Author name cluster

Akane Sano

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

1 author row

AAAI Conference 2026 Conference Paper

Uncovering Bias Paths with LLM-guided Causal Discovery: An Active Learning and Dynamic Scoring Approach

Khadija Zanna
Akane Sano

Ensuring fairness in machine learning requires understanding how sensitive attributes like race or gender causally influence outcomes. Existing causal discovery (CD) methods often struggle to recover fairness-relevant pathways in the presence of noise, confounding, or data corruption. Large language models (LLMs) offer a complementary signal by leveraging semantic priors from variable metadata. We propose a hybrid LLM-guided CD framework that extends a breadth-first search strategy with active learning and dynamic scoring. Variable pairs are prioritized for querying using a composite score combining mutual information, partial correlation, and LLM confidence, enabling more efficient and robust structure discovery. To evaluate fairness sensitivity, we introduce a semi-synthetic benchmark based on the UCI Adult dataset, embedding domain-informed bias pathways alongside noise and latent confounders. We assess how well CD methods recover both global graph structure and fairness-critical paths (e.g., sex→education→income). Our results demonstrate that LLM-guided methods, including our active, dynamically scored variant, outperform baselines in recovering fairness-relevant structure under noisy conditions. We analyze when LLM-driven insights complement statistical dependencies and discuss implications for fairness auditing in high-stakes domains.

PDF Details DOI

TMLR Journal 2025 Journal Article

Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models

Zeyu Yang
Han Yu
Peikun Guo
Khadija Zanna
Xiaoxue Yang
Akane Sano

Diffusion models have emerged as a robust framework for various generative tasks, including tabular data synthesis. However, current tabular diffusion models tend to inherit bias in the training dataset and generate biased synthetic data, which may influence discriminatory actions. In this research, we introduce a novel tabular diffusion model that incorporates sensitive guidance to generate fair synthetic data with balanced joint distributions of the target label and sensitive attributes, such as sex and race. The empirical results demonstrate that our method effectively mitigates bias in training data while maintaining the quality of the generated samples. Furthermore, we provide evidence that our approach outperforms existing methods for synthesizing tabular data on fairness metrics such as demographic parity ratio and equalized odds ratio, achieving improvements of over $10\%$. Our implementation is available at https://github.com/comp-well-org/fair-tab-diffusion.

PDF Details

NeurIPS Conference 2025 Conference Paper

CogPhys: Assessing Cognitive Load via Multimodal Remote and Contact-based Physiological Sensing

Anirudh Bindiganavale Harish
Peikun Guo
Bhargav Ghanekar
Diya Gupta
Akilesh Rajavenkatanarayan
MANOJ SHARMA
Maureen August
Akane Sano

Remote physiological sensing is an evolving area of research. As systems approach clinical precision, there is increasing focus on complex applications such as cognitive state estimation. Hence, there is a need for large datasets that facilitate research into complex downstream tasks such as remote cognitive load estimation. A first-of-its-kind, our paper introduces an open-source multimodal multi-vital sign dataset consisting of concurrent recordings from RGB, NIR (near-infrared), thermal, and RF (radio-frequency) sensors alongside contact-based physiological signals, such as pulse oximeter and chest bands, providing a benchmark for cognitive state assessment. By adopting a multimodal approach to remote health sensing, our dataset and its associated hardware system excel at modeling the complexities of cognitive load. Here, cognitive load is defined as the mental effort exerted during tasks such as reading, memorizing, and solving math problems. By using the NASA-TLX survey, we set personalized thresholds for defining high/low cognitive levels, enabling a more reliable benchmark. Our benchmarking scheme bridges the gap between existing remote sensing strategies and cognitive load estimation techniques by using vital signs (such as photoplethysmography (PPG) and respiratory waveforms) and physiological signals (blink waveforms) as an intermediary. Through this paper, we focus on replacing the need for intrusive contact-based physiological measurements with more user-friendly remote sensors. Our benchmarking demonstrates that multimodal fusion significantly improves remote vital sign estimation, with our fusion model achieving $<3~BPM$ (beats per minute) error for vital sign estimation. For cognitive load classification, the combination of remote PPG, remote respiratory signals, and blink markers achieves $86. 49$% accuracy, approaching the performance of contact-based sensing ($87. 5$%) and validating the feasibility of non-intrusive cognitive monitoring.

PDF Details

TMLR Journal 2024 Journal Article

AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis

Han Yu
Peikun Guo
Akane Sano

Time series data analysis is a critical component in various domains such as finance, healthcare, and meteorology. Despite the progress in deep learning for time series analysis, there remains a challenge in addressing the non-stationary nature of time series data. Most of the existing models, which are built on the assumption of constant statistical properties over time, often struggle to capture the temporal dynamics in realistic time series and result in bias and error in time series analysis. This paper introduces the Adaptive Wavelet Network (AdaWaveNet), a novel approach that employs Adaptive Wavelet Transformation for multi-scale analysis of non-stationary time series data. AdaWaveNet designed a lifting scheme-based wavelet decomposition and construction mechanism for adaptive and learnable wavelet transforms, which offers enhanced flexibility and robustness in analysis. We conduct extensive experiments on 10 datasets across 3 different tasks, including forecasting, imputation, and a newly established super-resolution task. The evaluations demonstrate the effectiveness of AdaWaveNet over existing methods in all three tasks, which illustrates its potential in various real-world applications.

