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Fahmida Liza Piya, Rahmatollah Beheshti

The exponential growth of unstructured medical data presents a unique opportunity and challenge for advancing healthcare. Traditional methods struggle to extract meaningful insights from this complex data due to its inherent noise, ambiguity, and heterogeneity. To address these limitations, we propose a novel hybrid approach that integrates Knowledge Graphs (KGs) with Large Language Models (LLMs) within a Retrieval-Augmented Generation (RAG) framework. By leveraging the structured knowledge of KGs and the contextual understanding of LLMs, we aim to improve the precision of feature extraction and disease progression modeling. Our research focuses on refining the KG representation through advanced entity extraction and relation extraction techniques, ensuring that the KG accurately captures the semantic nuances and temporal dynamics of medical data. By integrating this enhanced KG with the RAG framework, we can derive more precise and informative insights for clinical decision-making.

Ruth-Emely Pierau

My thesis primarily focuses on hyper-spectral image generation from frequency spectrums for downstream computer vision tasks. Hyper-spectral images are images with more than three channels commonly created by special hyper-spectral cameras or from frequency spectrums of various sensing applications such as radargrams or distributed acoustic sensing (DAS) systems. The range of frequencies considered in a frequency spectrum is typically too large to map one frequency to one image channel, i.e. we generally consider a frequency spectrum of 2500 Hz. Frequencies need to be binned together in frequency bands where each band forms one image channel. Usually, frequency bands are created either by expert knowledge or trial-and-error. I research how filters can be trained to automatically select frequencies and bin them into frequency bands. My aim is to represent a variety of signal information and decrease noise. Signal representation is optimised for object detection on time-sequenced images with a set number of image channels. The object detection task consists of localising and classifying events in the generated hyper-spectral images. Events are typically types of intrusions, structural changes, or defined actions and structures, e.g. someone climbing a fence. Events and noise often share at least some frequencies and vary between application types.

Debalina R Padariya

Despite the advancement of generative model-based synthetic datasets, several challenges, such as privacy attacks and limitations of current privacy-preserving approaches, undermine the trust in this field. This research attempts to alleviate these challenges by developing a novel privacy-preserving framework that will contribute to the practical advancements of synthetic data generation across industry and the public sector.

Subash Neupane

Healthcare information is scattered across heterogeneous data sources, such as patient medical records, clinical guidelines, research literature, and online knowledge bases. Segmented information, both structured and unstructured, when integrated together using context augmentation - a knowledge fusion technique, has the ability to contextualize broader medical context. Current approaches lack knowledge aggregation that is necessary to generate personalized healthcare recommendations. I propose novel AI frameworks that leverage language models and hybrid retrieval techniques to aggregate multi source knowledge, enabling the generation of contextual and accurate medical response.

Manisha Natarajan

Efficient human-agent collaboration requires understanding each other’s capabilities and establishing appropriate reliance. My thesis focuses on optimizing performance in mixed-initiative settings, where humans and agents dynamically contribute to decisions and actions. I first explore key factors shaping human reliance on decision-support agents, then examine how agents can model this reliance to initiate actions. My proposed work aims to enable agents to jointly provide decision and action support in multi-objective tasks, using bi-directional communication to enhance collaboration.

Amina Mević

My PhD research focuses on developing a highly accurate and explainable multi-output virtual metrology system for semiconductor manufacturing. Using machine learning, we predict the physical properties of metal layers from process parameters captured by production equipment sensors. Key contributions include a model-agnostic explanatory method based on projective operators, providing insights into the most influential features for multi-output predictions and feature selection algorithms for these tasks.

Yuxing Lu

The advancements in Knowledge Graphs (KGs) and Large Language Models (LLMs) are driving transformative changes across various research fields, including metabolomics. These tools present exceptional opportunities to elucidate complex metabolic pathways and identify biomarkers essential to biological systems. My research focuses on harnessing the potential of KGs and LLMs within metabolomics, specifically making interactions between them and with biological researches. KGs, with their structured representation of metabolic entities and relationships, provide a robust foundation for managing extensive multimodal metabolomic knowledge. Recently, I developed a metabolite-centric knowledge graph and explored innovative methodologies to leverage KGs and LLMs for enhancing predictive modeling in clinical settings. My future research aims to fully exploit the capabilities of KGs and LLMs in metabolomics, advancing our understanding and applications in this field.

