(1) I lead applied AI research and live systematic trading with multi-billion dollar notional sizes at Hessian Matrix. We develop scalable systematic strategies with deep learning, reinforcement learning and bayesian learning for thin-tailed and fat-tailed distributions. We are a systematic global hedge fund led by a team with deep experience formerly from GIC, NUS, and NVIDIA.
(2) I am also an NVIDIA Deep Learning Institute instructor leading all deep learning industry workshops in NUS, Singapore and conducting workshops across Southeast Asia.
(3) I’m into applied research for systematic trading strategies with deep learning at NUS where I am a research scholar in NExT (NUS)) and a PYI Fellow.
(4) My passion for enabling anyone to leverage on deep learning has led to the creation of Deep Learning Wizard where I have taught and still continue to teach more than 3000 undergraduates, graduates and professionals in over 60 countries around the world.
I spent 2.5 years leading artificial intelligence with my colleagues in ensemblecap.ai, an AI hedge fund based in Singapore comprising research scientists, engineers, quants, and traders from NVIDIA and JP Morgan. I have built the whole AI tech stack in a production environment with rigorous time-sensitive and fail-safe software testing powering multi-million dollar trades daily. Additionally, I led, as portfolio manager, our deep learning systematic portfolio, delivering high positive returns in highly volatile years like 2018 (US-China trade war initiation) and 2020 YTD (covid-19 virus)
I was previously conducting research in meta-learning for hyperparameter optimization for deep learning algorithms in NExT Search Centre that is jointly setup between National University of Singapore (NUS), Tsinghua University and University of Southampton led by co-directors Prof Tat-Seng Chua (KITHCT Chair Professor at the School of Computing), Prof Sun Maosong (Dean of Department of Computer Science and Technology, Tsinghua University), and Prof Dame Wendy Hall (Director of the Web Science Institute, University of Southampton).
I graduated from NUS where I was an NUS Global Merit Scholar, Chua Thian Poh Community Leadership Programme Fellow, Philip Yeo Innovation Associate, and NUS Enterprise I&E Praticum Award recipient. I was awarded the IT Youth Leader of the Year Award in 2019.
Deep Learning Courses with Deep Learning Wizard
- These are some of the courses/tutorials I created that will gradually build up your deep learning capabilities. We are an NVIDIA Inception Partner and supported by Amazon AWS Activate.
- Deep Learning and Deep Reinforcement Learning Tutorials (Libraries: Python, PyTorch, Gym, NumPy, Matplotlib and more)
- Course Progression
- Linear Regression
- Logistic Regression
- Feedforward Neural Network (FNN)
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Long Short-Term Memory Network (LSTM)
- Autoencoders (AE)
- Fully Connected Overcomplete Autoencoders
- Derivative, Gradient and Jacobian
- Forward- and Backward-propagation and Gradient Descent (From Scratch FNN Regression)
- From Scratch Logistic Regression Classification
- From Scratch CNN Classification
- Learning Rate Scheduling
- Optimization Algorithms
- Weight Initialization and Activation Functions
- Supervised to Reinforcement Learning
- Markov Decision Processes and Bellman Equations
- Dynamic Programming
- Speed Optimization Basics Numba
- Machine Learning Tutorials (Libraries: Python, cuDF RAPIDS, cuML RAPIDS, pandas, numpy, scikit-learn and more)
- Programming Tutorials (Libraries: C++, Python, Bash and more)
- Scalable Database Tutorials (Libraries: Apache Cassandra, Bash, Python and more)
Presentations and Publications
- Detecting Waterborne Debris with Sim2Real and Randomization, AI for Social Good, ICML, 2019
- GFD: GPU Fractional Differencing for Rapid Large-scale Stationarizing of Time Series Data while Minimizing Memory Loss, NVIDIA, 2019
- Neural Optimizers with Hypergradients for Tuning Parameter-Wise Learning Rates, AutoML, ICML, 2017
Challenges at Conferences
- FinIR 2020 Grand Challenge at SIGIR 2020, SIGIR 2020, Co-organized by NExT++ and Hessian Matrix (Ritchie Ng)
