The impact of algorithmic decision-making systems on individuals has raised significant interest in addressing fairness concerns within such systems. Designing fair systems entails several critical components, which have garnered considerable attention from the research community. However, notable gaps persist in three key components. Specifically, in this thesis, we address gaps in following components: i) evaluating existing approaches and systems for (un)fairness, ii) updating deployed algorithmic systems fairly, and iii) designing new decision-making systems from scratch. Firstly, we evaluate fairness concerns within foundation models. The primary challenge is that fairness definitions are task-specific while foundation models can be used for diverse tasks. To address this problem, we introduce a broad taxonomy to evaluate the fairness of popular foundation models and their popular bias mitigation approaches. Secondly, we tackle the issue of fairly updating already deployed algorithmic decision-making systems. To this end, we propose a novel notion of update-fairness and present measures and efficient mechanisms to incorporate this notion in binary classification.  However, in cases where there is no deployed system or updating an existing system is prohibitively complex, we must design new fair decision-making systems from scratch. Lastly, we develop new fair decision-making systems for three key application scenarios. Major challenges in designing these systems include computational complexity, lack of existing approaches to tackle fairness issues and designing human-subject based studies. We develop a computationally efficient mechanism for fair influence maximization to make the spread of information in social graphs fair. Additionally, we address fairness concerns under model uncertainty, i.e., uncertainty arising due lack of data or the knowledge about the best model. We propose a novel approach for training nondiscriminatory systems that differentiate errors based on their uncertainty origin and provide efficient methods to identify and equalize errors occurring due to model uncertainty in binary classification. Furthermore, we investigate whether algorithmic decision-aids can mitigate inconsistency among human decision-makers through a large-scale study testing novel ways to provide machine advice.

Reward functions are central in reinforcement learning (RL), guiding agents towards optimal decision-making. The complexity of RL tasks requires meticulously designed reward functions that effectively drive learning while avoiding unintended consequences. Effective reward design aims to provide signals that accelerate the agent’s convergence to optimal behavior. Crafting rewards that align with task objectives, foster desired behaviors, and prevent undesirable actions is inherently challenging. This thesis delves into the critical role of reward signals in RL, highlighting their impact on the agent’s behavior and learning dynamics and addressing challenges such as delayed, ambiguous, or intricate rewards. In this thesis work, we tackle different aspects of reward shaping. First, we address the problem of designing informative and interpretable reward signals from a teacher’s/expert’s perspective (teacher-driven). Here, the expert, equipped with the optimal policy and the corresponding value function, designs reward signals that expedite the agent’s convergence to optimal behavior. Second, we build on this teacher-driven approach by introducing a novel method for adaptive interpretable reward design. In this scenario, the expert tailors the rewards based on the learner’s current policy, ensuring alignment and optimal progression. Third, we propose a meta-learning approach, enabling the agent to self-design its reward signals online without expert input (agent-driven). This self-driven method considers the agent’s learning and exploration to establish a self-improving feedback loop

Cloud systems are composed of multiple inter-connected independent systems. These systems are complex in nature as they are made of heterogeneous components rife with complicated interactions, operate in dynamic conditions, and exhibit unpredictable behaviors. Despite all the complexity, developers of these systems are tasked to efficiently design, implement, optimize, operate, and improve these systems in a continuous fashion. A proposed way of managing the complexity for designing, implementing, and optimizing these systems is to automate these tasks. There are three major roadblocks preventing this automation from becoming reality - (i) lack of abstractions for design and implementation and design exploration of cloud systems; (ii) lack of abstractions and tooling converting user's high level design intent into actual implementations;  (iii) lack of abstractions for leveraging runtime information for optimizing cloud systems. I propose new abstractions for cloud systems, with a special focus on microservice systems, for automating developer tasks. The work I will present takes us one step closer to the vision of automating the design, implementation, and optimization of cloud systems whilst managing the inherent complexity of these systems.

