Microscopy techniques generate vast amounts of complex image data that in principle can be used to discover simpler, interpretable, and parsimonious forms to reveal the underlying physical structures, such as elementary building blocks in molecular systems or order parameters and phases in crystalline materials. Variational Autoencoders (VAEs) provide a powerful means of constructing such low-dimensional representations, but their performance heavily depends on multiple non-myopic design choices, which are often optimized through trial-and-error and empirical analysis. To enable automated and unbiased optimization of VAE workflows, we investigated reward-based strategies for evaluating latent space representations. Using Piezoresponse Force Microscopy data as a model system, we examined multiple policies and reward functions that can serve as a foundation for automated optimization. Our analysis shows that approximating the latent space with Gaussian Mixture Models (GMM) and Bayesian Gaussian Mixture Models (BGMM) provides a strong basis for constructing reward functions capable of estimating model efficiency and guiding the search for optimal parsimonious representations.

Disputes between two parties occur in almost all domains such as taxation, insurance, banking, healthcare, etc. The disputes are generally resolved in a specific forum (e.g., consumer court) where facts are presented, points of disagreement are discussed, arguments as well as specific demands of the parties are heard, and finally a human judge resolves the dispute by often favouring one of the two parties. In this paper, we explore the use of large language models (LLMs) as assistants for the human judge to resolve such disputes, as part of our DRAssist system. We focus on disputes from two specific domains -- automobile insurance and domain name disputes. DRAssist identifies certain key structural elements (e.g., facts, aspects or disagreement, arguments) of the disputes and summarizes the unstructured dispute descriptions to produce a structured summary for each dispute. We then explore multiple prompting strategies with multiple LLMs for their ability to assist in resolving the disputes in these domains. In DRAssist, these LLMs are prompted to produce the resolution output at three different levels -- (i) identifying an overall stronger party in a dispute, (ii) decide whether each specific demand of each contesting party can be accepted or not, (iii) evaluate whether each argument by each contesting party is strong or weak. We evaluate the performance of LLMs on all these tasks by comparing them with relevant baselines using suitable evaluation metrics.

We introduce ResPlan, a large-scale dataset of 17,000 detailed, structurally rich, and realistic residential floor plans, created to advance spatial AI research. Each plan includes precise annotations of architectural elements (walls, doors, windows, balconies) and functional spaces (such as kitchens, bedrooms, and bathrooms). ResPlan addresses key limitations of existing datasets such as RPLAN (Wu et al., 2019) and MSD (van Engelenburg et al., 2024) by offering enhanced visual fidelity and greater structural diversity, reflecting realistic and non-idealized residential layouts. Designed as a versatile, general-purpose resource, ResPlan supports a wide range of applications including robotics, reinforcement learning, generative AI, virtual and augmented reality, simulations, and game development. Plans are provided in both geometric and graph-based formats, enabling direct integration into simulation engines and fast 3D conversion. A key contribution is an open-source pipeline for geometry cleaning, alignment, and annotation refinement. Additionally, ResPlan includes structured representations of room connectivity, supporting graph-based spatial reasoning tasks. Finally, we present comparative analyses with existing benchmarks and outline several open benchmark tasks enabled by ResPlan. Ultimately, ResPlan offers a significant advance in scale, realism, and usability, providing a robust foundation for developing and benchmarking next-generation spatial intelligence systems.

Current Visual Simultaneous Localization and Mapping (VSLAM) systems often struggle to create maps that are both semantically rich and easily interpretable. While incorporating semantic scene knowledge aids in building richer maps with contextual associations among mapped objects, representing them in structured formats like scene graphs has not been widely addressed, encountering complex map comprehension and limited scalability. This paper introduces visual S-Graphs (vS-Graphs), a novel real-time VSLAM framework that integrates vision-based scene understanding with map reconstruction and comprehensible graph-based representation. The framework infers structural elements (i.e., rooms and corridors) from detected building components (i.e., walls and ground surfaces) and incorporates them into optimizable 3D scene graphs. This solution enhances the reconstructed map's semantic richness, comprehensibility, and localization accuracy. Extensive experiments on standard benchmarks and real-world datasets demonstrate that vS-Graphs outperforms state-of-the-art VSLAM methods, reducing trajectory error by an average of 3.38% and up to 9.58% on real-world data. Furthermore, the proposed framework achieves environment-driven semantic entity detection accuracy comparable to precise LiDAR-based frameworks using only visual features. A web page containing more media and evaluation outcomes is available on https://snt-arg.github.io/vsgraphs-results/.

As part of the Kingspan research team, my passions lie with the development of structural mechanics and how we can further enhance the technological development of the built environment. As a part of my masters thesis I was working on the applications of Artificial Intelligence (AI) and Machine Learning (ML) in the AEC industry. My research looked at how AI and ML are shaping the way we work, how projects are managed and delivered and most importantly, the question of whether the industry is ready to embrace this level of digital ingenuity. It's no secret that public attention on AI has rapidly increased recently, despite the fact that the technology has been slowly developing for the past 70 years. If we consider that structural mechanics has been developing accurate theoretical models for predicting strain and stresses for the past few decades and that these theoretical models require a fixed set of input parameters such as material properties, boundary conditions etc. to produce results such as deflection, stresses etc. – it comes as no surprise that this is a pretty complex and time-consuming process. Therefore, because of these complexities, experienced engineers are often needed to interpret the results for other parties.

Structural data(age, gender, weight) from Electronic Health Records (EHRs) are exploited by Computer Aided Systems (CADS) as complementary information for disease prediction. Such systems, however, fail to weight the structural data based its relevance to the disease at hand. A model capable of evaluating the significance of every element of the structural data and performing the prediction task based on the selective and weighted procedure for elements of structural data is required. Such scheme will boost more semantic automatic disease prediction task Recently multi-modal data is processed using deep learning methods like Convolutional Neural Networks(CNNs)[9], Autoencoders[6], Modified Restricted Boltzman Machine[8] etc. These methods provide richer and discriminant feature space which helps to exploit the global complementary information from available modalities, however, fail to address the problem of unequal relevance. Structural data gives statistical information about the population as a whole. This is taken into consideration more recently using graphs, providing a more semantic way of using multi-modal data[7, 4]. These methods focus more on the association between the subjects with respect to either of the modalities and then solve the tasks such as disease prediction with features from other modalities.