Faculty Profiles - SAIGO HIROTO

Information

写真a

SAIGO HIROTO

Organization

Faculty of Information Science and Electrical Engineering Department of Informatics Associate Professor
Joint Graduate School of Mathematics for Innovation （Concurrent）
School of Sciences Department of Physics（Concurrent）
Graduate School of Information Science and Electrical Engineering Department of Information Science and Technology（Concurrent）

Contact information

Tel

0928023783

Profile

His research interest is in developing methods for data mining and artificial intelligence, and applying them to problems in biology and chemistry. He also serves as a program committee member for international conferences in bioinformatics and machine learning.

Homepage

http://www.i.kyushu-u.ac.jp/~saigo/index_ja.html

External link

Research Areas

Informatics / Life, health and medical informatics
Informatics / Statistical science
Informatics / Intelligent informatics

Degree

Doctor of Informatics

Research History

2010-2015　九州工業大学（准教授） 2008-2010 Max Planck Institute for Informatics, Germany (Research Scientist) 2006-2008 Max Planck Institute for Biological Cybernetics, Germany (Research Scientist)

Education

Kyoto University 情報学研究科

2001.4 - 2006.3

Research Interests・Research Keywords

Research theme： Machine Learning for High-Level Radioactive Waste Management: An Approach Based on Control of High-Temperature Multiphase Melts

Keyword： machine learning, high-level radioactive waste, high-temperature multiphase melts

Research period： 2023.4
Research theme： A machine learning approach to automatic design of genes, proteins and chemical compounds

Keyword： machine learning, protein squence, chemical compound, design

Research period： 2022.9
Research theme： Development of machine learning methods towards manufacturing informatics

Keyword： machine learning, data mining, statistics

Research period： 2018.4
Research theme： Development of a GWAS method　that considers interaction among genetic factors and environmental factors

Keyword： GWAS, interaction

Research period： 2013.3
Research theme： Development and application of statistical learning methods to the problems associated with Human Immunodeficiency Virus (HIV).

Keyword： HIV, statistical learning, pattern mining

Research period： 2008.6
Research theme： Integration of frequent pattern mining with machine learning algorithms

Keyword：頻出パターンマイニング、ブースティング、線形計画法、SVM

Research period： 2006.6
Research theme： Development of kernel methods for detecting remote homology between protein sequences.

Keyword： kernel methods, protein homology detection, alignment, SVM

Research period： 2002.4 - 2006.3

Awards

奨励賞

2007.6 人工知能学会奨励賞
Best Paper Award

2006.6 Mining and Learning with Graphs Committee 論文賞

Papers

Einstein-Roscoe regression for the slag viscosity prediction problem in steelmaking Reviewed International journal

@Saigo, H., Bahadur, K.C.D, @Saito, N.

Scientific Reports 12 2022.4

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Language：English Publishing type：Research paper (scientific journal)

Other Link： https://www.nature.com/articles/s41598-022-10278-w
Automatically mining relevant variable interactions via sparse Bayesian learning Reviewed International journal

#Yafune, R., #Sakuma, D., Tabei, Y., @Saito, N., @Saigo, H.

International Conference on Pattern Recognition 2021.1

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Language：English Publishing type：Research paper (international conference proceedings)
KDE: a Kernel-based approach to detecting high-order genetic Epistasis Reviewed International journal

Kodama, K., Saigo, H.

The 27th International Conference on Genome Informatics (GIW2016) 2016.10

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Protein Structure Comparison Based on 3D Molecular Visualization Images Reviewed International journal

Suryanto, C. H., Saigo, H., Fukui, K.

2016.8

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Extracting sets of chemical substructures and protein domains governing drug-target interactions Reviewed International journal

Yamanishi, Y., Pauwels, E., Saigo, H., Stoven, V.

51 ( 5 ) 1183 - 1194 2011.5

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Learnig from past treatments and their outcome improves prediction of in vivo response t anti-HIV therapy Reviewed International journal

Saigo, H., Altmann, A., Bogojeska, J., Mueller, F., Nowozin, S., and Lengauer, T.

10 ( 1 ) 2011.1

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Reaction graph kernels that predict EC numbers of unknown enzymatic reactions in the secondary metabolism of plant Reviewed International journal

Saigo, H., Hattori, M., Kashima, H., and Tsuda, K.

Asia Pacific Bioinformatics Conference (APBC2010) 2010.1

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gBoost: A mathematical programming approach to graph classification and regression Reviewed International journal

Saigo, H., Nowozin, S., Kadowaki, T., Kudo, T., and Tsuda, K.

75 ( 1 ) 69 - 89 2009.4

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Iterative Subgraph Mining for Principal Component Analysis Reviewed International journal

Saigo, H. and Tsuda, K.

IEEE International Conference on Data Mining (ICDM2008) 2008.12

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Partial Least Squares Regression for Graph Mining Reviewed International journal

Saigo, H., Kraemer, N. and Tsuda, K.

ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2008) 2008.8

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Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism Reviewed International journal

Saigo, H., M. Hattori and K. Tsuda:

NIPS Workshop on Machine Learning in Computational Biology, 2007.12

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Graph boosting for molecular QSAR analysis Reviewed International journal

Saigo, H., Kadowaki, T., Kudo, T. and Tsuda, K.

NIPS Workshop on Machine Learning in Computational Biology, 2006.12

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A Linear Programming Approach for Molecular QSAR analysis Reviewed International journal

Saigo, H., Kadowaki, T. and Tsuda, K.

International Workshop on Mining and Learning with Graphs (MLG2006) 2006.9

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Optimizing amino acid substitution matrices with a local alignment kernel Reviewed International journal

Saigo, H., Vert J.-P. and Akutsu, T.

7 ( 246 ) 1 - 12 2006.5

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Functional census of mutation sequence spaces: The example of p53 cancer rescue mutants Reviewed International journal

Danziger, S. A., Swamidass, S. J., Zeng, J., Dearth, L. R., Lu, Q., Cheng, J. H., Cheng, J. L., Hoang, V. P., Saigo, H., Luo, R., Baldi, P., Brachmann, R. K. and Lathrop, R. H.

3 ( 2 ) 114 - 125 2006.4

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A novel representation of protein sequences for prediction of subcellular location using support vector machines Reviewed International journal

Matsuda, S., Vert, J.-P., Saigo, H., Ueda, N., Toh, H. and Akutsu, T.

14 2804 - 2813 2005.1

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Graph Kernels for Chemical Informatics Reviewed International journal

Ralaivola, L., Swamidass, J. S., Saigo, H. and Baldi, P.

18 ( 8 ) 1093 - 1110 2005.1

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Multi-level PEnet: A Robust Three-Stage Model for Parameter Estimation in Non-Gaussian Noise-Driven Stochastic Differential Equations

Li, SY; Cheng, YZ; Ruan, Y; Saigo, H

NONLINEAR DYNAMICS 2025.5 （ ISSN:0924-090X eISSN:1573-269X ）

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DOI： 10.1007/s11071-025-11318-6

Web of Science
Benchmarking a Wide Range of Unsupervised Learning Methods for Detecting Anomaly in Blast Furnace

Itakura K., Bahadur D., Saigo H.

International Conference on Pattern Recognition Applications and Methods 1 641 - 650 2024 （ ISBN:9789897586842 ）

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Publisher：International Conference on Pattern Recognition Applications and Methods

Steel plays important roles in our daily lives, as it surrounds us in the form of various products. Blast furnace, one of the main facility in steel production process, is traditionally monitored by skilled workers to prevent incidents. However, there is a growing demand to automate the monitoring process by leveraging machine learning. This paper focuses on investigating the suitability of unsupervised learning methods for detecting anomalies in blast furnaces. Extensive benchmarking is conducted using a dataset collected from blast furnaces, encompassing a wide range of unsupervised learning methods, including both traditional approaches and recent deep learning-based techniques. The computational experiments yield results that suggest the effectiveness of traditional methods over deep learning-based methods. To validate this observation, additional experiments are performed on publicly available non time series datasets and complex time series datasets. These experiments serve to confirm the superiority of traditional methods in handling non time series datasets, while deep learning methods exhibit better performance in dealing with complex time series datasets. We have also discovered that dimensionality reduction before anomaly detection is beneficial in eliminating outliers and effectively modeling the normal data points in the blast furnace dataset.

DOI： 10.5220/0012310800003654

Scopus
A Branch-and-Bound Approach to Efficient Classification and Retrieval of Documents

Ii K., Saigo H., Tabei Y.

International Conference on Pattern Recognition Applications and Methods 1 205 - 214 2024 （ ISBN:9789897586842 ）

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Publisher：International Conference on Pattern Recognition Applications and Methods

Text classification and retrieval have been crucial tasks in natural language processing. In this paper, we present novel techniques for these tasks by leveraging the invariance of feature order to the evaluation results. Building on the assumption that text retrieval or classification models have already been constructed from the training documents, we propose efficient approaches that can restrict the search space spanned by the test documents. Our approach encompasses two key contributions. The first contribution introduces an efficient method for traversing a search tree, while the second contribution involves the development of novel pruning conditions. Through computational experiments using real-world datasets, we consistently demonstrate that the proposed approach outperforms the baseline method in various scenarios, showcasing its superior speed and efficiency.

DOI： 10.5220/0012310600003654

Scopus
pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model Invited Reviewed International journal

Pratyush, P., Pokharel, S., @Saigo, H., KC.D.B.

