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.

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.

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.

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.

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.