About Me

About Me

Andres Hernandez-Matamoros

I have a degree in Electronics Engineering from the Metropolitan Autonomous University in Mexico, which I obtained in 2011. I pursued advanced studies, earning a Master's degree in Microelectronics in 2013 and a Ph.D. in Communications and Electronics in 2018, the latter with distinction from the Polytechnic National Institute in Mexico. During my academic journey, I have participated in several international research opportunities. From 2012 to 2013, I conducted research at the Politecnico di Milano in Italy. Later, as part of the JUSST program, I spent a year (2015-2016) at the University of Electro-Communications in Tokyo, Japan. In 2019-2020, I completed a postdoctoral fellowship at Iwate Prefectural University, Japan. I then worked as a project researcher at the University of Tokyo from 2020-2022, where I developed anomaly detection systems using deep learning techniques. Most recently, I was a postdoctoral researcher at Meiji University until March 2025. Full CV

My Google Scholar Profile

Google Scholar

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Areas of interest

Machine Learning

Machine Learning

Deep Learning

Deep Learning

Processing Digital Signals

Processing Digital Signals

Data Privacy

Data Privacy


Academic Experience

Projects

Data Privacy

Local Differential Privacy (LDP) faces major challenges with high-dimensional, correlated healthcare and smartwatch data. To address this, we leverage Bayesian Ridge Regression for improved handling of highly correlated attributes, outperforming standard methods like LOPUB and LOCOP. Prof. Kikuchi introduces “Castell,” an approach that refines data perturbation and aggregation, enabling accurate joint probability estimation. Experimental results confirm Castell’s enhanced privacy safeguards and data utility, marking a notable advancement in privacy-safe analytics.

[Link]
Data Privacy

Anomaly Detection

We propose a real-time stuck-prediction approach using autoencoder-based models. By tracking anomalies through elevated reconstruction errors, this method identifies warning signs earlier and more accurately than earlier supervised approaches, improving overall system reliability.

[Link]
Anomaly Detection

Creating Synthetic Data

This project introduces a Bidirectional Recurrent Neural Network with an added statistical stage to generate five types of synthetic biomedical signals (ECG, EEG, BCG, PPG, and respiratory impedance). Our approach addresses data scarcity challenges and outperforms GAN-based techniques by producing highly accurate, event-specific synthetic data crucial for medical diagnoses.

[Link]

Forecasting of COVID19

We developed a region-based ARIMA model to predict COVID-19 spread across six global regions by incorporating population data. The model achieves an average RMSE of 144.81, highlighting its accuracy and potential. Future improvements may include other variables such as climate factors and cultural differences for even more precise predictions.

[Link]
Forecasting of COVID19

Facial Expression Recognition

Our system automatically detects a face, segments it into forehead/eyes and mouth regions, and then divides each region into blocks. We extract Gabor-based features and reduce them via PCA, classifying with a fuzzy clustering approach. The system achieves about 97% accuracy using only one region, and up to 99% when both are used, remaining robust even when one region is occluded.

[Link]


Publications & Invited talks

Full list of Invited talks (IT), journal articles (J), books (B) and refereed conference publications (C)
2025

J8

Comparative Analysis of Local Differential Privacy Schemes in Healthcare Datasets

Andres Hernandez-Matamoros, Hiroaki Kikuchi,
Appl. Sci. 2024, 14, 2864. [Link]

B2

Advances and Trends in Artificial Intelligence. Theory and Applications

Fujita, H., Cimler, R., Hernandez-Matamoros A., & Ali, M. (Eds.). (2024).
Springer Singapore

B1

New Trends in Intelligent Software Methodologies, Tools and Techniques

Fujita, H., Pérez Meana, H. M., Hernandez-Matamoros A. (Eds.). (2024).
IOS Press

C13

Prediction of heat transfer coefficient and pressure drop

Galicia, E.S., A. Hernandez-Matamoros, Miyara, A., 2024.
"Prediction of heat transfer coefficient and pressure drop of flow boiling and condensation using machine learning"
Journal of Physics: Conference Series

C12

Meaningful performance analysis on Healthcare Data under Local Differential Privacy

A. Hernandez-Matamoros, Hiroaki Kikuchi
SoMeT 2024: 398-411, ISBN 978-1-64368-538-0.
[Link]

C11

Machine learning-based approach to correct saturated flow boiling heat transfer correlations

Galicia, E.S., A. Hernandez-Matamoros, Miyara, A.
SoMeT 2024: 235-248, ISBN 978-1-64368-538-0.
[Link]

C10

Secure Aggregation of Smartwatch Health Data with LDP

A. Hernandez-Matamoros, H. Kikuchi, presented at SocialSec 2024.
[Link]

2023

J7

Ozone responses to reduced precursor emissions

Vazquez Santiago, J., Jaimes Palomera, M., Resendiz Martinez, C., A. Hernandez-Matamoros, et al.
Science of The Total Environment, 2023, 169180,
[Link]