- Preparing for Disruption in an AI Future, Prudential Singapore, January 2020
- Machine Learning for Humans, MAS Fintech Festival, November 2019
- Large-scale Stationarizing of Time Series while Maximizing Memory, Visa Data Summit, November 2019
- GPU Fractional Differencing, DBS, Singapore September 2019
- GPU Fractional Differencing, Big Data & AI Leaders Summit, Singapore, September 2019
- Computer Vision with Deep Learning Fundamentals, AI Summer Camp, NVIDIA and NUS, July 2019
- Introduction to AI, DSTA and NVIDIA, Singapore, June 2019
- Detecting Waterborne Debris with Sim2Real and Randomization, ICML, Los Angeles, USA, June 2019
- Foundations of Deep Learning, African Masters of Machine Intelligence (AMMI), Google & Facebook, Kigali, Rwanda, November 2018
- AI and Unstructured Analytics in Fintech, Nanjing, China, November 2018 Post Link
- PyTorch Developer Conference, Facebook, San Francisco, USA, October 2018
- Hyperparameter Optimization with Neural Optimizers, Big Data & AI Leaders Summit, Singapore, September 2018
- Image Classification Workshop, NUS-NUH-MIT Datathon, NVIDIA, Singapore, July 2018
- Object Detection with DIGITS, NVIDIA, Singapore, June 2018
- Image Classification with DIGITS, NVIDIA, Singapore, May 2018
- Meta Learning, AutoML, ICML, Sydney, 2017
- Deep Learning for Self-Driving Cars and Medical Diagnostics, NVIDIA, Singapore, 2017
- Scalable Hyperparameter Optimization, REWORK Deep Learning Summit, Singapore, 2017
- Residual Networks with TensorFlow
- Wide Residual Networks with TensorFlow
- Large Scale Identification of Multiple Digits from Real-world Images with Convolutional Neural Networks (CNN)
- Training a Smart Cab (Reinforcement Learning)
- Identifying Customer Segments (Unsupervised Learning)
- Building a Student Intervention System (Supervised Learning)
- Predicting Boston House Prices
- The Incredible PyTorch, curated list of tutorials and projects in PyTorch
- DLAMI, deep learning Amazon Web Service (AWS) that’s free and open-source
- RAPID Fractional Differencing to Minimize Memory Loss While Making a Time Series Stationary, 2019
- The Great Conundrum of Hyperparameter Optimization, REWORK, 2017
IT Youth Leader of The Year 2019, Singapore Computer Society
Prestigious award for my industry, academic and charitable work in ensemblecap.ai, Deep Learning Wizard, NVIDIA and NUS
Chua Thian Poh Community Leadership Programme Fellow 2018, NUS
Established with generous gifts from Mr Chua Thian Poh, the Centre aims to nurture Singapore’s next generation of community leaders. These leaders will not only be intellectually engaged with social and community issues, but will also be passionate about addressing social and community challenges in Singapore.
Philip Yeo Innovation Fellowship 2017, NUS
Award with mentorship by Philip Yeo, Chairman of Spring Singapore.
I am fortunately also under the mentorship of Kiren Kumar (AMD, EDB) and Abel Ang (CEO, EDIS).
Valedictorian (Reserve) Class of 2018, NUS
Global Merit Scholarship 2014-2018, NUS
NUS top scholarship with only 4 awarded in NUS across all faculties for the year of my admission.
Full scholarship amounting to more than $100,000 covering tuition, allowance, accommodation, and overseas trips.
I&E Practicum Award 2017, NUS
Dean’s List 2015/2016, NUS
Top 5% of my cohort.
Languages, Libraries and Frameworks
|Machine Learning||Database||General Programming|
I would like to thank all my readers for their encouraging participation on this Github page. I would also like to thank Github Pages for serving this respository of notes for free.
I would like to give full credit to the respective authors for their free courses and materials online like Andrew Ng, Data School and Udemy where my notes are from them. These personal notes are meant for my personal review but I have open-sourced my repository of personal notes as a lot of people found it useful.
Take note that I’m currently concentrating entirely on building materials for Deep Learning with PyTorch from mastering deep learning, to deploying deep learning algorithms in production, and to to solve many problems through Deep Learning Wizard.