Systems Architect | AI-RAN & 5G/6G | Co-founder, NeuroRAN
Lead Engineering at NeuroRAN, AI-enabled Radio Access Networks
Singapore 487372
Email: (1) vanmao_ngo @ sutd dot edu dot sg (2) vanmao_ngo @ alumni.sutd dot edu dot sg
I am a Systems Architect specializing in AI-driven 5G/6G and Open RAN (O-RAN) systems, currently working at the Future Communications Programme (FCP), Singapore University of Technology and Design (SUTD), where he has been since 2022. Prior to this, he was a Research Scientist at the Institute for Infocomm Research (I2R), A*STAR, Singapore (2021–2022), and a Postdoctoral Research Fellow at SUTD (2020–2021). Before pursuing the Ph.D., Mao worked as a software engineer for 4+ years.
He received a B.Eng. (Hons.) degree in Telecommunications Engineering from the National Technical University of Ukraine, Kyiv Polytechnic Institute, Ukraine, in 2011, and received M.Eng. degree in Electronic Engineering from Ho Chi Minh City University of Technology, Vietnam, in 2015. He earned the Ph.D. in Information Systems Technology and Design (ISTD) from SUTD, in 2020, under the supervision of Prof. Tony Q.S. Quek at Singapore University of Technology and Design (SUTD), Prof. Tony T. Luo at Missouri University of Science and Technology, and Dr. Derek Leong at I²R, A*STAR, Singapore.
His research interests include AI-RAN, applied ML for RAN intelligent controller in Open RAN (O-RAN), Machine Learning (ML) for Cyber Security, Federated Learning, Edge Computing, and Data Mining for the Internet of Things (IoT). His earlier research focused on wireless sensor networks and scheduling in IoT networks.
I work to build intelligent RAN solutions with AI, bring Cloud computing to IoT devices, and safeguard networks against vulnerabilities.
My research interests include (but hopefully not limited to):
AI-Driven Radio Access Networks (AI-RAN)
Open RAN (O-RAN) and RAN Intelligent Controller (RIC)
Private 5G and Edge-Cloud AI Systems
Network Automation and Intent-Driven Networking
AI/ML for Cyber Security and Network Reliability
6G-Enabling Technologies
Software engineering skills: Python, C, C++, Java, Pytorch, Tensorflow, databases (e.g., MySql, Sqlite), network programming, PLEX-C, ASA.
Ph.D. degree in Information Systems Technology and Design, Singapore University of Technology and Design (SUTD) in 2020.
M.Eng. degree in Electronic Engineering from Ho Chi Minh City University of Technology (HCMUT) in 2015.
B.Eng. degree in Telecommunications Engineering (first class honor) from Kiev Polytechnic Institute (KPI), Ukraine in 2011.
High School for the Gifted in the subject Mathematics from Hue University of Science, Hue City, Vietnam.
Future Communications Research & Development Programme (FCP), SUTD, Systems Architect | 2022 – Present
Architected and deployed multi‑vendor private 5G and O‑RAN systems in Singapore
Led AI‑RAN xApp/rApp development for traffic analytics, interference mitigation, and resource optimization
Contributed to O‑RAN testing and certification at the Asia & Pacific OTIC
Represented Singapore’s AI‑RAN research at international forums (MWC Barcelona 2023-2026, 5G/6G summits)
NeuroRAN (SUTD Spin‑off), Co‑founder & Lead Engineer | 2024 – Present
Co‑founded a Singapore deep‑tech start‑up commercializing AI‑RAN technologies
Leading system architecture, product roadmap, and customer engagement
Translating research prototypes into deployable AI‑enabled RAN solutions
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Research Scientist, 2021-2022.
Research and implement malware detection AI models, e.g., deep learning, incremental learning, active learning, and transfer learning.
Singapore University of Technology and Design (SUTD), Research Fellow, July 2020 - 2021.
Research and implement a federated learning framework to collaboratively train a shared anomaly detection model without leaking local training data that belong to different enterprises for edge computing networks.
Singapore University of Technology and Design (SUTD), Ph.D. Candidate, Sep 2016 - July 2020
Design an adaptive scheme to select one of the machine learning models based on the contextual information extracted from input data, to perform anomaly detection. The selection is formulated as a contextual bandit problem and is characterized by a single-step Markov decision process, with the objective of achieving high detection accuracy and low detection delay simultaneously.
Design a hierarchical edge computing system that jointly supports container migration and base station handover for mobile users. Source code and Video demo.
Adaptive MAC scheduler for wireless body sensor network with a cross-layer approach.
Telecom Sud Paris, Research Visiting, 09/2019-12/2019, under supervision of Prof. Hakima Chaouchi
Using Machine learning for Distributed Anomaly Detection in IoT systems.
National Taiwan University (NTU), Research Visiting 04/2019, under supervision of Prof. Ai-Chun Pang
Machine Learning in Mobile Edge Computing.
Singapore University of Technology and Design (SUTD), Research Assistant 07/2015 - 09/2016
Setup experiments in the laboratory for Wireless Body Area Networks, and measure networks’ performance.
Implement adaptive MAC scheduling schemes for wireless communications, i.e., IEEE 802.15.4e and Bluetooth Low Energy (BLE).
DEK Technologies, Research and Development Engineer 10/2011 – 06/2015
Research and implement proposal for new features of APZ-AXE systems that have been the main project of Ericsson Company for many years.
Involve in some projects and all phases of the software design process from writing proposals, planning, and implementation.