Machine Learning Seminar

The evolution of AI has progressed rapidly from traditional machine learning models for prediction and classification to large language models (LLMs) for generative AI and now to autonomous, agentic AI systems. Despite these advancements, ensuring AI remains human-centered and trustworthy is a critical societal need. In this talk, Dr. Kush R. Varshney will explore the specifications required to ensure AI systems demonstrate reliability, human interaction, purpose alignment, and respect for human agency and dignity. He will discuss the current gaps in theoretical foundations for LLM-based agentic AI and propose how a systems theory approach could enhance prediction, control, and optimization in AI.

Speaker Bio: Dr. Kush R. Varshney is an IBM Fellow and leads Human-Centered Trustworthy AI Research at the IBM Thomas J. Watson Research Center, NY. His contributions to AI include developing well-known open-source tools such as AI Fairness 360, AI Explainability 360, Uncertainty Quantification 360, and AI FactSheets 360. He has been recognized with multiple IBM Corporate Technical Awards and is a Fellow of IEEE. His book Trustworthy Machine Learning (2022) explores the theoretical underpinnings of fair and explainable AI.

About Joint Machine Learning Seminar Series We are excited to announce the first seminar in our newly launched Joint Machine Learning Seminar Series, a collaborative initiative across three schools at the University of Sydney, co-organized by Dr. Chang Xu (School of Computer Science), Prof. Dmytro Matsypura (Business School), and Yiming Ying (School of Mathematics & Statistics). The goal of this initiative is to foster interdisciplinary interaction and collaboration on cutting-edge research in Machine Learning (ML) and Artificial Intelligence (AI). We welcome suggesions of potential future speakers for this seminar series.

Future Seminars: To maintain a high-quality seminar series, we aim to feature speakers with impactful contributions to ML and AI research. However, if no suitable speaker is available for a given session, we will organize canned seminar talks in the School of math and statistics, focusing on the mathematical and statistical aspects of machine learning, ensuring continuous engagement with fundamental and advanced topics in the field. We invite researchers, faculty, and students across disciplines to join us for this engaging talk and networking opportunity over coffee!