BMC Bioinformatics 24 ( 1 ) 2023.2

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DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction (vol 21, 63, 2020)

Thapa, N; Chaudhari, M; McManus, S; Roy, K; Newman, RH; Saigo, H; Kc, DB

BMC BIOINFORMATICS 23 ( 1 ) 349 2022.8 （ ISSN:1471-2105 ）

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After publication of this supplement article [1], it is requested to correct the below errors in the article: On page 1, the Result of Abstract should be changed to: Results: Using an independent test set of experimentally identified succinylation sites, our method achieved efficiency scores of 79%, 68.7% and 0.27 for sensitivity, specificity and MCC respectively, with an area under the receiver operator characteristic (ROC) curve of 0.8. In side-by-side comparisons with previously described succinylation site predictors, DeepSuccinylSite produces similar or better results compared to the other state-of-the-art predictors. On page 7, Last paragraph on right should be changed from Consequently, DeepSuccinylSite achieved a significantly higher performance as measured by MCC. Indeed, DeepSuccinylSite exhibited an ~ 62% increase in MCC when compared to the next highest method, GPSuc. to: Consequently, DeepSuccinylSite achieved an MCC score (at decision boundary of 0.5) on par with the top performingmethod, GPSuc. On page 2, in Table 1, the negative data of Independent Test should be 2977 rather than 254. On page 8, in Table 6, the MCC data of DeepSuccinylSite should be 0.27 rather than 0.48.

DOI： 10.1186/s12859-022-04844-2

Web of Science

Scopus

PubMed
Sparse nonnegative interaction models Reviewed International journal

#Takayanagi, M., Tabei, Y., @Suzuki, E., @Saigo, H.

IEEE Access 2021.8

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DOI： 10.1109/ACCESS.2021.3099473

Other Link： https://ieeexplore.ieee.org/document/9493878
Topic modeling for sequential documents based on hybrid inter-document topic dependency Reviewed International journal

#Li, W. and @Saigo, H. and Tong, E. and @Suzuki, E.

Journal of Intelligent Information Systems, 56 ( 3 ) 453 - 458 2021.6

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DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins Reviewed International journal

Chaudhari, M., Thapa, N., S., Roy, K., Newman, R.H., @Saigo, H., KC, D.B.

Molecular Omics 2020.10

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Context-Aware Latent Dirichlet Allocation for Topic SegmentationWenbo Li,?Tetsu Matsukawa,?Hiroto Saigo,?Einoshin Suzuki: Reviewed International journal

#Wenbo Li,?Tetsu Matsukawa,?Hiroto Saigo,?Einoshin Suzuki

PAKDD 2020.5

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DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction, Reviewed International journal

Thapa, N., Chaudhari, M., McManus, S., Roy, K., Newman, R.H., Saigo, H., KC, D.B.

BMC Bioinformatics 2020.4

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RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites Reviewed International journal

Al-barakati, H.J., Thapa, N., Saigo, H., Roy, K., Newman, R.H., Bahadur, K.C.D.

Computational and Structural Biotechnology Journal 2020.2

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SVM-GlutarySite: A support vector machine-based prediction of Glutarylation sites from protein sequences Reviewed International journal

Albarakati, H., Saigo, H., Newman, R.H., KC, D.B.

Joint GIW/ABACBS-2019 Bioinformatics Conference 2019.9

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RF-GlutarySite: a random forest predictor for glutarylation sites Reviewed International journal

Al-barakati, H.J., @Saigo, H., Newman, R.H., Bahadur, K.C.D.

Molecular Omics ( 15 ) 189 - 204 2019.4

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DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction Reviewed International journal

Thapa, N., Chaudhari, M., McManus, S., Roy, K., Newman, R.H., Saigo, H., KC, D.B.

MCBIOS 2019.3

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Entire regularization path for sparse nonnegative interaction model Reviewed International journal

#Takayanagi, M., Tabei, Y., Saigo, H.

ICDM 2018.11

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Structural Class Classification of 3D Protein Structure Based on Multi-View 2D Images Reviewed

Chendra Hadi Suryanto, Hiroto Saigo, Kazuhiro Fukui

IEEE/ACM Transactions on Computational Biology and Bioinformatics 15 ( 1 ) 286 - 299 2018.1

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Computing similarity or dissimilarity between protein structures is an important task in structural biology. A conventional method to compute protein structure dissimilarity requires structural alignment of the proteins. However, defining one best alignment is difficult, especially when the structures are very different. In this paper, we propose a new similarity measure for protein structure comparisons using a set of multi-view 2D images of 3D protein structures. In this approach, each protein structure is represented by a subspace from the image set. The similarity between two protein structures is then characterized by the canonical angles between the two subspaces. The primary advantage of our method is that precise alignment is not needed. We employed Grassmann Discriminant Analysis (GDA) as the subspace-based learning in the classification framework. We applied our method for the classification problem of seven SCOP structural classes of protein 3D structures. The proposed method outperformed the k-nearest neighbor method (k-NN) based on conventional alignment-based methods CE, FATCAT, and TM-align. Our method was also applied to the classification of SCOP folds of membrane proteins, where the proposed method could recognize the fold HEM-binding four-helical bundle (f.21) much better than TM-Align.

DOI： 10.1109/TCBB.2016.2603987
CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes Reviewed International journal

Clarence White, Hamid D. Ismail, Hiroto Saigo, K. C. Dukka B.

BMC Bioinformatics 18(16): 221-232 2017.12

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RF-NR: Random forest based approach for improved classification of Nuclear Receptors Reviewed International journal

Ismail, H.D., Saigo, H., Bahadur, K.C.D.

IEEE Transactions on Computational Biology and Bioinformatics 2017.11

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The Nuclear Receptor (NR) superfamily plays an important role in key biological, developmental and physiological processes. Developing a method for the classification of NR proteins is an important step towards understanding the structure and functions of the newly discovered NR protein. The recent studies on NR classification are either unable to achieve optimum accuracy or are not designed for all the known NR subfamilies. In this study we developed RF-NR, which is a Random Forest based approach for improved classification of nuclear receptors. The RF-NR can predict whether a query protein sequence belongs to one of the eight NR subfamilies or it is a non-NR sequence. The RF-NR uses spectrum-like features namely: Amino Acid Composition, Di-peptide Composition and Tripeptide Composition. Benchmarking on two independent datasets with varying sequence redundancy reduction criteria, the RF-NR achieves better (or comparable) accuracy than other existing methods. The added advantage of our approach is that we can also obtain biological insights about the important features that are required to classify NR subfamilies.
CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes Reviewed International journal

Clarence White, Hamid D. Ismail, Hiroto Saigo, K. C. Dukka B.

INCOB2017 2017.9

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KDSNP: A kernel-based approach to detecting high-order SNP interactions Reviewed

Kento Kodama, Hiroto Saigo

Journal of Bioinformatics and Computational Biology 14 ( 5 ) 1 - 16 2016.10

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Despite the accumulation of quantitative trait loci (QTL) data in many complex human diseases, most of current approaches that have attempted to relate genotype to phenotype have achieved limited success, and genetic factors of many common diseases are yet remained to be elucidated. One of the reasons that makes this problem complex is the existence of single nucleotide polymorphism (SNP) interaction, or epistasis. Due to excessive amount of computation for searching the combinatorial space, existing approaches cannot fully incorporate high-order SNP interactions into their models, but limit themselves to detecting only lower-order SNP interactions. We present an empirical approach based on ridge regression with polynomial kernels and model selection technique for determining the true degree of epistasis among SNPs. Computer experiments in simulated data show the ability of the proposed method to correctly predict the number of interacting SNPs provided that the number of samples is large enough relative to the number of SNPs. For cases in which the number of the available samples is limited, we propose to perform sliding window approach to ensure suffciently large sample/SNP ratio in each window. In computational experiments using heterogeneous stock mice data, our approach has successfully detected subregions that harbor known causal SNPs. Our analysis further suggests the existence of additional candidate causal SNPs interacting to each other in the neighborhood of the known causal gene.

DOI： 10.1142/S0219720016440030
Scalable Partial Least Squares Regression on Grammar- Compressed Data Matrices Reviewed International journal

Tabei, Y., Saigo, H., Yamanishi, Y., Puglisi, S.

The 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2016) 2016.8

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Mining Discriminative Patterns from Graph Data with Multiple Labels and Its Application to Quantitative Structure-Activity Relationship (QSAR) Models Reviewed

Zheng Shao, Yuya Hirayama, Yoshihiro Yamanishi, Hiroto Saigo

JOURNAL OF CHEMICAL INFORMATION AND MODELING 55 ( 12 ) 2519 - 2527 2015.12

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Graph data are becoming increasingly common in machine learning and data mining, and its application field pervades to bioinformatics and cheminformatics. Accordingly, as a method to extract patterns from graph data, graph mining recently has been studied and developed rapidly. Since the number of patterns in graph data is huge, a central issue is how to efficiently collect informative patterns suitable for subsequent tasks such as classification or regression. In this paper, we consider mining discriminative subgraphs from graph data with multiple labels. The resulting task has important applications in cheminformatics, such as finding common functional groups that trigger multiple drug side effects, or identifying ligand functional groups that hit multiple targets. In computational experiments, we first verify the effectiveness of the proposed approach in synthetic data, then we apply it to drug adverse effect prediction problem. In the latter dataset, we compared the proposed method with L1-norm logistic regression in combination with the PubChem/Open Babel fingerprint, in that the proposed method showed superior performance with a much smaller number of subgraph patterns. Software is available from https://github.com/axot/GLP.

