Seoul National University · 数学
Professor Jun-Gi Jang's research lab specializes in scalable and efficient tensor decomposition techniques for large-scale, dynamic, and irregular data, with a focus on enabling fast, accurate, and memory-efficient analysis of multidimensional data in both static and streaming environments. The lab develops advanced algorithms—such as D-Tucker, Zoom, DP AR2, and others—that address challenges in Tucker and PARAFAC2 decompositions for dense, temporal, and irregular tensors, including handling missing values and time-range queries. Their work bridges the gap between theoretical tensor methods and real-world data mining applications, such as phenotype discovery, fault detection, and time-series pattern recognition. The lab emphasizes practical efficiency, scalability, and adaptability to real-world data complexities.
Figures are computed from collected data and may differ slightly.
Given a dense tensor, how can we find latent patterns and relations efficiently? Existing Tucker decomposition methods based on Alternating Least Square (ALS) have limitations in terms of time and space since they directly handle large dense tensors to obtain the result of Tucker decomposition. Although few methods have tried to reduce their computational time by sampling tensors, sketching tensors, and efficient matrix operations, their speed and memory efficiency are limited. In this paper, we
Given a temporal dense tensor and an arbitrary time range, how can we efficiently obtain latent factors in the range? Tucker decomposition is a fundamental tool for analyzing dense tensors to discover hidden factors, and has been exploited in many data mining applications. However, existing decomposition methods do not provide the functionality to analyze a specific range of a temporal tensor. The existing methods are one-off, with the main focus on performing Tucker decomposition once for a who
Given a dense tensor, how can we efficiently discover hidden relations and patterns in static and online streaming settings? Tucker decomposition is a fundamental tool to analyze multidimensional arrays in the form of tensors. However, existing Tucker decomposition methods in both static and online streaming settings have limitations of efficiency since they directly deal with large dense tensors for the result of Tucker decomposition. In a static setting, although few static methods have tried
Given an irregular dense tensor, how can we ef-ficiently analyze it? An irregular tensor is a collection of matrices whose columns have the same size and rows have different sizes from each other. PARAFAC2 decomposition is a fundamental tool to deal with an irregular tensor in applications including phenotype discovery and trend analysis. Although several PARAFAC2 decomposition methods exist, their efficiency is limited for irregular dense tensors due to the expensive computations involved with
Given a temporal irregular tensor with missing values, how can we perform accurate decomposition for the tensor? Many real-world data can be represented as a temporal irregular tensor which is a collection of matrices whose rows corresponding to the time dimension have different sizes, but columns have the same size. PARAFAC2 decomposition is a powerful tool for analyzing an irregular tensor in many interesting applications such as phenotype discovery and fault detection. However, existing PARAF
Given multiple time series data, how can we efficiently find latent patterns in an arbitrary time range? Singular value decomposition (SVD) is a crucial tool to discover hidden factors in multiple time series data, and has been used in many data mining applications including dimensionality reduction, principal component analysis, recommender systems, etc. Along with its static version, incremental SVD has been used to deal with multiple semi-infinite time series data and to identify patterns of
How can we efficiently and accurately analyze an irregular tensor in a dual-way streaming setting where the sizes of two dimensions of the tensor increase over time? What types of anomalies are there in the dual-way streaming setting? An irregular tensor is a collection of matrices whose column lengths are the same while their row lengths are different. In a dual-way streaming setting, both new rows of existing matrices and new matrices arrive over time. PARAFAC2 decomposition is a crucial tool
How can we recommend existing bundles to users accurately? How can we generate new tailored bundles for users? Recommending a bundle, or a group of various items, has attracted widespread attention in e-commerce owing to the increased satisfaction of both users and providers. Bundle matching and bundle generation are two representative tasks in bundle recommendation. The bundle matching task is to correctly match existing bundles to users while the bundle generation is to generate new bundles th
How can we accurately identify new memory workloads while classifying known memory workloads? Verifying DRAM (Dynamic Random Access Memory) using various workloads is an important task to guarantee the quality of DRAM. A crucial component in the process is open-set recognition which aims to detect new workloads not seen in the training phase. Despite its importance, however, existing open-set recognition methods are unsatisfactory in terms of accuracy since they fail to exploit the characteristi
How can we efficiently analyze a specific time range on an irregular tensor? PARAFAC2 decomposition is widely used when analyzing an irregular tensor which consists of several matrices with different row sizes. A crucial task related to PARAFAC2 decomposition is to analyze sub-tensors corresponding to various time ranges of a given tensor, instead of analyzing the entire tensor. Although many recent works have developed efficient PARAFAC2 decomposition methods, existing PARAFAC2 decomposition me
How can we efficiently compress Convolutional Neural Network (CNN) while retaining their accuracy on classification tasks? Depthwise Separable Convolution (DSConv), which replaces a standard convolution with a depthwise convolution and a pointwise convolution, has been used for building lightweight architectures. However, previous works based on depthwise separable convolution are limited when compressing a trained CNN model since 1) they are mostly heuristic approaches without a precise underst
What are the key structures existing in a large real-world MMORPG (Massively Multiplayer Online Role-Playing Game) graph? How can we compactly summarize an MMORPG graph with hierarchical node labels, considering substructures at different levels of hierarchy? Recent MMORPGs generate complex interactions between entities inducing a heterogeneous graph where each entity has hierarchical labels. Succinctly summarizing a heterogeneous MMORPG graph is crucial to better understand its structure; howev
How can we efficiently and accurately analyze an irregular tensor in a dual-way streaming setting where the sizes of two dimensions of the tensor increase over time? What types of anomalies are there in the dual-way streaming setting? An irregular tensor is a collection of matrices whose column lengths are the same while their row lengths are different. In a dual-way streaming setting, both new rows of existing matrices and new matrices arrive over time. PARAFAC2 decomposition is a crucial tool
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