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Author: RSTZZZ

_research_directions/online-crime.md

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 Sex trafficking impacts 4.8 million people globally and is a $99 billion USD industry that often operates undetected, including in Canada. Technology has become a critical tool for traffickers, enabling recruitment and exploitation while making these crimes harder to trace. However, innovative analytics can uncover hidden patterns, identify victims, and provide much-needed support to those impacted. Our interdisciplinary team of AI and criminology experts is dedicated to developing context-aware, human-centered solutions to tackle this issue responsibly. Through advanced techniques like data mining and anomaly detection, we are working to bring a data-driven approach to the fight against human trafficking in Canada.
 
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 {% include sub_research-directions.html category="online-crime" %}
 
 # Core Team Members

_research_directions/temporal-graph-learning.md

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 # Why should we care?
 Temporal graph learning is crucial for analyzing dynamic networks that evolve over time, such as social media or financial systems. Unlike static graphs, these networks require modeling both the temporal and structural features, which presents unique challenges. However, incorporating time-based information enhances the predictive power of graph algorithms, making them valuable for applications like recommendation systems, fraud detection, and disease modeling. As interest in this area grows, it spans fields like machine learning, AI, and public health. Temporal graph learning bridges gaps across disciplines and opens up new opportunities for solving real-world problems
 
+# Topics
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 {% include sub_research-directions.html category="temporal-graph-learning" %}
 
 # Core Team Members