Model development in AI is shaped by developer decisions. While there is significant research on the opportunities and risks of multiplicity, little attention has been paid to how developer decisions impact multiplicity. My thesis focuses on (a) introducing broader frameworks to better situate and analyze developer decisions in AI, (b) identifying theoretical connections to characterize the influence of these decisions on multiplicity, and (c) operationalizing these insights across various applications, thus building responsible AI models with multiplicity.
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The PhD studies of the author focus on a human-centered and computationally-challenging interdisciplinary problem of the Stable Roommates problem and its variations. Motivated by real-world applications, and by the fact that the Stable Roommates problem does not always admit a stable solution, the goal is to develop novel computational methods to solve these problems, that are not only computationally efficient but also yield solutions that are fair, personalized, and applicable in real-world to benefit humans.
The safe deployment of artificial intelligence systems hinges on their ability to recognize and appropriately handle inputs they have not been trained for. Out-of-Distribution (OOD) detection aims to provide this capability, yet most existing methods are developed under idealized assumptions that do not hold in the real world. This thesis challenges these assumptions by systematically addressing four key practical challenges: the semantic ambiguity of unlabeled data, the presence of domain shifts and class imbalances, the scarcity of labeled training data, and the need to operate on dynamic video streams instead of static images. The core of this research is a suite of four novel deep learning frameworks, each designed to overcome one of these specific limitations. My contributions push the field of OOD detection from a laboratory problem towards a robust and practical technology, essential for building trustworthy AI.
Large language models (LLMs) have achieved remarkable success in natural language processing tasks but still struggle with complex causal and logical reasoning. Previous neuro-symbolic methods can be summarized into a two-stage framework: first translating natural language (NL) problems into symbolic language (SL) representation, and then performing the symbolic reasoning process. To facilitate this direction, we provide a comprehensive survey, summarizing two main challenges including complex logical question-answering (QA) and cross-question logical consistency, and further propose a new taxonomy. To achieve precise symbolic representation and enhance the accuracy of LLMs’ logical reasoning, we propose several effective and efficient approaches, including adaptively selecting the most suitable SL for each QA problem, a data-driven approach to determine the fine-tuning samples order, and an efficient multi-agent debate framework with sparse communication. Our future research will focus on theoretical analysis for optimal SL selection, translation refinement and robust neuro-symbolic approach to improve LLMs' reasoning.
Autonomous driving has shown significant progress in recent years. The combination of advanced sensors, ample data, and machine learning algorithms has led to the deployment of autonomous vehicles (AVs) in cities like Los Angeles, San Francisco, and Phoenix. While not all humans can drive perfectly, AVs should be able to plan, adapt, and react to environmental disturbances, including irrational human drivers. My research focuses on applying reinforcement learning (RL) techniques to validate AV-related cyber-physical systems (CPS) in realistic environments. I develop a custom RL environment that simulates highway driving scenarios with multiple vehicles. This environment includes a CPS model of adaptive cruise control (ACC), a lane-changing model (MOBIL), and an adversarial agent that learns to drive irrationally. My work extends interpretable RL techniques to continuous control tasks like autonomous driving.
Causal discovery is the task of learning a causal model from a source of information. Traditionally, the community has focused on algorithms that infer causal models from observational and/or interventional data, while alternative approaches have been only marginally explored. The proposed work aims to contribute to the theoretical foundations connecting agent-based systems with causal modeling, and to identify conditions under which newly developed causal discovery algorithms can be applied to elicit causal knowledge from agents.
The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents in an environment without collisions, subject to constraints on the lengths of paths. The real-world applications of MAPF require flexible, lifelong, robust and explainable solutions. In this study, these challenges are being addressed.
While Reinforcement Learning (RL) has demonstrated remarkable success in solving complex sequential decision-making problems, its application in real-world, safety-critical systems is hindered by its reliance on carefully engineered reward functions. Designing effective rewards is notoriously challenging and can lead to unintended or unsafe behaviors, a phenomenon known as reward hacking. Specification-guided RL has emerged as a principled alternative, leveraging formal methods to directly encode high-level objectives, safety requirements, and behavioral constraints. However, the practical utility of this approach is often limited by coarse or under-specified logical formulas and the computational challenge of enforcing safety at scale. This thesis addresses these limitations by developing a unified framework for the automated refinement, scalable enforcement, and flexible adaptation of formal specifications in RL.