PDF Details

TMLR Journal 2024 Journal Article

ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text

Han Yu
Peikun Guo
Akane Sano

The utilization of deep learning on electrocardiogram (ECG) analysis has brought the advanced accuracy and efficiency of cardiac healthcare diagnostics. In this work, we address a critical challenge in the field of ECG analysis with deep learning: learning robust representation without large-scale labeled datasets. We propose ECG Semantic Integrator (ESI), a novel multimodal contrastive pretraining framework that jointly learns from ECG signals and associated textual descriptions. ESI employs a dual objective function that comprises a contrastive loss and a captioning loss to develop representations of ECG data. To create a sufficiently large and diverse training dataset, we develop a retrieval-augmented generation (RAG)-based Large Language Model (LLM) pipeline, called Cardio Query Assistant (CQA). This pipeline is designed to generate detailed textual descriptions for ECGs from diverse databases. The generated text includes information about demographics and waveform patterns. This approach enables us to compile a large-scale multimodal dataset with over 660,000 ECG-text pairs for pretraining ESI, which then learns robust and generalizable representations of 12-lead ECG. We validate our approach through various downstream tasks, including arrhythmia detection and ECG-based subject identification. Our experimental results demonstrate substantial improvements over strong baselines in these tasks. These baselines encompass supervised and self-supervised learning methods, as well as prior multimodal pretraining approaches. Our work shows the potential of combining multimodal pretraining to improve the analysis of ECG signals.

PDF Details

JBHI Journal 2023 Journal Article

Psychotic Relapse Prediction in Schizophrenia Patients Using A Personalized Mobile Sensing-Based Supervised Deep Learning Model

Bishal Lamichhane
Joanne Zhou
Akane Sano

Mobile sensing-based modeling of behavioral changes could predict an oncoming psychotic relapse in schizophrenia patients for timely interventions. Deep learning models could complement existing non-deep learning models for relapse prediction by modeling the latent behavioral features relevant to prediction. However, given the inter-individual behavioral differences, model personalization might be required. In this work, we propose RelapsePredNet, a Long Short-Term Memory (LSTM) neural network-based model for relapse prediction. The model is personalized for a particular patient by using data from patients most similar to the given patient based on their demographics or baseline mental health scores. RelapsePredNet was compared with a deep learning-based anomaly detection model for relapse prediction. Additionally, we investigated if RelapsePredNet could complement ClusterRFModel (a random forest model leveraging clustering and template features proposed in prior work) in a fusion model. The CrossCheck dataset consisting of continuous mobile sensing data obtained from 63 schizophrenia patients, each monitored for up to a year, was used for our evaluations. RelapsePredNet outperformed the deep learning-based anomaly detection for relapse prediction with an F2 score of 0. 21 and 0. 52 in the full test set and the Relapse Test Set (consisting of data from patients who have had relapse only), respectively, representing a 29. 4% and 38. 8% improvement. Patients' social functioning scale (SFS) score was found to be the best personalization metric to define patient similarity. RelapsePredNet complemented the ClusterRFModel as it improved the F2 score by 26. 1% with a fusion model, resulting in an F2 score of 0. 30 in the full test set.

Details DOI

JBHI Journal 2019 Journal Article

Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks

Akane Sano
Weixuan Chen
Daniel Lopez-Martinez
Sara Taylor
Rosalind W. Picard

Unobtrusive and accurate ambulatory methods are needed to monitor long-term sleep patterns for improving health. Previously developed ambulatory sleep detection methods rely either in whole or in part on self-reported diary data as ground truth, which is a problem, since people often do not fill them out accurately. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep onset/offset using a type of recurrent neural network with long-short-term memory (LSTM) cells for synthesizing temporal information. We collected 5580 days of multimodal data from 186 participants and compared the new method for sleep/wake classification and sleep onset/offset detection to, first, nontemporal machine learning methods and, second, a state-of-the-art actigraphy software. The new LSTM method achieved a sleep/wake classification accuracy of 96. 5%, and sleep onset/offset detection $F_1$ scores of 0. 86 and 0. 84, respectively, with mean absolute errors of 5. 0 and 5. 5 min, res-pectively, when compared with sleep/wake state and sleep onset/offset assessed using actigraphy and sleep diaries. The LSTM results were statistically superior to those from nontemporal machine learning algorithms and the actigraphy software. We show good generalization of the new algorithm by comparing participant-dependent and participant-independent models, and we show how to make the model nearly realtime with slightly reduced performance.

Details DOI