DOI： 10.1021/acs.jcim.5b00376
RF-NR: Random forest based approach for improved classification of Nuclear Receptors Reviewed

Ismail, H.D, Saigo, H, Bahadur, K.C, D

International Conference on Genome Informatics & International Conference on Bioinformatics (GIW/InCoB2015) 2015.9

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RF-NR: Random forest based approach for improved classification of Nuclear Receptors
Fast Iterative Mining Using Sparsity-Inducing Loss Functions Reviewed

Hiroto Saigo, Hisashi Kashima, Koji Tsuda

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS E96D ( 8 ) 1766 - 1773 2013.8

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Apriori-based mining algorithms enumerate frequent patterns efficiently, but the resulting large number of patterns makes it difficult to directly apply subsequent learning tasks. Recently, efficient iterative methods are proposed for mining discriminative patterns for classification and regression. These methods iteratively execute discriminative pattern mining algorithm and update example weights to emphasize on examples which received large errors in the previous iteration. In this paper, we study a family of loss functions that induces sparsity on example weights. Most of the resulting example weights become zeros, so we can eliminate those examples from discriminative pattern mining, leading to a significant decrease in search space and time. In computational experiments we compare and evaluate various loss functions in terms of the amount of sparsity induced and resulting speed-up obtained.

DOI： 10.1587/transinf.E96.D.1766
Protein Clustering on Grassman Manifold, Pattern Recognition in Bioinformatics Reviewed International journal

Suryanto, C.H., Saigo, H., Fukui, K.

Pattern Recognition in Bioinformatics (PRIB2012) 2012.11

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A Bayesian Approach to Graph Regression with Relevant Subgraph Selection Reviewed International journal

Chiappa, S., Saigo, H. and Tsuda, K.

Siam International Conference on Data Mining (SDM2009) 2009.4

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Regression with Interval Output Values Reviewed International journal

Kashima, H., Yamasaki, K., Saigo, H. and Inokuchi, A.

International Conference on Pattern Recognition (ICPR2008) 2008.1

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Regression with Intervals Reviewed International journal

Kashima, H., Yamazaki, K., Saigo, H. and Inokuchi, A.

International Workshop on Data-Mining and Statistical Science (DMSS2007) 2007.10

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Mining complex genotypic features for predicting HIV-1 drug resistance Reviewed

Hiroto Saigo, Takeaki Uno, Koji Tsuda

BIOINFORMATICS 23 ( 18 ) 2455 - 2462 2007.9

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Motivation: Human immunodeficiency virus type 1 (HIV-1) evolves in human body, and its exposure to a drug often causes mutations that enhance the resistance against the drug. To design an effective pharmacotherapy for an individual patient, it is important to accurately predict the drug resistance based on genotype data. Notably, the resistance is not just the simple sum of the effects of all mutations. Structural biological studies suggest that the association of mutations is crucial: even if mutations A or B alone do not affect the resistance, a significant change might happen when the two mutations occur together. Linear regression methods cannot take the associations into account, while decision tree methods can reveal only limited associations. Kernel methods and neural networks implicitly use all possible associations for prediction, but cannot select salient associations explicitly.
Results: Our method, itemset boosting, performs linear regression in the complete space of power sets of mutations. It implements a forward feature selection procedure where, in each iteration, one mutation combination is found by an efficient branch- and- bound search. This method uses all possible combinations, and salient associations are explicitly shown. In experiments, our method worked particularly well for predicting the resistance of nucleotide reverse transcriptase inhibitors ( NRTIs). Furthermore, it successfully recovered many mutation associations known in biological literature.

DOI： 10.1093/bioinformatics/btm353
Functional census of mutation sequence spaces: The example of p53 cancer rescue mutants

SA Danziger, SJ Swamidass, J Zeng, LR Dearth, Q Lu, JH Chen, JL Cheng, VP Hoang, H Saigo, R Luo, P Baldi, RK Brachmann, RH Lathrop

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 3 ( 2 ) 114 - 125 2006.4

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Many biomedical problems relate to mutant functional properties across a sequence space of interest, e. g., flu, cancer, and HIV. Detailed knowledge of mutant properties and function improves medical treatment and prevention. A functional census of p53 cancer rescue mutants would aid the search for cancer treatments from p53 mutant rescue. We devised a general methodology for conducting a functional census of a mutation sequence space by choosing informative mutants early. The methodology was tested in a double-blind predictive test on the functional rescue property of 71 novel putative p53 cancer rescue mutants iteratively predicted in sets of three ( 24 iterations). The first double-blind 15-point moving accuracy was 47 percent and the last was 86 percent; r = 0.01 before an epiphanic 16th iteration and r = 0.92 afterward. Useful mutants were chosen early ( overall r = 0.80). Code and data are freely available (http://www.igb.uci.edu/research/research.html, corresponding authors: R. H. L. for computation and R. K. B. for biology).

DOI： 10.1109/TCBB.2006.22
Large-scale prediction of disulphide bridges using kernel methods, two-dimensional recursive neural networks, and weighted graph matching

JL Cheng, H Saigo, P Baldi

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 62 ( 3 ) 617 - 629 2006.2

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The formation of disulphide bridges between cysteines plays an important role in protein folding, structure, function, and evolution. Here, we develop new methods for predicting disulphide bridges in proteins. We first build a large curated data set of proteins containing disulphide bridges to extract relevant statistics. We then use kernel methods to predict whether a given protein chain contains intrachain disulphide bridges or not, and recursive neural networks to predict the bonding probabilities of each pair of cysteines in the chain. These probabilities in turn lead to an accurate estimation of the total number of disulphide bridges and to a weighted graph matching problem that can be addressed efficiently to infer the global disulphide bridge connectivity pattern. This approach can be applied both in situations where the bonded state of each cysteine is known, or in ab initio mode where the state is unknown. Furthermore, it can easily cope with chains containing an arbitrary number of disulphide bridges, overcoming one of the major limitations of previous approaches. It can classify individual cysteine residues as bonded or nonbonded with 87% specificity and 89% sensitivity. The estimate for the total number of bridges in each chain is correct 71% of the times, and within one from the true value over 94% of the times. The prediction of the overall disulphide connectivity pattern is exact in about 51% of the chains. In addition to using profiles in the input to leverage evolutionary information, including true (but not predicted) secondary structure and solvent accessibility information yields small but noticeable improvements. Finally, once the system is trained, predictions can be computed rapidly on a proteomic or protein-engineering scale. The disulphide bridge prediction server (DIpro), software, and datasets are available through www.igb.uci.edu/servers/pass.html.

DOI： 10.1002/prot.20787
Protein homology detection using string alignment kernels

H Saigo, JP Vert, N Ueda, T Akutsu

BIOINFORMATICS 20 ( 11 ) 1682 - 1689 2004.7

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Motivation: Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVMs) are currently the most effective methods for the problem of superfamily recognition in the Structural Classification Of Proteins (SCOP) database. The performance of SVMs depends critically on the kernel function used to quantify the similarity between sequences.
Results: We propose new kernels for strings adapted to biological sequences, which we call local alignment kernels. These kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. When tested in combination with SVM on their ability to recognize SCOP superfamilies on a benchmark dataset, the new kernels outperform state-of-the-art methods for remote homology detection.

DOI： 10.1093/bioinformatics/bth141

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Books

Matrix Decomposition-based Dimensionality Reduction on Graph Data In Sakr, S. and Pardede, E. editors Graph Data Management: Techniques and Applications

Saigo, H., Tsuda, K.（Role：Joint author）

IGI Global 2011.1

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Responsible for pages：260-284 Language：English Book type：Scholarly book
Graph Mining for Chemoinformatics In Lodhi, H and Yamanishi, Y. editors Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Method and Collaborative Technique

Saigo, H., Tsuda, K.（Role：Joint author）

2010.1

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Responsible for pages：95-128 Language：English Book type：Scholarly book
Graph Classification In Sakr, C.C.C. and Wang, H. editors Managing and Mining Graph Data

Saigo, H., Tsuda, K.（Role：Joint author）

Springer 2010.1

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Responsible for pages：337-364 Language：English Book type：Scholarly book
Graph Kernels for Chemoinformatics In Lodhi, H and Yamanishi, Y. editors Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Method and Collaborative Techniques

Kashima, H., Saigo, H., Hattori, M., Tsuda, K.（Role：Joint author）

IGI Global 2010.1

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Responsible for pages：1-15 Language：English Book type：Scholarly book
Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction in "Methods in Molecular Biology"

Subash C Pakhrin, Suresh Pokharel, @Hiroto Saigo, Dukka B Kc（Role：Joint author）

Springer 2022.6 （ ISSN:10643745 ）

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Posttranslational modification (PTM) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology. Recent advances in experimental approaches, mainly mass-spectrometry-based approaches, have immensely helped in obtaining and characterizing PTMs. However, experimental approaches are not enough to understand and characterize more than 450 different types of PTMs and complementary computational approaches are becoming popular. Recently, due to the various advancements in the field of Deep Learning (DL), along with the explosion of applications of DL to various fields, the field of computational prediction of PTM has also witnessed the development of a plethora of deep learning (DL)-based approaches. In this book chapter, we first review some recent DL-based approaches in the field of PTM site prediction. In addition, we also review the recent advances in the not-so-studied PTM, that is, proteolytic cleavage predictions. We describe advances in PTM prediction by highlighting the Deep learning architecture, feature encoding, novelty of the approaches, and availability of the tools/approaches. Finally, we provide an outlook and possible future research directions for DL-based approaches for PTM prediction.

