I conduct research in the field of sensor networks and real-time sensor data analytics and machine learning, focusing on the following areas:
Theoretical frameworks and approaches:
· CK Tham, Short course on “Internet of Things and Data Analytics for Industry”, Temasek Defence Systems Institute, Oct 2019; May 2020.
· CK Tham, Keynote Speaker on “Internet of Things and AI at the Edge”, Maybank-China Mobile International “Living in the Age of the Internet of Things” event, 27 Sept 2018.
· CK Tham, “Accelerating Reinforcement Learning in Engineering Systems”, NVIDIA AI Conference 2017, Singapore, 24 Oct 2017.
· CK Tham, “Edge Analytics on EA-Hubs”, NUS Enterprise “Intelligence at the Edge” Industry event, Singapore, 26 Oct 2017.
· CK Tham, Tutorial on Mobile Cloud Computing and Cloudlets at IEEE CloudCom 2015, Vancouver, BC, Canada, 30 Nov 2015.
Research attachments (sabbatical):
University of British Columbia (UBC), Vancouver, Canada (2016)
University of Illinois at Urbana-Champaign (UIUC), Urbana-Champaign, Illinois, USA (2016)
Shanghai Jiao Tong University (SJTU), Shanghai, China (2016)
THAM Chen Khong is an Associate Professor at the Department of Electrical and Computer Engineering (ECE) of the National University of Singapore (NUS). His current research focuses on wireless sensor networks/IoT, machine learning architectures and sensor data analytics involving cyber-physical systems, edge computing and participatory sensing. He was an early proponent of the SensorGrid architecture. He was seconded to A*STAR Institute for Infocomm Research (I2R) Singapore and served as principal scientist and department head of the Networking Protocols Dept, and programme manager of the Personalization, Optimization & Intelligence through Services (POISe) (Services) Programme. He was the programme manager of a multi-institution research programme on UWB-enabled Sentient Computing (UWB-SC) funded by A*STAR Singapore. He obtained his Ph.D.* and M.A. degrees in Electrical and Information Sciences Engineering from the University of Cambridge, United Kingdom, and was an Edward Clarence Dyason Universitas21 Fellow at the University of Melbourne, Australia. He is a Senior Member of the IEEE, is in the editorial board of the International Journal of Network Management, was the general chair of the IEEE SECON 2014, IEEE AINA 2011 and IEEE APSCC 2009 conferences, and the volunteers chair of IEEE GLOBECOM 2017 and organizing chair of IEEE ICCS 2012. He and his co-authors won the Best Paper award at IEEE ICUWB 2008 and ACM MSWiM 2007. Prior to joining NUS, he worked as an IT consultant at Accenture and a quality and reliability engineer at Hewlett-Packard. He currently serves as the deputy head for undergraduate studies and student life at ECE NUS, and was formerly the chair of the joint academic committee of the NUS computer engineering programme.
* PhD thesis: “Modular Online Function Approximation for Scaling up Reinforcement Learning”, University of Cambridge, UK, 1994.
This work is cited in Richard Sutton and Andrew Barto’s classic book Reinforcement Learning: An Introduction, 1st (1998) and 2nd (2018) editions.
I lecture the following graduate and undergraduate courses at NUS:
EE2028 Microcontroller Programming and Interfacing (see the ECE Connect article (page 3) on an earlier version of the course EE2024 Programming for Computer Interfaces) till 2019 only
EE5132 Wireless and Sensor Networks / EE5023 Wireless Networks / EE5024 Sensor Networks
EE4802 Learning from Data (compulsory module in Minor in Data Engineering)
- Co-PI of 2 projects on Traffic Modelling and Plan Generation using Machine Learning/AI Techniques, ST Engineering-NUS Joint Lab, 2020-2022.
- PI of MOE Tier 1 project: “Distributed Fast Predictive Modelling over Wireless Networks”, 2017-20
- NUS Co-Lead-PI of NRF NUS-Shanghai Jiaotong University CREATE Programme on Energy and Environmental Sustainability Solutions for Megacities (E2S2), Project SP2: “Cloud of Clouds: Peta-scale Urban Sensing and Data Management”, 2012-18.
- PI of MOE Tier 1 project: “System Architecture and Techniques for Event Analytics” (in collaboration with CS, SoC, NUS), 2015-17
- Towards Designing Flexible, Cost Effective and Secured Service Provisioning Strategies for Heterogeneous Data Centers in a Cloud-of Clouds Infrastructure (A*STAR SERC TSRP on Future Data Centre Technologies (FDCT)) (recently completed in July 2014)
- Event Driven Autonomic Services Architecture with Composition and Event Processing (EDASACEP) (A*STAR SERC TSRP on Data Value Chain as a Service (DVCaaS))
- UWB-enabled Sentient Computing Architecture & Middleware with Coordinated QoS (USCAM-CQ) (A*STAR UWB-SC TSRP)
- Stream Processing for Machine Learning & Control (NUS ARF Grant)
We have developed an Android-based application called ContriSense:Bus that uses participatory sensing to derive useful crowd-sourced real-time information on public buses.
See also our earlier work on SensorGrid.
General Chair, 11th Annual IEEE Communications Society Conference on Sensing, Communications and Networks (IEEE SECON 2014), 30 June-3 July 2014, Singapore.
Organizing Chair, IEEE Singapore International Conference on Communication Systems (ICCS) 2012, Singapore, 21-23 Nov 2012, Singapore.
System architectures for machine learning and
Cyber-Physical Systems (CPS)
Wireless mesh and sensor networks
Event driven architecture
Web address (this page)
Department of Electrical and Computer Engineering,
National University of Singapore,
4 Engineering Drive 3,
Block E4, Level 5, Room 42, or Block E4, Level 8, Room 6
Faculty of Engineering
National University of Singapore
(+65) 6516 2262 / 7959
(+65) 6779 1103