Theory of Mind (ToM) enables agents to model others' mental states, but in mixed-motive games, this capacity can lead to deceptive behaviour and alignment risks. My research investigates how ToM affects strategic behaviour in partially observed games, contributing: (1) a formal model of ToM-driven manipulation in a preference elicitation task, (2) evidence that excessive ToM leads to paranoid-like overmentalisation, and (3) the Aleph-IPOMDP model, a framework for multi-agent systems that balances ToM reasoning with game-theoretic principles to prevent manipulation, deterring capable agents from deceiving. My work contributes to the understanding of deceptive AI, overcoming deception in multi-agent systems and applications to computational model of human cognition.
Conceptualisation and Implementation of Human-centric Privacy Preserving Framework for Explainable AI
PDF ↗Explainability has emerged as a pillar of Trustworthy AI for ensuring safety in high-risk application domains. However, the incorporation of explainability to boost the transparency of black-box AI systems can inadvertently introduce unforeseen vulnerabilities. Previous research has drawn attention to privacy leakage, malicious or otherwise, from explainable interfaces leading to identification of individuals and exposure of sensitive personal information. Privacy preservation methods used in response to this leakage are found to adversely affect the utility of the system, including the degradation of model accuracy and explanation quality. The proposed thesis will examine the advancement of Privacy Enhancing Technologies (PETs) in Explainable AI (XAI) while ensuring that users remain at the core of the design process. The main objectives of this research are: (1) determining defenses for privacy attacks in XAI (2) building interpretable algorithms for private models and (3) examining user requirements for privacy preserving XAI. This research is expected to yield characteristics of privacy preserving XAI, guidelines and recommendations for effectively building privacy compliant XAI while considering the diverse needs of end users. The research outcomes will enable developers and researchers in designing XAI that is safe for deployment and considers the balance between privacy, explainability and utility.
Towards Offline Imitation Learning: Strictly Batch Settings, Generalization, and Variability in Expertise
PDF ↗My doctoral research develops a unified framework for offline imitation learning (IL) that tackles three central challenges: achieving sample efficiency in strictly batch settings, ensuring robustness and generalization under dynamics shifts, and learning from demonstrations of varying quality. At the core of this work is a new paradigm for strictly offline IL based on enforcing the Markov Balance Equation (MBE), a fundamental structural property of trajectory data. Using advanced conditional density estimation, I developed two algorithms, CKIL and MBIL, which achieve state-of-the-art performance in high-dimensional continuous-control tasks. Building upon this foundation, I developed the first Distributionally Robust Offline IL framework under a stationarity constraint, enabling robustness to transition-model mismatch without requiring any additional interaction. I am now extending this direction through Robust Behavior Foundation Models (RBFMs), which aim to generalize across dynamics shifts for a wide range of tasks. Finally, I propose a variational approach for learning from crowdsourced demonstrations by inferring and accounting for demonstrator expertise. Together, these contributions yield principled and practical IL algorithms with strong performance and robustness, broadening the applicability of IL to real-world domains such as robotics, healthcare, and autonomous systems.
This perspective article discusses an emerging research direction: to what extent can foundation models yield usable structure for modeling the physical world? We offer a Markovian formulation of structured world models and outline the notion of multi-level hybrid world representations that support compositional structure. We then review and suggest possible discovery paradigms, spanning distillation, interaction-driven continual learning, and ensemble learning.
This paper surveys studies on the use of neural networks for optimization in the training-data-free setting. Specifically, we examine the dataless application of neural network architectures in optimization by re-parameterizing problems using fully connected (or MLP), convolutional, graph, and quadratic neural networks. Although MLPs have been used to solve linear programs a few decades ago, this approach has recently gained increasing attention due to its promising results across diverse applications, including those based on combinatorial optimization, inverse problems, and partial differential equations. The motivation for this setting stems from two key (possibly over-lapping) factors: (i) data-driven learning approaches are still underdeveloped and have yet to demonstrate strong results, as seen in combinatorial optimization, and (ii) the availability of training data is inherently limited, such as in medical image reconstruction and other scientific applications. In this paper, we define the dataless setting and categorize it into two variants based on how a problem instance—defined by a single datum—is encoded onto the neural network: (i) architecture-agnostic methods and (ii) architecture-specific methods. Additionally, we discuss similarities and clarify distinctions between the dataless neural network (dNN) settings and related concepts such as zero-shot learning, one-shot learning, lifting in optimization, and over-parameterization.
Machine Learning Models Assisting the Development of Antibody Therapeutics and Vaccines – an Emerging Trend
PDF ↗The development of novel effective medical treatments is one of the most important and expected beneficial effects of the AI revolution. This decade is witnessing the rise of AI models able to predict complex properties for protein-protein interactions that hold great promise in assisting in the development of antibody therapeutics and vaccines, including for diseases that long eluded us in the pursuit of an effective treatment. This paper introduces this area of research in a language accessible to an AI researcher, exploring the biological problems that can be solved by AI models, as well as the general context to make solutions feasible in practical scenarios. We survey the main current trends and works in this research area and point towards current still unsolved challenges and trade offs. We expect this paper will be extremely helpful for AI researchers trying to join the field, as well as for researchers already working in one of the subtopics that wish to have a better understanding of the general context around it.
