Curriculum Vitae.

SheoYon Jhin

  • ACE NODE : Attentive Co-Evolving Neural Ordinary Differential Equations

    Sheo Yon Jhin, Minju Jo, Taeyong Kong, Jinsung Jeong, and Noseong Park

    Abstract

    Neural ordinary differential equations (NODEs) presented a new paradigm to construct (continuous-time) neural networks. While showing several good characteristics in terms of the number of parameters and the flexibility in constructing neural networks, they also have a couple of well-known limitations: i) theoretically NODEs learn homeomorphic mapping functions only, and ii) sometimes NODEs show numerical instability in solving integral problems. To handle this, many enhancements have been proposed. To our knowledge, however, integrating attention into NODEs has been overlooked for a while. To this end, we present a novel method of attentive dual co-evolving NODE (ACE-NODE): one main NODE for a downstream machine learning task and the other for providing attention to the main NODE. Our ACE-NODE supports both pairwise and elementwise attention. In our experiments, our method outperforms existing NODE-based and non-NODE-based baselines in almost all cases by non-trivial margins.

  • Attentive Neural Controlled Differential Equations for Time Series Classfication and Forecasting

    Sheo Yon Jhin, Heejoo Shin, Seoyoung Hong, Solhee Park, and Noseong Park

    Abstract

    Neural networks inspired by differential equations have proliferated for the past several years, of which neural ordinary differential equations (NODEs) and neural controlled differential equations (NCDEs) are two representative examples. In theory, NCDEs exhibit better representation learning capability for time-series data than NODEs. In particular, it is known that NCDEs are suitable for processing irregular time-series data. Whereas NODEs have been successfully extended to adopt attention, methods to integrate attention into NCDEs have not yet been studied. To this end, we present Attentive Neural Controlled Differential Equations (ANCDEs) for time-series classification and forecasting, where dual NCDEs are used: one for generating attention values, and the other for evolving hidden vectors for a downstream machine learning task. We conduct experiments on three real-world time-series datasets and ten baselines. After dropping some values, we also conduct experiments on irregular time-series. Our method consistently shows the best accuracy in all cases by non-trivial margins. Our visualizations also show that the presented attention mechanism works as intended by focusing on crucial information.

  • DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation

    Jungeun kim, Kookjin Lee, Dongeun Lee, Sheo Yon Jhin, and Noseong Park

    Abstract

    We present a method for learning dynamics of complex physical processes described by time-dependent nonlinear partial differential equations (PDEs). Our particular interest lies in extrapolating solutions in time beyond the range of temporal domain used in training. Our choice for a baseline method is physics-informed neural network (PINN) because the method parameterizes not only the solutions, but also the equations that describe the dynamics of physical processes. We demonstrate that PINN performs poorly on extrapolation tasks in many benchmark problems. To address this, we propose a novel method for better training PINN and demonstrate that our newly enhanced PINNs can accurately extrapolate solutions in time. Our method shows up to 72% smaller errors than state-of-the-art methods in terms of the standard L2-norm metric.

Publications

  • EXIT: Extrapolation-based Neural Controlled Differential Equations for time-series Classfication and forecasting

    Sheo Yon Jhin, Jaehoon Lee, Minju Jo, Seungji Kook, Jinsung Jeon, Jihyeon Hyeong, Jayoung Kim and Noseong Park

    Abstract

    Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series modeling with neural controlled differential equations (NCDEs) is considered as a breakthrough. In many cases, NCDE-based models not only provide better accuracy than recurrent neural networks (RNNs) but also make it possible to process irregular time-series. In this work, we enhance NCDEs by redesigning their core part, i.e., generating a continuous path from a discrete time-series input. NCDEs typically use interpolation algorithms to convert discrete time-series samples to continuous paths. However, we propose to i) generate another latent continuous path using an encoder-decoder architecture, which corresponds to the interpolation process of NCDEs, i.e., our neural network-based interpolation vs. the existing explicit interpolation, and ii) exploit the generative characteristic of the decoder, i.e., extrapolation beyond the time domain of original data if needed. Therefore, our NCDE design can use both the interpolated and the extrapolated information for downstream machine learning tasks. In our experiments with 5 real-world datasets and 12 baselines, our extrapolation and interpolation-based NCDEs outperform existing baselines by non-trivial margins.

  • LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations

    Jaehoon Lee, Jinsung Jeon, Sheo yon Jhin, Jihyeon Hyeong, Jayoung Kim, Minju Jo, Seungji Kook, and Noseong Park

    Abstract

    The problem of processing very long time-series data (e.g., a length of more than 10,000) is a long-standing research problem in machine learning. Recently, one breakthrough, called neural rough differential equations (NRDEs), has been proposed and has shown that it is able to process such data. Their main concept is to use the log-signature transform, which is known to be more efficient than the Fourier transform for irregular long time-series, to convert a very long timeseries sample into a relatively shorter series of feature vectors. However, the logsignature transform causes non-trivial spatial overheads. To this end, we present the method of LOweR-Dimensional embedding of log-signature (LORD), where we define an NRDE-based autoencoder to implant the higher-depth log-signature knowledge into the lower-depth log-signature. We show that the encoder successfully combines the higher-depth and the lower-depth log-signature knowledge, which greatly stabilizes the training process and increases the model accuracy. In our experiments with benchmark datasets, the improvement ratio by our method is up to 75% in terms of various classification and forecasting evaluation metrics.

  • Learnable Path in Neural Controlled Differential Equations

    Sheo Yon Jhin, Minju Jo, Seungji Kook, Noseong Park

    Abstract

    Neural controlled differential equations (NCDEs), which are continuous analogues to recurrent neural networks (RNNs), are a specialized model in (irregular) time-series processing. In comparison with similar models, e.g., neural ordinary differential equations (NODEs), the key distinctive characteristics of NCDEs are i) the adoption of the continuous path created by an interpolation algorithm from each raw discrete time-series sample and ii) the adoption of the Riemann--Stieltjes integral. It is the continuous path which makes NCDEs be analogues to continuous RNNs. However, NCDEs use existing interpolation algorithms to create the path, which is unclear whether they can create an optimal path. To this end, we present a method to generate another latent path (rather than relying on existing interpolation algorithms), which is identical to learning an appropriate interpolation method. We design an encoder-decoder module based on NCDEs and NODEs, and a special training method for it. Our method shows the best performance in both time-series classification and forecasting.

  • Precursor-of-Anomaly detection for irregular time series

    Sheo yon Jhin, Jaehoon Lee and Noseong Park

    Abstract

    Anomaly detection is an important field that aims to identify unexpected patterns or data points, and it is closely related to many real-world problems, particularly to applications in finance, manufacturing, cyber security, and so on. While anomaly detection has been studied extensively in various fields, detecting future anomalies before they occur remains an unexplored territory. In this paper, we present a novel type of anomaly detection, called Precursor-of-Anomaly (PoA) detection. Unlike conventional anomaly detection, which focuses on determining whether a given time series observation is an anomaly or not, PoA detection aims to detect future anomalies before they happen. To solve both problems at the same time, we present a neural controlled differential equation-based neural network and its multi-task learning algorithm. We conduct experiments using 17 baselines and 3 datasets, including regular and irregular time series, and demonstrate that our presented method outperforms the baselines in almost all cases. Our ablation studies also indicate that the multitasking training method significantly enhances the overall performance for both anomaly and PoA detection.

  • Neural networks inspired by differential equations have proliferated for the past several years, of which neural ordinary differential equations (NODEs) and neural controlled differential equations (NCDEs) are two representative examples. In theory, NCDEs exhibit better representation learning capability for time-series data than NODEs. In particular, it is known that NCDEs are suitable for processing irregular time-series data. Whereas NODEs have been successfully extended to adopt attention, methods to integrate attention into NCDEs have not yet been studied. To this end, we present attentive neural controlled differential equations (ANCDEs) for time-series classification and forecasting, where dual NCDEs are used: one for generating attention values and the other for evolving hidden vectors for a downstream machine learning task. We conduct experiments on 5 real-world time-series datasets and 11 baselines. After dropping some values, we also conduct experiments on irregular time-series. Our method consistently shows the best accuracy in all cases by non-trivial margins. Our visualizations also show that the presented attention mechanism works as intended by focusing on crucial information.

  • Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization

    Sheo yon Jhin, Seojin Kim and Noseong Park

    Abstract

    Time series forecasting has been an essential field in many different application areas, including economic analysis, meteorology, and so forth. The majority of time series forecasting models are trained using the mean squared error (MSE). However, this training based on MSE causes a limitation known as \textbf{\textit{prediction delay}}. The prediction delay, which implies the ground-truth precedes the prediction, can cause serious problems in a variety of fields, e.g., finance and weather forecasting --- as a matter of fact, predictions succeeding ground-truth observations are not practically meaningful although their MSEs can be low. This paper proposes a new perspective on traditional time series forecasting tasks and introduces a new solution to mitigate the prediction delay. We introduce a continuous-time gated recurrent unit (GRU) based on the neural ordinary differential equation (NODE) which can supervise explicit time-derivatives. We generalize the GRU architecture in a continuous-time manner and minimize the prediction delay through our time-derivative regularization. Our method outperforms in metrics such as MSE, Dynamic Time Warping (DTW) and Time Distortion Index (TDI). In addition, we demonstrate the low prediction delay of our method in a variety of datasets.

  • HINTS: Extraction of Human Insights from Time-Series without External Sources

    Sheo yon Jhin, Noseong Park

    Abstract

    Human decision-making, emotions, and collective psychology are complex factors that shape the temporal dynamics observed in financial and economic systems. Many recent time series forecasting models leverage external sources (e.g.,news and social media) to capture human factors, but these approaches incur high data dependency costs in terms of financial, computational, and practical implications. In this study, we propose HINTS, a self-supervised learning framework that extracts these latent factors endogenously from time series residuals without external data. HINTS leverages the Friedkin-Johnsen (FJ) opinion dynamics model as a structural inductive bias to model evolving social influence, memory, and bias patterns. The extracted human factors are integrated into a state-of-the-art backbone model as an attention map. Experimental results using nine real-world and benchmark datasets demonstrate that HINTS consistently improves forecasting accuracy. Furthermore, multiple case studies and ablation studies validate the interpretability of HINTS, demonstrating strong semantic alignment between the extracted factors and real-world events, demonstrating the practical utility of HINTS.

  • Point-wise Anomaly Detection via Fold-bifurcation ODE

    SheoYon Jhin, Noseong Park


    Anomaly detection in time series is essential for applications from industrial monitoring to financial risk management. Recent methods --- including forecasting error models, representation learning, augmentation, and weak-label learning --- have achieved strong results for specific anomaly types such as sudden point or gradual collective anomalies. While many prior works report window-level metrics that may mask errors, several recent methods evaluate at the point level as well. Our goal is to use a stricter point-wise protocol to make masking effects explicit. We introduce FOLD (Point-wise Anomaly Detection via fold-bifurcation), a framework that reframes detection as tracking a system’s proximity to a critical transition. FOLD extracts stress signals from a forecasting model and integrates them with a fold-bifurcation inspired ODE to produce the risk state, flagging anomalies once it crosses a threshold calibrated on normal data. This requires no anomaly labels and no additional detector training, enabling a parameter-free and efficient detection process. By modeling anomalies as stress accumulation toward a tipping point, FOLD naturally aligns with point-wise detection, providing a unifying and interpretable perspective that complements type-specific methods. Experiments on 40 benchmarks against 34 state-of-the-art baselines show that FOLD achieves competitive or superior performance, with particular strength under strict point-wise evaluation.

Failure CV

This section is where I write down my failures during my research.

2021 IJCAI (ACE-NODE) -> This paper was accepted in KDD 2021

2022 IJCAI (LEAP) -> This paper was accepted in AAAI 2023

2022 NeurIPS, ICLR 2023 NeurIPS, ICLR (CONTIME) -> This paper was accepted in KDD 2024

2023 ICLR (?) -> (?)

2024 NeurIPS (?) -> (?)

2024 ICLR (?) -> (?)

2025 ICML (?) -> (?)

2025 CIKM, WSDM (HINTS) -> This paper was accepted in AAAI 2026 (AI4TS workshop..)

2026 ICLR (?) -> (?)

Education

Feb 2017 ~ Feb 2021 B.A. in IT Engineering, Sookmyung Women's University

Feb 2021 ~ Feb 2023 M.S. in Artificial Intelligence, Yonsei University

Feb 2023 ~ Feb 2025 Ph.D. in Artificial Intelligence, Yonsei University

Feb 2025 ~ Ongoing Ph.D. in KAIST School of computing