GenAI4ELab/papercli-papers huggingface dataset:
Understanding Microtargeting Pattern on Social Media
Authors: Tunazzina Islam
Venue: AAAI 2025 (main)

Abstract: 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.
Data Monitoring for Large Scale Public Health Data
Authors: Ananya Joshi
Venue: AAAI 2025 (main)

Abstract: 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.
Investigating and Mitigating Undesirable Biases in Large Language Models
Authors: Mahammed Kamruzzaman
Venue: AAAI 2025 (main)

Abstract: 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.
Toward Causal Generative Modeling: From Representation to Generation
Authors: Aneesh Komanduri
Venue: AAAI 2025 (main)

Abstract: 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.
Mobility Data Representations for Spatiotemporal Tasks
Authors: Cristiano Landi
Venue: AAAI 2025 (main)

Abstract: 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.
Towards Trustworthy, Efficient, and Scalable Machine Learning
Authors: Yezi Liu
Venue: AAAI 2025 (main)

Abstract: 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.
Knowledge Graph and Large Language Model for Metabolomics
Authors: Yuxing Lu
Venue: AAAI 2025 (main)

Abstract: 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.
Human Interpretable Virtual Metrology in the Semiconductor Manufacturing
Authors: Amina Mević
Venue: AAAI 2025 (main)

Abstract: 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.
Adaptive Agents for Mixed-Initiative Human-AI Collaborations
Authors: Manisha Natarajan
Venue: AAAI 2025 (main)

Abstract: 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.
Intelligent Clinical Assistant for Personalized Responses and Clinical Summaries
Authors: Subash Neupane
Venue: AAAI 2025 (main)

Abstract: 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.
A Privacy-Preserving Framework for Generative Model-driven Synthetic Datasets
Authors: Debalina R Padariya
Venue: AAAI 2025 (main)

Abstract: 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.
Hyper-Spectral Image Generation from Frequency Spectrums
Authors: Ruth-Emely Pierau
Venue: AAAI 2025 (main)

Abstract: 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.
Advancing Feature Extraction in Healthcare through the Integration of Knowledge Graphs and Large Language Models
Authors: Fahmida Liza Piya; Rahmatollah Beheshti
Venue: AAAI 2025 (main)

Abstract: 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.
Understanding GenAI for Teaching and Learning in Secondary Classrooms
Authors: Heidi Reichert
Venue: AAAI 2025 (main)

Abstract: Large Language Models (LLMs) and Generative AI (GenAI) have markedly changed the landscape of many fields, including education. While these tools have significant capabilities, they also require understanding to effectively and responsibly use them. Additionally, little work has been done to evaluate how these tools can best benefit education at the secondary level, with design insights from instructors. My work focuses on informing secondary instructors of these tools, receiving their input on how to make these tools work best for them, and finally using this input to create and evaluate an in-class Retrieval-Augmented Generation (RAG)-based chatbot for their students to use to improve learning outcomes. This work aims to bridge the gap between the latest in computing technology and secondary education classrooms.
Towards Autonomous Network Management: AI-Driven Framework for Intelligent Log Analysis, Troubleshooting and Documentation
Authors: Shaghayegh Shajarian
Venue: AAAI 2025 (main)

Abstract: As modern network management grows increasingly complex, administrators are tasked with navigating vast volumes of log data, often resulting in inefficiencies, errors, and operational challenges. My doctoral research addresses these pressing issues by leveraging advanced AI techniques to minimize human intervention and pave the way for fully automated network operations. I propose a novel AI-driven framework that integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) and a human-in-the-loop process to effectively automate key network management tasks, including log analysis, troubleshooting recommendations, and documentation generation. By enhancing the accuracy and efficiency of these tasks, this study aims to improve network reliability, reduce operational complexity, and contribute to the evolution of self-running networks.
Reliable Uncertainty Quantification in Machine Learning via Conformal Prediction
Authors: Yuanjie Shi
Venue: AAAI 2025 (main)

Abstract: Deploying machine learning (ML) models in high-stakes domains such as healthcare and autonomous systems requires reliable uncertainty quantification (UQ) to ensure safe and accurate decision-making. 
Conformal prediction (CP) offers a robust, distribution-agnostic framework for UQ, providing valid prediction sets that guarantee a specified coverage probability. 
However, existing CP methods are often limited by assumptions that are violated in real-world scenarios, such as non-i.i.d. data, and by a lack of integration with modern machine learning workflows, particularly in large generative models. 
This research aims to address these limitations by advancing CP techniques to operate effectively in non-i.i.d. settings, improving predictive efficiency without sacrificing theoretical guarantees, and integrating CP directly into model training processes. 
These developments will enhance the practical applicability of CP for a wide range of ML tasks, enabling more reliable and interpretable models in high-stakes applications.
Developing LLM-Powered Trustworthy Agents for Personalized Learning Support
Authors: Benyamin Tabarsi
Venue: AAAI 2025 (main)

Abstract: Large Language Models (LLMs) have shown promise in educational applications, but challenges such as hallucinations, lack of contextual relevance, and limited personalization impede their practical adoption. To address these issues, my research introduces MerryQuery, an LLM-powered educational agent that integrates Retrieval-Augmented Generation (RAG), rule-based content control, and Reinforcement Learning from Human Feedback (RLHF). The system features a dynamic learning profile module for adaptive personalization and a multi-step verification framework that cross-checks responses against external sources to enhance trustworthiness. A functional prototype of MerryQuery is being piloted in a real-world classroom. Preliminary results demonstrate improved response reliability and student understanding.
Design and Evaluation of a Generative Artificial Intelligence-Based Tool for Students with Learning Disabilities in Kenya
Authors: G. Mercy Wairimu
Venue: AAAI 2025 (main)

