the dept of CS, and joins from UT-Chatanoga 

https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscholar.google.com%2Fcitations%3Fuser%3DcoMF0I4AAAAJ%26hl%3Den&data=05%7C02%7Chqin%40odu.edu%7Ca29cad16afb04e2e44e708dcb70d4c32%7C48bf86e811a24b8a8cb368d8be2227f3%7C0%7C0%7C638586514951450934%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=W%2Fu9thB9Kp0xZTYry05dD%2Ba7PnnL6a7F5LUnJ0veokc%3D&reserved=0
https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fhongqin&data=05%7C02%7Chqin%40odu.edu%7Ca29cad16afb04e2e44e708dcb70d4c32%7C48bf86e811a24b8a8cb368d8be2227f3%7C0%7C0%7C638586514951454185%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=fmECnsfbJvZbCdDnJRAf9hMWtgDpOOXgRsWimb%2FKh5U%3D&reserved=0

* Dushan Wadduwage a member of the School of Data Science and the dept of CS, and joins from Harvard: 

https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscholar.google.com%2Fcitations%3Fuser%3DLHmeoN4AAAAJ%26hl%3Den&data=05%7C02%7Chqin%40odu.edu%7Ca29cad16afb04e2e44e708dcb70d4c32%7C48bf86e811a24b8a8cb368d8be2227f3%7C0%7C0%7C638586514951457313%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=5lIjOC1dSKhi6IiGOrPJZBeqfd05Xo9cIe62tAEsocA%3D&reserved=0
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* Dana Willner is a lecturer in the School of Data Science and the undergraduate program advisor.  She joins us from William & Mary. As a lecturer, she doesn't have research responsibilities, but has an extensive biomed history:

https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpubmed.ncbi.nlm.nih.gov%2F%3Fterm%3Ddana%2520willner%26sort%3Ddate%26sort_order%3Ddesc&data=05%7C02%7Chqin%40odu.edu%7Ca29cad16afb04e2e44e708dcb70d4c32%7C48bf86e811a24b8a8cb368d8be2227f3%7C0%7C0%7C638586514951463696%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=%2BOKi0bfa1XIO1AOr55EutVR1MOk%2B6Lm9NAvxSHBTPn0%3D&reserved=0
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* Nirmala Karunarathna is a lecturer in the School of Data Science and joins us from the CS dept at ODU.  As a lecturer, she doesn't have research responsibilities, but also has biomed experience:

https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscholar.google.com%2Fcitations%3Fuser%3DiAGIoxQAAAAJ%26hl%3Den&data=05%7C02%7Chqin%40odu.edu%7Ca29cad16afb04e2e44e708dcb70d4c32%7C48bf86e811a24b8a8cb368d8be2227f3%7C0%7C0%7C638586514951470179%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=lOqE%2BNDAgFKLGdBjkLQiU5jJb9oJ%2FKnu6jyDRrPM%2FFg%3D&reserved=0

* Trent Buskirk is starting his 2nd year with us (School of Data Science and College of Business), and has an interest in public health:

https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscholar.google.com%2Fcitations%3Fuser%3DZym2GDMAAAAJ%26hl%3Den&data=05%7C02%7Chqin%40odu.edu%7Ca29cad16afb04e2e44e708dcb70d4c32%7C48bf86e811a24b8a8cb368d8be2227f3%7C0%7C0%7C638586514951473237%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=otr0LRBSGaeETGH%2FIR0bQwlcFCI35v7dMIWuQZ7ddjA%3D&reserved=0
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* Heather Richter is the director of the Hampton Roads Biomedical Research Consortium, and has been with us 3 (?) years:

https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fhrbrc.org%2Fabout%2Fteam%2F&data=05%7C02%7Chqin%40odu.edu%7Ca29cad16afb04e2e44e708dcb70d4c32%7C48bf86e811a24b8a8cb368d8be2227f3%7C0%7C0%7C638586514951482402%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=fWT%2Bz7Bp%2BKMQJ0JNwUHe2h4KD67fM%2FkvCmI2Oe0BWV0%3D&reserved=0
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Michael L. Nelson mln@cs.odu.edu https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftwitter.com%2Fphonedude_mln&data=05%7C02%7Chqin%40odu.edu%7Ca29cad16afb04e2e44e708dcb70d4c32%7C48bf86e811a24b8a8cb368d8be2227f3%7C0%7C0%7C638586514951495339%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=sl2k9Kf%2BqQEYoWIeQHaB3HpCO87m8ef2k9uAI1lsjKk%3D&reserved=0
Deputy Director, School of Data Science https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftwitter.com%2FODUDataScience&data=05%7C02%7Chqin%40odu.edu%7Ca29cad16afb04e2e44e708dcb70d4c32%7C48bf86e811a24b8a8cb368d8be2227f3%7C0%7C0%7C638586514951499598%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=xzihPDwp7lRggWH6IBn9AmHK9auxF9xGpaAfvSi%2BJBY%3D&reserved=0
Web Sciences and Digital Libraries Research Group https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftwitter.com%2FWebSciDL&data=05%7C02%7Chqin%40odu.edu%7Ca29cad16afb04e2e44e708dcb70d4c32%7C48bf86e811a24b8a8cb368d8be2227f3%7C0%7C0%7C638586514951503649%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=rmH7CrxR3TDlv%2FWGLCuSCse6Q6R6pcpZlk5X%2B6WEZJI%3D&reserved=0
Department of Computer Science, Old Dominion University, Norfolk VA 23529
Virginia Modeling, Analysis, and Simulation Center, 1030 University Blvd, Suffolk, VA 23435
+1 757 683-6393 +1 757 683-4900 (f) +1 757 570-7376 (c)