Showing 64 results for Author: Ramakrishnan {ramki} Kannan
Sep, 2026
Conference Paper
Many forms of real-world data across diverse domains encode relationships among multiple objects, with networks and point cloud data being common examples. Hypergraphs naturally capture such higher-order relations, and Hypergraph Neural Networks (HGNNs) have been shown to outperform their graph-based counterparts (GNNs) on many tasks. Some recent HGNN architectures leverag…
Sep, 2026
Conference Paper
We introduce a scalable system for constructing scientific Knowledge Graphs (KGs) from neuromorphic computing literature using open-source, lightweight Large Language Models (LLMs), and for leveraging these graphs as structured retrieval substrates in a GraphRAG framework. Our pipeline integrates extraction, merging, canonicalization, and clustering to transform unstructur…
Sep, 2026
Conference Paper
Nonnegative Least Squares (NNLS) is a fundamental constrained optimization problem encountered in many applications such as image deblurring, signal processing, nonnegative matrix factorization, magnetic microscopy, and hyperspectral imaging. Active-set based methods are a common class of algorithms for solving NNLS which identify the optimal variable set of the NNLS solut…
Sep, 2026
Conference Paper
Designing performant neuromorphic models requires reasoning across neuroscience, neuromorphic computing, and machine learning, making it a natural target for cross-domain hypothesis generation. Our primary contribution is a multi-corpus knowledge graph spanning all three domains, which we show substantially increases cross-domain retrieval novelty over single-corpus baseli…
Sep, 2026
Conference Paper
Protein homology search is foundational to bioinformatics: it supports annotation transfer, structure/function inference, and evolutionary analysis over rapidly expanding sequence repositories (e.g., UniProtKB). Profile hidden Markov models (pHMMs), as implemented in HMMER, remain the most widely trusted approach because they provide statistically calibrated E-values; howe…
May, 2026
Journal
Graph neural networks (GNNs) are increasingly widely used for community detection in attributed networks. They combine structural topology with node attributes through message passing and pooling. However, their robustness or lack thereof with respect to different perturbations and targeted attacks in conjunction with community detection tasks is not well understood. To sh…
Mar, 2026
Conference Paper
Phase field (PF) simulations are computationally expensive but remain a key analysis tool to understand the complex mechanisms of additive manufacturing (AM) processes. Each PF simulation-aided analysis requires thousands of node hours on leadership-class supercomputers. One of the main goals of these analyses is the study of microstructure evolution during the build proce…
Mar, 2026
Conference Paper
Lithium-ion batteries, widely used for their durability and high energy storage, face the risk of internal short circuits leading to catastrophic thermal runaway events. These events, triggered by external stimuli like mechanical loads, pose safety concerns in applications such as electric vehicles. Detecting and understanding thermal runaway events is crucial, but physics…
Mar, 2026
Conference Paper
Deep learning architectures have achieved state-of-the-art (SOTA) performance on computer vision tasks such as object detection and image segmentation. This may be attributed to the use of over-parameterized, monolithic deep learning architectures executed on large datasets. Although such large architectures lead to increased accuracy, this is usually accompanied by a larg…
Mar, 2026
Conference Paper
The new reality of exascale computing faces many challenges in achieving optimal performance on large numbers of nodes. A key challenge is the efficient utilization of the message-passing interface (MPI), a critical component for process communication. This paper explores communication optimization strategies to harness the GPU-accelerated architectures of these supercompu…
Mar, 2026
Conference Paper
Sparse symmetric tensors are an important class of tensors, and their decompositions serve as powerful tools for revealing low-rank structures. This paper introduces SymProp, a novel approach for scaling sparse symmetric Tucker decomposition by propagating symmetry through intermediate computations. SymProp optimizes two key computational kernels: Sparse Symmetric Tensor T…
Mar, 2026
Conference Paper
Detecting anomalous events of a particular area in a timely manner is an important task. Geo-tagged social media data are useful resource for this task; however, the abundance of everyday language in them makes this task still challenging. To address such challenges, we present TopicOnTiles, a visual analytics system that can reveal information relevant to anomalous events…