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School of Computer Science and Engineering
Data Science and AI Center (DSAIR)
Nanyang Technological University (NTU), Singapore
Tel.: +65 6790 6300
Address: SCSE, Block N4, Nanyang Avenue, Singapore 639798
- Postdoctoral scholar, PhD and Research Assistant positions available in deep learning and graph science.
- Software engineer positions available.
- No internship offered at this time.
- [Feb 19] I am pleased to offer a new edition of my industrial training on AI and Deep Learning at the Institute for Pure and Applied Mathematics (IPAM), UCLA, May 16-17, 2019, here.
- [Feb 19] Gave an invitated talk at the workshop on "Mathematics of Imaging" organized by J.F Aujol, J. Delon, A. Desolneux, J. Fadili, B. Galerne, G. Peyre, Institut Henri Poincaré, Paris, Feb 5, 2019, here.
- [Jan 19] Invited to give a talk at the opening of the joint NTU-WeBank Research Centre on FinTech on 31 Jan 2019, here.
- [Jan 19] Gave a lecture at the Tsinghua-NTU theoretical computer science winter school, here.
- [Jan 19] Joined the editorial board of SIAM Journal on Imaging Sciences (SIIMS) here. Consider submitting your research work to this great journal (highest rank in imaging science and applied mathematics).
- [Dec 18] Geometric deep learning is going mainstream! Great job on the introductory video by Siraj Raval here.
- [Nov 18] New journal paper w/ F. Monti, R. Levie, M. Bronstein on spectral ConvNets with Cayley function. Paper is here and GitHub code is here.
- [Nov 18] Will co-organize w/ Y. LeCun, R. Vidal, S. Other and R. Willett in May 20-24 2019 a workshop on geometry and learning from data, check out our amazing speakers and apply to the program here.
- [Oct 18] Just finished my 2-day industrial course in AI at UCLA. Glad the course was successful and will be offered again next year, here. Contact me if you interested in a in-house AI training.
- [Aug 18] Will teach the first graduate course on deep learning at NTU, Singapore. The course has 20 lectures, 800 slides, 50 PyTorch exercises. Website of the course is here and the GitHub folder of the course is here.
- [July 18] We will deliver a tutorial on "Geometric Deep Learning on Graphs and Manifolds" at the 2018 SIAM Annual Meeting (AN18) on July 12, 2018, Portland, US, here.
- [June 18] Will deliver my 2-day industrial training in deep learning at IPAM, UCLA in October 1-2. This course is designed for industry professionals who want to get started in deep learning and apply DL to their projects. Register here.
- [May 18] Our work on "Fetal Brain MRI Reconstruction" will be presented at the Organization for Human Brain Mapping Annual Meeting, here.
- [May 18] A part of our large-scale (917GB/343days) music dataset FMA was used for the "WWW 2018 Challenge: Learning to Recognize Musical Genre" website.
- [May 18] Gave an introductory talk to the radiology department at the CHUV hospital, Switzerland, about what AI can do and its limitations. AI has the potential to release the mental labor of radiologits.
- [May 18] Congrats to my FYP student Mr. Suyash Lakhotia who got his first research paper accepted to ACM CW 2018 on deep learning for text document categorization. Code is available on GitHub.
- [May 18] Congrats to my FYP student Mr. Ho Song Yan who won the Most Enterprising project during the Technovation Day at NTU for applying reinforcement learning to self/driving car, see video.
- [April 18] In the list of top 200 french AI scientists by usinenouvelle.
- [April 18] Our paper "Deep Geometric Matrix Completion" will be presented at IEEE ICASSP.
- [March 18] Our paper "An Experimental Study of Neural Networks for Variable Graphs" accepted to ICLR18 Workshop. See openreview, arXiv and GitHub code.
- [Feb 18] Slides and video of my IPAM talk "ConvNets on Graphs" are available here and here resp. Yann LeCun and his collaborators spurred the original idea and we leveraged it to reach linear complexitiy.
- [Feb 18] Our IPAM Workshop on "New Methods in Deep Learning" at UCLA has been the most successful IPAM workshop in term of participants. The videos are available on the IPAM YouTube channel and the slides on the website. Thanks to the speakers Sam Bowman, Emily Fox, Ellie Pavlick, Leonidas Guibas, Alán Aspuru-Guzik, Daniel Rueckert, Kyle Cranmer, Stéphane Mallat, Michael Elad, Federico Monti, Joan Bruna, Jure Leskovec, Arthur Szlam, Sanja Fidler, Raquel Urtasun, Pratik Chaudhari, Stefano Soatto, Tom Goldstein, Stanley Osher, Michael Bronstein, Sainbayar Sukhbaatar, Zuowei Shen, Wei Zhu, and Yann LeCun.