Yezi Liu

Throughout the development of machine learning, researchers have increasingly focused on the challenges of trustworthiness, efficiency, and scalability. Our research specifically addresses these critical aspects.

Cristiano Landi

Mobility data from smartphones, connected cars, and GPS devices are widely used for tasks such as transportation mode classification and suspicious movement detection. Time series research, a closely related field, focuses more on classification methods. Yet, Mobility Data analysis faces unique challenges like geographic transferability and limited public data due to privacy issues. My PhD work focuses on developing reusable, interpretable MD representations. I created Trajectory Interval Forest and later Geolet, a shapelet-based transformation to improve MD classification across geographic regions. Ongoing research explores improving geographic transferability and event-based trajectory clustering.

Aneesh Komanduri

Deep learning has given rise to the field of representation learning, which aims to automatically extract rich semantics from data. However, there have been several challenges in the generalization capabilities of deep learning models. Recent works have highlighted beneficial properties of causal models that are desirable for learning robust models under distribution shifts. Thus, there has been a growing interest in causal representation learning for achieving generalizability in tasks involving reasoning and planning. The goal of my dissertation is to develop theoretical intuitions and practical algorithms that uncover the nature of causal representations and their applications. In my work, I focus on causal generative modeling with an emphasis on either representation or generation. For representation learning, I investigate the disentanglement of causal representations through the lens of independent causal mechanisms. For generation tasks, I develop algorithms for counterfactual generation under weak supervision settings by leveraging recent advances in generative modeling. The proposed approaches have been empirically shown to be effective in achieving disentanglement and generating counterfactuals.

Mahammed Kamruzzaman

The rise of large language models (LLMs) has revolutionized natural language processing, offering immense capabilities across various applications. The widespread integration of these models into commonplace technology has brought to light deep concerns about the biases they encompass, which could serve to perpetuate negative preconceptions and social injustices. The scope of my research includes social biases, brand biases, the impact of personas on bias, and stereotypes in low-resource languages. My contributions aim to deepen our understanding of these biases and develop methodologies to mitigate them, enhancing the fairness and utility of LLMs across diverse global applications.

Ananya Joshi

Modern public health data contains information about changes in disease dynamics that can have significant downstream benefits if these phenomena can be identified. However, systemic data quality issues hamper automated analysis of these vast data volumes, and there is now far too much data (3-4 million data points/day) for public health data experts to inspect manually as they may have done in the past. This interdisciplinary thesis addresses practical questions about large-scale data monitoring that impact public health data users and are also reflected in the larger public health community. This work has been deployed for over a year and a half at the Delphi Research Group at Carnegie Mellon University, a national public health data curator, where data reviewers have been able to detect approximately 200 significant outbreaks, data issues, or changes in disease dynamics from 15 million new data points weekly.

Tunazzina Islam

We now live in a world where we can reach people directly through social media, without relying on traditional media such as television and radio. On the other hand, social media platforms collect vast amounts of data and create very specific profiles of different users through targeted advertising. Various interest groups, including politicians, advertisers, and stakeholders, utilize these platforms to target potential users to advance their interests by adapting their messaging. This process, known as microtargeting, relies on data-driven techniques that exploit the rich information collected by social networks about their users. Microtargeting is a double-edged sword. It enhances the relevance and efficiency of targeted content, can influence people to take action based on personal beliefs. This could be great, increasing the relevance based on users to help guide people in making better health decisions and offering them opportunities for career growth. On the other hand, it can influence people to make decisions against their own interests, foster echo chambers, and increase polarization. My research is motivated by the fact that some of these risks can be mitigated by providing transparency, identifying conflicting or harmful messaging choices, and indicating bias introduced in messaging in a nuanced way. I provide computational frameworks to analyze microtargeting patterns, which will help policymakers make better decisions. This is crucial for promoting healthy public discourse in the digital age and maintaining a cohesive society.

Joseph Marvin Imperial

Standards, or expert-defined preferences, are documented guidelines describing strict specifications for text-based content such as books, manuals, and reports. These guidelines are curated, defined, and continuously improved by domain experts in various fields, such as education, policy, and healthcare, and are used for maintaining quality. In my dissertation, I focus on evaluating and teaching large language models (LLMs) to capture standards to improve generation quality across diverse language generation tasks. I draw motivation from my preliminary published works, where I explored how open and commercial LLMs can learn complex constraints from standards in education and language assessment to produce classroom-ready narrative content. In this proposal, I also discuss the technical novelty, impact, and target contributions and highlight how this line of work can be scaled and generalized for other domains where standards are also used as a reference of quality.

