Material Informatics:

Data science approach to new material discovery

Machine Learning + Uncertainty Quantification.


Bioinformatics and Bioimaging:

Automated annotation and data analysis pipeline for C. elegans

A five-year-long cross-disciplinary and cross-institute project on studying whole brain neuronal activity of Caenorhabditis elegans (C. elegans) has taken part in Japan since 2013. The goal is to understand the relationship between the complicated neural system dynamics and animal behaviors through recent advancements of 3D imaging and data analysis techniques. Pan-neuronal calcium imaging is performed to capture neural activities of a large number of C. elegans samples. On top of the typical measurement noise issue, the heterogeneity of the samples posts significant challenges on image registration and annotation. My research develops a series of statistical techniques that are suitable for automatic annotation and data analysis. As a result, one can exploit the increasing availability of large data sets of high resolution pan-neuronal images for more reliable data analysis as compared to the existing studies. My methodology combines the advantages from the Motion Coherent Theory, Integer Linear Programing and Weighted Majority Voting to achieve accurate and computationally efficient annotation algorithm.


Hierarchical Bayesian Modeling:

General procedure for approximating nested integrals in Bayesian analysis

(Collaboration with Dr. Panagiotis Angelikopoulos, Dr. Georgios Arampatzis and Prof. Costas Papadimitriou)

Hierarchical model is used across many fields, both in scientific and engineering research. The earliest concept of a hierarchical model appears in statistics, known as random effect model. When putting the problem into a Bayesian framework, there can be different types of hierarchical models. In general, such a complex model significantly increases the computational demand. My research develops an efficient way of handling hierarchical models for Bayesian inference. Previous applications include Molecular Dynamics and Pharmacokinetics models.


Earthquake Early Warning (EEW) application:

Earthquake Probability-based Automated Decision-making (ePAD)

Due to the high uncertainty of the stress and strength distributions within the tectonic plates on Earth, earthquakes are one of the most unpredictable natural hazards. Accurate prediction of when an earthquake will happen is still not possible, but the concept of EEW can be achieved because of the rapid development of computing power and network communication. A major challenge of developing applications for EEW is the short lead time, ranging from a few seconds to minutes depending on the geographics.As a result, a robust automated decision process about whether to initiate a mitigation action is essential. One of the recent approaches proposes taking an action upon exceedance of a fixed threshold for an intensity measure or damage or loss measure, but the determination of the threshold value remains as an open-ended question. Other approaches propose a decision-making framework based on cost-benefit analysis. However, a general framework that can handle multiple-action decisions, lead time and its uncertainty is still missing. My research focuses on developing a more robust decision criterion based on a new cost-benefit analysis procedure, which is proposed as part of an earthquake probability-based automated decision-making (ePAD) framework.


Complex network:

Efficient reliability analysis using Subset Simulation

(Collaboration with Prof. Konstantin M. Zuev)


Bayesian regression problems:

Compressive sensing application

(Collaboration with Dr. Yong Huang)


Bayesian Optimization and Decision theory:

Sensor placement strategy based on value of information

(Collaboration with Prof. Ikumasa Yoshida)


Sequential Monte Carlo:

Earthquake Early Warning Multi-source identification

(Collaboration with Prof. Masumi Yamada)