- [Jan 18] Invitation by Google Singapore for the 2018 Faculty Learning Summit.
- [Jan 18] I will teach this semester at NTU the new undergraduate course "Introduction to Data Science", see slides, python notebooks, installation info on GitHub.
- [Dec 17] Our NIPS'17 tutorial on "Geometric Deep Learning on Graphs and Manifolds" with M. Bronstein (USI), J. Bruna (NYU), A. Szlam (Facebook) and Y. LeCun (Facebook), link, slides and video.
- [Nov 17] Gave my 3-day data science training to Optum, one of the largest health care companies in the US, here.
- [Nov 17] GitHub code for our NIPS'17 paper on recommender systems with multi-graph neural networks, here.
- [Nov 17] Our paper w/ Facebook, Google, LMU, CSULB on reseeding strategy for unsupervised clustering accepted in Springer’s “Mathematics and Visualization” series, here.
- [Nov 17] My talk on spectral graph ConvNets at Newton Institute in Cambridge UK, here.
- [Oct 17] My talk at Singapore Management University, here.
- [Oct 17] Posted on GitHub the PyTorch implementation of our NIPS’16 spectral graph convnets paper, here.
- [Sept 17] My (cut) interview to newspaper The Straits Times on benefits of data science for smart cities, here.
- [Sept 17] Will give a live streaming talk at Isaac Newton Institute for Mathematical Sciences for the workshop "Generative models, parameter learning and sparsity", Oct 30 - Nov 3, list of talks here.
- [Sept 17] Accepted paper at NIPS'17: Recommender systems with deep learning on graphs, link.
- [Aug 17] Accepted tutorial at NIPS'17: "Geometric Deep Learning on Graphs and Manifolds" with M. Bronstein (USI), J. Bruna (Berkeley), A. Szlam (Facebook) and Y. LeCun (Facebook) accepted at NIPS'17, link.
- [Aug 17] Dr. Yi-Qing Wang (PhD CMLA Cachan) joined the team as Postdoctoral Scholar.
- [July 17] Presented a 3-hour tutorial "Deep Learning on Graphs" with M. Bronstein (USI), A. Szlam (Facebook) at CVPR'17. Slides are here. Video will follow at a later date.
- [June 17] Gave a 3-day data science training at EPFL, see here. Evaluation: 90% of participants would recommend the course to their colleagues.
- [June 17] Gave a 1-day deep learning training at Swiss Center for Electronics and Microtechnology, link.
- [June 17] Gave a talk at the NUS Workshop on Sparse Representation for Complex Data link.
- [May 17] We have released FMA, the world biggest open research music dataset for deep learning link.
- [Apr 17] Committee member for the curriculum of the coming NTU Master in Data Science and AI.
- [Apr 17] Will give a talk at the inaugural workshop of the new NTU Data Science and AI Center link.
- [Mar 17] We are organising with Facebook (LeCun, Szlam), NYU (Bruna) and USI (Bronstein) a workshop at IPAM-UCLA on new deep learning techniques link.
- [Feb 17] Excited to be part of the new NTU Data Science and AI Center. Mission: research, teaching and technological transfer link.
- [Jan 17] Honored to receive the competitive Singapore NRF Fellowship of 2.5M US$! link
- [Jan 17] Start position as Assoc Prof in Computer Science at NTU, Singapore, link.
- [Dec 16] We released a large-scale music dataset for deep learning, link.
- [Dec 16] Our paper on deep learning for graphs selected as Spotlight Video at NIPS'16, link.
- [Nov 16] Delivered 3-day teaching on data science techniques at Deloitte University.
- [Nov 16] Our NIPS paper on clustering in collaboration with Facebook FAIR, link.
- [Oct 16] Our JMLR paper on consistency analysis (for optimal graph scaling) is out link.
- [Sep 16] Delivered first 3-day data science training for continuing education at EPFL, see next training in June 2017 here.
- [Sep 16] My data science course: 500slides, 43codes, 20datasets: clust, recom, visual, graphs, deep learning, etc link.
- [Sep 16] My talk on fast ConvNets for graph-structured data link.
- [Sep 16] Two papers accepted at NIPS'16! link.
- [Aug 16] Best Student Paper Award to Rodrigo Pena at IEEE IVMSP, link.