Chicago Data Night: Sonja Petrović (TrustVector)

Please join us for our monthly Chicago Data Night, cohosted by Drive Capital, ChiData and the DSI. Practitioners, academics, and aficionados within the Chicago area are all invited to be part of a community at the intersection of industry and academia, brought together by a mutual interest in data. Each month, guest speakers will cover a specific data-related topic.

Hors d’oeuvres and drinks will be provided. Admission is free, and we strongly encourage an RSVP to attend.

Meeting Location
Drive Capital, Fulton East Building
215 N Peoria St
Chicago, IL 60607

AGENDA
5:30pm: Networking Reception
6:00pm: Welcome Remarks
6:05pm: Guest Speaker
6:50pm: Q&A
7:00pm: Networking Reception
7:30pm: Event Concludes

Title: Building the ecosystem of AI safety and trust

Abstract: The widespread implementation of AI is here. How do we turn AI from a strategic risk to a responsible advantage? We will take a data science perspective to distill this question. Are data science teams equipped to drive responsible innovation, or are they susceptible to common AI challenges such as algorithmic bias and unreliable outputs? This focused session will explore key technical evaluations vital for data science practitioners before deploying AI models. Learn about a framework for ensuring your AI systems are built on robust data, evaluated with appropriate metrics, and designed with transparency and explainability in mind. This will enable you to transform AI’s analytical capabilities into trustworthy, actionable insights and build a sustainable advantage for your organization.

Bio: As a co-founder and Chief Scientific Officer (CSO), Sonja is responsible for evaluating and setting TrustVector's scientific priorities, envisioning and leading development of scientific capabilities, and building academic partnerships. Sonja is also a Professor in the Department of Applied Mathematics, College of Computing, at Illinois Institute of Technology. She discovers, studies, and models structure in relational data we encounter in our daily lives.

Sonja’s academic research portfolio spans theoretical and applied problems including development of statistical models for discrete relational data, such as social networks, and use of machine learning to predict and improve the behavior of algebraic computations.