American University of Beirut

• Zeenat Patrawala on  Generative AI being underestimated in the field of Healthcare - what is the truth of LLMs and Diffusion Models with a specific focus on clinical application tools
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  Talk Abstract:
  
  The rapid advancements in Generative AI, particularly in the form of Large Language Models (LLMs) and Diffusion Models, have sparked significant interest and debate within the healthcare industry. This talk aims to explore whether the true potential of these transformative technologies is being underestimated, with a specific focus on their clinical application tools.
  
  
  The discussion will begin by defining the key concepts of AI, LLMs, and Diffusion Models, while also acknowledging the continuously evolving landscape of foundation models. This foundation will set the stage for a deeper examination of what the new era of AI in healthcare might entail.
  
  
  Drawing on a case study, the talk will delve into how Generative AI can provide strong clinical application tools for pregnant women in critical need during humanitarian crises. This real-world example will illustrate the tangible benefits and practical applications of these cutting-edge technologies in the healthcare domain.  A second topic will include the power of on demand tools (radiology/AI and ultrasound/AI) that can be utilized on the field and enable critical care professionals to gain access to best in class medical information from the global experts (and training data) to provide immediate care, thereby increasing the ability to save life since time is extremely critical.
  
  
  Furthermore, the presentation will draw comparisons to past hype cycles, allowing the audience to gain a nuanced understanding of the current state of Generative AI in healthcare and to envision the future of healthcare tools and applications. This comparative analysis will help separate fact from fiction and provide a balanced perspective on the true potential of these transformative technologies.
  
  
  By the end of the talk, attendees will have a comprehensive understanding of the current capabilities and limitations of Generative AI in healthcare, as well as a glimpse into the promising future that these technologies hold for improving patient outcomes and revolutionizing clinical practices.​
  

• Vida Hamad on Leveraging Data for Risk Mitigation and Crisis Response​​
  

  
  Talk Abstract:
  ​In an increasingly interconnected world, data plays a pivotal role in managing risks and responding effectively to crises. The presentation will outline how data-driven approaches enhance risk mitigation, empower crisis response, and safeguard against misinformation risks.​​
  

Track 2: Data Science for Humanitarian Logistics and Supply Chains in Conflict Zones​

• Tanveer Syeda-Mahmood on Responsible Development of Foundational Models
  
  Keynote Talk Abstract:
  

  With advances in generative artificial intelligence (AI), it is now possible to produce realistic-looking automated reports for a variety of applications ranging from customer service, to preliminary reads in radiology. Such reports can expedite existing workflows, improve accuracy and reduce overall costs. However, it is also well-known that such models often hallucinate, leading to false findings in the generated reports.

  
  

  In this talk,  Dr. Tanveer Syeda-Mahmood​ will describe industry and open source efforts in building responsible, trustworthy, and explainable AI models that begin with responsible data collection and curation, a community-driven consensus knowledge creation and inherent fact-checking. Dr. Tanveer Syeda-Mahmood will highlight some of the research advances in this field for both textual and image-driven fact-checked models. Future generative AI approaches can use these emerging methods to validate their reports leading to a more responsible use of AI in expediting workflows.​​
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• ​Alia Gharaibeh on Leveraging Data Analytics for Humanitarian Supply Chain Resilience
  

  
  Talk Abstract:
  Humanitarian actors today face a multitude of operational and environmental risks that can result in supply chain disruptions. Building resilient supply chains is key to ensuring the availability of life-saving commodities and begins with understanding the operating environment and the contextual factors that make them susceptible to disruptions. In this talk, Alia Gharaibeh introduces the Supply Chain Resilience (SCR) tool, an approach to simulate potential disruptions to humanitarian supply chains that can empower organizations to enhance supply chain resilience proactively. Through a System Dynamics model, this simulation fosters systems thinking and provides a comprehensive analysis of supply chain operations, identifying bottlenecks and testing diverse scenarios. These insights inform the creation of data-driven supply strategies that offer a roadmap for organizations to mitigate risks, reduce disruptions, and ensure aid reaches vulnerable populations, even in the most challenging environments.

• Tina Comes on AI for Crisis Decisions & Humanitarian Logistics
  
  Talk Abstract:
  The world is confronted with a series of super-wicked problems, ranging from climate change to refugee crises. AI and data science have have been recognized as vital to improve resilience especially in the face of adverse events. At the same time, computational tools also have created new vulnerabilities, and changed the way we interact and make decisions. Major challenges remain in rapidly identifying and analysing different data sources and develop from there meaningful and actionable information. Through several case studies, I will outline key resilience principles, highlight how data and information can be used to improve disaster response and humanitarian logistics. Further, I will highlight the possible dilemmas arising given the different time frames of decision-making and discuss requirements for responsible design, and outline directions for future research​

• Erica Gralla on Monitoring Complex Agricultural Market Systems: Framework and Insights from Uganda
  
  Talk Abstract:
  Meeting the United Nations Sustainable Development Goals (SDGs) will require adapting or redirecting a variety of very complex global and local human systems. Tools are needed to understand the dynamics of these systems and the key drivers of their behavior, such as barriers to progress and leverage points for driving sustainable change. System dynamics tools are well suited to address this challenge, but they must first be adapted for the data-poor and fragmented environment. This talk describes a framework for capturing and gaining insight from diverse qualitative and quantitative data, developed through a 4-year engagement with USAID/Uganda. It is illustrated with an application to agricultural financing in Uganda and the management of supply chain shocks during COVID-19.