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Luisa Werner

The goal of this thesis is to address knowledge graph completion tasks using neuro-symbolic methods. Neuro-symbolic methods allow the joint utilization of symbolic information defined as meta-rules in ontologies and knowledge graph embedding methods that represent entities and relations of the graph in a low-dimensional vector space. This approach has the potential to improve the resolution of knowledge graph completion tasks in terms of reliability, interpretability, data-efficiency and robustness.

Pulkit Verma

The vast diversity of internal designs of taskable black-box AI systems and their nuanced zones of safe functionality make it difficult for a layperson to use them without unintended side effects. My dissertation focuses on developing paradigms that enable a user to assess and understand the limits of an AI system's safe operability. We develop a personalized AI assessment module that lets an AI system execute instruction sequences in simulators and answer queries about these executions. Our results show that such a primitive query-response interface is sufficient to efficiently derive a user-interpretable model of a system's capabilities.

Fiona Anting Tan

Causality expresses the relation between two arguments, one of which represents the cause and the other the effect (or consequence). Causal text mining refers to the extraction and usage of causal information from text. Given an input sequence, we are interested to know if and where causal information occurs. My research is focused on the end-to-end challenges of causal text mining. This involves extracting, representing, and applying causal knowledge from unstructured text. The corresponding research questions are: (1) How to extract causal information from unstructured text effectively? (2) How to represent extracted causal relationships in a graph that is interpretable and useful for some application? (3) How can we capitalize on extracted causal knowledge for downstream tasks? What tasks or fields will benefit from such knowledge? In this paper, I outline past and on-going works, and highlight future research challenges.

Aaquib Tabrez

Policy explanation, a process for describing the behavior of an autonomous system, plays a crucial role in effectively conveying an agent's decision-making rationale to human collaborators and is essential for safe real-world deployments. It becomes even more critical in effective human-robot teaming, where good communication allows teams to adapt and improvise successfully during uncertain situations by enabling value alignment within the teams. This thesis proposal focuses on improving human-machine teaming by developing novel human-centered explainable AI (xAI) techniques that empower autonomous agents to communicate their capabilities and limitations via multiple modalities, teach and influence human teammates' behavior as decision-support systems, and effectively build and manage trust in HRI systems.

Nisha Simon

Humans have been using stories to entertain, educate, and persuade audiences for centuries. The advent of modern AI tools in the form of Large Language Models (LLMs) such as chatGPT continues to fulfill this purpose. However while recent work has shown that LLMs can successfully be used for narrative generation, they lack coherence and can be prone to repetition and stilted language. Automated Planning can therefore be combined with Natural Language text generation to create narratives (stories) that are logical, coherent, and believable. A planning model provides scaffolding to an LLM so that the LLM's language generation is context-dependent, in order to allow users to create more coherent, logical, and believable stories in a variety of domains.

Ke Shen

The advent of powerful transformer-based discriminative language models and, more recently, generative GPT-family models, has led to notable advancements in natural language processing (NLP), particularly in commonsense reasoning tasks. One such task is commonsense reasoning, where performance is usually evaluated through multiple-choice question-answering benchmarks. Till date, many such benchmarks have been proposed and `leaderboards' tracking state-of-the-art performance on those benchmarks suggest that transformer-based models are approaching human-like performance. However, due to documented problems such as hallucination and bias, the research focus is shifting from merely quantifying accuracy on the task to an in-depth, context-sensitive probing of LLMs' generalization and robustness. To gain deeper insight into diagnosing these models' performance in commonsense reasoning scenarios, this thesis addresses three main studies: the generalization ability of transformer-based language models on commonsense reasoning, the trend in confidence distribution of these language models confronted with ambiguous inference tasks, and a proposed risk-centric evaluation framework for both discriminative and generative language models.

Naman Shah

Although state-of-the-art hierarchical robot planning algorithms allow robots to efficiently compute long-horizon motion plans for achieving user desired tasks, these methods typically rely upon environment-dependent state and action abstractions that need to be hand-designed by experts. On the other hand, non-hierarchical robot planning approaches fail to compute solutions for complex tasks that require reasoning over a long horizon. My research addresses these problems by proposing an approach for learning abstractions and developing hierarchical planners that efficiently use learned abstractions to boost robot planning performance and provide strong guarantees of reliability.