Haimin Hu

Autonomous robots are becoming more versatile and widespread in our daily lives. From autonomous vehicles to companion robots for senior care, these human-centric systems must demonstrate a high degree of reliability in order to build trust and, ultimately, deliver social value. How safe is safe enough for robots to be wholeheartedly trusted by society? Is it sufficient if an autonomous vehicle can avoid hitting a fallen cyclist 99.9% of the time? What if this rate can only be achieved by the vehicle always stopping and waiting for the human to move out of the way? I argue that, for trustworthy deployment of robots in human-populated space, we need to complement standard statistical methods with clear-cut robust safety assurances under a vetted set of operation conditions. We need runtime learning to minimize the robot’s performance loss during safety-enforcing maneuvers by reducing its inherent uncertainty induced by its human peers, for example, their intent (does a human driver want to merge, cut behind, or stay in the lane?) or response (if the robot comes closer, how will the human react?). We need to close the loop between the robot’s learning and decision-making so that it can optimize efficiency by anticipating how its ongoing interaction with the human may affect the evolving uncertainty, and ultimately, its long-term performance.

Hans W. A. Hanley

Misinformation and propaganda undermine trust in institutions, spread falsehoods, and sometimes incite violence. However, recent advancements in transformer-based AI models can help combat the proliferation of disinformation globally and in real time. In this work, I propose and develop a system using these models to scalably identify, track, and analyze the spread of narratives from over 40,000 international news websites. First, by employing novel multilingual Matryoshka embeddings and hierarchical level-wise clustering, my proposed system identifies news stories, topics, and themes across these thousands of news websites. Second, by utilizing multilingual stance detection, my system assesses the biases and factual inconsistencies in news articles, enabling the identification of websites that spread propaganda or misinformation. Finally, through network inference methods, my system uncovers connections among websites disseminating slanted or false content. My approach illustrates how AI can be utilized to mitigate the global spread of harmful misinformation and propaganda.

Amar Halilovic

As the use of autonomous mobile robots expands into dynamic and complex environments, the need for them to provide understandable explanations for their actions becomes crucial. This thesis addresses the challenge of developing explainability for robot navigation by leveraging a hybrid model that combines machine learning techniques with symbolic reasoning methods. Furthermore, the thesis explores the modeling of human explanation preferences and the impact of different explanation attributes on explanation recipients' understanding, satisfaction, and trust. The goal is to integrate different explanation aspects and approaches into a unified framework to support explainable navigation in robotics.

Filippos Gouidis

The basic objective of my research work is to address the challenging problem of recognizing object states in a visual context by integrating data-driven and symbolic approaches. In particular, I focus on the Zero-shot variation of this task. The contributions made so far include the development of novel methods that exhibit state-of-the-art (SOTA) performance, the creation of a new object states dataset, the formulation of novel problems, the successful integration of low-level and high-level approaches, and comprehensive analyses that highlight the specific challenges posed by the problem.

Zahra Ghorrati

Artificial Intelligence (AI) continues to evolve rapidly, impacting numerous fields, including time series (TS) classification and human activity recognition (HAR). Despite the advancements in deep learning models, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), these models face several challenges, including the need for extensive labeled datasets, significant computational resources, and lack of interpretability. This research aims to address these limitations by developing an adaptive hierarchical deep neural network framework that integrates fuzzy logic principles and adaptive learning techniques for robust, computationally efficient, and interpretable real-time TS analysis. The reduction in the number of parameters and the efficient learning of hierarchical features mean that less training data is needed to achieve robust performance. The model's ability to generalize from hierarchical representations allows it to make effective use of smaller datasets, which is particularly advantageous in scenarios where data is limited or expensive to obtain.The proposed framework specifically targets HAR applications using data from wearable sensors.

Lea Demelius

Developing trustworthy AI requires advancing methods that meet key requirements such as privacy or fairness while maintaining strong utility, as well as understanding the intricate interdependencies between these dimensions, which often manifest as trade-offs. My PhD research focuses on differential privacy, which is widely regarded as the state-of-the-art for protecting privacy in data analysis and machine learning. I investigate the relationships between differential privacy, utility and fairness, with the goal of advancing the adoption of differentially private machine learning in real-world settings.