DOI： 10.1007/978-1-0716-2317-6_15

Scopus

PubMed
Local Alignment Kernels for Biological Sequences, In Bernhard Scheolkopf, Koji Tsuda and Jean-Philippe Vert editors, Kernel Methods in Computational Biology

Vert, J.-P., Saigo, H., Akutsu, T.（Role：Joint author）

MIT Press 2004.1

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Responsible for pages：131-153 Language：English Book type：Scholarly book

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Presentations

Automatically mining relevant variable interactions via sparse Bayesian learning International conference

#Yafune, R., #Sakuma, D., Tabei, Y., @Saito, N., @Saigo, H.

International Conference on Pattern Recognition 2021.1

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Event date： 2021.1

Language：English Presentation type：Oral presentation (general)

Venue：Online (originally Milan) Country：Italy
Entire regularization path for sparse nonnegative interaction model International conference

#Takayanagi, M., Tabei, Y., Saigo, H.

International Conference on Data Mining (ICDM) 2018.11

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Event date： 2017.9

Language：English Presentation type：Oral presentation (general)

Venue：Singapore Country：Japan

本研究では相互作用を考慮した非負値最小二乗法に対する正則化パス追跡アルゴリズムを提案した。
組み合わせ空間の効率的な探索のための枝刈りを実装した本手法は、LASSOよりも大幅に小さい解集合を得られることを計算機実験で示した。
HIVデータを用いた実験では、重要な遺伝子要因の組み合わせを自動的に探索することにより、薬剤耐性モデルを正確に推定出来ることを示した。
Towards predicting the epistasis in genome wide association study International conference

Saigo, H.

BMIRC2015 2015.3

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Language：English

Venue：Iizuka Country：Japan
Towards predicting the epistasis in genome wide association study International conference

Saigo, H.

BMIRC2015 2015.3

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Language：English

Venue：Iizuka Country：Japan
Mining and learning with structured data International conference

Saigo, H.

BIT2016 2016.3

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Language：English

Venue：Taipei Country：Taiwan, Province of China
Reaction graph kernels that predict EC numbers of unknown enzymatic reactions in the secondary metabolism of plant International conference

Saigo, H., Hattori, M., Kashima, H., and Tsuda, K.

Asia Pacific Bioinformatics Conference (APBC2010) 2010.1

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Language：English

Venue：Bangalore Country：India
KDE: a Kernel-based approach to detecting high-order genetic Epistasis International conference

Kodama, K., Saigo, H.

The 27th International Conference on Genome Informatics (GIW2016) 2016.10

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Language：English

Venue：Shanghai Country：China
大規模データ学習に向けたスケーラブルなノイズ拡張カーネル法：タニモトカーネルとガウスカーネルの性能比較研究• Chen Minrui, 西郷浩人

Chen Minrui, 西郷浩人

第27回情報論的学習理論ワークショップ（IBIS2024） 2024.11

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Event date： 2024.11

Language：English Presentation type：Poster presentation

Venue：さいたま Country：Japan
Protein Property Prediction with Sequence Alignment and Gradient-Based Optimization International conference

Ogawa. N., Saigo. H.

Asia Pacific Bioinformatics Joint Conference (APBJC) 2024.10

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Event date： 2024.10

Language：English Presentation type：Poster presentation

Venue：Okinawa Country：Japan
Early detection of Auditory Brainstem Response waveforms using k-means International conference

Imahashi, A., Saigo, H.

Asia Pacific Bioinformatics Joint Conference (APBJC) 2024.10

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Event date： 2024.10

Language：English Presentation type：Poster presentation

Venue：Okinawa Country：Japan
Optimization of amino acid substitution matrices by Gaussian process and sequence alignment

#小川直紀，西郷浩人

生命医薬情報学連合大会 2023.9

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Event date： 2024.5

Language：Japanese Presentation type：Symposium, workshop panel (public)

Venue：柏 Country：Japan
Improving the Efficiency of Auditory Brainstem Response Testing Using k-means

#今橋輝，西郷浩人

生命医薬情報学連合大会 2023.9

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Event date： 2024.5

Language：Japanese Presentation type：Symposium, workshop panel (public)

Venue：柏 Country：Japan
Benchmarking a wide range of unsupervised learning methods for detecting anomaly in blast furnace International conference

# Kendai Itakura, Dukka Bahadur KC, Hiroto Saigo

International Conference of Pattern Recognition Applications and Methods (ICPRAM2024) 2024.2

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Event date： 2024.2

Language：English Presentation type：Symposium, workshop panel (public)

Venue：Rome Country：Italy
A branch-and-bound approach to efficient classification and retrieval of documents International conference

# Kotaro Ii, Yasuo Tabei, Hiroto Saigo

International Conference of Pattern Recognition Applications and Methods (ICPRAM2024) 2024.4

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Event date： 2024.2

Language：English Presentation type：Symposium, workshop panel (public)

Venue：Rome Country：Italy
機械学習・深層学習を用いた高炉の教師なし異常検知

#板倉, @西郷

情報学的学習理論ワークショップ(IBIS2022) 2022.11

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Event date： 2023.11

Language：Japanese

Venue：つくば国際会議場 Country：Japan
深層学習を利用した高炉内の異常検知 International conference

#木崎亮介, @西郷浩人

情報学的学習理論ワークショップ 2022.6

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Event date： 2021.11

Language：Japanese Presentation type：Oral presentation (general)

Venue：Online Country：Japan
深層学習を利用した高炉内の異常検知

#木崎亮介, @西郷浩人

人工知能基本問題研究会 2021.3

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Event date： 2021.3

Language：Japanese Presentation type：Oral presentation (general)

Venue：Online Country：Japan
Context-Aware Latent Dirichlet Allocation for Topic SegmentationWenbo Li, Tetsu Matsukawa, Hiroto Saigo, Einoshin Suzuki: International conference

#Wenbo Li, Tetsu Matsukawa, Hiroto Saigo, Einoshin Suzuki

PAKDD 2020.5

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Event date： 2020.5

Language：English Presentation type：Oral presentation (general)

Venue：Online Country：Japan
Bayesian Optimization for Sequence Data International conference

#Kohei Oyamada and Hiroto Saigo

10th International Conference on Bioscience, Biochemistry and Bioinformatics 2020.1

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Event date： 2020.1

Language：English Presentation type：Oral presentation (general)

Venue：Kyoto University Country：Japan
A Sparse Bayesian Approach to Combinatorial Feature Selection and Its Applications to Biological Data International conference

#Ryoichiro Yafune, #Daisuke Sakuma, Yasuo Tabei, Noritaka Saito, Einoshin Suzuki and Hiroto Saigo

10th International Conference on Bioscience, Biochemistry and Bioinformatics 2020.1

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Event date： 2020.1

Language：English Presentation type：Oral presentation (general)

Venue：Kyoto University Country：Japan
SVM-GlutarySite: A support vector machine-based prediction of Glutarylation sites from protein sequences International conference

Albarakati, H., Saigo, H., Newman, R.H., KC, D.B.

Joint GIW/ABACBS-2019 Bioinformatics Conference 2019.9

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Event date： 2019.12

Language：English Presentation type：Oral presentation (general)

Venue：Sydney Country：Australia
変数間作用を考慮した非負スパースモデルの正則化経路探索

#高柳未来1、田部井靖生、@西郷浩人

人工知能学会全国大会（第３３回） 2019.6

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Event date： 2019.6

Language：Japanese Presentation type：Oral presentation (general)

Venue：朱鷺メッセ新潟コンベンションセンター Country：Japan
アイテムセットを用いたスパースベイズ学習

#矢船僚一朗、@西郷浩人

人工知能学会全国大会（第３３回） 2019.6

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Event date： 2019.6

Language：Japanese Presentation type：Oral presentation (general)

Venue：朱鷺メッセ新潟コンベンションセンター Country：Japan
DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction International conference

Thapa, N., Chaudhari, M., McManus, S., Roy, K., Newman, R.H., Saigo, H., KC, D.B.

MCBIOS 2019.3

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Event date： 2019.3

Language：English Presentation type：Oral presentation (general)

Venue：Birmingham Country：United States
Mining and Learning with Structured Data International conference

Hiroto Saigo

Japan America Germany Frontiers of Science Symposium 2017.9

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Event date： 2017.9

Language：English Presentation type：Symposium, workshop panel (public)

Venue：Steigenberger Hotel Bad Neuenahr Country：Germany
CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes International conference

Clarence White, Hamid D. Ismail, Hiroto Saigo, K. C. Dukka B.

INCOB2017 2017.9

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Event date： 2017.9

Language：English Presentation type：Oral presentation (general)

Venue：Shenzhen Country：Japan
Learning from treatment history to predict response to anti-HIV therapy

Saigo, H.

BMIRC2013 2013.2

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Language：English

Venue：Iizuka Country：Japan
Mining discriminative patterns from graph data with multiple labels

Saigo, H.

BMIRC2014 2014.1

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Language：English

Venue：Iizuka Country：Japan
Mining and learning with structured data

Saigo, H.

久留米大学バイオ統計センター公開セミナー 2016.1

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Language：Japanese

Venue：Kurume Country：Japan
Multiple response regression for graph mining International conference

Saigo, H.

Department of Computing Seminar, Imperial College London 2013.10

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Language：English

Venue：London Country：United Kingdom
Multiple response regression for graph mining International conference

Saigo, H.

Friedrich Miescher Lab. Seminar 2013.11

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Language：English

Venue：Tuebingen Country：Germany
Protein Clustering on Grassman Manifold, Pattern Recognition in Bioinformatics International conference

Suryanto, C.H., Saigo, H., Fukui, K.

