





Ø Joined AIT in 2019, founded AIT Brain Lab, currently Assistant Professor in Computer Science and Information Management and Data Science and AI, School of Engineering, AIT
Ø Academic Program Coordinator, Computer Science, Asian Institute of Technology (AIT)
Ø Chair of the Sustainability Positioning Task Force at AIT
Ø Previously served in the Ethics Committee
Ø Co-founder, AI Brain Lab Co. Ltd.
Ø Currently serving in the DPRC & Faculty Relations Committee
1. Ph.D. in Computer Science at Center of Human Engaged Computing, Kochi University of Tech, Japan
2. Recipient of a KAKENHI Young Scientist grant, a Young Scientist grant by TRF, a National Science and Technology Development Agency Toray Science grant, a JST grant project, a NBTC grant project. Moreinformationcanbefoundhere.
Ø This project aims to develop a brain-computer interface-based speller that would benefit disabled users or elderly people.
Ø The primary purpose of this work is to develop the Thai language-based P300-BCI visual speller as an assistive technology option for disabled native Thai speakers. An experiment of a checkerboard paradigm with a 8 x 9 matrix layout containing 44 Thai alphabets, 16 vowels and 7 numbers was conducted. Results show that RegLDA with xDAWN outperformed other models on P300 speller performance, achieving cross validation accuracy of 93% and online accuracy of 76%. AIT Brain Lab will continue this project by employing hybrid P300-SSVEP for further improvements in speed and accuracy.
Ø Non-Invasive Blood Glucose Measuring Using Raman Spectroscopy represents a cutting-edge approach to diabetes management, offering a non-invasive and convenient method for monitoring blood glucose levels.
Ø By harnessing the principles of Raman spectroscopy, this technology enables the detection of glucose concentrations in the bloodstream through analysis of the chemical composition of fingernails. Raman spectroscopy works by shining laser light onto a sample and analyzing the scattered light to identify molecular vibrations unique to glucose molecules.
Ø This technique eliminates the need for painful finger pricks or invasive procedures, providing individuals with diabetes a hasslefree way to track their glucose levels. Moreover, the ability to measure glucose levels through fingernails offers greater accessibility and convenience, as it can be performed anytime, anywhere, without the need for specialized equipment.
Ø In collaboration with Rsquare, our research lab is currently delving into the innovative realm of liveness detection.
Ø This project aims to enhance security measures by developing advanced techniques to discern between live human presence and synthetic or replicated representations, such as images or videos.
Ø By leveraging cutting-edge technologies and methodologies, we aspire to fortify authentication systems, ensuring robust protection against fraudulent activities in various domains, including biometrics, identity verification, and secure access control.
Detection of fake video
Detection of real video
Partner with Rsquare
Ø Our research project is focused on the development of a sales x CRM (Customer Relationship Management) x Telemarketing system in collaboration with a Thai insurance company.
Ø Combining the power of conversational AI with the expertise of Thai Insurance company on in language solutions, our aim is to revolutionize the way businesses manage customer interactions and sales processes.
Ø The chatbot will serve as a virtual sales assistant, capable of understanding and responding to inquiries in the Thai language while seamlessly integrating with CRM systems. By leveraging natural language processing and machine learning algorithms, our chatbot will streamline sales workflows, automate routine tasks, and provide personalized recommendations, ultimately enhancing productivity and driving revenue growth for businesses operating in Thai-speaking markets.
Ø Establish an AI and Simulation Lab to support the faculty of the School of Engineering and Technology (SET) in their research.
Ø Initial investments from donors of around 8 million baht
Ø Demonstrations related to artificial intelligence, deep learning/machine learning, and computer simulation.
Ø This lab will serve as a hub for cuttingedge research, innovation, and interdisciplinary collaboration.
Donors:
• Global water & sanitation center, AIT
• AI center, AIT
- ContactSETifyouareinterestedtobedonor Coordinators:
• Dr. Chaklam Silpasuwanchai
• Dr. Sarawut Ninsawat
• Dr. Sanit Arunplod
• Dr. Mongkol Ekpanyapong
• SET Deans and Faculties
Ø On 26 April 2024, AIT and Beijing University of Posts and Telecommunications (BUPT) signed a MoU for the co - establishment of The Future Lab, AIT X BUPT, and inaugurated the new joint facility at AIT that was witnessed by senior diplomats of the Embassy of China in Thailand.
Ø BUPT is one of the top public universities in China in a number of technological fields, ranking high on global university tables in areas such as information and communication engineering, computer science and technology and electrical and electronic engineering.
Ø The Future Lab is intended to leverage the two institutions' capabilities in education and research in ICT, A.I., sustainability, and innovation, and address the challenges of achieving the SDGs.
Coordinators:
• Ms. Danielle Duan Duan
• Mr. Shawn Kelly
• Dr. Chaklam Silpasuwanchai Faculty helping:
• Dr. Chutiporn Anutariya
• Dr. Chantri Polprasert
• Dr. Sarawut Ninsawat
Ø Dr. Chaklam Silpasuwanchai and Mr. Raymond Chee co-founded AI Brain Lab Co., Ltd. and successfully obtaining BOI privileges.
Ø AI Brain Lab signed a MoU with AIT on May 14, 2023. AI Brain Lab will set up the office in AIT under AIT BOI Science and Technology Park.
Ø Under the MoU, AI Brain Lab will contribute to AIT by
• Providing opportunities for students to intern/hiring at company.
• Providing scholarships
• Participating in AIT Career Fairs
Ø Medical Imaging for Stroke
Ø EEG for stroke, dementia, depression
Ø Brain-Computer Interface Speller
Ø Glucose monitoring using Raman Spectroscopy
Ø Sign2Text and Text2Sign (Sign Languages for the Deaf)
Ø Multimodal learning
Ø Voice2Text and Text2Voice
Ø Image2Text and Text2Image
Ø Visual Question Answering
Ø Medical Visual Question Answering
Ø Small Language Models