Every mystery can be explained,
with the right materials and tools.

My principle research interests lie in the field of data analysis and mining, probabilistic and statistical modeling as well as similar prediction and convex optimization techniques for creating real world application such as recommendation systems.

I also am interested in using new approaches, techniques, and technologies such as big data, deep learning, stream data, online learning, and trend analysis to expand existed knowledge and support people life.

Education

2012/10 〜

PhD. Student Department of Informatics, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies, Japan. 5-year PhD international course with granted scholarship from National Institute of Informatics (NII), which covers admission fee, tuition fee, and monthly allowance. Latent Models for Movie Recommendation System -- PhD. dissertation progress (equivalent to Master’s thesis) I extended proposed Bayesian probabilistic model, with an assumption the movies popularity is as decreased as they are getting older. The model is able to group up the same characteristics such as rating patterns by their age. It also shows that not all movies go along with the stated assumption. The prediction accuracy is also improved by a help from support vector regression as in previous work

2008/05 — 2012/03

Bachelor of Science Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Thailand. First author of "Regular Full Paper" titled "Multicriteria Collaborative Filtering by Bayesian Model-Based User Profiling" which published in IEEE IRI 2012. Model-Based Recommender System on Multi-Criteria -- Final year project I studied various kinds of algorithms for recomender system, especially probabilistic graphical model. I extended an existing probabilistic model for multi-criteria user-movie ratings input, and provided model learning method via expectation-maximization algorithm.

Experience

2017/11 〜	Data Analyst & Scientist – Machine Learning & Artificial Intelligence Engineer, Arblet Inc., Japan Research, surveying, filtering, validating, and presenting knowledge in work related topics. Data acquisition and experimental design. Developing cutting-edge algorithms, machine learnings, artificial intelligence for further product production. Experiment result evaluation, conclusion, and visualization.
2017/04	Invited Presentation & Talk, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Thailand. Introduction to Bayesian probabilistic graphical model and statistical inference, i.e Gibbs sampling and variational Bayes, and recent work on polylingual topic model.
2012/10 — 2017/09	Research Assistant, Takasu Laboratory, National Institute of Informatics (NII), Japan. Performing research experiments, reports, and presentations in various fields, such as feature extraction, natural language processing, text mining, topic modeling, probabilistic modeling, neural network, etc. Supporting lab members, such as managing study group, maintaining and monitoring a server.
2012/04 — 2012/09	Outsourced Web Developer, www.eventpro.in.th, Thailand (Project discontinued.) Provided effective APIs and back-end system, such as login system, ticket reservation system for event ticket saling website and mobile application

Interest

Information Retrieval	Multivariate Statistic
Natural Language Processing	Nueral Network & Sequential Variant Models
Numerical Analysis & Stochastic Methods	Pattern Recognition
Predictive Models: Regression, Vector Machine, etc.	Probability Graphical Model & Bayesian Inference
Recommendation Systems	Signal Processing

Skills

• Scientific programming: Expertise in Python with well-known libraries and tools, such as gensim, joblib, nltk, numpy, pandas, scikit-learn, scipy, matplotlib, Jupyter-Notebook, TensorFlow.
• Web development: Usually use for making demo or prototype, and data visualization with HTML5, CSS, JavaScript (D3.js).
• General: Markdown, , Google Drive.

Hobbies

• Co-operative, multiplayer online battle arena (MOBA) games.
• Photography

Publications

1. Bayesian Model for a Multicriteria Recommender System with Support Vector Regression
   Oct 24, 2013 • 2013 IEEE 14th International Conference on Information Reuse and Integration (IRI).
2. Extended Bayesian Model for Multi-criteria Recommender System
   Jan 4, 2013 • Information Fundamentals and Access Technologies.
3. Multicriteria Collaborative Filtering by Bayesian Model-Based User Profiling
   Sep 17, 2012 • 2012 IEEE 13th International Conference on Information Reuse and Integration (IRI).
4. Item Age Effect Against Global User Preference in Latent Model of Recommendation System
   Aug 05, 2015 • IEICE technical report, 115(177), 49-54.