Abstract: Students with learning disabilities (LDs) face significant challenges in key academic areas such as reading comprehension, cognitive organization, self-expression, mathematics, and handwriting. These difficulties increase their susceptibility to discrimination and mental health related issues. Although existing studies have primarily focused on AI’s diagnostic capabilities, there is limited research examining how Generative AI (GenAI) can be utilized to produce measurable learning outcomes and enhance learning experiences for students with LDs. Moreover, GenAI is increasingly gaining prominence in educational settings. Therefore, the relationship between GenAI tools, LDs, and instructional methods needs to be further examined. This research aims to develop a comprehensive theoretical framework for helping design and implement tools specifically tailored to the unique needs of students with LDs. A prototype based on this framework will be implemented in selected educational settings to assess its effectiveness in improving learning outcomes and providing targeted support to students with LDs. The prototype will provide mobile phone integration to ensure scalability and enhance educational accessibility. The expected findings will contribute to the promotion of more inclusive learning environments for students with LDs.
Developing Multimodal Healthcare Foundation Model: From Data-driven to Knowledge-enhanced
Authors: Xiaochen Wang
Venue: AAAI 2025 (main)

Abstract: Foundation models in general domains have leveraged multimodal knowledge graphs to great effect, yet the healthcare sector lacks such comprehensive structures, presenting a significant gap in current research. Based on previous exploration with pure data-driven approaches, this proposal describes a two-stage project aiming to enhance multimodal healthcare foundation model with domain knowledge. The first stage is to construct a robust multimodal healthcare knowledge graph based on established healthcare taxonomies, such as UMLS, and enriched with data from multimodal clinical databases like MIMIC-CXR. This knowledge graph will incorporate medical images as cross-modal instances linked to healthcare terminologies, enhancing the depth and applicability of the graph. In the second stage, the knowledge graph will serve as a foundational tool in training healthcare foundation models with enhanced capabilities, particularly in reducing hallucination and managing concept ambiguity through the novel use of reinforcement learning techniques like Direct Preference Optimization (DPO). This research is expected to make significant contributions to the domain of healthcare AI by enabling more accurate, reliable, and explainable AI-driven diagnostics and interventions.
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks
Authors: Ximing Wen
Venue: AAAI 2025 (main)

Abstract: Pretrained transformer-based Language Models (LMs) are well-known for their ability to achieve significant improvement on NLP tasks, but their black-box nature, which leads to a lack of interpretability, has been a major concern. My dissertation focuses on developing intrinsically interpretable models when using LMs as encoders while maintaining their superior performance via prototypical networks. I initiated my research by investigating enhancements in performance for interpretable models of sarcasm detection. My proposed approach focuses on capturing sentiment incongruity to enhance accuracy while offering instance-based explanations for the classification decisions. Later, we develop a novel white-box multi-head graph attention-based prototypical framework designed to explain the decisions of text classification models without sacrificing the accuracy of the original black-box LMs. In addition, I am working on extending the attention-based prototypical framework with contrastive learning to redesign an interpretable graph neural network for document classification, aiming to enhance both the interpretability and performance of the model in document classification.
Kernel Representation Learning for Time Sequence: Algorithm, Theory, and Applications
Authors: Kunpeng Xu
Venue: AAAI 2025 (main)

Abstract: Time sequences are essential in fields such as finance, healthcare, and environmental science, where understanding temporal dependencies and making accurate predictions are crucial. These sequences often exhibit complexities like nonlinearity, noise, and concept drift. Traditional models struggle to capture the intricate dynamics of multivariate and co-evolving sequences, particularly in contexts where relationships between variables shift unpredictably. This thesis introduces a range of Kernel Representation Learning (KRL) methodologies to address these challenges. We develop kernel self-representation learning to capture the temporal dependencies and hidden structures, while identifying concept drift in co-evolving sequences. Additionally, we explore theoretical connections between KRL and advanced deep-learning models. The proposed methods are validated through real-world applications, showing improvements in predictive accuracy, interpretability, and robustness.
The Mainstays of Trustworthy Machine Learning
Authors: Chhavi Yadav
Venue: AAAI 2025 (main)

Abstract: While machine learning (ML) models of today have the potential to be useful in many societal applications, they also harbor the potential for great harm, be it perpetuating biases or compromising privacy.  To prevent these harms, many (evolving) regulatory guardrails have been put in place; for instance European Union's GDPR and Biden's Executive Order which demand explainability, privacy, fairness and so on from models deployed in societal applications. Yet, most technical solutions in the Trustworthy ML literature which claim to meet these regulatory requirements are brittle and often fail at the task in hand. To this end, my research aims to make the field of Trustworthy ML reliable using mainstay concepts of Measurement, Mitigation and Maintenance. With these concepts, I develop end-to-end solutions for trustworthy ML by (1)  exploring the limitations of existing approaches and (2) providing principled novel solutions exploiting interconnections with cryptography.
Advancing Intelligent Software Development and Trustworthy Models Through the Synergy of Software Engineering and LLMs
Authors: Guanqun Yang
Venue: AAAI 2025 (main)

Abstract: Integrating Large Language Models (LLMs) into software engineering unlocks new opportunities to automate manual processes but raises challenges around reliability, safety, and scalability. My research centers on this synergy, with two key objectives: first, harnessing LLMs to solve software engineering tasks traditionally dependent on labor-intensive, domain-specific methods, and second, applying robust software engineering principles to improve LLM safety and performance. This dual focus creates a powerful feedback loop, where LLMs drive innovation while engineering rigor ensures these systems meet the high standards required for real-world applications.