Pattern Recognition in Bioinformatics (PRIB2012) 2012.11

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Language：English

Venue：Tokyo Country：Japan
A Bayesian Approach to Graph Regression with Relevant Subgraph Selection International conference

Chiappa, S., Saigo, H. and Tsuda, K.

Siam International Conference on Data Mining (SDM2009) 2009.4

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Language：English

Venue：Nevada Country：United States
Iterative Subgraph Mining for Principal Component Analysis International conference

Saigo, H. and Tsuda, K.

IEEE International Conference on Data Mining (ICDM2008) 2008.12

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Language：English

Venue：Pisa Country：Italy
Regression with Interval Output Values International conference

Kashima, H., Yamasaki, K., Saigo, H. and Inokuchi, A.

International Conference on Pattern Recognition (ICPR2008) 2008.1

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Language：English

Venue：Florida Country：United States
Partial Least Squares Regression for Graph Mining International conference

Saigo, H., Kraemer, N. and Tsuda, K.

ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2008) 2008.8

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Language：English

Venue：Las Vegas Country：United States
Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism International conference

Saigo, H., M. Hattori and K. Tsuda:

NIPS Workshop on Machine Learning in Computational Biology, 2007.12

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Language：English

Venue：Whistler Country：Canada
Regression with Intervals International conference

Kashima, H., Yamazaki, K., Saigo, H. and Inokuchi, A.

International Workshop on Data-Mining and Statistical Science (DMSS2007) 2007.10

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Language：English

Venue：Tokyo Country：Japan
Graph boosting for molecular QSAR analysis International conference

Saigo, H., Kadowaki, T., Kudo, T. and Tsuda, K.

NIPS Workshop on Machine Learning in Computational Biology, 2006.12

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Language：English

Venue：Whistler Country：Canada
A Linear Programming Approach for Molecular QSAR analysis International conference

Saigo, H., Kadowaki, T. and Tsuda, K.

International Workshop on Mining and Learning with Graphs (MLG2006) 2006.9

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Language：English

Venue：Berlin Country：Germany
Introduction to Chemoinformatics

Saigo, H.

鹿児島大学分子腫瘍学分野セミナー 2015.1

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Language：Japanese

Venue：Kagoshima Country：Japan
Pteris Vittata analysis report

Saigo, H.

東北大学自然システム共生学分野セミナー 2013.2

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Language：Japanese

Venue：Sendai Country：Japan
Clustering approach to drug discovery International conference

Saigo, H.

Novartis Animal Health Department Seminar 2012.12

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Language：English

Venue：Basel Country：Switzerland
Learn- ing from past treatments and their outcome improved prediction of in vivo response to anti-HIV therapy International conference

Saigo, H.

Ecole des Mines de Paris and Paris Tech in Paris Seminar 2010.2

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Language：English

Venue：Paris Country：France
Incorporating detailed information on treatment history improves prediction of response to anti-HIV therapy International conference

Saigo, H.

Eidgenoesische Technische Hochschule (ETH) Zuerich Seminar 2009.12

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Language：English

Venue：Basel Country：Switzerland
Incorporating detailed information on treatment history improves prediction of response to anti-HIV therapy International conference

Saigo, H.

Universitet van Amsterdam Seminar 2009.12

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Language：English

Venue：Amsterdam Country：Netherlands
Partial Least Squares Regression for Graph Mining International conference

Saigo, H.

Ecole des Mines de Paris and Paris Tech in Paris Seminar 2008.5

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Language：English

Venue：Paris Country：France
A Linear Programming Approach for Molecular QSAR analysis International conference

Saigo, H.

Fraunhofer FIRST Seminar 2006.9

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Language：English

Venue：Berlin Country：Germany
Classification of chemical compounds using graph kernels

Saigo, H.

Information-Based Induction Science (IBIS) 2005 2005.10

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Language：Japanese

Venue：Toyo Country：Japan
SNP間相互作用探索アルゴリズム

池田直人, 西郷浩人

第30回情報処理学会バイオ情報学研究会 2012.8

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Language：Japanese

Venue：九州工業大学 Country：Japan
カイ二乗検定によるｐ値の下限値を利用した遺伝子相互作用の効率的な数え上げ

山口拓郎, 西郷浩人

第42回バイオ情報学研究会 2015.6

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Language：Japanese

Venue：沖縄科学技術先端大学院大学 Country：Japan
RF-NR: Random forest based approach for improved classification of Nuclear Receptors

Ismail, H.D., Saigo, H., Bahadur, K.C.D.

The 26th International Conference on Genome Informatics & International Conference on Bioinformatics (GIW/InCoB2015) 2015.9

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Venue：Tokyo Country：Japan
応答変数が連続値の際の組み合わせ仮説に対する多重検定補正法

井ノ口敬章, 永野竜輝, 西郷浩人

第103回人工知能基本問題研究会 2017.3

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Language：Japanese

Venue：湯布院公民館 Country：Japan
Scalable Partial Least Squares Regression on Grammar- Compressed Data Matrices International conference

Tabei, Y., Saigo, H., Yamanishi, Y., Puglisi, S.

The 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2016) 2016.8

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Venue：San Francisco Country：United States

文法圧縮されたデータ行列上のスケーラブルなPLS回帰モデル学習を提案した。
Mining and Learning with Structured Data, Japan America Germany Frontiers of Science Symposium Invited International conference

西郷浩人

Japan America Germany Frontiers of Science Symposium 2017.9

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Country：Other

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MISC

QSARモデルの構築; 機械学習と部分構造マイニングによるアプローチ

西郷浩人

日本化学会情報化学部会誌 2013.7

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局所アラインメントカーネルを用いたアミノ酸置換行列の最適化

西郷浩人, ジャンフィリップ・ヴェール, 阿久津達也

情報処理学会研究報告数理モデル化と問題解決（MPS） 2006.3

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Optimizing amino acid substitution matrices with local alignment kernels
Detecting similarity between protein sequence is one of the core problems in bioinformatics, and detecting weak similarities is known as a hard problem. We have proposed a local alignmnet kernel for this purpose and showed good performance in the previous research. The local alignment kernel depdends on amino acid substitution matrices. We show that we can analytically calculate the derivatives of the local alignment kernels with respect to amino acid substitution matrix as well as their efficient calculation through dynamic programming. Then we plug them into the gradient based optimization procedure which is designed to discriminate true homologs from non-homologs. The local alignment kernel exhibits better performance when it uses the matrices and gap parameters optimized by this procedure than when it uses the matrices optimized for the Smith-Waterman algorithm. Furthermore, the matrices and gap parameters optimized for the local alignment kernel can also be used successfully by the Smith-Waterman algorithm.

Professional Memberships

Japanese Society of Bioinformatics (JSBi)
Japanese Society of Artificial Intelligence (JSAI)
Japanese Society of Statistics (JSS)
The Iron and Steel Institute of Japan (ISIJ)
THE JAPAN STATISTICAL SOCIETY

Academic Activities

学術論文等の審査 International contribution

Role(s)： Peer review

2024

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Type：Peer review

Number of peer-reviewed articles in foreign language journals：10
Pattern Recognition International contribution

2023.12 - Present

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Type：Academic society, research group, etc.
Screening of academic papers

Role(s)： Peer review

2023

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Type：Peer review

Number of peer-reviewed articles in foreign language journals：2

Number of peer-reviewed articles in Japanese journals：1

Proceedings of International Conference Number of peer-reviewed papers：10
Screening of academic papers

Role(s)： Peer review

2022

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Type：Peer review

Number of peer-reviewed articles in foreign language journals：2

Number of peer-reviewed articles in Japanese journals：2

Proceedings of International Conference Number of peer-reviewed papers：9

Proceedings of domestic conference Number of peer-reviewed papers：0
プログラム編集委員長

電気・情報関係学会九州支部連合大会（ Online Japan ） 2021.9

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Type：Competition, symposium, etc.
Screening of academic papers

Role(s)： Peer review

2021

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Type：Peer review

Number of peer-reviewed articles in foreign language journals：3

Number of peer-reviewed articles in Japanese journals：1

Proceedings of International Conference Number of peer-reviewed papers：4

Proceedings of domestic conference Number of peer-reviewed papers：0
プログラム委員 International contribution

Neural Information Processing Systems (Neurips2020) （ Online ） 2020.12

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Type：Competition, symposium, etc.
プログラム委員

生命医薬情報学連合大会 (IIBMP2020) （ Online ） 2020.9

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Type：Competition, symposium, etc.
プログラム委員 International contribution

International Conference on Machine Learning (ICML2020) （ Virtual ） 2020.7

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Type：Competition, symposium, etc.
Frontiers in Bioinformatics International contribution

2020.5 - Present

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Screening of academic papers

Role(s)： Peer review

2020

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Type：Peer review

Number of peer-reviewed articles in foreign language journals：5

Proceedings of International Conference Number of peer-reviewed papers：14

Proceedings of domestic conference Number of peer-reviewed papers：22
プログラム委員 International contribution

International Conference on Genome Informatics (GIW2019) （ Sydney Australia ） 2019.12

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Type：Competition, symposium, etc.
プログラム委員 International contribution

Neural Information Processing Systems (NIPS2019) （ Vancouver Canada ） 2019.12

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Type：Competition, symposium, etc.
プログラム委員

Information Based Induction Systems (IBIS2019) （ Nagoya Japan ） 2019.11

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Type：Competition, symposium, etc.
プログラム委員 International contribution

International Conference on Machine Learning (ICML2019) （ Long Beach, California UnitedStatesofAmerica ） 2019.6

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プログラム委員 International contribution

Artificial Intelligence and Statistics Conference (AISTATS2019) （ Naha, Okinawa Japan ） 2019.4