As machine learning (ML) models continue to scale in size and capability, they expand the surface area for safety and privacy risks, raising concerns about model trustworthiness and responsible data use. My research uncovers and mitigates these risks. In this presentation, I will focus on the three cornerstones of trustworthy foundation models and agents: safety, privacy, and generalization. For safety, I will introduce our comprehensive benchmarks designed to evaluate trustworthiness risks in Large Language Models (LLMs) and LLM-based code agents. For privacy, I will present a solution for protecting data privacy with a synthetic text generation algorithm under differential privacy guarantees. The algorithm requires only LLMs inference API access without model training, enabling efficient safe text sharing. For generalization, I will introduce our study on the interplay between the memorization and generalization of LLMs in logical reasoning during the supervised fine-tuning (SFT) stage. Finally, I will conclude with my future research plan for assessing and improving trustworthiness in foundation model-powered ML systems.

Despite widespread enthusiasm for AI from governments worldwide, ensuring that AI adoption positively impacts society remains a challenge. Focusing on applications in criminal justice and social services, this talk will examine the significant gaps between current AI capabilities and the demands of real-world high-stakes decision-making. I will demonstrate critical shortcomings of current approaches to AI transparency, fairness, and effective human oversight, and discuss my work on addressing these issues, and its impact on policy and UK public services to date.  Concretely, I will first show how we used statistical modelling to uncover racial bias in algorithmic risk assessment instruments used for bail and sentencing decisions. Next, I will turn to my work on human-AI interaction that combines large language model speed with human accuracy to extract information from unstructured documents with high-precision. This system has served practitioners across disciplines, and is a core component of our pioneering effort to enable researcher access to UK Crown Court transcripts. I will conclude by outlining my research vision for developing urgently needed evaluation and auditing tools for human-AI systems deployed in high-risk decision-making contexts.

Collecting user data is crucial for advancing machine learning, social science, and government policies, but the privacy of the users whose data is being collected is a growing concern. Organizations often deal with a massive volume of user data on a regular basis — the storage and analysis of such data is computationally expensive. Thus developing algorithms that not only preserve formal privacy but also perform efficiently is a challenging and important necessity. Since preserving privacy inherently involves some data distortion which potentially sacrifices accuracy for smaller populations, a complementary challenge is to develop responsible privacy practices that ensure that the resulting privacy implementations are equitable. My talk will focus on Differential Privacy (DP) --- a rigorous mathematical framework that preserves the privacy of individuals in the input dataset, and explore the nuanced landscape of privacy-preserving algorithms through three interconnected perspectives: the systematic design of both time and space-efficient private algorithms, and strategic approaches to creating equitable privacy practices.

Optical data center networks (DCNs) are transforming cloud infrastructure, yet current architectures remain closed ecosystems tightly bound to specific optical hardware. In this talk, we unveil an innovative open framework that decouples software from hardware, empowering researchers and practitioners to freely explore and deploy diverse software solutions across multiple optical platforms. Building on this flexible foundation, we tackle three critical system challenges—time synchronization, routing, and transport protocols—to enable optical DCNs to achieve nanosecond-precision, high throughput, and ultra-low latency. This presentation highlights the fundamental design shifts brought by optical DCNs and demonstrates how our breakthrough solutions surpass traditional DCN performance, setting new standards for future cloud networks.

Millions of everyday users are interacting with technologies built with generative AI, such as voice assistants, search engines, and chatbots. While these AI-based systems are being increasingly integrated into modern life, they can also magnify risks, inequities, and dissatisfaction when providers deploy unreliable systems. A primary obstacle to having reliable systems is the opacity of the underlying large language models - we lack a systematic understanding of how models work, where critical vulnerabilities may arise, why they are happening, and how models must be redesigned to address them. In this talk, I will first describe my work in investigating large language models to illuminate when and how they acquire knowledge and capabilities. Then, I will describe my work on building methods to enable greater data transparency for large language models, that allows stakeholders to make sense of the information available to models. Finally, I will describe my work on understanding how this information can get distorted in large language models, and implications for building the next generation of robust AI systems.