C9

New LDP Approach Using VAE

A. Hernandez-Matamoros, Kikuchi, H. (2023),
In: Li, S., Manulis, M., Miyaji, A. (eds) Network and System Security. NSS 2023, LNCS 13983.
[Link]

C8

An Efficient Local Differential Privacy Scheme Using Bayesian Ridge Regression

A. Hernandez-Matamoros and H. Kikuchi,
2023 20th Annual Int. Conference on Privacy, Security & Trust (PST), Copenhagen, Denmark, 2023.
[Link]

IT2

Webinar Medical Big Data - Machine Learning

2023/March/31
Webinar Details | Certificate

2022

IT1

International Congress of Geoeconomic Engineering

How to create synthetic biomedical signals using artificial neural networks. 2022/October/25
Event Details | Video | Certificate [Spanish] https://www.youtube.com/live/sefPhcfxUrI?si=-Cf4VvkzLB33Y2iX&t=238

C7

A Vulnerability in Video Anonymization - Privacy Disclosure from Face-obfuscated video

Kikuchi, H., Miyoshi, S., Mori, T., A. Hernandez-Matamoros,
2022 19th Annual Int. Conference on Privacy, Security & Trust (PST), Fredericton, Canada.
[Link]

2021

J6

Less complexity oneclass classification approach using construction error

T. Hayashi, H. Fujita, A. Hernandez-Matamoros,
Information Sciences, Volume 560, Pages 217-234, 06-2021,
[Link]

2020

J4

Recognition of ECG Signals Using Wavelet Based on Atomic Functions

A. Hernandez-Matamoros, Fujita, H., Nakano-Miyatake, M. et al.
Journal Biocybernetics and Biomedical Engineering, Volume 40, Issue 2, (04-2020).
[Link]

J5

A novel approach to create synthetic biomedical signals using BiRNN

A. Hernandez-Matamoros, Fujita, H., & Perez-Meana, H.
Information Sciences, Volume 541, Pages 218-241, (12-2020).
[Link]

J3

Forecasting of COVID19 per regions using ARIMA models and Polynomial Functions

A. Hernandez-Matamoros, Toshitaka H., Fujita, H., Perez-Meana, H.
Applied Soft Computing, Volume 96, 11-2020, ISSN 1568-4946.
[Link]

C6

Heart Beat Recognition using a novel preprocessing scheme and Neural Networks

A. Hernandez-Matamoros, Fujita, H., Perez-Meana, H.
SoMeT 2020, Frontiers in Artificial Intelligence and Applications; Volume 327.
[Link]

2019

J2

Scheme fuzzy approach to classify skin tonalities through geographic distribution

Hernandez-Matamoros, A., Fujita, H., Nakano-Miyatake, M. et al.
J. Ambient Intell Human Comput, 11, 2859-2870 (07-2019).
[Link]

C5

A Scheme to Classify Skin Through Geographic Distribution of Tonalities Using Fuzzy Based Classification Approach

A. Hernandez-Matamoros, Fujita, H., Nakano, M., Perez-Meana, H., & Hernandez-Escamilla E.(2019)
SoMeT 2019, Frontiers in Artificial Intelligence and Applications; Vol. 318.
[Link]

2017

C4

Facial expression recognition in unconstrained environment

A. Hernandez-Matamoros, T. Nagai, M. Attamimi, H. Perez-Meana
SoMeT 2017, (Frontiers in AI and Applications; Vol. 297, pages 525-538).
[Link]

2016

J1

Facial expression recognition with automatic segmentation of face regions using a fuzzy based classification approach

Andres Hernandez-Matamoros, Andrea Bonarini, E. Escamilla-Hernandez,
Mariko NakanoMiyatake, Hector Perez-Meana,
Knowledge-Based Systems, (2016), ISSN 0950-7051.
[Link]

2015

C3

A Facial Expression Recognition with Automatic Segmentation of Face Regions

A. Hernandez-Matamoros, A. Bonarini, E. Escamilla-Hernandez, M. Nakano-Miyatake, H. Perez-Meana,
SoMet 2015, Naples, Italy, LNCS 529-540, ISBN 978-3-319-22689-7.
[Link]

2014

C2

A supervised classifier scheme based on clustering algorithms

A. Hernandez-Matamoros, E. Escamilla-Hernandez, K. Perez-Daniel, M. Nakano-Miyatake, H. Perez-Meana,
CONCAPAN XXXIV, IEEE, Panama City, 2014.
[Link]

2013

C1

Learning an object through images obtained from the Internet using Unsupervised Learning

A. Hernandez Gerardo, H. M. Perez Meana, E. Escamilla Hernandez,
9th International Congress Technological Trends in Computation, 2013,
indexed to the journal Research in Computer Science, Vol. 69,
[PDF] | RCS