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Type：Competition, symposium, etc.
Screening of academic papers

Role(s)： Peer review

2019

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Type：Peer review

Number of peer-reviewed articles in foreign language journals：4

Number of peer-reviewed articles in Japanese journals：1

Proceedings of International Conference Number of peer-reviewed papers：19
プログラム委員 International contribution

Neural Information Processing Systems (NIPS2018) （ Montreal Canada ） 2018.12

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Type：Competition, symposium, etc.
プログラム委員 International contribution

International Conference on Genome Informatics (GIW2018) （ Yunnan China ） 2018.12

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Type：Competition, symposium, etc.
プログラム委員 International contribution

International Conference on Machine Learning (ICML2018) （ Stockholm Sweden ） 2018.10

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プログラム委員 International contribution

Artificial Intelligence and Statistics Conference (AISTATS2018) （ Playa Blanca, Lanzarote, Canary Islands Spain ） 2018.4

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担当幹事

人工知能学会　人工知能基本問題研究会（ Japan ） 2018.1

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プログラム委員 International contribution

Asia Pacific Bioinformatics Conference (APBC2018) （ Yokohama Japan ） 2018.1

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Type：Competition, symposium, etc.
Screening of academic papers

Role(s)： Peer review

2018

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Type：Peer review

Number of peer-reviewed articles in foreign language journals：2

Number of peer-reviewed articles in Japanese journals：2

Proceedings of International Conference Number of peer-reviewed papers：16
プログラム委員 International contribution

Neural Information Processing Systems (NIPS2017) （ Long Beach, California UnitedStatesofAmerica ） 2017.12

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Type：Competition, symposium, etc.
プログラム委員 International contribution

IEEE International Conference on Bioinformatics and Biomedicine (BIBM2017) （ Kansas City, MO UnitedStatesofAmerica ） 2017.11

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Type：Competition, symposium, etc.
プログラム委員 International contribution

Artificial Intelligence and Statistics Conference (AISTATS2017) （ Fort Lauderdale, Florida UnitedStatesofAmerica ） 2017.4

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Type：Competition, symposium, etc.
Screening of academic papers

Role(s)： Peer review

2017

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Type：Peer review

Number of peer-reviewed articles in foreign language journals：1

Proceedings of International Conference Number of peer-reviewed papers：32
担当幹事

人工知能学会　人工知能基本問題研究会（ Japan ） 2016.12

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座長（Chairmanship）

JSBi年会(2015) （ Japan ） 2015.10

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Type：Competition, symposium, etc.
座長（Chairmanship）

JSBi年会(2013) （ Japan ） 2013.10

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Type：Competition, symposium, etc.
座長（Chairmanship）

Information Based Induction Systems (IBIS2012) （ Japan ） 2012.11

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プログラム委員

JSBi年会(2015) （ Japan ）

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プログラム委員 International contribution

Asian Conference on Machine Learning (ACML2009) （ China ）

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プログラム委員 International contribution

IEEE International Conference on Bioinformatics and Biomedicine (BIBM2016) （ China ）

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Type：Competition, symposium, etc.
プログラム委員 International contribution

IEEE International Conference on Bioinformatics and Biomedicine (BIBM2015) （ UnitedStatesofAmerica ）

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プログラム委員 International contribution

IEEE International Conference on Bioinformatics and Biomedicine (BIBM2014) （ UnitedKingdom ）

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Type：Competition, symposium, etc.
プログラム委員 International contribution

IEEE International Conference on Bioinformatics and Biomedicine (BIBM2013) （ UnitedStatesofAmerica ）

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Type：Competition, symposium, etc.
プログラム委員 International contribution

IEEE International Conference on Bioinformatics and Biomedicine (BIBM2012) （ UnitedStatesofAmerica ）

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Type：Competition, symposium, etc.
プログラム委員 International contribution

IEEE International Conference on Bioinformatics and Biomedicine (BIBM2011) （ UnitedStatesofAmerica ）

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Type：Competition, symposium, etc.
プログラム委員 International contribution

Genome Informatics Workshop (GIW2014) （ Japan ）

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Type：Competition, symposium, etc.
プログラム委員 International contribution

Pattern Recognition in Bioinformatics (PRIB2013) （ France ）

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Type：Competition, symposium, etc.
プログラム委員 International contribution

Pattern Recognition in Bioinformatics (PRIB2012) （ Japan ）

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Type：Competition, symposium, etc.
プログラム委員

Information Based Induction Systems (IBIS2012) （ Japan ）

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Type：Competition, symposium, etc.
プログラム委員

JSBi年会(2013) （ Japan ）

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Type：Competition, symposium, etc.

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Research Projects

高レベル放射性廃棄物処理のための機械学習：高温多相融体の制御によるアプローチ

2023.4

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Authorship：Principal investigator
高レベル放射性廃棄物処理のための機械学習：高温多相融体の制御によるアプローチ研究

Grant number：23H03356 2023 - 2027

Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)

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Authorship：Principal investigator Grant type：Scientific research funding
高炉操業の診断・予測方法の開発

2023

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Grant type：Donation
A machine learning approach to automatic design of genes, proteins and chemical compounds

Grant number：22K19834 2022 - 2024

Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research Challenging Research(Exploratory)

西郷浩人

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Authorship：Principal investigator Grant type：Scientific research funding

科学の基本的なプロセスは仮説を立てて実験を行い、それを検証することの繰り返してである。近年は実験装置の機械化や測定装置の高精度化と高速化などにより、実験の質や量が急激に増える傾向にある。しかしながら、次にどのような実験を行うかを決定する実験計画は人間の勘に頼ったままである。そこで本研究課題が目指すのは機械学習を用いた実験計画の自動化である。
本提案課題では特に、タンパク質・化合物をターゲットとし、類似度の指標に滑らかな近似を導入することで局所解の効率的な探索を目指す。この結果として、次に実験を行うべきタンパク質や化合物を逐次的かつ効率的に行うことが可能となる。

CiNii Research
スラグみえる化研究会

2022

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Grant type：Donation
高炉操業の診断・予測方法の開発

2022

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Grant type：Donation
高温酸化物サスペンションのレオロジー特性に及ぼす界面電気物性の影響

Grant number：21H01684 2021

Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)

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Authorship：Coinvestigator(s) Grant type：Scientific research funding
スラグみえる化研究会

2021

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Grant type：Donation
高炉操業の診断・予測方法の開発

2021

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Grant type：Donation
スラグみえる化研究会

2020

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Grant type：Donation
高炉操業の診断・予測方法の開発

2020

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Grant type：Donation
製造インフォマティクスに向けた機械学習技術の開発と鉄鋼製造における評価

2019.6 - 2022.6

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Authorship：Principal investigator
Development of algorithms for manufacture informatics and its evaluation in steel industry

Grant number：19H04176 2019 - 2022

Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)

saigo hiroto

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Authorship：Principal investigator Grant type：Scientific research funding

In the "Anomaly Detection in Blast Furnaces" problem, we have developed approaches using unsupervised learning based on the work of Itakura et al. (IBIS2022), and supervised learning based on the work of Kizaki (IBIS2021). In the supervised learning approach using CNN, we have confirmed that utilizing data from 5 to 15 minutes prior leads to improved accuracy.
<BR>
We have also developed a method for "Viscosity Prediction of High-Temperature States through Multi-Task Learning" as described in the study by Saigo et al. (Scientific Reports, 2022). In addition to robust extrapolation prediction, we have proposed a transfer learning method that leverages room temperature experimental data for high-temperature experiments.

CiNii Research
スラグみえる化研究会

2019

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Grant type：Donation
高炉操業の診断・予測方法の開発

2019

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Grant type：Donation
転移学習を利用した高温二相流体のレオロジー特性予測システム構築

2018.4 - 2020.3

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Authorship：Coinvestigator(s)
転移学習を利用した高温二相流体のレオロジー特性予測システム構築継続中

2018 - 2020

Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)

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Grant type：Scientific research funding
転移学習を利用した高温二相流体のレオロジー特性予測システム構築

Grant number：18H01762 2018 - 2020

Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)

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Authorship：Coinvestigator(s) Grant type：Scientific research funding
構造データの学習とマイニング

2018

Japan Society for the Promotion of Science JSPS外国人招へい研究者

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Authorship：Principal investigator Grant type：Joint research
高炉操業の診断・予測方法の開発

2018

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Grant type：Donation
複数の遺伝要因及び環境要因の組み合わせを考慮したゲノムワイド相関解析法の開発

2017.3 - 2013.4

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Authorship：Principal investigator
深層学習によるタンパク質分類法の開発

2017

スタートアップ支援

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Authorship：Principal investigator Grant type：On-campus funds, funds, etc.
マルチモーダル多視点画像を用いたタンパク質立体構造の解析研究課題

2013.4 - 2015.3

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Authorship：Coinvestigator(s)
複数の遺伝要因及び環境要因の組み合わせを考慮したゲノムワイド相関解析法の開発

2013 - 2016

Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)

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Authorship：Principal investigator Grant type：Scientific research funding
複数の遺伝要因及び環境要因の組み合わせを考慮したゲノムワイド相関解析法の開発

Grant number：25700004 2013 - 2016

Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (A)

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Authorship：Principal investigator Grant type：Scientific research funding
マルチモーダル多視点画像を用いたタンパク質立体構造の解析

2013 - 2014

Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)

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Grant type：Scientific research funding
マルチモーダル多視点画像を用いたタンパク質立体構造の解析

Grant number：25540062 2013 - 2014

Grants-in-Aid for Scientific Research Grant-in-Aid for challenging Exploratory Research