Bringing the promise of quantum computation into reality requires not only building a quantum computer but also correctly programming it to run a quantum algorithm. To obtain asymptotic advantage over classical algorithms, quantum algorithms rely on the ability of data in quantum superposition to exhibit phenomena such as interference and entanglement. In turn, an implementation of the algorithm as a program must correctly orchestrate these phenomena in the states of qubits. Otherwise, the algorithm would yield incorrect outputs or lose its computational advantage. Given a quantum algorithm, what are the challenges and costs to realizing it as a program that can run on a physical quantum computer? In this talk, I answer this question by showing how basic programming abstractions upon which many quantum algorithms rely – such as data structures and control flow – can fail to work correctly or efficiently on a quantum computer. I then show how we can leverage insights from programming languages to re-invent the software stack of abstractions, libraries, and compilers to meet the demands of quantum algorithms. This approach holds out a promise of expressive and efficient tools to program a quantum computer and practically realize its computational advantage.

As learning algorithms become more capable, they are used to tackle an increasingly large spectrum of tasks. Their applications range from understanding images, speech and natural language to making socially impactful decisions, such as about people's eligibility for loans and jobs. Therefore, it is important to better understand both the consequences of algorithmic decisions and the mechanisms by which algorithms arrive at their outputs. Of particular interest in this regard are fairness when algorithmic decisions impact people's lives and the behavior of deep learning algorithms, the most powerful but also opaque type of learning algorithm. To this end, this thesis makes two contributions: First, we study fairness in algorithmic decision-making. At a conceptual level, we introduce a metric for measuring unfairness in algorithmic decisions based on inequality indices from the economics literature. We show that this metric can be used to decompose the overall unfairness for a given set of users into between- and within-subgroup components and highlight potential tradeoffs between them, as well as between fairness and accuracy. At an empirical level, we demonstrate the necessity for studying fairness in algorithmically controlled systems by exposing the potential for discrimination that is enabled by Facebook's advertising platform. In this context, we demonstrate how advertisers can target ads to exclude users belonging to protected sensitive groups, a practice that is illegal in domains such as housing, employment and finance, and highlight the necessity for better mitigation methods.

The second contribution of this thesis is aimed at better understanding the mechanisms governing the behavior of deep learning algorithms. First, we study the role that invariance plays in learning useful representations. We show that the set of invariances possessed by representations is of critical importance in determining whether they are useful for downstream tasks, more important than many other factors commonly considered to determine transfer performance. Second, we investigate memorization in large language models, which have recently become very popular. By training models to memorize random strings, we uncover a rich and surprising set of dynamics during the memorization process. We find that models undergo two phases during memorization, that strings with lower entropy are harder to memorize, that the memorization dynamics evolve during repeated memorization and that models can recall tokens in random strings with only a very restricted amount of information.

Neural networks optimized with generic learning objectives acquire representations that support remarkable behavioural flexibility—from learning from few examples to analogical reasoning—previously seen as uniquely human. While these artificial learning systems simulate how cognitive capacities can emerge through experience, these capacities arise from complex interactions between architecture, learning algorithm, and training data that we struggle to interpret and validate, limiting the value of neural networks as scientific models of cognition. My research addresses this epistemic challenge by connecting high-level computational properties of neural systems to their low-level mechanistic details, making these systems more interpretable and manipulable for science and practice alike. I will present two case studies demonstrating this approach: how meta-learning in neural networks can be reinterpreted through the lens of hierarchical Bayesian inference, and how sparse representations can emerge naturally through the dynamics of learning in neural networks. Through these examples, I'll illustrate how interpreting and analyzing neural networks sheds light on their emergent computational properties, laying the groundwork for a more productive account of how cognitive capacities arise in neural systems.

Traditional "one-size-fits-all" user interfaces (UIs) often fail to provide the necessary adaptability, leading to challenges in accommodating varying contexts and enhancing user capabilities. This talk explores my research on developing intelligent UIs that bridge the gap between static design and dynamic user engagement. My research focuses on facilitating the creation of intelligent UIs that support two key areas: assisting designers in building adaptive systems and capturing user behaviors to enable automatic interface adaptation. In this talk, I will focus on how to develop computational representations that embed domain-specific knowledge into AI models, providing intelligent suggestions while ensuring that designers maintain control over the design process. Additionally, I will discuss how to develop neural models that simulate and predict user behaviors, such as eye tracking on UIs, aligning interactions with users' unique abilities and preferences.

