The remarkable advances in AI have given rise to a growing interest in AI-assisted decision support in domains ranging from medicine and drug-discovery, to criminal justice and education. The ultimate goal in AI-assisted decision support is to optimally combine the complementary strengths of humans and AI models to achieve greater outcomes than either can achieve on their own, in short human-AI complementarity. Achieving this goal though, has shown to be a major challenge as it typically requires humans to understand when they can rely on the AI model for their decision, which has shown to be highly non-trivial.

My research shows that it is possible to circumvent this challenge and achieve human-AI complementarity by proposing an alternative design of decision support systems. The key principle underpinning this design lies on adaptively controlling the level of human agency by using an AI model to narrow down the decisions a human can take to a subset. In this talk, I introduce this design in the context of independent and sequential decision-making tasks, where I present provably data-efficient algorithmic methods to identify the level of human agency under which humans maximize their performance in the decision-making task. Under this optimal level of human agency, my proposed design shows to achieve human-AI complementarity in practice, based on evaluation through two large-scale human studies with a total of more than 4,000 participants. I conclude the talk by discussing challenges and opportunities in human-AI complementarity that open up by AI models generating natural language, highlighting emerging avenues for future research.

Privacy-Enhancing Technologies (PETs) are foundational for a future where data can be used without compromising privacy. While the community has largely focused on advancing the cryptographic foundations of PETs, real-world security of PETs is threatened by the very software systems designed to make them accessible, including PET-oriented compilers and frameworks.

The goal of my research is to ensure that the practical systems supporting PETs are dependable. In this talk, I will present my work on developing novel, automated techniques to systematically uncover critical vulnerabilities in the software systems of PETs. I will show two thrusts of my research: (1) automatically discovering severe logic bugs in domain-specific compilers for PETs, and (2) identifying and mitigating new, subtle security risks in PET-enhanced machine learning frameworks. The tools from this research have uncovered dozens of bugs (some with high security impact) in high-stakes PET systems and have been adopted by leading PET industry users. I will conclude by discussing my future research vision towards building provably dependable PET ecosystems.

Pattern matching is a fundamental feature of functional programming languages that is also being adopted by mainstream object-oriented languages, such as Java and Python. In this talk, I will discuss recent improvements and simplifications we proposed for pattern matching syntax. I will also present our ongoing follow-up work: we propose “composable recursive patterns and transformations”, a new way of writing data-oriented code that can be compiled efficiently and integrates well with structural types and subtyping, making writing such code safer, more modular, and more efficient.

Concurrent and distributed systems are pervasive, yet verifying their correctness remains challenging. A core difficulty is heterogeneity: verification techniques developed for one computational model rarely transfer to others. In this talk, I present compositional linearizability, a framework that reconstructs linearizability through a compositional lens. This perspective yields a general theory from which correctness criteria for specific domains can be systematically derived, as demonstrated in work on crash-aware systems and systems with liveness requirements. The framework leads to novel verification techniques that enable modular reasoning about complex concurrent objects, mechanized in the Rocq proof assistant. These results open promising new paths toward trustworthy distributed systems.

Current frontiers in machine learning, data science, and, more broadly, artificial intelligence reveal the limits of purely predictive approaches and motivate a shift toward decentralized, scalable, and causal systems. Such systems require processing and learning on increasingly complex networks. A promising heterogeneous toolbox, loosely grouped under the name of Topological Deep Learning (TDL), aims to design deep architectures that integrate ideas from algebraic topology, non-Euclidean geometry, and category theory to address this complexity. In Dr. Battiloro’s approach to TDL, the basic units of a network are cells, which generalize graph nodes. A cell may represent, for example, a single agent or a group of agents in an agentic AI system, a neuron or a brain region in a neural circuit, or a sensor or sensor type in an environmental monitoring network. Cells can be organized hierarchically and exhibit rich intra-and inter-cell interaction patterns. In this seminar, Dr. Battiloro will (1) introduce and discuss TDL’s current landscape and explain why developing a modern, coherent language for it matters broadly, (2) argue that this language should be grounded in the theory of poset sheaves, (3) briefly highlight goals TDL has already achieved—such as inferring higher-order, hierarchical goal-driven interactions in data or jointly modeling and relating subjective causal structures across cells—and (4) outline an ambitious sheaf-centric research pathway for TDL.