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Authorship：Coinvestigator(s) Grant type：Scientific research funding
全cDNA解析によるヒ素高蓄積植物土壌浄化システムの解析

2011.4 - 2012.3

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Authorship：Coinvestigator(s)
大量のタンパク質リガンドデータより相互作用の構造的特徴をマイニングする方法の開発研究課題

2011.4 - 2012.3

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Authorship：Principal investigator
大量のタンパク質リガンドデータより相互作用の構造的特徴をマイニングする方法の開発

Grant number：23700338 2011 - 2013

Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (B)

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Authorship：Principal investigator Grant type：Scientific research funding
大量のタンパク質リガンドデータより相互作用の構造的特徴をマイニングする方法の開発

2011 - 2012

Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)

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Authorship：Principal investigator Grant type：Scientific research funding
全cDNA解析によるヒ素高蓄積植物土壌浄化システムの解析

2011 - 2012

Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)

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Grant type：Scientific research funding
全cDNA解析によるヒ素高蓄積植物土壌浄化システムの解析

Grant number：23710085 2011 - 2012

Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (B)

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Authorship：Coinvestigator(s) Grant type：Scientific research funding

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Class subject

【通年】情報理工学研究Ⅰ

2024.4 - 2025.3 Full year
【通年】情報理工学講究

2024.4 - 2025.3 Full year
【通年】情報理工学演習

2024.4 - 2025.3 Full year
データ科学

2024.4 - 2024.9 First semester
情報理工学論議Ⅰ

2024.4 - 2024.9 First semester
情報理工学論述Ⅰ

2024.4 - 2024.9 First semester
情報理工学読解

2024.4 - 2024.9 First semester
サイバーセキュリティ基礎論

2024.4 - 2024.6 Spring quarter
機械学習特論Ⅱ

2023.12 - 2024.2 Winter quarter
Machine Learning II

2023.12 - 2024.2 Winter quarter
機械学習特論

2023.10 - 2024.3 Second semester
情報理工学論議Ⅱ

2023.10 - 2024.3 Second semester
情報理工学論述Ⅱ

2023.10 - 2024.3 Second semester
情報理工学演示

2023.10 - 2024.3 Second semester
生物情報科学

2023.10 - 2024.3 Second semester
機械学習特論Ⅰ

2023.10 - 2023.12 Fall quarter
Machine Learning I

2023.10 - 2023.12 Fall quarter
国際演示技法Ⅰ

2023.4 - 2024.3 Full year
Advanced Project Management II

2023.4 - 2024.3 Full year
Advanced Project Management I

2023.4 - 2024.3 Full year
Exercise in Teaching II

2023.4 - 2024.3 Full year
Exercise in Teaching I

2023.4 - 2024.3 Full year
Intellectual Property Management II

2023.4 - 2024.3 Full year
Intellectual Property Management I

2023.4 - 2024.3 Full year
Scientific English Presentation II

2023.4 - 2024.3 Full year
Scientific English Presentation I

2023.4 - 2024.3 Full year
先端プロジェクト管理技法Ⅱ

2023.4 - 2024.3 Full year
先端プロジェクト管理技法Ⅰ

2023.4 - 2024.3 Full year
ティーチング演習Ⅱ

2023.4 - 2024.3 Full year
ティーチング演習Ⅰ

2023.4 - 2024.3 Full year
知的財産技法Ⅱ

2023.4 - 2024.3 Full year
知的財産技法Ⅰ

2023.4 - 2024.3 Full year
国際演示技法Ⅱ

2023.4 - 2024.3 Full year
国際演示技法Ⅰ

2023.4 - 2024.3 Full year
Advanced Seminar in Information Science and Technology

2023.4 - 2024.3 Full year
Advanced Research in Information Science and Technology II

2023.4 - 2024.3 Full year
Advanced Research in Information Science and Technology I

