Symposium: Demystifying AI in Public Health

This symposium aims to bring together experts, researchers, and practitioners to share knowledge, discuss challenges, and explore opportunities in the intersection of AI and public health. It will serve as a platform for fostering collaboration and innovation in this rapidly evolving field.

Objective: To demystify the use of AI in public health at Tulane University and foster interdisciplinary collaboration among faculty, students, and community partners.

9:00 – 9:15Opening Remarks: Dean Thomas LaVeist 
 
  1. Welcome and Introduction
  2. Overview of the symposium's goals and objectives. 
9:15 – 9:30 Session 1: Introduction to AI in Public Health 
Speaker:  
 
  • Presentation on the importance of AI in addressing public health challenges. 
  • Overview of Tulane's current AI initiatives and future plans 
9:30 – 10:00 Session 2: Keynote Address
10:00 – 10:45Session 3: AI Research and Applications in Public Health (could break this into 2 sessions) 
 

Short 10-15 minute presentations from AI researchers, including:

  • Outside researchers
  • Yilu Lin, PhD and/or Lizheng Shi(HPAM) 4 
  • Sam Kakraba
  • Martha Silva (IHSD),2   
10:45 – 11:15BREAK: Student AI Posters / Meet and Greet 
11:15 – 12:30 Session 4: AI Research and Applications in Public Health (could break this into 2 sessions) 
 
  • Aron Culotta (Computer Science)1  
  • Simone Skeen (SPBS)3 
  • Others 
12:30 – 1:00     LUNCH
1:00 – 2:00 Session 4: Panel Discussion 
Moderator: 
 
  • What got you interested in AI and its applications for public health?  
  • Where do you see the field going over the next 1-2 years? 5-10 years? 
  • What do you see as the growth areas in this field? 

Or

Developing AI Competencies in SPHTM / New Certificate program

  • Discussion on the integration of AI and data science into the public health curriculum. (Sam Kakraba, Patrick Button?)
  • Discussion of new courses and areas of specialization in AI and public health.
 

 This may be a result of the Symposium rather than a full session [Session 5: Building AI Infrastructure and Collaborations

  • Plans for establishing a Center for Machine Learning & AI in Public Health.
  • Opportunities for collaboration with the School of Science and Engineering and the Connolly Alexander Institute for Data Science.]
2:00     WRAP-UP 

1 Li, X and Culotta, A. Forecasting COVID-19 Vaccination Rates using Social Media Data, Companion Proceedings of the ACM Web Conference 2023, p1020-1029. 
2 Silva M, Anaba U, Jani Tulsani N, Sripad P, Walker J, Aisiri A. Gender-Based Violence Narratives in Internet-Based Conversations in Nigeria: Social Listening Study. J Med Internet Res. 2023 Sep 15;25:e46814. doi: 10.2196/46814.  PubMed PMID: 37713260; PubMed Central PMCID: PMC10541644. 
3Skeen SJ, Jones SS, Cruse CM, Horvath KJ Integrating Natural Language Processing and Interpretive Thematic Analyses to Gain Human-Centered Design Insights on HIV Mobile Health: Proof-of-Concept Analysis  JMIR Hum Factors 2022;9(3):e37350.  
4 Shao H, Shi L, Lin Y, Fonseca V. Using modern risk engines and machine learning/artificial intelligence to predict diabetes complications: A focus on the BRAVO model. J Diabetes Complications. 2022 Nov;36(11):108316. doi: 10.1016/j.jdiacomp.2022.108316. Epub 2022 Oct 3Review. PubMed PMID: 36201893.