We build secure software on a seemingly obvious assumption: the processor executes instructions correctly and enforces the boundaries between programs, operating systems, and virtual machines. But what if the CPU itself breaks these rules? Over the past decade, research has found increasingly powerful ways in which processors violate the abstractions on which software relies. What started with tiny timing differences revealing secrets led to attacks such as Spectre and Meltdown, and more recently to CPU bugs that can directly corrupt data, bypass isolation, or expose information across security boundaries.

In this talk, I will show how seemingly harmless implementation details and useful optimizations turn into practical security vulnerabilities. Using examples from our research across Intel, AMD, and RISC-V processors, I will talk about how we discover and understand CPU vulnerabilities and ultimately turn them into security problems. The broader lesson goes beyond processor security: abstractions are only as strong as the layers implementing them. If we want trustworthy computer systems, hardware must increasingly be tested, analyzed, and secured with the same rigor that we already apply to software.

In regression and classification tasks with tabular datasets, most of the literature on explanation in ML (e.g. SHAP, permutation importance) has focused on predictive attribution: does feature X_i help predict outcome Y? At its heart, predictive attribution captures correlation, not causation. We instead ask a causal question: would manipulating feature X_i change outcome Y? We define a natural quantity to answer this question, the feature intervention effect: Δ_i = E[Y | do(X_i=+1)] − E[Y | do(X_i=−1)]. In general, estimating this quantity from observational data alone would entail strong causal assumptions. We propose FADO (Feature-wise Amortized Do-Operators), an amortized inference method using prior-fitted networks trained on synthetic tabular datasets generated by structural causal models. During training, the synthetic data generating process is known, so we can compute ground truth intervention effects Δ_i, and train a model to predict them at test time from a purely observational tabular dataset. As we demonstrate through extensive experiments, our new causal explainability method is easy to train and provides better explanations under a range of settings for which standard predictive attribution fails, including proxy variables, confounders, colliders, and placebos. We evaluate it on real, synthetic and semi-synthetic datasets, with and without hidden confounders, and compare it to existing predictive attribution methods and state-of-the-art causal inference methods.

Anomaly detection is one of the fundamental topics in machine learning and artificial intelligence. The aim is to identify instances that deviate from the norm, so-called anomalies. Such anomalies arise in many scenarios, ranging from attacks on computer or energy networks to critical faults in industrial systems or rare tumors in medical imaging data. In this talk, I will first provide an overview of the field of anomaly detection, with an emphasis on approaches based on deep neural networks. I will then present recent algorithms and theoretical advances in deep anomaly detection. In particular, I will discuss why certain methods work and how their behavior can be better understood using principled analysis. Furthermore, I will highlight recent developments in anomaly detection for time series data, including new benchmark datasets and theoretically-grounded evaluation metrics. Finally, I will conclude with an outlook on promising future research directions in anomaly detection.

Reinforcement learning (RL) has emerged as a fundamental approach to decision-making in machine learning, with applications across a wide range of real-world domains, and several practical extensions, such as multi-agent RL (MARL) and RL from human feedback (RLHF). Despite the growing successful applications of these systems, there exists an inherent threat when it comes to applying them in the real world, where ill-intentioned third parties may intervene in both their training process and deployment. This typically has catastrophic consequences, where even small and inexpensive perturbations to the environment may cause the system to substantially diverge from the desired behavior. The purpose of this thesis is to provide a thorough investigation of various adversarial attacks and robustness against such attacks to (MA)RL and (MA)RLHF systems. In particular, we study training-time and test-time attacks in MARL and propose algorithms that are shown to be provably robust against such attacks. Beyond MARL, we establish fundamental statistical results for RLHF, provide a rigorous characterization of poisoning attacks to RLHF, and develop robust extensions for MARLHF. Our work is centered around robust algorithmic approaches with provable guarantees under common assumptions.

A word is a sequence of symbols drawn from a finite set, and is often a device to represent the evolution of a (discretised) system. Words are central to both symbolic dynamics and verification. In the former discipline, words that represent the same system constitute a shift space, and are typically studied for their combinatorial structure. In verification, a quintessential objective is to certify that a system satisfies a logical specification by showing that its representative words are accepted by an automaton. In this thesis, we bridge the two perspectives through the following contributions.

1) We describe how the action of a deterministic finite transducer on an infinite word preserves the structure of the word. If the input word x has a recurrence property, then this property is preserved for the output word A(x); using the ergodic theory of shift spaces, we identify a large class of words x, including Sturmian words, such that A(x) is guaranteed to admit factor frequencies.

2) We show that we can determine whether an automaton accepts a word by reading its so-called S-adic construction. This allows us, e.g., to decide, given an automaton A, whether A accepts all Sturmian words, or all d-letter Arnoux-Rauzy words.

3) As applications, we study Monadic Second-Order (MSO) logic, and show that the MSO theory of the natural numbers remains decidable when expanded with the following sets of predicates: (i) powers of 2 and 3; (ii) powers of 2, 3, and 5, subject to Schanuel's conjecture; (iii) perfect squares and powers of 2, subject to the binary expansion of the square root of 2 being a disjunctive word.