Sangwon Seo

Effective teamwork translates to fewer preventable errors and higher task performance in collaborative tasks. However, in time-critical tasks, successful teamwork becomes highly challenging to attain. In such settings, often, team members have partial observability of their surroundings, incur high cost of communication, and have trouble estimating the state and intent of their teammates. To assist a team in improving teamwork at task time, my doctoral research proposes an automated task-time team intervention system. Grounded in the notion of shared mental models, the system first detects whether the team is on the same page or not. It then generates effective interventions to improve teamwork. Additionally, by leveraging past demonstrations to learn a model of team behavior, this system minimizes the need for domain experts to specify teamwork models and rules.

Deepayan Sanyal

Infants see a selective view of the world: they see some objects with high frequency and from a wide range of viewpoints (e.g., their toys during playing) while a much larger set of objects are seen much more rarely and from limited viewpoints (e.g., objects they see outdoors). Extensive, repeated visual experiences with a small number of objects during infancy plays a big role in the development of human visual skills. Internet-style datasets that are commonly used in computer vision research do not contain the regularities that result from such repeated, structured experiences with a few objects. This has led to a dearth of models that learn by exploiting these regularities. In my PhD dissertation, I use deep learning models to investigate how regularities in an infant's visual experience can be leveraged for visual representation learning.

Célian Ringwald

Seq-to-seq transformer models have recently been successfully used for relation extraction, showing their flexibility, effectiveness, and scalability on that task. In this context, knowledge graphs aligned with Wikipedia such as DBpedia and Wikidata give us the opportunity to leverage existing texts and corresponding RDF graphs in order to extract, from these texts, the knowledge that is missing in the corresponding graphs and meanwhile improve their coverage. The goal of my thesis is to learn efficient extractors targeting specific RDF patterns and to do so by leveraging the latest language models and the dual base formed by Wikipedia on the one hand, and DBpedia and Wikidata on the other hand.

Md Maklachur Rahman

Template learning transformer trackers have achieved significant performance improvement recently due to the longdependency learning using the self-attention (SA) mechanism. However, the typical SA mechanisms in transformers adopt a less discriminative design approach which is inadequate for focusing on the most important target information during tracking. Therefore, existing trackers are easily distracted by background information and have constraints in handling tracking challenges. The focus of our research is to develop a target-focused discriminative shallow transformer tracking framework that can learn to distinguish the target from the background and enable accurate tracking with fast speed. Extensive experiments will be performed on several popular benchmarks, including OTB100, UAV123, GOT10k, LaSOT, and TrackingNet, to demonstrate the effectiveness of the proposed framework.

Elizabeth Akinyi Ondula

My research integrates stochastic epidemic models with reinforcement learning to develop effective strategies or policies to inform operational decisions. The objective is to refine policies that are attuned to diverse outbreak dynamics and to offer a tool for informed planning in real-world settings.

Mbithe Nzomo

Non-communicable diseases are on the rise globally, resulting in accelerated efforts to develop personal health monitoring systems for early detection, prediction, and prevention of diseases. This is part of the vision of precision health, an emerging paradigm that focuses on preventing disease before it strikes by encouraging people to actively monitor and work towards improving their health. A key facilitator of this is the use of wearable sensors that can collect and measure physiological data.Although many sensor-based health monitoring systems have been proposed, interoperability of health data and processes, prediction of future health states, and uncertainty management remain open challenges. This research aims to alleviate these challenges through the development of a reusable framework integrating both data-driven and knowledge-driven AI within a hybrid AI architecture.