Most cognitive models are domain-specific, meaning that their scope is restricted to a single type of problem. The human mind, on the other hand, does not work like this -- it is a unified system whose processes are deeply intertwined. In this talk, I will present my ongoing work on foundation models of human cognition: models that cannot only predict behavior in a single domain but that instead offer a truly universal take on our mind. Furthermore, I outline my vision for how to use such behaviorally predictive models to advance our understanding of human cognition, as well as how they can be scaled to naturalistic environments.

The multi-layer blockchain architecture presents unique challenges, as issues in one layer can amplify or cause problems in another. These layers include the network layer, a peer-to-peer (P2P) network responsible for information dissemination; the consensus layer, where nodes reach agreement on the blockchain’s state; and the application layer, which hosts decentralized finance (DeFi) applications. My research investigates the interactions between these layers and the resulting challenges, with the goal of enhancing fairness and resilience in the blockchain ecosystem. In the first part of this talk, I will explore how financial value originating at the application layer can threaten the consensus layer, focusing on non-atomic arbitrage — arbitrage between on-chain and off-chain exchanges. I will demonstrate how this value, despite originating at the application layer, introduces centralizing forces and security vulnerabilities in the consensus layer. In the second part, I will show how these dynamics operate in the opposite direction. Specifically, I will highlight privacy flaws in Ethereum’s P2P network that threaten the consensus layer by enabling attacks targeting application layer value. As I will demonstrate, the P2P network leaks validator locations. This vulnerability allows malicious validators (i.e., consensus layer participants) to launch targeted attacks on validators handling blocks with significant application layer value and scoop the value from those blocks.

AI has made remarkable progress in recent years, enabling groundbreaking applications but also raising serious safety concerns. This talk will explore the robustness challenges in deep learning and large language models (LLMs), demonstrating how seemingly minor perturbations can lead to critical failures. I will present my research on evaluating and mitigating AI risks, including adversarial robustness, LLM jailbreak vulnerabilities, and the broader implications of AI safety. By developing rigorous benchmarks, novel evaluation methods, and foundational theoretical insights, my work aims to provide effective safeguards for AI deployment. Ultimately, I advocate for a systematic approach to AI risk mitigation that integrates technical solutions with real-world considerations to ensure the safe and responsible use of AI systems.

The ability to build an actionable representation of the environment of a robot is crucial for autonomy and prerequisite to a large variety of applications, ranging from home, service, and consumer robots to social, care, and medical robotics to industrial, agriculture and disaster response applications. Notably, a large part of the promise of autonomous robots depends on long-term operation in domains shared with humans and other agents. These environments are typically highly complex, semantically rich, and highly dynamic with agents frequently moving through and interacting with the scene. This talk presents an autonomy pipeline combining perception, prediction, and planning to address these challenges. We first present methods to detect and represent complex semantics, short-term motion, and long-term changes for real-time robot perception in a unified framework called Khronos. We then show how Dynamic Scene Graphs (DSGs) can represent semantic symbols in a task-driven fashion and facilitate reasoning about the scene, such as the prediction of likely future outcomes based on the data the robot has already collected. Lastly, we show how robots as embodied agents can leverage these actionable scene representations and predictions to complete tasks such as actively gathering data that helps them improve their world models, perception, and action capabilities fully autonomously over time. The presented methods are demonstrated on-board fully autonomous aerial, legged, and wheeled robots, run in real-time on mobile hardware, and are available as open-source software.

The prevalence and severity of online abuse are on the rise, from toxic content on social media to image-based sexual abuse, as new technologies are weaponized by people who do harm. Further, this abuse disproportionately harms people already marginalized in society, creating unacceptable disparities in safety and reinforcing oppression. Working in the areas of both computer security and privacy (S&P) and human-computer interaction (HCI), I address online abuse as the next frontier of S&P challenges. In this talk, I discuss my approach to sociotechnical threat modeling that (1) characterizes emerging S&P threats in digital safety, with particular attention to the technical and societal factors at play, (2) evaluates the existing support for online abuse, taking an ecosystem-level perspective, and (3) develops conceptual tools that bridge S&P and HCI towards societally informed S&P research. I conclude by outlining how sociotechnical security and privacy can work towards a world where all people using technology feel safe and connected.