The research trend of metacognitive AI deals with the study of artificial intelligence systems that can self-monitor and/or regulate resources. This concept has its roots in cognitive psychology studies on human metacognition. It has led to the understanding of how people monitor, control, and communicate their cognitive processes. An emerging research trend in artificial intelligence is to build systems that possess these capabilities. This paper summarizes the key ideas about metacognition from cognitive psychology, describes recent attempts to instantiate these concepts in AI systems, and discusses metacognitive capabilities observed in humans that are not thoroughly explored in AI research.
Multi-objective search (MOS) has emerged as a unifying framework for planning and decision-making problems where multiple, often conflicting, criteria must be balanced. While the problem has been studied for decades, recent years have seen renewed interest in the topic across AI applications such as robotics, transportation, and operations research, eflecting the reality that real-world systems rarely optimize a single measure. This paper surveys developments in MOS while highlighting cross-disciplinary opportunities, and outlines open challenges that define the emerging frontier of MOS research.
Marine ecosystems face increasing pressure due to climate change, driving the need for scalable, AI-powered monitoring solutions to inform effective conservation and restoration efforts. This paper examines the rapid emergence of underwater AI as a major research frontier and analyzes the factors that have transformed marine perception from a niche application into a catalyst for AI innovation. We identify three convergent drivers: i) environmental necessity for ecosystem-scale monitoring, ii) democratization of underwater datasets through citizen science platforms, and iii) researcher migration from saturated terrestrial computer vision domains. Our analysis reveals how unique underwater challenges—turbidity, cryptic species detection, expert annotation bottlenecks, and cross-ecosystem generalization—are driving fundamental advances in weakly supervised learning, open-set recognition, and robust perception under degraded conditions. We survey emerging trends in datasets, scene understanding and 3D reconstruction, highlighting the paradigm shift from passive observation toward AI-driven, targeted intervention capabilities. The paper demonstrates how underwater constraints are pushing the boundaries of foundation models, self-supervised learning, and perception, with methodological innovations that extend far beyond marine applications to benefit general computer vision, robotics, and environmental monitoring.
Autoformalization has emerged as a term referring to the automation of formalization in the context of the formalization of mathematics using interactive theorem provers (proof assistants). Its rapid development has been driven by progress in deep learning, especially large language models (LLMs). More recently, usage of the term has expanded beyond mathematics to describe tasks that involve translating natural language input into verifiable logical representations. At the same time, a growing body of research explores using LLMs to translate informal language into formal representations for reasoning, planning, and knowledge representation, but without explicitly referring to this process as autoformalization. As a result, despite addressing similar tasks, the largely independent development of these research areas has limited opportunities for shared methodologies, benchmarks, and theoretical frameworks that could accelerate progress. Our goal is to review - explicit or implicit - instances of what can be considered autoformalization and to propose a unified framework, encouraging cross-pollination between different fields to advance the development of next generation AI systems.
Conversational AI for Social Good (CAI4SG): An Overview of Emerging Trends, Applications, and Challenges
PDF ↗The integration of Conversational Agents (CAs) into daily life offers opportunities to tackle global challenges, leading to the emergence of Conversational AI for Social Good (CAI4SG). This paper examines the advancements of CAI4SG using a role-based framework that categorizes systems according to their AI autonomy and emotional engagement. This framework emphasizes the importance of considering the role of CAs in social good contexts, such as serving as empathetic supporters in mental health or functioning as assistants for accessibility. Additionally, exploring the deployment of CAs in various roles raises unique challenges, including algorithmic bias, data privacy, and potential socio-technical harms. These issues can differ based on the CA's role and level of engagement. This paper provides an overview of the current landscape, offering a role-based understanding that can guide future research and design aimed at the equitable, ethical, and effective development of CAI4SG.
This report explores the evolution and current state of neuro- symbolic artificial intelligence, an approach that integrates neural network capabilities with symbolic reasoning. We trace the historical context from early AI aspirations to modern implementations and successes, highlighting key paradigms, and other logical and semantical considerations. We argue against the “scaling is all you need” hypothesis, and point to persistent challenges in reliable symbolic reasoning with deep and large models. We conclude by suggesting that despite numerous implementation choices and the ”broad church” nature of neuro-symbolic AI, these approaches offer the most promising path towards AI systems that combine pattern recognition with robust reasoning, particularly for applications requiring structured knowledge, explainability, and trustworthiness.