2023.4 - 2024.3 Full year
情報理工学特別演習

2023.4 - 2024.3 Full year
情報理工学特別研究Ⅱ

2023.4 - 2024.3 Full year
情報理工学特別研究Ⅰ

2023.4 - 2024.3 Full year
Advanced Research in Data Science

2023.4 - 2024.3 Full year
データサイエンス特别講究

2023.4 - 2024.3 Full year
Advanced Project Management II

2023.4 - 2024.3 Full year
Advanced Project Management I

2023.4 - 2024.3 Full year
Exercise in Teaching II

2023.4 - 2024.3 Full year
Exercise in Teaching I

2023.4 - 2024.3 Full year
Intellectual Property Management II

2023.4 - 2024.3 Full year
Intellectual Property Management I

2023.4 - 2024.3 Full year
Scientific English Presentation II

2023.4 - 2024.3 Full year
Scientific English Presentation I

2023.4 - 2024.3 Full year
先端プロジェクト管理技法Ⅱ

2023.4 - 2024.3 Full year
先端プロジェクト管理技法Ⅰ

2023.4 - 2024.3 Full year
ティーチング演習Ⅱ

2023.4 - 2024.3 Full year
ティーチング演習Ⅰ

2023.4 - 2024.3 Full year
知的財産技法Ⅱ

2023.4 - 2024.3 Full year
知的財産技法Ⅰ

2023.4 - 2024.3 Full year
国際演示技法Ⅱ

2023.4 - 2024.3 Full year
【通年】情報理工学研究Ⅰ

2023.4 - 2024.3 Full year
【通年】情報理工学講究

2023.4 - 2024.3 Full year
【通年】情報理工学演習

2023.4 - 2024.3 Full year
データ科学

2023.4 - 2023.9 First semester
情報理工学論議Ⅰ

2023.4 - 2023.9 First semester
情報理工学論述Ⅰ

2023.4 - 2023.9 First semester
情報理工学読解

2023.4 - 2023.9 First semester
機械学習特論Ⅱ

2022.12 - 2023.2 Winter quarter
Machine Learning II

2022.12 - 2023.2 Winter quarter
情報理工学論議Ⅱ

2022.10 - 2023.3 Second semester
情報理工学論述Ⅱ

2022.10 - 2023.3 Second semester
情報理工学演示

2022.10 - 2023.3 Second semester
機械学習特論

2022.10 - 2023.3 Second semester
Machine Learning

2022.10 - 2023.3 Second semester
生物情報科学

2022.10 - 2023.3 Second semester
情報科学講究

2022.10 - 2023.3 Second semester
国際科学特論Ⅱ

2022.10 - 2022.12 Fall quarter
機械学習特論Ⅰ

2022.10 - 2022.12 Fall quarter
Machine Learning I

2022.10 - 2022.12 Fall quarter
基幹教育セミナー

2022.6 - 2022.8 Summer quarter
国際演示技法

2022.4 - 2023.3 Full year
情報理工学講究

2022.4 - 2023.3 Full year
情報理工学演習

2022.4 - 2023.3 Full year
情報理工学研究Ⅰ

2022.4 - 2023.3 Full year
Advanced Seminar in Informatics

2022.4 - 2023.3 Full year
Advanced Research in Informatics II

2022.4 - 2023.3 Full year
Advanced Research in Informatics I

2022.4 - 2023.3 Full year
情報学特別演習

2022.4 - 2023.3 Full year
情報学特別講究第二

2022.4 - 2023.3 Full year
情報学特別講究第一

2022.4 - 2023.3 Full year
Advanced Research in Data Science

2022.4 - 2023.3 Full year
データサイエンス特别講究

2022.4 - 2023.3 Full year
Advanced Project Management Technique

2022.4 - 2023.3 Full year
Exercise in Teaching

2022.4 - 2023.3 Full year
Intellectual Property Management

2022.4 - 2023.3 Full year
Scientific English Presentation

2022.4 - 2023.3 Full year
先端プロジェクト管理技法

2022.4 - 2023.3 Full year
ティーチング演習

2022.4 - 2023.3 Full year
知的財産技法

2022.4 - 2023.3 Full year
データ科学

2022.4 - 2022.9 First semester
情報理工学論議Ⅰ

2022.4 - 2022.9 First semester
情報理工学論述Ⅰ

2022.4 - 2022.9 First semester
情報理工学読解

2022.4 - 2022.9 First semester
電気情報工学入門

2022.4 - 2022.6 Spring quarter
サイバーセキュリティ基礎論

2022.4 - 2022.6 Spring quarter
(IUPE)Data Structure and Algorithms IB

2021.12 - 2022.2 Winter quarter
Machine Learning II

2021.12 - 2022.2 Winter quarter
機械学習特論Ⅱ

2021.12 - 2022.2 Winter quarter
機械学習特論

2021.10 - 2022.3 Second semester
情報理工学演示

2021.10 - 2022.3 Second semester
生物情報科学

2021.10 - 2022.3 Second semester
情報科学講究

2021.10 - 2022.3 Second semester
Machine Learning

2021.10 - 2022.3 Second semester
情報学論議Ⅱ

2021.10 - 2022.3 Second semester
情報学論述Ⅱ

2021.10 - 2022.3 Second semester
(IUPE)Data Structure and Algorithms IA

2021.10 - 2021.12 Fall quarter
Machine Learning I

2021.10 - 2021.12 Fall quarter
機械学習特論Ⅰ

2021.10 - 2021.12 Fall quarter
国際演示技法

2021.4 - 2022.3 Full year
[M2]情報学講究

2021.4 - 2022.3 Full year
情報理工学演習

2021.4 - 2022.3 Full year
情報理工学研究Ⅰ

2021.4 - 2022.3 Full year
情報学演習

2021.4 - 2022.3 Full year
Advanced Seminar in Informatics

2021.4 - 2022.3 Full year
Advanced Research in Informatics II

2021.4 - 2022.3 Full year
Advanced Research in Informatics I

2021.4 - 2022.3 Full year
情報学特別演習

2021.4 - 2022.3 Full year
情報学特別講究第二

2021.4 - 2022.3 Full year
情報学特別講究第一

2021.4 - 2022.3 Full year
Advanced Research in Data Science

2021.4 - 2022.3 Full year
データサイエンス特别講究

2021.4 - 2022.3 Full year
Advanced Project Management Technique

2021.4 - 2022.3 Full year
Exercise in Teaching

2021.4 - 2022.3 Full year
Intellectual Property Management

2021.4 - 2022.3 Full year
Scientific English Presentation

2021.4 - 2022.3 Full year
先端プロジェクト管理技法

2021.4 - 2022.3 Full year
ティーチング演習

2021.4 - 2022.3 Full year
知的財産技法

2021.4 - 2022.3 Full year
データ科学

2021.4 - 2021.9 First semester
[M2]情報学論議Ⅰ

2021.4 - 2021.9 First semester
[M2]情報学論述Ⅰ

2021.4 - 2021.9 First semester
情報理工学読解

2021.4 - 2021.9 First semester
(IUPE)Data Structure and Algorithms IB

2020.12 - 2021.2 Winter quarter
【修士】機械学習特論

2020.10 - 2021.3 Second semester
Machine Learning

2020.10 - 2021.3 Second semester
【博士】機械学習特論

2020.10 - 2021.3 Second semester
情報学論議Ⅱ

2020.10 - 2021.3 Second semester
情報学論述Ⅱ

2020.10 - 2021.3 Second semester
情報学演示

2020.10 - 2021.3 Second semester
(IUPE)Data Structure and Algorithms IA

2020.10 - 2020.12 Fall quarter
国際演示技法

2020.4 - 2021.3 Full year
Advanced Seminar in Informatics

2020.4 - 2021.3 Full year
Advanced Research in Informatics II

2020.4 - 2021.3 Full year
Advanced Research in Informatics I

2020.4 - 2021.3 Full year
情報学特別演習

2020.4 - 2021.3 Full year
情報学特別講究第二

2020.4 - 2021.3 Full year
情報学特別講究第一

2020.4 - 2021.3 Full year
Advanced Research in Data Science

2020.4 - 2021.3 Full year
データサイエンス特别講究

2020.4 - 2021.3 Full year
Advanced Project Management Technique

2020.4 - 2021.3 Full year
Exercise in Teaching

2020.4 - 2021.3 Full year
Intellectual Property Management

2020.4 - 2021.3 Full year
Scientific English Presentation

2020.4 - 2021.3 Full year
先端プロジェクト管理技法

2020.4 - 2021.3 Full year
ティーチング演習

2020.4 - 2021.3 Full year
知的財産技法

2020.4 - 2021.3 Full year
データ科学

2020.4 - 2020.9 First semester
【修士】機械学習特論

2019.10 - 2020.3 Second semester
生物情報科学

2019.10 - 2020.3 Second semester
【博士】機械学習特論

2019.10 - 2020.3 Second semester
情報学論議Ⅱ

2019.10 - 2020.3 Second semester
情報学論述Ⅱ

2019.10 - 2020.3 Second semester
情報学演示

2019.10 - 2020.3 Second semester
(IUPE)Data Structure and Algorithms I

2019.10 - 2019.12 Fall quarter
情報学演習

2019.4 - 2020.3 Full year
情報学講究

2019.4 - 2020.3 Full year
データ科学

2019.4 - 2019.9 First semester
情報学論議Ⅰ

2019.4 - 2019.9 First semester
情報学論述Ⅰ

2019.4 - 2019.9 First semester
情報学読解

2019.4 - 2019.9 First semester
機械学習特論

2018.10 - 2019.3 Second semester
生物情報科学

2018.10 - 2019.3 Second semester
情報学読解

2018.4 - 2018.9 First semester
情報学論議Ⅰ

2018.4 - 2018.9 First semester
情報学論述Ⅰ

2018.4 - 2018.9 First semester
サイバーセキュリティ基礎論

2018.4 - 2018.6 Spring quarter
情報学演示

2017.10 - 2018.3 Second semester
情報学論議Ⅱ

2017.10 - 2018.3 Second semester
情報学論述Ⅱ

2017.10 - 2018.3 Second semester
情報学読解

2017.4 - 2017.9 First semester
高度プログラミング

2017.4 - 2017.9 First semester
情報学論議Ⅰ

2017.4 - 2017.9 First semester
情報学論述Ⅰ

2017.4 - 2017.9 First semester
生物情報科学

2016.10 - 2017.3 Second semester
データ科学

2016.4 - 2016.9 First semester

▼display all

FD Participation

2023.5 Role：Participation Title：【シス情FD】農学研究院で進めているDX教育について

Organizer：[Undergraduate school/graduate school/graduate faculty]
2023.3 Role：Participation Title：【シス情FD】独・蘭・台湾での産学連携を垣間見る-Industy 4.0・量子コンピューティング・先端半導体-

Organizer：[Undergraduate school/graduate school/graduate faculty]
2022.6 Role：Participation Title：【シス情FD】電子ジャーナル等の今後について

Organizer：[Undergraduate school/graduate school/graduate faculty]
2022.5 Role：Participation Title：【シス情FD】若手教員による研究紹介④「量子コンピュータ・システム・アーキテクチャの研究～道具になることを目指して～」

Organizer：[Undergraduate school/graduate school/graduate faculty]
2022.3 Role：Participation Title：全学FD:メンタルヘルス講演会

Organizer：University-wide
2022.1 Role：Participation Title：【シス情FD】シス情関連の科学技術に対する国の政策動向（に関する私見）

Organizer：[Undergraduate school/graduate school/graduate faculty]
2021.10 Role：Participation Title：【シス情FD】熊本高専と九大システム情報との交流・連携に向けてー 3年半で感じた高専の実像ー

Organizer：[Undergraduate school/graduate school/graduate faculty]
2021.9 Role：Participation Title：博士後期課程の充足率向上に向けて

Organizer：[Undergraduate school/graduate school/graduate faculty]
2021.5 Role：Participation Title：先導的人材育成フェローシップ事業(情報・AI分野)について

Organizer：[Undergraduate school/graduate school/graduate faculty]
2020.12 Role：Participation Title：令和２年度第２回工学部ＦＤ（１日目）総合型選抜の実施に向けて―面接の全般的な内容（注意事項、採点方法など）

Organizer：[Undergraduate school/graduate school/graduate faculty]
2020.11 Role：Participation Title：マス・フォア・イノベーション卓越大学院について

Organizer：[Undergraduate school/graduate school/graduate faculty]
2020.9 Role：Participation Title：電気情報工学科総合型選抜（AO入試）について

Organizer：[Undergraduate school/graduate school/graduate faculty]
2019.10 Role：Participation Title：電子ジャーナルの現状と今後の動向に関する説明会

Organizer：[Undergraduate school/graduate school/graduate faculty]
2019.6 Role：Participation Title：8大学情報系研究科長会議の報告

Organizer：[Undergraduate school/graduate school/graduate faculty]
2016.6 Role：Participation Title：【全学ＦＤ(第2回)】教育の質向上支援プログラム（EEP）成果発表会

Organizer：University-wide

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Visiting, concurrent, or part-time lecturers at other universities, institutions, etc.

2024 理化学研究所革新的人工知能研究センター Classification:Affiliate faculty Domestic/International Classification:Japan
2024 九州工業大学 Classification:Part-time lecturer Domestic/International Classification:Japan
2023 九州工業大学 Classification:Part-time lecturer Domestic/International Classification:Japan
2023 理化学研究所革新的人工知能研究センター Classification:Affiliate faculty Domestic/International Classification:Japan
2022 理化学研究所革新的人工知能研究センター Classification:Affiliate faculty Domestic/International Classification:Japan
2022 九州工業大学 Classification:Part-time lecturer Domestic/International Classification:Japan
2021 理化学研究所革新的人工知能研究センター Classification:Affiliate faculty Domestic/International Classification:Japan
2020 理化学研究所 Classification:Affiliate faculty Domestic/International Classification:Japan
2019 京都大学 Classification:Affiliate faculty Domestic/International Classification:Japan
2019 東京大学 Classification:Part-time lecturer Domestic/International Classification:Japan
2016 九州工業大学 Classification:Part-time lecturer Domestic/International Classification:Japan
2016 鹿児島大学 Classification:Part-time lecturer Domestic/International Classification:Japan

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Participation in international educational events, etc.

2018.9

工学系国際教育支援センター

豪州クイーンズランド大学（UQ）におけるSTEM科目英語教育研修

Other educational activity and Special note

2024 Class Teacher 学部
2023 Class Teacher 学部
2022 Class Teacher 学部
2021 Class Teacher 学部
2020 Class Teacher 学部
Class Teacher 学部

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Acceptance of Foreign Researchers, etc.

North Carolina A&T State Univeristy

Acceptance period： 2018.6 - 2018.7 （Period）：1 month or more

Nationality：United States

Business entity：Japan Society for the Promotion of Science

Travel Abroad

2008.7 - 2010.3

Staying countory name 1：Germany Staying institution name 1：Max Planck Institute for Informatics
2006.6 - 2008.6

Staying countory name 1：Germany Staying institution name 1：Max Planck Institute for Biological Cybernetics
2003.8 - 2004.8

Staying countory name 1：United States Staying institution name 1：University of California, Irvine