Singular Learning
Classical laws break down near statistical singularities.
Distributed Intelligence
Towards model-parallelism and Hebbian learning.
Type-Theoretic Language
Communication and reasoning between intelligent entities.

2016 Future of AI

2016 Statistics

Singular Learning


20161111 ‘Deep Probability Flow,’ Brain Lab, SUTD.
20161028 ‘Deeper Learning for Smarter Cities,’ NVIDIA-SUTD Deep Learning Day, SUTD.
20161027 ‘From Deep Learning to Minimum Probability Flow,’ ZJU Data Science and Engineering Research Center, Hangzhou.
20161026 ‘Smarter Cities through Distributed Artificial Intelligence,’ SUTD-ZJU IDEA Workshop, Hangzhou.
20160715 ‘Deep Distributed Intelligence,’ Brain Lab, SUTD.
20160713 ‘Distributed Intelligence,’ WNDS Group, SUTD.
20160713 NRF Workshop on AI, CREATE, Singapore.
20160701 Future of AI, National Library, Singapore.
20160509 ‘The Singularity is Near: When Machines Transcend Data,’ Applied Algebra Seminar, Berkeley.
20160504 Panellist at ICCCRI 2016, Suntec Convention and Exhibition Centre, Singapore.
20160316 ‘Lessons on Statistical Singularities from Deep Learning,’ Yale-NUS Math Seminar.
20160309 6th Singapore Conference on Statistical Sciences, NUS.
20160304 ‘Deep Learning,’ NUS Guest Lecture.
20160226 ‘Big Data and Data Analytics,’ CSD&M Asia, SUTD.
20160203 ‘IoT Analytics,’ SMU Guest Lecture.

We Are Hiring!

I’m looking for mathematically-minded postdocs and Ph.D. students who are interested in working on distributed machine intelligence and deep reinforcement learning. A strong background in statistical learning and computer science will be preferred. Please read this or email me for more information.