Data privacy has become a focal point for public discourse. In response, Data protection and privacy regulations have been enacted across the world, including the GDPR and CCPA, and companies make various promises to end-users in their privacy policies. However, high profile privacy violations remain commonplace, in part because complying with privacy regulations and policies is challenging for applications and developers. This talk demonstrates how we can help developers achieve privacy compliance by designing new privacy-conscious systems and abstractions. This talk focuses on my work on Sesame (SOSP24), my system for end-to-end compliance with privacy policies in web applications. To provide practical guarantees, Sesame combines new static analysis for data leakage with advances in memory safe languages and lightweight sandboxing, as well as standard industry practices like code review. My work in this area also includes K9db (OSDI23), a privacy-compliant database that supports compliance-by-construction with GDPR-style subject access requests. By creating privacy abstractions at the systems level, we can offer applications privacy guarantees by design, in order to simplify compliance and improve end-user privacy.

Modern computing systems encounter significant challenges related to data movement in applications, such as data analytics and machine learning. Within a compute node, the physical separation of the processor from main memory necessitates retrieving data through a narrow memory bus. In big-data applications running across multiple nodes, data must be exchanged via narrow network interconnects. This movement of data —both within and across compute nodes— causes significant performance and energy overheads in modern and emerging applications. Moreover, today’s general-purpose computing stacks overlook the particular data needs of individual applications, missing crucial opportunities for untapped performance optimization. In this talk, I will present a cross-stack approach to designing application-aware computing stacks for cutting-edge applications, enabling new synergies between algorithms, systems software, and hardware. Specifically, I will demonstrate how integrating fine-grained application characteristics —such as input features, data access and synchronization patterns— across the layers of general-purpose computing stacks allows for tailoring stack components to meet the application’s specific data needs. This integration enables the stack components to work synergistically to reduce unnecessary or redundant data movement during application execution. I will present a few of my research contributions that propose hardware and software solutions for emerging applications, such as deep learning, and by capitalizing on the emerging processing-in-memory paradigm. Finally, I will conclude by outlining my future plans to design application-adaptive and sustainable computing stacks to significantly enhance performance and energy efficiency in cutting-edge applications.

In a profound sense, the law is an applied field. It exists because society needs rules to function. Even if these rules are seemingly "bright line", in limit cases they require interpretation. Even more so if the rule in question confines itself to enunciate a normative program, and leaves its implementation to the administration and the judiciary. The traditional response is hermeneutical. The legislator translates the normative intention into words. That way, it implicitly delegates spelling out what these words mean to the parties and the authorities involved in dissolving the concrete conflicts of life. This sketch of the law’s mission explains the traditional character of legal research. If a researcher adopts an "inside view", she engages in a division of labor with practicing lawyers. The quintessential product of this research is a "commentary". The researcher summarizes the state of the art thinking about a statutory provision (and maybe proposes an alternative reading). Alternatively, the researcher adopts an "outside view". In the spirit of a social scientist, she treats the law as her object of study. Typical products are more precise definitions of and empirical investigations into a class of social problems that legal rules are meant to address; or attempts at finding traces of judicial policy in the jurisprudence of a court. Large language models have the potential to deeply affect all of these strands of legal research. As the potential is more easily discernible for the "outside view", the talk will only briefly illustrate in which ways LLMs are likely to fuel this strand of legal research. It will drill deeper into the "inside view", and explain how an important part of this research, the summarization of the jurisprudence on a statutory provision (the guarantee of freedom of assembly in the German Constitution) can already today be delegated to the LLM. It is not difficult to predict that, ten years from now, legal research will look radically different.

I will present two recent joint works done with Arnaud Sangnier and Nathalie Sznajder. We study networks of processes that all execute the same finite protocol and communicate synchronously in two different ways: a process can broadcast one message to all other processes or send it to at most one other process. We study two coverability problems with a parameterised number of processes (state coverability and configuration coverability). It is already known that these problem are Ackermann-hard (and decidable) in the general case. This already holds when the processes communicate only by broadcasts. We show that when forbidding broadcasts, the two problems are EXPSPACE-complete. We also study a restriction on the protocol: a Wait-Only protocol has no state from which a process can send and receive messages. We show that without broadcasts, both problems are PTIME-complete in this restriction, and with broadcasts, state coverability is PTIME-complete and configuration coverability PSPACE-complete.

