Next week, Toronto becomes the epicenter of data science.
From August 3 to 7, Toronto will welcome KDD-2025, the premier conference for leading-edge research and AI applications, organized by the ACM’s Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD).
“While AI transforms the field, turning raw data into meaningful knowledge remains crucial.”
Jian Pei, KDD
KDD brings together thousands of researchers and tech leaders leading the data science sector.
It is a unique venue where academic research meets practical deployment, assembling people building AI’s infrastructure in labs, startups, and Fortune 500 companies to learn, collaborate, and shape the field’s future.
This year, leaders from Cohere, Apple, Google DeepMind, Amazon, and Cisco, among others, will engage in discussions covering model behavior, system deployment, safety, and governance.
The 2025 conference occurs during a major transition for the data science community, as generative AI’s rapid development has outpaced many field assumptions and created new challenges for those handling data at scale.
According to Xiaohui Yu, York University Professor and one of three KDD-2025 General Chairs, this year’s program was designed to reflect this urgency.
“The world of data science and AI is changing fast,” said Yu. “The KDD community, central to discovering insights from data for decades, is evolving with it.”
The changing landscape of data science
For decades, data scientists focused on extracting insights from structured datasets. Today, the scenario is vastly different.
“We are at an inflection point,” said Luiza Antonie, University of Guelph Professor and KDD General Chair.
One significant change Antonie observed recently is how users interact with AI. Instead of merely receiving model outputs, data scientists now work with systems responding fluidly, mimicking human intuition, and producing complex on-the-fly outputs.
“It’s like learning to converse with a very smart but sometimes overly literal assistant,” Antonie said. “You have to phrase things precisely to get what you need.”
These changes in AI interaction are also influencing its development. According to Jian Pei, KDD General Chair and Arthur S. Pearse Distinguished Professor at Duke University, many data scientists now fine-tune large, general-purpose systems instead of building models from scratch.