CV

My 2-page CV can be found [here].

Education

Ph.D. in Computer Engineering, UIUC (Dec 2018 - May 2022)

M.Sc. in Computer Engineering, UIUC (Aug 2015 - Dec 2018)

B.Comp. in Computer Engineering, School of Computing, NUS (Aug 2007 - May 2011)

Work experience

Software Engineer at Amazon AWS (Sept 2022 - present)

Data Science Intern at iManage (May 2022 - Aug 2022)

Research Assistant at UIUC (Aug 2015 - May 2022)

Software Engineer at ADSC (Sept 2011 - May 2015)

Software Engineer Intern at Temasek Lab, NUS (Oct 2010 - Mar 2011)

Research skills

My strongest suit is probabilistic thinking and a risk-informed approach to problem solving. I owe my understanding of probability to my academic advisor, Prof. David M. Nicol, who encouraged me to study Bayesian analysis earlier on in my research journey. My other source of inspiration came from a wonderful book written by E. T. Jaynes, "Probability: the logic of science". Since then, probability became the foundation that allowed me to explore other research topics such as random process, Monte Carlo methods, risk modeling and analysis, and other topics listed below.

Bayesian analysis: Bayesian belief network and Bayesian inference.
Monte Carlo methods: topics in rare event simulation (e.g., adaptive sampling based on cross-entropy method, population Monte Carlo) and variance reduction (importance sampling, stratified sampling).
Risk analysis: Uncertainty modeling and quantification, probabilistic risk analysis.
System reliability: Fault tree, event tree, network reliability, binary decision diagram.
Applied probability and statistics: Markov chain, Markov decision process, stochastic Petri nets, hidden Markov model, queueing theory, hypothesis testing, dependence modeling with copulas.
Graph theory, including graphs with random elements such as uncertain graphs.
Optimization: Global and heuristic optimization.
Machine learning: Neural networks, multi-armed bandit, regression analysis, reinforcement learning.

Other research projects I have completed or participated in at UIUC:

ECE598 Dependable AI systems: Developed rare-event simulation technique for safety evaluation of automated driving technology with machine-learning components. Evaluated the simulation technique using scenarios developed from Matlab’s Automated Driving Toolbox.
CS598 Reliability of cloud-scale systems: Proposed programmable network-based resiliency testing tool for data center networks.
Social simulation: Participated in UIUC’s team to compete in DARPA’s SocialSim Challenge. Developed agent-based simulation framework for modeling information propagation over social networks. Proposed model selection algorithm for composite model with discrete state transitions.

Software development skills

Melody

Our group at UIUC developed Melody, a cyber-physical systems co-simulation tool for generating nework traffic containing traces of cyber-attacks. The network packet capture (PCAP) generated by Melody can be used to train machine learning-based network intrusion detection systems. The documentation of Melody can be found [here]. A demonstration of how Melody can be used to simulation a cyber-attack on the 39-bus power system is given in the video below.

Living lab

At ADSC, I was an active member of the living lab project. I helped designing, developing, and deploying a wireless sensor network in the office space to study building energy efficiency. The living lab consists of 100+ Zigbee radio-powered sensors, including power plug sensors (provided by Korea Electronics Technology Institute), ambient light, temperature, and humidity sensors, PIR motion sensors, and CO2 sensors. I also developed a monitoring and management system for data collection, storage, and troubleshooting.