Modern machine learning (ML) has largely been driven by neural networks, delivering outstanding results not only in traditional ML applications, but also permeating into classical sciences solving open problems in physics, chemistry, and biology. This success came at a cost, as typical neural network architectures are inherently complex and their reasoning processes opaque. In domains where insights weigh more than prediction, such as modeling of systems biology, or in high-stakes decision making, such as healthcare and finance, the reasoning process of a neural network however needs to be transparent. Recently, the mechanistic interpretation of this reasoning process has gained significant attention, which is concerned with understanding the *internal reasoning process* of a network, including what information particular neurons respond to and how these specific neurons are organized into larger circuits. In this talk, I will (1) gently introduce the topic of mechanistic interpretability in machine learning, (2) show how to discover mechanistic circuits within a neural network, (3) discuss the relevance of mechanistic interpretability in real-world applications, and (4) discuss what is still missing in the field.

Declassification allows a programmer to purposfully reveal secret information. In my thesis, I will argue that treating higher-order programs explicitly and compositionally is important when dealing with declassification, and using purpose-built logical-relations semantics, we can reason about declassification in new ways while fully supporting higher-order programs. To support this claim, I will study two different styles of declassification and their semantics. In the first part of my thesis, I will show how to reason about where declassification, an intensional declassification policy, in the higher-order setting by adapting logical relations techniques developed for noninterference. This approach leads to the first compositional semantics for an intensional declassification policy in the higher-order setting. In the second part of my thesis, I turn towards a style of declassification that has already been more extensively studied in the higher-order setting using logical relation semantics: what declassification. I will present a new approach to what declassification in higher-order languages. Specifically, I will present a novel dependency analysis using a dependent type system and show that what-declassification guarantees can be obtained from this dependency analysis using logical relations semantics.

From finance and healthcare to criminal justice and transportation, various domains that require critical decisions, traditionally made by humans, have increasingly incorporated artificial intelligence (AI) systems into the decision making process. Despite unprecedented advances in AI capabilities, in most real-world contexts, AI systems do not make highly consequential decisions autonomously; instead, they operate in conjunction with a human decision maker. The promise of human-AI collaboration lies in the complementary strengths of each to lead to better decisions overall. In this thesis, I present contributions on multiple dimensions of AI-assisted decision making that range from technical methodologies to empirical findings. I start by focusing on ways that AI systems can support strategic reasoning—the ability of humans to make decisions based on expectations about others’ behavior—and I introduce algorithmic methods that support a human decision maker in allocating resources to a population of other (strategic) humans. Then, I switch focus to counterfactual reasoning—the ability to reflect on how different decisions could have led events of the past to turn out differently. In that context, I propose algorithmic methods to help a human decision maker improve their future decisions by highlighting specific past decisions that could have led to better outcomes and may serve as a good learning signal. I conclude the thesis by proposing a computational model based on counterfactual simulations to predict the extent to which people hold a human and an AI agent responsible for an outcome in cases where the two agents are working together towards a common goal. The performance of this model is evaluated through an experiment with human participants conducted online.

Deciding if two graphs are isomorphic, i.e., whether they are structurally identical, is a fundamental computational problem that has many important applications. Despite extensive efforts in the past decades, GI has remained one of only few natural problems contained in NP that are neither known to be NP-complete nor known to be contained in P. In 20216, Babai proved in a breakthrough result that GI can be solved in quasipolynomial time. In the first part of my talk, I will give an overview on recent developments on testing isomorphism. A closely related problem is that of determining the similarity between two graphs. Arguably, this problem is even more relevant for many applications such as machine learning tasks on graph-structured data. Natural approaches such as counting edge modifications required to turn one graph into the other are computationally intractable even on very restricted input graphs. As a result, various other methods have been proposed to measure similarity between graphs such as graph motif counts, i.e., measuring similarity based on counting certain patterns appearing in the graphs. In the second part of the talk, we will consider different ways of measuring similarity and discuss relations to standard heuristics for isomorphism testing that have been discovered in recent years.