Apart from research, I am very passionate about teaching. I have not only been a teaching assistant for several modules, but have also actively contributed in guiding Final Year Project students. By being the department representative who has the most direct, one-on-one contact with the students on a regular basis, I have been fortunate in creating a rich learning experience for undergraduate students. Below is the list of students who have been under my tutelage.
1) Lim Zhi Jian Nicholas (Jan 2012 - Dec 2012) - Scene understanding. Published a paper in ICCA, 2013.
2) Tian Chao (Aug 2012 - May 2013) - Face Recognition. Contributed to a poster presented at the annual graduate student symposium in 2014.
3) Lin Lin (Aug 2013 - May 2014) - Vehicle tracking and speed detection. Won three prestigious awards for the project she completed.
- IEEE Control Systems Chapter Prize
- IEEE Singapore Computer Society Book Prize, and
- Outstanding Undergraduate Researcher Award
4) Chen Liang (Aug 2013 - May 2014) - Scene understanding. Won two awards for improving upon Nicholas's work.
- 3rd place in the 2014 IEEE Region 10 Student Paper Contest
- Merit Award in the Faculty of Engineering 28th Innovation & Research Award (2014).
5) Liu Qi (Aug 2013 - May 2014) - Face Recognition.
6) Huang Liushu (Aug 2014 - May 2015) - Face Recognition. Published a paper in ASCC 2015
7) Han Ming Show (Aug 2014 - May 2015) - Vehicle tracking and speed detection. Published a paper in ICCA 2016 and a patent is pending.
8) Tan Hao Wei (Aug 2014 - May 2015) - Scene Understanding.
9) Ji Yahui (Aug 2015 - May 2016) - Helped to secure the practicum grant from NUS Enterprise.
10) Lim Anli (Aug 2015 - May 2016) - TL SEED Video Stabilization project. Writing a journal paper to be submitted to JCTA.
11) Asawari Agarwal (Aug 2015 - May 2016) - Scene Understanding.
12) Xiaocong Yang (Jan 2016 - Dec 2016) - Face recognition.
13) Sankalp Srivastava (Aug 2016 - May 2017) - Face recognition
14) Antony Karukapally (Aug 2016 - May 2017) - Scene recognition
15) Le Thi Ngoc Anh (Aug 2016 - May 2017) - Event-based Recognition. A paper submitted to BioCAS 2017.
16) Le Dang Khoi (Aug 2016 - May 2017) - Event-based Motion Estimation.
17) Cihang Zhou (Aug 2016 - May 2017) - Object Tracking.
18) Zhang Shihao (Aug 2017 - May 2018) - Event-based Object Tracking.
19) Jonathan Lee Wei (Aug 2017 - May 2018) - Event-based Object Tracking.
20) Hu Yang (Aug 2017 - May 2018) - Face recognition
21) Wei Junhao (Aug 2017 - May 2018) - Event-based Scene Classification
22) Justin Erh Wei Lun (Aug 2017 - May 2018) - Face recognition
23) Guo Jiaqi (Aug 2017 - May 2018) - Big Data Techniques for Large Scale Scene Classification
24) Jonathan Cheong (Aug 2018 - May 2019) - Event-based Object Recognition using Spiking Neural Networks
25) Matthew Ong Zhi Jian (Aug 2018 - May 2019) - Event-based Motion Estimation
26) Low Weng Fei (May 2019 - Aug 2020) - SLAM for Event Cameras
27) Chockalingam SenthilRajen (May 2019 - Aug 2019) - Event-based Object Tracking
28) Mohit Sarin (May 2019 - Aug 2019) - Event-based Object Tracking GUI
29) Khor Ru Shan (Aug 2019 - Dec 2019) - Object Classification for Event Cameras
30) Jonathan Lim Kangjie (Aug 2019 - Apr 2020) - Real-time Event-based Motion Estimation
31) Fu Chuanrong Gideon (Aug 2019 - Apr 2020) - Event-based Object Tracking