Rashmeet Kaur Nayyar

Reinforcement Learning (RL) in complex environments presents many challenges: agents require learning concise representations of both environments and behaviors for efficient reasoning and generalizing experiences to new, unseen situations. However, RL approaches can be sample-inefficient and difficult to scale, especially in long-horizon sparse reward settings. To address these issues, the goal of my doctoral research is to develop methods that automatically construct semantically meaningful state and temporal abstractions for efficient transfer and generalization. In my work, I develop hierarchical approaches for learning transferable, generalizable knowledge in the form of symbolically represented options, as well as for integrating search techniques with RL to solve new problems by efficiently composing the learned options. Empirical results show that the resulting approaches effectively learn and transfer knowledge, achieving superior sample efficiency compared to SOTA methods while also enhancing interpretability.

Deepa Muralidhar

Artificial intelligence system architects can increase user trust by designing systems that are inherently transparent. We propose the idea of representing an AI system as an amalgamation of the AI Model (algorithms), data (input and output, including outcomes), and the user interface with visual interpretations (e.g. graphs, Venn diagrams). By designing human controls and feedback mechanisms for AI systems that allow users to exert control over them we can integrate transparency into existing user interfaces. Our plan is to design prototypes of transparent user interfaces for AI systems using well-known usability principles. By conducting surveys we will study their impact to see if these principles help the user to work with the AI system with confidence and if the user perceives the system to be adequately transparent.

Sukanya Mandal

A Smart City is one that makes better use of city data to make our communities better places to live. Typically, this has 3 components: sensing (data collection), analysis and actuation. Privacy, particularly as it relates to citizen's data, is a cross-cutting theme. A Digital Twin (DT) is a virtual replica of a real-world physical entity. Cognitive Digital Twins (CDT) are DTs enhanced with cognitive AI capabilities. Both DTs and CDTs have seen adoption in the manufacturing and industrial sectors however cities are slow to adopt these because of privacy concerns. This work attempts to address these concerns by proposing a Privacy Preserving Federated Learning (PPFL) based Cognitive Digital Twin framework for Smart Cities.

Michael S. Lee

Demonstrations are a powerful way of increasing the transparency of AI policies to humans. Though we can approximately model human learning from demonstrations as inverse reinforcement learning, we note that human learning can differ from algorithmic learning in key ways, e.g. humans are computationally limited and may sometimes struggle to understand all of the nuances of a demonstration. Unlike related work that provide demonstrations to humans that simply maximize information gain, I leverage concepts from the human education literature, such as the zone of proximal development and scaffolding, to show demonstrations that balance informativeness and difficulty of understanding to maximize human learning.

Andrii Krutsylo

In a Continual Learning setting, models are trained on data with occasional distribution shifts, resulting in forgetting the information learned before each shift. Experience Replay (ER) addresses this challenge by retaining part of the old training samples and replaying them alongside current data, improving the model's understanding of the overall distribution in training batches. The crucial factor in ER performance is the diversity of samples within batches. The impact of sample diversity across a sequence of batches is investigated, introducing a new metric and an associated approach to assess and leverage this diversity. This exploration opens up significant potential for future work, as various strategies can be devised to ensure inter-batch diversity. Achieving optimal results may involve striking a balance between this novel metric and other inherent properties of a batch or sequence.

Changhoon Kim

My doctoral research delves into the realm of generative model fingerprinting, aiming to assign responsibility for the generated images. I introduce frameworks that modify generative models to incorporate each user's distinct digital fingerprint. This ensures that every piece of generated content carries a traceable identifier linked to its originator. The primary objective of my research is to achieve optimal attribution accuracy while ensuring minimal compromise on the model's performance. Additionally, I present strategies designed to enhance robustness against common adversarial manipulations, which malicious users might employ to obscure or remove these fingerprints.

Md. Khairul Islam

The widespread use of Artificial Intelligence (AI) has highlighted the importance of understanding AI model behavior. This understanding is crucial for practical decision-making, assessing model reliability, and ensuring trustworthiness. Interpreting time series forecasting models faces unique challenges compared to image and text data. These challenges arise from the temporal dependencies between time steps and the evolving importance of input features over time. My thesis focuses on addressing these challenges by aiming for more precise explanations of feature interactions, uncovering spatiotemporal patterns, and demonstrating the practical applicability of these interpretability techniques using real-world datasets and state-of-the-art deep learning models.