Shedding Light on Quality

Wei Siong Tan, the founder of Glow-Ai, is dedicated to leveraging Artificial Intelligence and Machine Vision in the manufacturing industry. Previously, he held several senior management positions at companies like Stadium Group PLC and Laird, where he gained extensive experience in operations, project management, engineering, costing, and supply chain across diverse industries. Fluency in multiple languages, including English, Malay, Mandarin, Hokkien, and Cantonese, further enhances Wei Siong's ability to communicate effectively.

Wei Siong's academic achievements include an Executive MBA from Rutgers University, where he proudly served as the valedictorian of the 2011 class. He also holds a Master's and Bachelor's degree in Industrial & Management System Engineering from the University of Nebraska Lincoln.

As a results-oriented professional, Wei Siong has consistently excelled in business leadership and operational excellence. Notably, he successfully led the Mechanism operation, achieving an exceptional 300% growth from $20 million to $80 million. His strategic acumen was instrumental in establishing a robust supply chain infrastructure for the Raspberry Pi3 production, prioritizing lead time reduction, risk mitigation, and flexibility enhancement.

Furthermore, Wei Siong spearheaded the transformation of the Automotive Connectivity Unit at Laird, strategically leveraging functions such as Supply Chain, Category Management, Design Sourcing, and Material Management to gain a competitive edge for the organization.

Blaine Bateman has over 35 years industrial experience including 10 years of experience in Machine Learning,  and spearheads new innovative solutions at Glow-Ai. He graduated with Special Honors in ChE & later Cert. in Quality Mgmt. (CU Boulder) & Machine Learning (Stanford). His career spans US govt. R&D, several start-ups, and 13 years with Laird (Global VP Mktg) prior to forming the consulting business EAF LLC. Blaine’s core skills include Statistics, Machine Learning, Electronics, Automotive, & Antennas/RF, and he holds 18 patents in Instruments, Antennas, and RF Design. As a consultant, Blaine served Fortune 1000 and FTSE 250 companies, including global strategy, deep analytics, and forecasting. Blaine supports several start ups with Machine Learning including Autonet and Maike as well as providing mentoring in a number of roles.

After ten years in government (Deputy Director, National Measurement Lab (US DoC NIST) and Chief, Chemical Engineering Division of NIST), Blaine developed 100s of products and several patents in various startups.  He led efforts to bring manufacturing of telematics antennas to China for Centurion, and was key in creating an Automotive Connectivity Unit in Laird. Blaine was Global Head of Marketing in Laird and led efforts to improve data quality and analytics. Blaine is also the author of The Pandas Workshop: A comprehensive guide to using Python for data analysis with real-world case studies.​

John Yu career has spanned diverse high-tech industries including semiconductors, electronic design automation (EDA), consumer electronics, and Internet-of-Things (IoT).  An integrated circuit designer and computer architect by training, John has held engineering as well as management positions in global Fortune 500 companies as well as co-founded a number of startups in US and China. 

John led a number of impactful technology and product developments prior to Glow-Ai, including the development and deployment of the world’s first Bluetooth-mesh IoT gateway, a novel smart IoT lighting system that not only senses the environment to automatically optimize ambient lighting to save energy consumption, but also acts as an indoor location tracking system of people and things, China’s first all-in-one multimedia System-on-Chip (SoC) for camera phones and smart phones, the world’s first SoC that integrated all of the magnetic disk drive’s electronics including read-channel, controller, CPU and application-specific hardware on a single die, a comprehensive delay calculation, clock tree and timing modeling methodology that accounted for the impact of IR-drop and cross-coupling interconnect noise on the performance of very large-scale integrated circuits (VLSI), and signal integrity design methodologies for fault-tolerant enterprise mainframes.