32) Jaspreet Kaur Pawa (Apr 2020 - Jun 2020) - Event-based Feature Extraction
33) Naitik Khandelwal (Oct 2020 - Dec 2020) - Modelling visual object recognition mechanisms
34) Ankit Sonthalia (April 2021 - Dec 2021) - Mid-level Statistical Modeling for Event-based Cameras
35) Karthikay Gundepudi (Aug 2021 - Dec 2022) - Analysis of EEG signals for Sleep detection
36) Praneeth Thota (Jan 2022 - Dec 2022) - Predicting EEG signals using Machine Learning
37) Abhinav Kumar Goswami (Dec 2021 - April 2022) - Event-based Pattern Detection Systems
38) Madhav Walia (Nov 2022 - Present) -Decentralized Federated Learning
39) Sai Krishna (Dec 2022 - Present) - Event-based pattern recognition in the wild
40) Shivanshu Raj (Dec 2022 - Present) - Real-time event-based tracking
Grad Students
1) Zheng Xiaoxu (Aug 2016 - Apr 2017) - Tracking Learning and Detection Systems
2) Leng Yusong (Aug 2016 - Dec 2017) - Scene Hierarchy for improving scene classification
3) Zhen Xie, PhD Scholar, (Jun 2016- Dec 2016) - Event-based SLAM
4) Zhoajin Sun, Masters, (Jan 2017 - May 2017) - Study of Mindfulness Using Video Analytics.
5) Yang Hong, Masters, (Aug 2017- Dec 2017) - Event-based Object Recognition. TPAMI Paper accepted!
6) Tarun Pulluri, Masters, (Dec 2019 - Jun 2020) - Event-based Object Tracking. Two papers submitted to TNNLS!
7) Andres Ussa, Masters (Dec 2018 - Dec 2021) - Hardware Implementation of Neuromorphic Object Recognition and Tracking Systems
8) Tan Yan Rui, PhD Scholar, (Sep 2020 - Apr 2021) - Event-based Flow Detection
9) Priyanka Bharadwaj, PhD Scholar, (Sep 2021 - Apr 2022) - Low-power Object Detection using Neuromorphic Cameras
10) Qiuyu Shen (Aug 2021 - Apr 2022) - Implementation of blockchain platforms for distributed artificial neural networks
11) Li Chao (Aug 2021 - Apr 2022) - Design and Implementation of Distributed Artificial Neural Networks for Blockchain Platforms
12) Wu Hongchi (Aug 2021 - Apr 2022) - Design and implementation of neuromorphic intelligent systems for blockchain platforms
13) Fang Binhao (Aug 2021 - Apr 2022) - Implementation of blockchain platforms for distributed neuromorphic systems
14) Chen JIahui (Aug 2021 - Apr 2022) - Swarm Learning framework for decentralized and distributed machine learning
15) Fan Kanglong (Aug 2021 - Apr 2022) - An Interpretable Scene Understanding Framework via Graph Learning
16) Liu Wei (Aug 2021 - Apr 2022) - Scene Understanding via Saliency Consensus
17) Chen Xiaowen (Aug 2021 - Apr 2022) - Parsing Networks for Scene Understanding
18) Gao Shihao (Aug 2022 - Present) - Edge IoT Systems on Blockchains
19) Su Hongxu (Jan 2023 - Present) - Decentralized Federated Learning
20) Long Yinyun (Jan 2023 - Present) - Decentralized Deep Learning
Besides mentoring students, I have been the teaching assistant for various modules.
1) Neural Networks EE5904R - Sem 2 Ph.D
2) Instrumentation and Sensors MCH5206 -Sem 3 Ph.D
3) Neural Networks EE5904R - Sem 4 Ph.D
4) Computer Control Systems EE5103R - Sem 5 Ph.D
5) Neural Networks EE5904R - Sem 6 Ph.D
6) Computer Control Systems EE5103R - Sem 7 Ph.D
While the teaching load takes time away from research, it is an extremely valuable experience in thinking and speaking on one's feet. It helps you learn to transform passive into active knowledge. Also, teaching is something of a social responsibility, a payment for the privilege of satisfying our curiosity in doing basic research!