trustworthy machine learning

Learning under Limited Information across Federated, Multi-Agent, and LLM Settings

Salma Kharrat, Ph.D. Student, Computer Science

May 7, 15:00 - 16:45

B3 R5220

Federated learning personalized learning decentralized learning Reinforcement Learning black-box optimization prompt optimization decentralized systems combinatorial optimization observability inference Trustworthy AI trustworthy machine learning intelligent systems LLM

This dissertation studies learning under structural information constraints across three major paradigms: federated learning, cooperative multi-agent reinforcement learning, and black-box optimization of large language models.

What Survives When Code Doesn’t?

Dr. Laurent Bindschaedler, Research Group Leader, Max Planck Institute for Software Systems (MPI-SWS)

May 4, 12:00 - 13:00

B9 R2325

Trustworthy AI trustworthy machine learning coding AI axplainable AI software development

This talk explores how AI-driven code generation shifts the role of software from a durable artifact to a disposable implementation and argues for a new computational model for agentic software that formalizes the fundamental guarantees of intent, state, composition, and effect into explicit, enforceable contracts.

Yulian Wu

Ph.D. Student, Computer Science

machine learning privacy Trustworthy AI decision making decision sciences privacy-preserving AI trustworthy machine learning Robustness Reinforcement Learning quantum machine learning bioinformatics direct preference optimization

Yulian Wu is a Ph.D. candidate under the supervision of Professor Francesco Orabona at King Abdullah University of Science and Technology (KAUST).

Salma Kharrat

Ph.D. Student, Computer Science

Federated learning Trustworthy AI trustworthy machine learning intelligent systems decentralized systems combinatorial optimization personalized learning decentralized learning Reinforcement Learning observability inference black-box optimization LLM prompt optimization